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  • Published: 27 April 2021

Urbanization: an increasing source of multiple pollutants to rivers in the 21st century

  • Maryna Strokal   ORCID: orcid.org/0000-0002-8063-7743 1 ,
  • Zhaohai Bai   ORCID: orcid.org/0000-0001-7685-5441 2 ,
  • Wietse Franssen 1 ,
  • Nynke Hofstra 1 ,
  • Albert A. Koelmans 3 ,
  • Fulco Ludwig 1 ,
  • Lin Ma   ORCID: orcid.org/0000-0003-1761-0158 2 ,
  • Peter van Puijenbroek   ORCID: orcid.org/0000-0001-6370-2411 4 ,
  • J. Emiel Spanier 1 ,
  • Lucie C. Vermeulen   ORCID: orcid.org/0000-0002-8403-2442 5 ,
  • Michelle T. H. van Vliet   ORCID: orcid.org/0000-0002-2597-8422 6 ,
  • Jikke van Wijnen 7 &
  • Carolien Kroeze 1  

npj Urban Sustainability volume  1 , Article number:  24 ( 2021 ) Cite this article

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Most of the global population will live in urban areas in the 21st century. We study impacts of urbanization on future river pollution taking a multi-pollutant approach. We quantify combined point-source inputs of nutrients, microplastics, a chemical (triclosan) and a pathogen ( Cryptosporidium ) to 10,226 rivers in 2010, 2050 and 2100, and show how pollutants are related. Our scenarios consider socio-economic developments and varying rates of urbanization and wastewater treatment. Today, river pollution in Europe, South-East Asia and North America is severe. In the future, around 80% of the global population is projected to live in sub-basins with multi-pollutant problems in our high urbanization scenarios. In Africa, future river pollution is projected to be 11–18 times higher than in 2010, making it difficult to meet Sustainable Development Goals. Avoiding future pollution is technically possible with advanced wastewater treatment in many regions. In Africa, however, clean water availability is projected to remain challenging. Our multi-pollutant approach could support effective water pollution assessment in urban areas.

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Introduction.

Urban areas currently accommodate more than half of the global population 1 and generate over two-thirds of the world gross domestic products (GDP) 2 , 3 . In 2050, more than two-thirds of the global population will live in cities 1 , 4 , 5 . Rapid urbanization creates opportunities for economic developments 6 , but may also increase the use of freshwater resources 4 , 6 , 7 , 8 , 9 . This will increase competition for water between cities and agriculture 4 . More urban waste is likely to result in contamination of water with multiple pollutants such as nutrients 10 and pathogens 11 , 12 from human excretion, plastics 13 , 14 , 15 , 16 , 17 , 18 , and chemicals 19 , 20 from personal care products. River pollution poses a threat to the availability of clean water in large parts of the world 7 , 21 , challenging the achievement of Sustainable Development Goal 6 (SDG, clean water for all) and 11 (sustainable cities). Recent studies on impacts of rapid urbanization on water stress or water scarcity worldwide exist 4 , but often ignore water quality 7 .

Previous global studies likely underestimate the impact of urbanization on water pollution because of their strong focus on single pollutants 10 , 16 , 20 , 22 , 23 , 24 (Fig. 1 ). Urbanization (e.g., sewer connections in cities) is, however, often a common, point source of multiple pollutants in rivers, contributing to multiple impacts. Examples are eutrophication problems caused by nitrogen (N) and phosphorus (P) in many world regions 25 , 26 , and diarrhea caused by pathogens (e.g., Cryptosporidium ) especially in developing countries 11 , 27 . A multi-pollutant approach is, thus, urgently needed to account for interactions between drivers of urbanization (e.g., population, economy) and pressures such as emissions of different pollutants 21 . This can help to identify effective solutions accounting for synergies and trade-offs in pollution control. Furthermore, reducing multiple pollutants in rivers from urban-related sources might be easier (e.g., improved wastewater treatment) than from diffuse sources such as agricultural runoff (e.g., delay effects of reduction options due to accumulation of substances in soils). This may have a positive effect on the overall water quality status depending on diffuse sources.

figure 1

The figure shows a difference between single-pollutant approaches (most existing studies) and a multi-pollutant approach (this study) to assess the impacts of the rapid urbanization on future global river quality. We take N (nitrogen), P (phosphorus), pathogens and plastics as examples. Advances of the multi-pollutant approach are discussed in the main text.

In this paper, we study the impacts of urbanization on river pollution in the 21st century, taking a multi-pollutant perspective. We define multi-pollutant problems as increasing levels of more than one pollutant to rivers in future decades. We analyze, simultaneously, the following groups of pollutants: nutrients (N and P), pathogens (such as Cryptosporidium ), microplastics and chemicals (such as triclosan). These pollutants are selected because of their increasing pollution in many rivers worldwide 18 , 20 , 23 , 28 , 29 , 30 . Yet, these pollutants have common urban sources such as sewer systems (worldwide) and open defecation. We quantify point-source inputs of the pollutants to 10,226 rivers for 2010, 2050 and 2100 associated with urbanization: sewer systems and open defecation. For this, we use a global model of Strokal et al. 31 that takes the sub-basin scale modelling approach of Strokal et al. 32 for nutrients and integrates modelling approaches for other pollutants 18 , 20 , 23 (Supplementary Tables 1 , 2 and 3 ). We develop this model further for multiple-pollutants and future analyses based on evaluated, modelling approaches (see the “Methods” section).

To assess the impacts of urbanization, we develop five scenarios with different levels of urbanization and wastewater treatment rates (Fig. 2 ). The storylines are interpretations of the five Shared Socio-economic Pathways (SSPs) 33 , 34 , 35 , 36 (Supplementary Tables 4 , 5 and 6 ). These SSPs are five pathways with different levels of socio-economic challenges for mitigation and adaptation 33 , 34 , 35 , 36 . SSP1 is a Green Road pathway with low socio-economic challenges (e.g., low population growth), but with high economic and urbanization development. It is largely oriented towards achieving sustainable goals (see Supplementary Tables 4 , 5 and 6 ). SSP2 is a middle of the road pathway with medium challenges to mitigation and adaptation. Future trends will not be very different from historical trends. SSP3 is a Rocky Road pathway with high challenges to mitigation and adaptation. It is a world with difficulties to control the population growth and has low economic and urbanization development (see Supplementary Tables 4 , 5 and 6 ). SSP4 is a Road Divided pathway with high challenges to mitigation and low to adaptation. It has a large gap between urban and rural development with the high urbanization rates especially in urban areas. SSP5 is a taking the highway pathway with high challenges to mitigate, but low challenges to adapt. It is a word with priorities towards economy (see Supplementary Tables 4 , 5 and 6 ).

figure 2

Low, moderate and high urbanization is defined here as the increasing number of urban people and total people with sewer connections (see a and b panels and Supplementary Tables 4 – 6 ). The number of people opens defecating directly to water is assumed to decrease with sewer connection. Higher sewer connections imply that more wastewater treatment plants will be constructed to maintain the increasing volumes of the waste (see the “Methods” section). Low, moderate and high wastewater treatment levels refer here to a shirt (low, moderate, high) towards a next treatment type: e.g., from primary to secondary to tertiary ( a , b , Supplementary Tables 4 – 6 ). This implies the low, moderate and high ambitions to improve wastewater treatment ( b ). Future years are 2050 and 2100. Supplementary Tables 1 – 6 give quantitative interpretations of the storylines for our multi-pollutant model (see also the “Methods” section). GDP is the gross domestic product. Sources for the technologies are in the main text and in Supplementary Table 3 .

Our five scenarios incorporate socio-economic pathways of SSPs, but with quantitative interpretations of aspects related to urbanization and wastewater treatment (see the “Methods” section). Our scenarios aim to show the impact of urbanization on multiple pollutants in rivers. Thus, the names of our five scenarios correspond to the different levels of urbanization and wastewater treatment: from low urbanization and low wastewater treatment rates towards high urbanization and high wastewater treatment rates. This results in the following scenarios: low urbanization and low wastewater treatment rates (Low urb –Low wwt , based on SSP3), moderate urbanization and moderate wastewater treatment rates (Mod urb –Mod wwt , based on SSP2), high urbanization and low wastewater treatment rates (High urb –Low wwt , based on SSP4), high urbanization and moderate wastewater treatment rates (High urb –Mod wwt , based on SSP5), and high urbanization and high wastewater treatment rates (High urb –High wwt , based on SSP1) (Fig. 2 ). The five scenarios consider interactions between global change (socio-economic pathways), urbanization, sanitation and wastewater treatment.

Low, moderate and high urbanization reflect different levels of increases in urban population, and, indirectly, people with sewer connections between 2010 and future years (see the “Methods” section). As a net effect, the number of people practicing open defecation (direct inputs of human waste to rivers) may decrease. Increasing sewer connections assume higher capacities of treatment plants to manage increasing volumes of the wastewater. Low, moderate and high rates of wastewater treatment are defined based on a shift towards a next treatment type: e.g., from primary (technologies with <10% removal rates 10 , 18 , 20 , low) to secondary (50% removal rates 10 , 18 , 20 , 37 , moderate) or to tertiary (>75% removal rates 10 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , high, see the “Methods” section). The differences between the Low urb –Low wwt, and High urb –Low wwt scenarios indicate the impact of urbanization in terms of increasing numbers of people with sewer connections with low ambitions to improve the wastewater treatment under different socio-economic developments. The Mod urb –Mod wwt scenario could be considered business as usual. The differences between the High urb –Low wwt , High urb –Mod wwt and High urb –High wwt scenarios indicate the impact of improving the wastewater treatment in highly urbanized areas. Details are given in the “Methods” section on qualitative and quantitative descriptions of the five urbanization scenarios.

River pollution today

River pollution in Europe, South-East Asia and North America is already severe today. For these regions, we calculate high inputs of N (>50 kg km −2  year −1 ), P (>30 kg km −2  year −1 ), triclosan (>10 g km −2  year −1 ), microplastics (>5 kg km −2  year −1 ) and Cryptosporidium (>100 × 10 17 oocysts km −2 year −1 ) to many rivers in 2010 (Fig. 3 ). These regions experience severe water pollution problems 9 , 16 , 21 , 25 , 45 , contributing to negative impacts 21 such as eutrophication 45 and waterborne diseases (South-East Asian countries). For African sub-basins, pollution levels are not as high as in those regions (Fig. 3 ). However, some impacts of polluted water on children’s health are already indicated 21 . Globally, 9.5 Tg of N, 1.6 Tg of P, 0.45 Tg of microplastics, 0.72 kton of triclosan and 1.6 × 10 17 oocysts of Cryptosporidium entered rivers in 2010 (Fig. 4 , Supplementary Table 7 ). More than half of these inputs are to rivers in South-East Asia. Most of the pollutants in rivers are from sewer systems (see details in Supplementary Figs. from 1 to 29 ). Exceptions are some sub-basins in Africa and South-East Asia where open defecation contributes to over 20% of N, P and Cryptosporidium to their rivers. Existing assessments 9 , 10 , 13 , 20 , 23 reveal similar global estimates, but with diverse spatial scales. Our consistent spatial and temporal scales increase the robustness of our comparisons between multiple pollutants worldwide (e.g., Fig. 4 ).

figure 3

Units are kg km −2 of sub-basin area year −1 for nitrogen (N), phosphorus (P) and microplastics (MP), g km −2 of sub-basin area year −1 for triclosan (TCS) and 10 17 oocysts km −2 of sub-basin area year −1 for Cryptosporidium . Source: the global multi-pollutant model (model description is provided in the “Methods” section, and in Supplementary Tables 1 – 6 , model inputs are in Supplementary Figs. 1 – 14 ). Model uncertainties are discussed in the “Methods” section.

figure 4

a – e Future trends for individual pollutants. Pies show the shares of the surface areas by region as % of the global surface area. Spatially explicit results are shown in Fig. 3 for 2010 and Fig. 5 for the future. The description of the scenarios is in Fig. 2 , in the “Methods” section and Supplementary Tables 1 – 6 . Source: the global multi-pollutant model (model description is provided in the “Methods” section, and in Supplementary Tables 1 – 6 , model inputs are in Supplementary Figs. 1 – 14 ). Model uncertainties are discussed in the “Methods” section.

High pollution levels result from the net effect of population densities, sewer connection rates (Supplementary Figs. 1 , 2 and 3 ), production of pollutants in human waste (Supplementary Figs. 4 , 5 , 6 , 7 and 8 for individual pollutants) and wastewater treatment efficiencies (Supplementary Figs. 9 , 10 , 11 , 12 and 13 for individual pollutants) in countries (Supplementary Figs. 14 and 15 ). For South-East Asia, high pollution levels are driven by high population densities (Supplementary Figs. 3 and 16 ). This region accommodates approximately half of the global population (3 billion people, Supplementary Fig. 1 ) on 12% of the global surface area (Fig. 4 ). For comparison, sub-basins of Europe (excluding Russia) and North America accommodate around 10% of the global population (0.8 billion people, Supplementary Fig. 1 ) on 20% of the global surface area (Fig. 4 ). Approximately 20% of the total population in 2010 was connected to sewer systems (Supplementary Fig. 1 ) with relatively low wastewater treatment efficiencies (removal levels <50% for most pollutants, Supplementary Figs. 9 – 13 ). For Europe and North America, the high pollution levels per km 2 of sub-basins are driven by high connection rates to sewer systems especially in urban areas. Here, over two-thirds of the population live in urban areas and are largely connected to sewer systems with removal efficiencies above 50% for the studied pollutants (Supplementary Figs. 9 – 13 ). Supplementary Fig. 17 shows the results of the sensitivity analysis indicating the importance of wastewater treatment and human development in river pollution (see the “Discussion” section).

Future river pollution globally

In the future, ~80% of the global population is projected to live in sub-basins with multi-pollutant problems (Figs. 5 and 6 ). These sub-basins cover over half of the global surface area (Fig. 6 ) for which inputs of more than one pollutant will increase at least 30% (Fig. 5 ) between 2010 and 2050 or 2100. This is for all scenarios, except for High urb –High wwt . In the scenario assuming low urbanization and low wastewater treatment (Low urb –Low wwt ), global inputs of most pollutants will less than double between 2010 and 2050 (Fig. 4 ). In this scenario, the population growth is high, and almost doubles between 2010 and 2100 (Supplementary Fig. 3 ). Approximately one-third of the total population globally will be connected to sewer systems. This number is much lower than in the other scenarios in 2100 (Supplementary Fig. 3 ). As a net effect of the low sewer connection (Supplementary Fig. 3 ) and low wastewater treatment (Supplementary Figs. 9 – 13 ), future inputs of pollutants to rivers from sewage are lower in the Low urb –Low wwt scenario than in the others (Fig. 3 ). However, as a trade-off, more nutrients and Cryptosporidium are projected to enter rivers from open defecation, mainly in developing countries (see Supplementary Figs. 14 and 15 ) compared to the other scenarios.

figure 5

Maps show changes in inputs of pollutants to rivers during the periods of 2010–2050, 2010–2100 and 2050–2100 according to the five scenarios. We classify sub-basins based on the number of pollutants for which the increases are higher or lower than 30% (Note: 30% is arbitrary; see Supplementary Figs. 18 and 20 for results based on 10 and 50% thresholds). The pollutants include Cryptosporidium , microplastic, triclosan, nitrogen and phosphorus. More information is available in Supplementary Figs. 18 – 29 . The description of the five scenarios is in Fig. 2 , in the “Methods” section and Supplementary Tables 1 – 6 . Results for 2010 are in Fig. 3 . Source: the global multi-pollutant model (model description is provided in the “Methods” section, and in Supplementary Tables 1 – 6 , model inputs are in Supplementary Figs. 1 – 14 ). Model uncertainties are discussed in the “Methods” section.

figure 6

Sub-basins are classified based on the number of pollutants for which the increases are higher or lower than 30% during the periods of 2010–2050, 2010–2100 and 2050–2100 according to the five scenarios. Graphs show the number of sub-basins ( a ), sub-basin areas ( b ), total population ( c ) and urban population ( d ) for the sub-basins with the increases of higher or lower than 30% (Note: 30% is arbitrary; see Supplementary Figs. 19 and 21 for results based on 10% and 50% thresholds). More information is available in Supplementary Figs. 18 – 29 . See Fig. 5 for the changes in inputs of pollutants during the periods of 2010–2050, 2010–2100 and 2050–2100. The description of the scenarios is in Fig. 2 , in the “Methods” section and Supplementary Tables 1 – 6 . Results for 2010 are in Fig. 3 . Source: the global multi-pollutant model (model description is provided in the “Methods” section, and in Supplementary Tables 1 – 6 , model inputs are in Supplementary Figs. 1 – 14 ). Model uncertainties are discussed in the “Methods” section.

The future inputs of most pollutants to rivers are projected to be higher in the scenarios with moderate (Mod urb –Mod wwt ) and high urbanization (High urb –Low wwt , High urb –Mod wwt , Fig. 4 ). The population grows not as fast as in the Low urb –Low wwt scenario, but the rate of urbanization is much higher, especially in the High urb –Low wwt and High urb –Mod wwt scenarios (Supplementary Tables 4 – 6 ). As a result, over two-thirds of the global population is projected to be connected to sewer systems in 2100 (Supplementary Fig. 3 ). Wastewater treatment efficiency is slightly improved (Mod urb –Mod wwt , High urb –Mod wwt ) depending on the economic development (Supplementary Figs. 9 – 13 ). As a net effect, the High urb –Low wwt and High urb –Mod wwt scenarios project, generally, higher inputs of most pollutants to rivers than the Low urb –Low wwt and Mod urb –Mod wwt scenarios (Fig. 4 ).

Pollutants differ in their future trends. For example, High urb –Low wwt projects the highest inputs of Cryptosporidium , microplastics and triclosan globally in 2100 compared to the other pollutants and scenarios (Fig. 4 ). For N and P, High urb -Low wwt and High urb -Mod wwt project somewhat similar amounts globally (Fig. 4 ). All these differences between pollutants and scenarios are a net effect of three important factors: socio-economic development (e.g., population, GDP), urbanization rates (population connected to sewer systems) and treatment efficiencies. For example, higher GDP results generally in higher N and P excretion rates per capita because of changes towards protein-rich diets 31 , 46 (Supplementary Figs. 4 – 5 ). Developed countries (Human Developing Index, HDI > 0.785) have generally lower infection rates, leading to less per capita excretion of Cryptosporidium 23 (Supplementary Fig. 8 ), but may lead to higher production of microplastics from car tyres 31 (Supplementary Fig. 7 ) as a result of industrialization. All these interactions are considered together with different trends in the population growth (Supplementary Fig. 3 ), urbanization rates (Supplementary Figs. 1 and 2 ) and treatment levels (Supplementary Figs. 9 – 13 ) among scenarios and regions.

Future river pollution in Africa

Future river pollution is projected to be 11–18 times higher than in 2010 in the scenario with high urbanization and low wastewater improvements (High urb –Low wwt ). This range is for increasing inputs of the five pollutants by at least 30% during the period of 2010–2100 (Fig. 5 ). Africa may become a major contributor to river pollution in the world (Fig. 4 ). For example, by 2100, up to half of the global inputs of multiple pollutants are projected in Africa in High urb –Low wwt (Fig. 4 ). For comparison: in 2010 the contribution of African rivers to the global river pollution was <5% (Fig. 4 ). All scenarios project increasing river pollution in the future for Africa (Figs. 5 and 6 ). This is largely associated with the projected population growth and assumed wastewater treatment. The African population is projected to more than double in many sub-basins during 2010–2100 in all scenarios (Supplementary Fig. 3 ). Many people will live in urban areas (High urb –Low wwt and High urb –Mod wwt , Supplementary Figs. 1 – 3 ). More people will inevitably generate more waste, and this may not be treated effectively enough (e.g., High urb –Low wwt ). This all explains the large future increases in river pollution in Africa (Fig. 5 ). In the low urbanization scenario (Low urb –Low wwt ), less people will live in urban areas, and a lower percentage of people will be connected to sewer systems. Thus, open defecation may continue in Low urb –Low wwt especially by 2050. This is an important source of nutrients and Cryptosporidium to African rivers in this scenario. Supplementary Figs. 18 , 19 , 20 and 21 show results for increasing inputs of the five pollutants by at least 10% and 50% during the period of 2010–2100. Supplementary Figs. 22 , 23 , 24 , 25 and 26 show future trends in river pollution by individual pollutants. Supplementary Figs. 27 , 28 and 29 show scenarios and sub-basins where open defecation is an important source of P, N and Cryptosporidium in rivers.

Future river pollution in Asia

Future river pollution is projected to be 2–3 times higher than in 2010 in the scenario with high urbanization and low wastewater improvements (High urb –Low wwt ). This range is for at least 30% increases in inputs of the five pollutants for the period 2010–2100 (Fig. 5 ). Exceptions are rivers in sub-basins of China (Fig. 5 ). These rivers are projected to be cleaner in 2100 than in 2050, but inputs of the pollutants may still be higher in 2100 than in 2010 in the urbanized scenarios with the low (High urb –Low wwt and Low urb –Low wwt ) and moderate (Mod urb –Mod wwt and High urb –Mod wwt ) wastewater treatment improvements (Fig. 5 ). The Chinese population is projected to decrease in the future in all scenarios (Supplementary Fig. 3 ). However, with the rapid urbanization (Supplementary Figs. 1 – 2 ), the wastewater treatment (Supplementary Figs. 9 – 13 ) may not keep up with the pollution loads. This explains higher river pollution levels. This is different for some other Asian countries such as India and Pakistan. By 2050, the total population of India and Pakistan will have increased (Supplementary Fig. 3 ). By 2100, the total population will have decreased or increased depending on the socio-economic development in the scenarios (Supplementary Fig. 3 , Supplementary Tables 4 – 6 for the scenario description). However, the wastewater treatment is poorer or absent compared to the Chinese sub-basins (Supplementary Figs. 9 – 13 ), resulting in more pollutants in rivers (Fig. 5 , Supplementary Figs. 18 – 21 ).

Future river pollution in Europe and North America

Many rivers in Europe and North America may be cleaner in the future. European rivers (Western, Northern and Southern) may get cleaner in the future because of high removal efficiencies to treat wastewater (Supplementary Figs. 9 – 13 ). However, in the High urb –Mod wwt scenario, high wastewater treatment efficiencies (>50% for all pollutants) may not be enough to reduce future pollution to the level below 2010. For American rivers, future trends differ largely between South and North in the scenarios with the low (Low urb –Low wwt ) and high (High urb –Low wwt ) urbanization trends. In the Low urb –Low wwt scenario, lower increases (<30%) in inputs of pollutants are projected for many Northern rivers whereas higher increases (>30%) for most Southern rivers (Fig. 5 , Supplementary Figs. 18 – 21 ). This difference can be explained by the higher population growth (Supplementary Figs. 1 – 3 ) and less efficient wastewater treatment (Supplementary Figs. 9 – 13 ) in South America compared to North America. In the High urb –Low wwt scenario, higher increases in river pollution are projected for South America by 2050, but lower by 2100. This is associated with the decreased population (Supplementary Fig. 3 ) and with the increased efficiencies of wastewater treatment between 2050 and 2100 (Supplementary Figs. 9 – 13 ). Rivers in Australia may be more polluted in the future (Fig. 5 ). Exceptions are the Low urb –Low wwt and High urb –Low wwt scenarios with less pollution in 2100 than in 2050. This is largely associated with the decreasing population during 2050–2100 (Supplementary Figs. 1 – 13 , 18 – 21 ).

Reducing future river pollution

Advanced wastewater treatment can reduce future river pollution in many world regions, but not in Africa (High urb –High wwt ). In High urb –High wwt, all developed countries (HDI > 0.785) will shift completely towards tertiary treatment with enough capacities and high efficiencies to remove pollutants from the wastewater (>75% for all pollutants, Supplementary Figs. 1 – 14 ). Examples of such technologies are annomox 47 for N, calcium precipitation for P 48 , disinfection by Ultraviolet radiation for Cryptosporidium 42 , reverse osmosis for nutrients 41 and microplastics 49 . Developing countries (HDI < 0.785) will also shift towards tertiary technologies, but in combination with secondary technologies 10 , 46 (Supplementary Figs. 1 – 14 ). Open defecation will stop by 2100. Thus, High urb –High wwt shows the technical potential of advanced technologies with enough treatment capacities to reduce future pollution from highly urbanized areas.

It will be difficult to reduce future river pollution in Africa to the level of 2010, even with advanced technologies (High urb –High wwt , Fig. 5 ). Inputs of most pollutants to many African rivers are projected to increase by at least 30% during 2010–2100 in High urb –High wwt (Fig. 5 ). The main reason is an increase in the total population, which is much higher (>doubling) than in other world regions (Supplementary Fig. 3 ). As a result, implementing advanced technologies in 2100 may help to reduce inputs of most pollutants to the level of 2050, but not to the level of 2010. For many other world’s rivers, advanced technologies with enough treatment capacities are projected to lower future inputs of pollutants in High urb –High wwt (Fig. 5 , Supplementary Fig. 20 ). This may have a positive impact on the overall pollution status depending also on the contribution of diffuse sources from agriculture. However, for some rivers in Asia (e.g. India, Pakistan), inputs of most pollutants from point sources will still increase by 2050, but may be lower by 2100 in High urb –High wwt (Fig. 5 ). Some rivers in North America, Middle Asia and Australia are projected to have higher inputs of pollutants in 2100 than in 2050, but lower than in 2010 (Fig. 5 , Supplementary Fig. 20 ). These trends are the net effect of the population growth, urbanization and wastewater treatment in High urb –High wwt (Figs. 2 , 5 and 6 ).

Scenario analyses are widely used to explore possible futures 1 , 34 , 36 , 50 , 51 , 52 . Our five scenarios are a combination of possible trends in urbanization, socio-economic development (existing SSPs 1 , 36 , 53 ) and our assumptions on sanitation, wastewater treatment capacities and removal efficiencies of pollutants. Our assumptions may, however, seem ambitious (Supplementary Tables 5 and 6 ). For example, we assume the full implementation of advanced technologies with enough treatment capacities in High urb –High wwt for all developed countries. We did this to show the effects of sustainable practices in urban areas on increasing the availability of clean water for people and nature. This assumption, however, might be ambitious to achieve. In our scenarios, we reflect a relation between urbanization (e.g., more urban people) and sewer connections (see High urb –Low wwt, High urb –Med wwt ) with sustainable urbanization practices (see High urb –High wwt ). This relation may, however, not emerge everywhere in the world. On the other hand, we explore possible futures; we do not state how likely or desirable these futures are. Our scenarios aim to identify impacts of future urbanization (e.g., differences between Low urb –Low wwt and High urb –Low wwt ) and the technical potentials of proven wastewater treatment technologies to reduce future river pollution from point sources (e.g., differences between High urb –Low wwt and High urb –High wwt ). Our insights may contribute to the formulation of sustainable urbanization practices where wastewater treatment is effective enough to reduce pollutants in the urban waste (e.g., SDG11) and thus to increase the availability of clean water in the future (e.g., SDG6).

Our global multi-pollutant model quantifies, simultaneously, five pollutants in rivers with consistent datasets in space and time. However, uncertainties exist. The model is developed based on existing, evaluated models for pollutants 11 , 18 , 20 , 23 , 29 , 32 (e.g., comparisons with observed concentrations and sensitivity analyses). We further evaluate our combined model using five approaches 54 (see the “Methods” section). First, we compare our model outputs with existing studies (see the “Methods” section, Supplementary Table 7 ), showing a good agreement for the five pollutants. Second, we compare the spatial pattern of pollution problems with existing models 8 , 9 , 10 , 11 , 12 , 16 , 55 , 56 , indicating the river pollution in densely populated and highly urbanized areas (Figs. 3 – 5 , Supplementary Tables 7 and 8 ). However, existing studies did not focus on a simultaneous reduction of the five pollutants from urbanized activities in the 21st century, which is a multi-pollutant perspective of our study. Third, we performed a sensitivity analysis for pollution hotspots. We define multi-pollutant hotspots as places with >30% increases in two or more pollutants between 2010 and future years (Fig. 5 ). This is an elegant way to combine the five pollutants. We realize that the 30% threshold is arbitrary. The results should, therefore, be interpreted as warning signals of future river pollution. In the sensitivity analysis, we changed the 30% threshold to 10% (Supplementary Figs. 18 – 19 ) and 50% (Supplementary Figs. 20 – 21 ). The results confirm the robustness of our main messages about future multi-pollutant hotspots. Fourth, we performed a sensitivity analysis for all important model inputs underlying the calculations (Supplementary Tables 9 , 10 , 11 and 12 , Supplementary Fig. 17 ). In total, 25 model inputs are changed with ±10%, resulting in 50 model runs for 10,226 sub-basins and five pollutants. The results show that the model is not very sensitive to changes in most model inputs. For most sub-basins, the model outputs are relatively sensitive to changes in <5 model inputs. These inputs are related to HDI, wastewater treatment types and removal efficiencies. The 10% changes in these inputs, resulted in up to 5% change in model output for sub-basins covering over two-thirds of the global surface area (see details in the “Methods” section for all sub-basins). Fifth, we compare model inputs with independent datasets (Supplementary Table 8 , Supplementary Figs. 15 and 16 ). All this gives trust in the model performance (see the “Methods” section).

Our results are future oriented. We focus on trends in future hotspots of multi-pollutant problems in the world. We believe that not all model uncertainties affect our main messages about trends. We also realize that our results are relatively sensitive to the assumptions on future HDI and wastewater treatment (see Approach 4 in the “Methods” section and sensitivity analysis). For HDI, we assumed an increase of 0, 10 and 20% between 2010 and 2050 and further increase by 2100 depending on scenario (Supplementary Tables 5 – 6 ). For wastewater treatment rates, we assumed a shift towards a next treatment type between 2010 and future years (e.g., 0–50% shift depending on scenario). To increase trust in our assumptions for future trends, we compared our model inputs with other independent studies. We did this for our five scenarios (Supplementary Table 8 , Supplementary Fig. 15 ). For example, future trends in our HDI between 2010 and future years are strongly in line with an independent study 57 ( R 2 above 0.88, see Supplementary Fig. 15 ). Crespo Cuaresma and Lutz 57 took into account differences in human development and their socio-economic wealth in projecting future HDI. Our wastewater treatment types in 2050 are also well compared with an independent study 10 (Supplementary Table 8 ).

Another potential source of uncertainties relates to the local variation in pollution levels. For example, sewage overflows may happen under heavy rain events, causing local peaks in water pollution. Such events are time dependent and may also contribute to global pollution levels 58 . We do not account for such local events in our model. We, however, believe that such omissions of events do not affect our messages for the multi-pollutants worldwide. This is because we explore future trends in the multi-pollutant hotspots worldwide that are influenced by global change, urbanization and wastewater treatment. Local analyses should, however, account for the impact of local events on local water quality (e.g., cities).

Our study aims to analyze the impact of the socio-economic drivers (e.g., GDP) and urbanization on future inputs of pollutants to rivers from point sources worldwide. However, we do not consider the transport of pollutants to rivers from agricultural fields, nor the impact of climate change on future river pollution. Next steps could be to further develop our global multi-pollutant model by calculating inputs of pollutants from agricultural fields and associated river export of pollutants. This will allow to explicitly combine the impact of both climate change and of socio-economic developments.

A multi-pollutant approach supports the search for effective solutions. A multi-pollutant approach might be more effective in reducing river pollution than a single-pollutant approach (Fig. 1 ). For example, reducing one pollutant may reduce (synergies) or increase (trade-offs) another pollutant. Our study serves as an illustrative example for the five pollutants. For example, increasing sewer connections may increase inputs of the five pollutants to rivers, but decrease inputs of N, P and Cryptosporidium from open defecation (Low urb –Low wwt ; trade-off). Higher economic developments may lead to less excreted Cryptosporidium per capita because of lower infection risks in developed countries 11 , 23 (Supplementary Fig. 8 ), but may generate more N and P in human excreta (Supplementary Figs. 4 – 5 ) as a result of protein-rich food consumption 10 , 46 (trade-off). Synergies also exist. For example, increasing sewer connections with advanced technologies and sufficient wastewater treatment capacities is projected to decrease the inputs of all five pollutants to many rivers in the future (High urb –High wwt ). This is also associated with synergies in treatment technologies to remove multiple pollutants. Some technologies are developed to target specific pollutants (e.g., N 47 , P 48 , Cryptosporidium 42 ). This implies that implementing technologies for one pollutant may not strongly influence another pollutant. However, technologies exist to treat more than one pollutant (e.g., 10 , 38 , 39 , 40 , 42 , 59 ). For example, secondary treatment with removal efficiencies of around 40–50% (assumed in Mod urb –Mod wwt and High urb –Mod wwt ) converts organic N into inorganic and gas, removing N from the waste 10 . They can also facilitate the biodegradation of triclosan 59 . Microplastics can host microorganisms (e.g., Cryptosporidium ) and serve as vectors for chemicals 15 , 49 , 60 . As a result, biofilms and flocs can form in, for example, activated sludge ponds and then settle down 49 . Triclosan can sorb to large particles and also settle down with other pollutants 38 , 39 , 59 . Advanced technologies (assumed in High urb –High wwt ) such as efficient ultrafiltration methods can reduce Cryptosporidium 42 and microplastics 49 , and reverse osmosis can recover nutrients 41 and reduce microplastics 49 . Nature-based solutions such as stabilization ponds and constructed wetlands are largely effective to reduce Cryptosporidium 42 and nutrients 61 . Accounting for synergies and trade-offs is essential to identify effective solutions for multiple pollutants. This can support the achievement of SDG11 for sustainable cities and SDG6 for clean water.

Our results can support policy assessment of water pollution in urban areas, and form the basis for actionable and region-specific solutions. We identify hotspots of urban-related river pollution and show possible effects of future urbanization on river quality under global change. This could help to prioritize short-term actions to avoid river pollution in the 21st century. Improving wastewater treatment is important to avoid multi-pollutant problems in an urbanized world (Fig. 5 , differences between High urb –High wwt and High urb –Low wwt ). Our sensitivity analysis indicates where improved wastewater treatment could have a larger impact (Supplementary Fig. 17 ). Our model indicates that water pollution is related to human development (expressed as human development index). This is important to realize when reducing Cryptosporidium and microplastics. Some countries in the world already introduced policies such as a ban of detergents and triclosan in products. Combing such policies with improved wastewater treatment may contribute to synergetic solutions for achieving SDGs and reducing river pollution from urban waste. For Africa, improving wastewater treatment may not be enough. Controlling the African population growth to reduce waste production in the future may be needed in urban and water policy assessments.

Our study quantifies future trends in inputs of five pollutants to rivers for five scenarios. We argue that a multi-pollutant perspective is needed in quantitative analyses of future trends in global change, urbanization, sanitation and wastewater treatment. We analyzed multiple pollutants simultaneously in a consistent way. We did this for 10,226 sub-basins for 2010, 2050 and 2100. Our insights are in how future trends differ between pollutants, sub-basins and how hotspots of multi-pollutant problems change in the 21st century. Our study provides an example of multi-pollutant problems from urban point sources. We show that future inputs of pollutants are projected to increase with increasing urbanization. We also show that it is technically possible to avoid these increases with advanced proven technologies to treat wastewater, except in Africa. In Africa, clean water availability is projected to remain a challenge because of the fast increasing population. This will consequently challenge the achievement of SDGs 6 and 11 in Africa. Our model may serve as an example for multi-pollutant modelling of diffuse sources such as agricultural runoff and other pollutants, such as pesticides 62 , antibiotics 24 and antimicrobial resistance. Another opportunity is to analyze the economic (e.g., costs), societal, institutional and political feasibilities of future pollution reduction options. This is important to identify region-specific solutions. Our long-term projections can help to increase the awareness of society and decision makers about pollution hotspots in the 21st century. This can facilitate short-term actions in different regions to avoid pollution in the future and contribute to achieve SDGs 6 and 11.

Model description and inputs

We used a model of Strokal et al. 31 that takes the sub-basin scale modelling approach of Strokal et al. 32 for nutrients and integrates modelling approaches for other pollutants 18 , 20 , 23 . We developed it further for future analyses of point-source inputs of pollutants to rivers (Supplementary Table 1 ). Our model quantifies inputs of five pollutants to 10,226 rivers: nitrogen (N), phosphorus (P), microplastics, triclosan and Cryptosporidium for 2010, 2050 and 2100. The model of Strokal et al. 31 was developed for 2010 taking the sub-basin modelling approach of Strokal, et al. 32 for N 29 , 32 , P 29 , 32 and integrating the existing modelling approaches for microplastics 18 , triclsan 20 and Cryptosporidium 23 . We developed the model for the years 2050 and 2100 based on the urbanization storylines of the SSPs and our assumptions. Our multi-pollutant model quantifies simultaneously annual inputs of the five pollutants to rivers at the sub-basin scale using the consistent spatial and temporal dataset for model inputs for 2010, 2050 and 2100. The model quantifies inputs of the five pollutants from sewer systems and open defecation. These are the point sources of the pollutants in rivers. Sewer systems discharge five pollutants to rivers. Open defecation is a point source of N, P and Cryptosporidium in our model. Model evaluation is presented below after the scenario descriptions.

Inputs of the pollutants to rivers from open defecation are quantified as a function of the population that is open defecating and the excretion or consumption rates of pollutants per person per year (Supplementary Tables 1 and 2 ). Inputs of pollutants from sewer systems are quantified as a function of the population that is connected to sewer systems, the excretion or consumption rates of pollutants per person per year and removal efficiencies of pollutants during treatment. We quantified inputs of the pollutants at 0.5° grid and then aggregate the results to 10,226 river sub-basins (Supplementary Table 1 ). Model inputs for 2010 are directly from Strokal, et al. 31 . Model inputs for 2050 and 2100 are based on the SSPs with different trends in urbanization and wastewater treatment (see scenario descriptions below).

Below, we explain how model inputs were derived (Supplementary Tables 1 – 6 ). Population for 2010, 2050 and 2100 are aggregated to 0.5° grid from the global, 0.125 degree cell database of Jones and O’Neill 53 . The number of people with sewer connections and open defecation are quantified at 0.5° grid using the population map of 0.5° grid and the fraction of people with sewer connections or open defecation. For 2010, the fraction of urban and rural people with sewer systems and open defecation were available by country from the Joint Monitoring Program (see details in Strokal et al. 31 and Hofstra and Vermeulen 11 ). We assigned the national values to grids of 0.5° grid. Then, we multiplied the number of people per grid (aggregated from Jones and O’Neill 53 ) with the fraction of people connected to sewer systems or open defecating (based on Hofstra and Vermeulen 11 ). For 2050 and 2100, we made assumptions for the fractions of people connected to sewer systems and with open defecation. These assumptions were based on storylines of SSPs for economy, population and urbanization (Fig. 2 , Supplementary Tables 4 – 6 ). Our assumptions differ among urban and rural people, and among developing and developed countries (see scenario descriptions below).

Excretion or consumption rates of pollutants were largely derived based on existing, evaluated approaches and sources. Excretion rates of N and P in human waste per person are quantified as a function of GDP (gross domestic product) at purchasing power parity, following the approach of Van Drecht et al. 46 , but adjusted to the unit of 2005 (see details in Strokal et al. 31 , Supplementary Tables 1 – 6 ). For 2010, 2050 and 2100, GDP at 0.5° grid was derived from the global SSP database with the projections from the International Institute for Applied Systems Analysis (IIASA, 63 ). P in detergents was from Van Drecht et al. 46 for the world regions (Supplementary Tables 1 – 6 ).

Excretion rates of Cryptosporidium were quantified based on the infection rate in developed (5%) and developing (10%) countries and the excretion rate per ill person (10 9 oocysts) according to Hofstra et al. 23 . For 2010, developed and developing countries were defined based on the Human Development Index (HDI), following the approach of Hofstra et al. 23 : HDI > 0.785 (developed) and HDI < 0.785 (developing). For 2050 and 2100, we made assumptions for HDI for countries depending on SSP storylines for the economy, population growth and urbanization (see scenario descriptions below and Supplementary Tables 4 – 6 ).

Consumption rates of microplastics per person per year were derived directly from Siegfried et al. 18 , but with some modifications (details are in Strokal et al. 31 ). Microplastics in sewer systems result from car tyres, PCPs (personal care products), household dusts and laundry. For PCPs, dust and laundry, consumption rates are 0.071, 0.08 and 0.12 kg of microplastics per person per year according to Siegfried, et al. 18 . We assumed that these values do not change over time. For tyres, this is different. Strokal et al. 31 assumed that developed countries will contribute more microplastics to sewage from car tyres as a side-effect of economic and infrastructural developments. Thus, we assigned 0.18 kg of microplastics from tyres per person for developed countries (HDI > 0.785) and 0.018 kg of microplastics from tyres per person for developing countries (HDI < 0.785) according to Strokal et al. 31 . We assumed changes in HDI by country in the future based on the SSPs storylines (see scenario descriptions below and Supplementary Tables 1 – 6 ).

Consumption rates of triclosan per person in the world were directly taken van Wijnen et al. 20 (0.5 kg per person per year for 2010). We assumed that the consumption rate will not change largely in the future and thus will remain as in 2010.

Removal efficiencies of pollutants during treatment were derived based on the existing studies. For N, P and Cryptosporidium , removal efficiencies were quantified by country using the national distribution of wastewater treatment types (primary, secondary, tertiary, no treatment) and their treatment efficiencies for pollutants, following the approaches of 11 , 23 , 46 (see Supplementary Tables 1 – 6 , Supplementary Figs. 1 – 14 ). The quantified national removal efficiencies were then assigned to corresponding grids of 0.5°. For 2010, national distributions of wastewater treatment types were derived from Hofstra and Vermeulen 11 with a few corrections for countries with missing data (details are in Strokal et al. 31 ). For 2050 and 2100, we assumed changes (low, moderate, high) in the distribution of the treatment types depending on the storylines of SSPs (see scenario descriptions below). These changes imply a shift towards a next treatment type: e.g., from primary to secondary to tertiary (Supplementary Tables 1 – 6 ). Removal efficiencies of pollutants for different treatment types were taken directly from literature (see Supplementary Table 3 ) and do not vary among years.

For triclosan and microplastics, removal efficiencies were quantified based on the approaches of van Wijnen et al. 20 and Siegfried et al. 18 (details are in Strokal et al. 31 ). We used the known removal rate of phosphorus to assume the removal of triclosan and microplastics. For our assumptions, we used data about the removal of triclosan and microplastics from literature 39 , 59 , 64 , 65 , 66 . Based on these data, we related average phosphorus removal in a watershed to triclosan removal. We formulated three classes of triclosan removal (0, 60 or 90%) and related these to known phosphorus removal in each sub-basin (details are in van Wijnen et al. 20 ). A similar approach was carried out for microplastics. We formulated four microplastics removal classes based on literature and related those to the known average phosphorus removal in each sub-basin 18 , 30 . These classes represent an average microplastics removal in each sub-basin. Microplastic removal depends on the size and density of the microplastics. Therefore, the removal at each individual WWTP will be dependent on these and other characteristics. In our study, on a global scale, we chose to assume average removal for each sub-basin.

Scenario description

Storylines of the five scenarios are summarized in Fig. 2 , Supplementary Tables 1 – 6 and Supplementary Figs. 1 – 14 . Our five scenarios are with low urbanization and low wastewater treatment rates (Low urb –Low wwt ), moderate urbanization and moderate wastewater treatment rates (Mod urb –Mod wwt ), high urbanization and low wastewater treatment rates (High urb –Low wwt ), high urbanization and moderate wastewater treatment rates (High urb –Mod wwt ), and high urbanization and high wastewater treatment rates (High urb –High wwt ) (Fig. 2 ). These scenarios follow future trends in the socio-economic development based on the existing SSPs 1 , 63 , combined with our assumptions for population with sewer connections, open defecation and for wastewater treatment capacities and technologies (Supplementary Tables 4 – 6 ). Below, we describe each scenario. Quantitative interpretations of the scenario assumptions are presented in Supplementary Tables 4 – 6 for 2050 and 2100, and inputs are given in Supplementary Figs. 1 – 14 .

The Low urb -Low wwt scenario is based on SSP3 projections for the socio-economic development (Fig. 2 , Supplementary Tables 4 – 6 ). The scenario assumes a fragmented world with difficulties to control population growth. In this world, It is projected a low economic development with low urbanization rates and high population growth. For example, a global population of approximately 12 billion people is projected for 2100, of which 58% will be urban (Supplementary Figs. 1 – 3 ). Low economic developments will not allow to develop technologies largely. For 2050, HDI is assumed to stay as in 2010 and increase by 10% between 2050 and 2100 on a county level (Supplementary Tables 4 – 6 ). The society will not focus on reducing or avoiding future river pollution. As a result, the fraction of the population with sewer connections (around one-third of the global population) and the treatment efficiencies of wastewater (e.g., 14–18% globally depending on pollutant) will remain in 2050 as in 2010 (Supplementary Figs. 3 , 9 – 13 ). The same holds for the wastewater treatment capacities. However, by 2100 more people may be connected to sewer systems (above one-third of the global population). This will result in higher capacities of the wastewater treatment plants with slightly improved treatment technologies (e.g., 21–24% of removal efficiencies globally depending on pollutant). However, future wastewater treatment efficiencies vary largely among world countries: e.g., 0–96% in 2100 depending on region and pollutant. In general, higher wastewater treatment efficiencies are projected for Europe, North America and Australia (Supplementary Figs. 9 – 13 ),

The Mod urb -Mod wwt scenario is based on SSP2 projections of the middle of the road for the socio-economic development (Fig. 2 , Supplementary Tables 4 – 6 ). The scenario assumes a moderate economic development, moderate urbanization rates and moderate population growth compared to the other scenarios. For example, 9 billion people are projected globally for 2100 and 80% will be urban (Supplementary Figs. 1 – 3 ). From 2010, HDI is assumed to increase by 10% by 2050 and further increase by 10% by 2100 on a county level (Supplementary Tables 4 – 6 ). Technological development follows the business as usual trends. As a result, more people will be connected to sewer systems than today (45% in 2050 and 68% in 2100 globally, Supplementary Fig. 3 ). A number of wastewater treatment plants will be constructed to maintain the increasing volume of the wastewater from connected population to sewer systems. The amount of waste that is collected will be treated with slightly improved wastewater treatment. For example, on average, 33–42% of removal efficiencies globally are projected for 2100. This range is for the five pollutants. The removal efficiencies vary largely among regions (0–97% depending on region and pollutant, Supplementary Figs. 9 – 13 ). The number of people connected to sewer systems will be larger for urban (over two-thirds) than for rural (less than one-third) population. Some people may still experience open defecation in 2050. By 2100, all people who opened defecated in 2050 will become connected to sewer systems.

The High urb -Low wwt scenario is based on SSP4 projections for the socio-economic development (Fig. 2 , Supplementary Tables 4 – 6 ). The scenario assumes a large gap between urban and rural developments. The economic development is projected to be moderate compared to the other scenarios. HDI is projected to increase as in the Mod urb –Mod wwt scenario (Supplementary Tables 1 – 3 , Supplementary Fig. 14 ). The population is projected to increase in the future, but not largely: e.g., around 30% between 2010 and 2100 globally. By 2100, the global population is projected to reach 9.3 billion people (Supplementary Fig. 3a ). However, the urban population will develop faster than the rural. Urbanization will be high: e.g., 76% and 90% of the global population will be urban in 2050 and 2100, respectively. As a result, the connection rate of the population to sewer systems will increase in the future for urban areas. For example, 80% of urban and 11% of rural population globally is projected to be connected to sewer systems in 2100 (Supplementary Figs. 1 – 3 ). Wastewater treatment capacities will be enough to maintain the waste from sewer systems and treatment will be improved as in the Mod urb -Mod wwt scenario. For rural areas, the fraction of people connected to sewer systems in 2050 may remain the same as in the Low urb -Low wwt scenario and will be improved by 2100 (Supplementary Tables 4 – 6 ). By 2050, some rural people may still open defecate. By 2100, all rural people who opened defecated in 2050 will become connected to sewer systems with better treatment.

The High urb –Mod wwt scenario is based on SSP5 projections for the socio-economic development (Supplementary Tables 4 – 6 , Fig. 2 ). The scenario assumes a high economic development with high urbanization and low population growth (Fig. 2 , Supplementary Table 4 ). For example, the total population globally is projected to increase by less than 10% between 2010 and 2100, reaching 7.4 billion people in 2100 (Supplementary Fig. 3a ). However, more than 90% of the global population will be urban in 2100. From 2010, HDI is assumed to increase by 20% by 2050 and further increase by 20% by 2100. The technological development is relatively high compared to the Mod urb -Mod wwt scenario. This will lead to a higher population with sewer connections. More than half of the global population will be connected to sewer systems in 2050. For 2100, this number is over two-thirds of the global population (Supplementary Figs. 1 – 3 ). The capacities of the wastewater treatment plants will be enough to manage the amount of waste from sewer systems. However, people will invest less in improving wastewater treatment. People will focus more on the economy rather than on reducing river pollution. As a result, wastewater treatment may follow the business as usual trends. For example, on average, 34–44% of the wastewater treatment efficiencies are projected globally for 2100. However, these efficiencies vary largely among regions (0–97% depending on area and pollutant, Supplementary Figs. 9 – 13 ). Furthermore, some people may still open defecate in nearby water systems in the future. By 2100, all people who opened defecated in 2050 will become connected to sewer systems.

The High urb –High wwt scenario is based on SSP1 projections for the socio-economic development (Fig. 2 , Supplementary Tables 4 – 6 ). The society will develop fast with high urbanization rates as comparable to the High urb –Mod wwt scenario. The global population is projected to reach 6.9 billion people in 2100 (Supplementary Fig. 3a ). The share of urban people globally is projected to be 77% in 2050 and 92% in 2100 (Supplementary Figs. 1 – 3 ). The share of the total connected people to sewer systems is projected to be 55% in 2050 and 82% in 2100. HDI is projected to increase in the same rate as in the High urb –Mod wwt scenario. However, in this world, a strong focus is on reducing or avoiding river pollution by using the best available advanced technologies in all areas. Technological development is high because of the high economic development. People will invest in improving technologies to treat wastewater with multiple pollutants. There will be opportunities to develop technologies for multiple pollutants and combine them with nature-based solutions. As a result, the wastewater treatment is assumed to be improved largely with high removal efficiencies (60–98% depending on year, area and pollutant, Supplementary Figs. 9 – 13 ).

Model evaluation

We evaluated the uncertainties in our model using four approaches following a building trust circle method 54 . This method has been applied in several water quality studies 32 , 67 , 68 . First, we compare model outputs with existing studies. Second, we compare the spatial pattern of the pollution problems with existing models for individual pollutants. Third, we perform a sensitivity analysis for pollution hotspots. Fourth, we perform a comprehensive sensitivity analysis for all important model inputs underlying the calculations. Fifth, we compare model inputs with independent datasets. Model validation against observed concentrations is, unfortunately, challenging. This is because our model does not quantify concentrations. Some of the existing global models calculate concentrations and were evaluated against observations (Supplementary Tables 7 – 8 ). Thus, we used those models to compare their results with ours for individual pollutants. Below, we elaborate on these five approaches. Details are in Supplementary Tables 7 – 12 and Supplementary Figs. 15 , 17 .

Approach 1: evaluating model outputs by comparing them with other models and studies for individual pollutants. This comparison is presented in Supplementary Table 7 . The results show that our model outputs for global inputs of nitrogen, phosphorus, microplastics, triclosan and Cryptosporidium are generally in line with other models and studies. For example, our model quantified 9.5 Tg of nitrogen to rivers from point sources in 2010. Other models quantified 6.4–10.4 Tg of nitrogen to rivers from points sources during 2000–2010 10 , 46 , 69 (Supplementary Table 7 ). For phosphorus, we quantified 1.6 Tg in 2010 whereas the other models quantified 1.0–1.5 Tg for the period of 2000–2010 10 , 46 , 69 . For 2050, we quantified 5.4–21.0 Tg of nitrogen and 0.6–3.5 Tg of phosphorus in 2050 (ranges for the five scenarios). van Puijenbroek et al. 10 quantified 13.5–17.9 Tg of nitrogen and 1.6–2.4 Tg of phosphorus in 2050 under the five SSPs. For Cryptosporidium , our model quantified 1.6 × 10 9 oocysts in 2010 which is 1.1–1.4 × 10 9 oocysts in another model in 2000–2010 11 , 23 (Supplementary Table 7 ). For 2050, our model quantified 0.4–2.9 × 10 9 oocysts (range for the five scenarios). For the Low urb -Low wwt scenario, this value is 2.44 × 10 9 oocysts, which is comparable with 2.28 × 10 9 oocysts from the other model 11 , 23 . To our knowledge, van Wijnen, et al. 20 is the only study quantifying triclosan export by rivers. Our estimates for Danube, Zhujiang and Ganges are comparable with estimates of van Wijnen et al. 20 (Supplementary Table 7 ). For microplastics, our model quantified 0.45 Tg entering rivers globally in 2010. Best 9 indicated loads of 0.41–4.00 Tg of plastics in 32 world’s rivers. This is higher than our estimate because Best 9 accounts for macro- and microplastics whereas we only consider microplastics. Avio et al. 13 indicated 0.27 Tg of plastics to oceans in some regions in the world. This is lower than our estimate because we quantify inputs of plastics to rivers and not to the oceans. The other reasons for the differences between our model and other studies are in data inputs and the spatial level of detail. We focus on sub-basin analyses with the consistent model inputs for multiple pollutants (Supplementary Table 7 , Supplementary Figs. 1 – 13 ).

Approach 2: evaluating model outputs by comparing the spatial variability in pollution hotspots with other studies. We reviewed the literature on pollution hotspots in the world for individual pollutants 8 , 9 , 10 , 11 , 12 , 16 , 55 , 56 , 70 . Our pollution hotspots for multiple pollutants are in line with the existing studies for individual pollutants. For example, most pollution often happens in densely populated and highly urbanized areas 8 , 9 , 10 , 11 , 12 , 16 , 55 , 56 . For example, Best 9 indicated over 80% of large transboundary rivers in the world with multiple pollutants. For many large cities in polluted regions, the demand for water already exceeds its availability. For example, water scarcity (ratio between the water demand and availability) has been already reported for cities in countries such as China (e.g., Shanghai, Beijing), India (e.g., Delhi, Kolkata, Bangalore, Hyderabad), Mexico, North America (e.g., Los Angeles) 70 . In the future, river pollution will further decrease the availability of clean water in many urban regions 4 , 7 , 8 , 71 . We show that it is technically possible to increase the availability of clean water with implementing advanced technologies (High urb _High wwt , Figs. 3 – 6 ). However, future analyses for multi-pollutant hotspots are lacking in the existing literature. A few global models performed future analysis for individual pollutants 10 , 11 , 18 , 20 where urbanization was taken into account by 2050. Their results indicate pollution hotspots where human activities are most intensive, which is in line with our study. However, studies exploring trends in multi-pollutant hotspots by 2100 do not exist. We explore trends in pollution hotspots for multi-pollutant problems covering the entire 21st century under the five scenarios with different socio-economic developments and levels of wastewater treatment.

Approach 3: evaluating model outputs for pollution hotspots by sensitivity analysis. In Fig. 5 , we showed multi-pollutant hotspots. These hotspots were defined as at least a 30% increase in inputs of more than one pollutant to rivers during 2010–2050, 2010–2100 and 2050–2100. This definition is modest and easier to understand and interpret. We checked if the pollution hotspots remain the same by changing a 30% increase to 10% (Supplementary Figs. 18 – 19 ) and 50% (Supplementary Fig. 20 – 21 ). Results of this sensitivity analysis indicate that our main messages stay the same: Africa will become a hotspot region with multiple pollutants in rivers in the 21st century and advanced technologies may help to reduce pollution in many rivers of the world.

Approach 4: evaluating model inputs by a sensitivity analysis. We performed a comprehensive sensitivity analysis for all important model inputs underlying the calculations. In total, there are 25 model input parameters included in this analysis. Every model input was changed by +10% and −10%. As a result, we did 50 runs of the model for the year 2010. We analyzed the results of the 50 runs for 10,226 sub-basins and five pollutants: Cryptosporidium , nitrogen, phosphorus, triclosan and microplastics. Details can be found in Supplementary Tables 9 – 12 and Supplementary Fig. 17 .

In general, increasing the model inputs (13 out of 25) that are responsible for excretion or consumption rates of pollutants in urban waste lead to more pollutants in rivers (Supplementary Tables 10 – 12 ). The opposite is observed when these model inputs are decreased. An exception is HDI for Cryptosporidium and microplastics. Model inputs that are responsible for wastewater treatments (6 out of 25) have the following effect on the model outputs: increases in these inputs lead to less pollutants in rivers and vice versa. Model inputs (6 out of 25) that are responsible for the number of people (urban and rural) connected to sewage systems have the following effect on the model outputs: increases in these inputs lead to more pollutants in rivers and vice versa (Supplementary Tables 10 – 12 ).

We find that model outputs are most sensitive to changes in 2–5 out of the 25 model inputs. The sensitivities vary among sub-basins and pollutants. These model inputs are HDI (sensitive for Cryptosporidium and microplastics), the fractions of secondary (sensitive for triclosan and microplastics) and tertiary (sensitive for all five pollutants) treatment, and the removal efficiencies of secondary (sensitive for triclosan and microplastics) and tertiary (sensitive for all five pollutants) treatment. We analyze model outputs for 10,226 sub-basins that are sensitive to changes in those 2–5 model inputs. Supplementary Tables 11 – 12 show the percentages of the sub-basin areas where model outputs for the five pollutants change by: <5%, 5–10%, 10–50% and >50%. Supplementary Fig. 17 shows the location of the sub-basins for which model outputs are sensitive to one or more model inputs.

The model results for sub-basins covering over two-thirds of the global surface area changed by less than 5% (Supplementary Tables 11 – 12 ). For ≤13% of the global surface area the model outputs changed between 5–10%. This is for all pollutants. For ≤8% of the global area, the changes are between 10–50% in the model outputs. Exceptions are Cryptosporidium and microplastics, which are relatively sensitive for HDI. In one-third of the sub-basin area the model output for Cryptosporidium changed 10–50% as a result of changes in HDI. For microplastic, the changes may be even higher. However, the number of basins with changes above 50% is small. These results show that HDI is an important model input for Cryptosporidium and microplastics (see Supplementary Tables 1 , 9 – 12 ).

Approach 5: evaluating model inputs by comparing them with independent datasets. We provide this comparison in Supplementary Table 8 , Supplementary Figs. 15 and 16 . Comparison results build trust in our model inputs. We compared the following important model inputs for 2010 and 2050 scenarios: total population, population with sewer connections, distribution of treatment types, removal efficiencies of pollutants, nutrients in human excretion (Supplementary Table 8 ). We compared these inputs with van Puijenbroek et al. 10 who recently published global analyses of nutrient inputs to rivers from point sources. We also compared our population from Jones and O’Neill 53 with another global dataset from Kc and Lutz 34 (Supplementary Fig. 16 ). Our model inputs are well compared with the mentioned studies. Furthermore, we compared our HDI index for 2010 and 2050 with the HDI index from Crespo Cuaresma and Lutz 57 (Supplementary Fig. 15 ). HDI is an important input in our model to quantify the excretion of Cryptosporidium . HDI influences the treatment developments and consumption of microplastics associated with the use of car tyres. Our values for HDI under the five scenarios are well compared with the values of Crespo Cuaresma and Lutz 57 ( R 2  > 0.88 for the five scenarios).

Results of these five approaches give us trust in using our multi-pollutant model to explore future trends in inputs of multiple pollutants to rivers from urbanization activities. All data are available in Strokal et al. 72 and Strokal et al. 73 .

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

All the datasets generated and analysed during this study are publicly available in the Data Archiving and Networked Services (DANS Easy) repository: https://doi.org/10.17026/dans-zyx-jce3 73 . The data will be available for download from 01–04–2021. The data supporting the findings of this study are described in the following metadata record: https://doi.org/10.6084/m9.figshare.13333796 72 .

Code availability

All equations to the model are provided in the supplementary information files of this study and in the Data Archiving and Networked Services (DANS Easy) repository: https://doi.org/10.17026/dans-zyx-jce3 . The data will be available for download from 01–04–2021.

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M.S. (the corresponding author) was financially supported by a Veni-grant (0.16.Veni.198.001) and a KNAW-MOST SURE + project (5160957392).

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M.S. led this manuscript. M.S. was responsible for designing the manuscript, developing a multi-pollutant model, and analyzing and writing the manuscript. C.K. substantially assisted in designing the manuscript, developing the model and analyzing the results. Z.B., W.F., N.H., A.A.K., L.V., M.T.H.V., J.E.S. and J.W., contributed largely in developing the global multi-pollutant model that was used in the manuscript for future analyses of the impact of urbanization on river pollution. They and other authors provided information to the manuscript and advised on the analyses. All authors assisted the interpretations of the Shared Socio-economic Pathways. These pathways are used in the manuscript for multiple pollutants. All authors read and commented on the text. All authors approved the final version and were involved in the accountability for all aspects of the manuscript.

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Strokal, M., Bai, Z., Franssen, W. et al. Urbanization: an increasing source of multiple pollutants to rivers in the 21st century. npj Urban Sustain 1 , 24 (2021). https://doi.org/10.1038/s42949-021-00026-w

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Review article, effects of water pollution on human health and disease heterogeneity: a review.

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  • 1 Research Center for Economy of Upper Reaches of the Yangtse River/School of Economics, Chongqing Technology and Business University, Chongqing, China
  • 2 School of Economics and Management, Huzhou University, Huzhou, China

Background: More than 80% of sewage generated by human activities is discharged into rivers and oceans without any treatment, which results in environmental pollution and more than 50 diseases. 80% of diseases and 50% of child deaths worldwide are related to poor water quality.

Methods: This paper selected 85 relevant papers finally based on the keywords of water pollution, water quality, health, cancer, and so on.

Results: The impact of water pollution on human health is significant, although there may be regional, age, gender, and other differences in degree. The most common disease caused by water pollution is diarrhea, which is mainly transmitted by enteroviruses in the aquatic environment.

Discussion: Governments should strengthen water intervention management and carry out intervention measures to improve water quality and reduce water pollution’s impact on human health.

Introduction

Water is an essential resource for human survival. According to the 2021 World Water Development Report released by UNESCO, the global use of freshwater has increased six-fold in the past 100 years and has been growing by about 1% per year since the 1980s. With the increase of water consumption, water quality is facing severe challenges. Industrialization, agricultural production, and urban life have resulted in the degradation and pollution of the environment, adversely affecting the water bodies (rivers and oceans) necessary for life, ultimately affecting human health and sustainable social development ( Xu et al., 2022a ). Globally, an estimated 80% of industrial and municipal wastewater is discharged into the environment without any prior treatment, with adverse effects on human health and ecosystems. This proportion is higher in the least developed countries, where sanitation and wastewater treatment facilities are severely lacking.

Sources of Water Pollution

Water pollution are mainly concentrated in industrialization, agricultural activities, natural factors, and insufficient water supply and sewage treatment facilities. First, industry is the main cause of water pollution, these industries include distillery industry, tannery industry, pulp and paper industry, textile industry, food industry, iron and steel industry, nuclear industry and so on. Various toxic chemicals, organic and inorganic substances, toxic solvents and volatile organic chemicals may be released in industrial production. If these wastes are released into aquatic ecosystems without adequate treatment, they will cause water pollution ( Chowdhary et al., 2020 ). Arsenic, cadmium, and chromium are vital pollutants discharged in wastewater, and the industrial sector is a significant contributor to harmful pollutants ( Chen et al., 2019 ). With the acceleration of urbanization, wastewater from industrial production has gradually increased. ( Wu et al., 2020 ). In addition, water pollution caused by industrialization is also greatly affected by foreign direct investment. Industrial water pollution in less developed countries is positively correlated with foreign direct investment ( Jorgenson, 2009 ). Second, water pollution is closely related to agriculture. Pesticides, nitrogen fertilizers and organic farm wastes from agriculture are significant causes of water pollution (RCEP, 1979). Agricultural activities will contaminate the water with nitrates, phosphorus, pesticides, soil sediments, salts and pathogens ( Parris, 2011 ). Furthermore, agriculture has severely damaged all freshwater systems in their pristine state ( Moss, 2008 ). Untreated or partially treated wastewater is widely used for irrigation in water-scarce regions of developing countries, including China and India, and the presence of pollutants in sewage poses risks to the environment and health. Taking China as an example, the imbalance in the quantity and quality of surface water resources has led to the long-term use of wastewater irrigation in some areas in developing countries to meet the water demand of agricultural production, resulting in serious agricultural land and food pollution, pesticide residues and heavy metal pollution threatening food safety and Human Health ( Lu et al., 2015 ). Pesticides have an adverse impact on health through drinking water. Comparing pesticide use with health life Expectancy Longitudinal Survey data, it was found that a 10% increase in pesticide use resulted in a 1% increase in the medical disability index over 65 years of age ( Lai, 2017 ). The case of the Musi River in India shows a higher incidence of morbidity in wastewater-irrigated villages than normal-water households. Third, water pollution is related to natural factors. Taking Child Loess Plateau as an example, the concentration of trace elements in water quality is higher than the average world level, and trace elements come from natural weathering and manufacture causes. Poor river water quality is associated with high sodium and salinity hazards ( Xiao et al., 2019 ). The most typical water pollution in the middle part of the loess Plateau is hexavalent chromium pollution, which is caused by the natural environment and human activities. Loess and mudstone are the main sources, and groundwater with high concentrations of hexavalent chromium is also an important factor in surface water pollution (He et al., 2020). Finally, water supply and sewage treatment facilities are also important factors affecting drinking water quality, especially in developing countries. In parallel with China rapid economic growth, industrialization and urbanization, underinvestment in basic water supply and treatment facilities has led to water pollution, increased incidence of infectious and parasitic diseases, and increased exposure to industrial chemicals, heavy metals and algal toxins ( Wu et al., 1999 ). An econometric model predicts the impact of water purification equipment on water quality and therefore human health. When the proportion of household water treated with water purification equipment is reduced from 100% to 90%, the expected health benefits are reduced by up to 96%.. When the risk of pretreatment water quality is high, the decline is even more significant ( Brown and Clasen, 2012 ).

To sum up, water pollution results from both human and natural factors. Various human activities will directly affect water quality, including urbanization, population growth, industrial production, climate change, and other factors ( Halder and Islam, 2015 ) and religious activities ( Dwivedi et al., 2018 ). Improper disposal of solid waste, sand, and gravel is also one reason for decreasing water quality ( Ustaoğlua et al., 2020 ).

Impact of Water Pollution on Human Health

Unsafe water has severe implications for human health. According to UNESCO 2021 World Water Development Report , about 829,000 people die each year from diarrhea caused by unsafe drinking water, sanitation, and hand hygiene, including nearly 300,000 children under the age of five, representing 5.3 percent of all deaths in this age group. Data from Palestine suggest that people who drink municipal water directly are more likely to suffer from diseases such as diarrhea than those who use desalinated and household-filtered drinking water ( Yassin et al., 2006 ). In a comparative study of tap water, purified water, and bottled water, tap water was an essential source of gastrointestinal disease ( Payment et al., 1997 ). Lack of water and sanitation services also increases the incidence of diseases such as cholera, trachoma, schistosomiasis, and helminthiasis. Data from studies in developing countries show a clear relationship between cholera and contaminated water, and household water treatment and storage can reduce cholera ( Gundry et al., 2004 ). In addition to disease, unsafe drinking water, and poor environmental hygiene can lead to gastrointestinal illness, inhibiting nutrient absorption and malnutrition. These effects are especially pronounced for children.

Purpose of This Paper

More than two million people worldwide die each year from diarrhoeal diseases, with poor sanitation and unsafe drinking water being the leading cause of nearly 90% of deaths and affecting children the most (United Nations, 2016). More than 50 kinds of diseases are caused by poor drinking water quality, and 80% of diseases and 50% of child deaths are related to poor drinking water quality in the world. However, water pollution causes diarrhea, skin diseases, malnutrition, and even cancer and other diseases related to water pollution. Therefore, it is necessary to study the impact of water pollution on human health, especially disease heterogeneity, and clarify the importance of clean drinking water, which has important theoretical and practical significance for realizing sustainable development goals. Unfortunately, although many kinds of literature focus on water pollution and a particular disease, there is still a lack of research results that systematically analyze the impact of water pollution on human health and the heterogeneity of diseases. Based on the above background and discussion, this paper focuses on the effect of water pollution on human health and its disease heterogeneity.

Materials and Methods

Search process.

This article uses keywords such as “water,” “water pollution,” “water quality,” “health,” “diarrhea,” “skin disease,” “cancer” and “children” to search Web of Science and Google Scholar include SCI and SSCI indexed papers, research reports, and works from 1990 to 2021.

Inclusion-Exclusion Criteria and Data Extraction Process

The existing literature shows that water pollution and human health are important research topics in health economics, and scholars have conducted in-depth research. As of 30 December 2021, 104 related literatures were searched, including research papers, reviews and conference papers. Then, according to the content relevancy, 19 papers were eliminated, and 85 papers remained. The purpose of this review is to summarize the impact of water pollution on human health and its disease heterogeneity and to explore how to improve human health by improving water pollution control measures.

Information extracted from all included papers included: author, publication date, sample country, study methodology, study purpose, and key findings. All analysis results will be analyzed according to the process in Figure 1 .

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FIGURE 1 . Data extraction process (PRISMA).

The relevant information of the paper is exported to the Excel database through Endnote, and the duplicates are deleted. The results were initially extracted by one researcher and then cross-checked by another researcher to ensure that all data had been filtered and reviewed. If two researchers have different opinions, the two researchers will review together until a final agreement is reached.

Quality Assessment of the Literature

The JBI Critical Appraisal Checklist was used to evaluate the quality of each paper. The JBI (Joanna Briggs Institute) key assessment tool was developed by the JBI Scientific Committee after extensive peer review and is designed for system review. All features of the study that meet the following eight criteria are included in the final summary:1) clear purpose; 2) Complete information of sample variables; 3) Data basis; 4) the validity of data sorting; 5) ethical norms; (6); 7) Effective results; 8) Apply appropriate quantitative methods and state the results clearly. Method quality is evaluated by the Yes/No questions listed in the JBI Key Assessment List. Each analysis paper received 6 out of 8.

The quality of drinking water is an essential factor affecting human health. Poor drinking water quality has led to the occurrence of water-borne diseases. According to the World Health Organization (WHO) survey, 80% of the world’s diseases and 50% of the world’s child deaths are related to poor drinking water quality, and there are more than 50 diseases caused by poor drinking water quality. The quality of drinking water in developing countries is worrying. The negative health effects of water pollution remain the leading cause of morbidity and mortality in developing countries. Different from the existing literature review, this paper mainly studies the impact of water pollution on human health according to the heterogeneity of diseases. We focuses on diarrhea, skin diseases, cancer, child health, etc., and sorts out the main effects of water pollution on human health ( Table 1 ).

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TABLE 1 . Major studies on the relationship between water pollution and health.

Water Pollution and Diarrhea

Diarrhea is a common symptom of gastrointestinal diseases and the most common disease caused by water pollution. Diarrhea is a leading cause of illness and death in young children in low-income countries. Diarrhoeal diseases account for 21% of annual deaths among children under 5 years of age in developing countries ( Waddington et al., 2009 ). Many infectious agents associated with diarrhea are directly related to contaminated water ( Ahmed and Ismail, 2018 ). Parasitic worms present in non-purifying drinking water when is consumed by human beings causes diseases ( Ansari and Akhmatov., 2020 ) . It was found that treated water from water treatment facilities was associated with a lower risk of diarrhea than untreated water for all ages ( Clasen et al., 2015 ). For example, in the southern region of Brazil, a study found that factors significantly associated with an increased risk of mortality from diarrhoea included lack of plumbed water, lack of flush toilets, poor housing conditions, and overcrowded households. Households without access to piped water had a 4.8 times higher risk of infant death from diarrhea than households with access to piped water ( Victora et al., 1988 )

Enteroviruses exist in the aquatic environment. More than 100 pathogenic viruses are excreted in human and animal excreta and spread in the environment through groundwater, estuarine water, seawater, rivers, sewage treatment plants, insufficiently treated water, drinking water, and private wells ( Fong and Lipp., 2005 ). A study in Pakistan showed that coliform contamination was found in some water sources. Improper disposal of sewage and solid waste, excessive use of pesticides and fertilizers, and deteriorating pipeline networks are the main causes of drinking water pollution. The main source of water-borne diseases such as gastroenteritis, dysentery, diarrhea, and viral hepatitis in this area is the water pollution of coliform bacteria ( Khan et al., 2013 ). Therefore, the most important role of water and sanitation health interventions is to hinder the transmission of diarrheal pathogens from the environment to humans ( Waddington et al., 2009 ).

Meta-analyses are the most commonly used method for water quality and diarrhea studies. It was found that improving water supply and sanitation reduced the overall incidence of diarrhea by 26%. Among Malaysian infants, having clean water and sanitation was associated with an 82% reduction in infant mortality, especially among infants who were not breastfed ( Esrey et al., 1991 ). All water quality and sanitation interventions significantly reduced the risk of diarrhoeal disease, and water quality interventions were found to be more effective than previously thought. Multiple interventions (including water, sanitation, and sanitation measures) were not more effective than single-focus interventions ( Fewtrell and Colford., 2005 ). Water quality interventions reduced the risk of diarrhoea in children and reduced the risk of E. coli contamination of stored water ( Arnold and Colford., 2007 ). Interventions to improve water quality are generally effective in preventing diarrhoea in children of all ages and under 5. However, some trials showed significant heterogeneity, which may be due to the research methods and their conditions ( Clasen et al., 2007 ).

Water Pollution and Skin Diseases

Contrary to common sense that swimming is good for health, studies as early as the 1950s found that the overall disease incidence in the swimming group was significantly higher than that in the non-swimming group. The survey shows that the incidence of the disease in people under the age of 10 is about 100% higher than that of people over 10 years old. Skin diseases account for a certain proportion ( Stevenson, 1953 ). A prospective epidemiological study of beach water pollution was conducted in Hong Kong in the summer of 1986–1987. The study found that swimmers on Hong Kong’s coastal beaches were more likely than non-swimmers to complain of systemic ailments such as skin and eyes. And swimming in more polluted beach waters has a much higher risk of contracting skin diseases and other diseases. Swimming-related disease symptom rates correlated with beach cleanliness ( Cheung et al., 1990 ).

A study of arsenic-affected villages in the southern Sindh province of Pakistan emphasized that skin diseases were caused by excessive water quality. By studying the relationship between excessive arsenic in drinking water caused by water pollution and skin diseases (mainly melanosis and keratosis), it was found that compared with people who consumed urban low-arsenic drinking water, the hair of people who consumed high-arsenic drinking water arsenic concentration increased significantly. The level of arsenic in drinking water directly affects the health of local residents, and skin disease is the most common clinical complication of arsenic poisoning. There is a correlation between arsenic concentrations in biological samples (hair and blood) from patients with skin diseases and intake of arsenic-contaminated drinking water ( Kazi et al., 2009 ). Another Bangladesh study showed that many people suffer from scabies due to river pollution ( Hanif et al., 2020 ). Not only that, but water pollution from industry can also cause skin cancer ( Arif et al., 2020 ).

Studies using meta-analysis have shown that exposure to polluted Marine recreational waters can have adverse consequences, including frequent skin discomfort (such as rash or itching). Skin diseases in swimmers may be caused by a variety of pathogenic microorganisms ( Yau et al., 2009 ). People (swimmers and non-swimmers) exposed to waters above threshold levels of bacteria had a higher relative risk of developing skin disease, and levels of bacteria in seawater were highly correlated with skin symptoms.

Studies have also suggested that swimmers are 3.5 times more likely to report skin diseases than non-swimmers. This difference may be a “risk perception bias” at work on swimmers, who are generally aware that such exposure may lead to health effects and are more likely to detect and report skin disorders. It is also possible that swimmers exaggerated their symptoms, reporting conditions that others would not classify as true skin disorders ( Fleisher and Kay. 2006 ).

Water Pollution and Cancer

According to WHO statistics, the number of cancer patients diagnosed in 2020 reached 19.3 million, while the number of deaths from cancer increased to 10 million. Currently, one-fifth of all global fevers will develop cancer during their lifetime. The types and amounts of carcinogens present in drinking water will vary depending on where they enter: contamination of the water source, water treatment processes, or when the water is delivered to users ( Morris, 1995 ).

From the perspective of water sources, arsenic, nitrate, chromium, etc. are highly associated with cancer. Ingestion of arsenic from drinking water can cause skin cancer and kidney and bladder cancer ( Marmot et al., 2007 ). The risk of cancer in the population from arsenic in the United States water supply may be comparable to the risk from tobacco smoke and radon in the home environment. However, individual susceptibility to the carcinogenic effects of arsenic varies ( Smith et al., 1992 ). A high association of arsenic in drinking water with lung cancer was demonstrated in a northern Chilean controlled study involving patients diagnosed with lung cancer and a frequency-matched hospital between 1994 and 1996. Studies have also shown a synergistic effect of smoking and arsenic intake in drinking water in causing lung cancer ( Ferreccio et al., 2000 ). Exposure to high arsenic levels in drinking water was also associated with the development of liver cancer, but this effect was not significant at exposure levels below 0.64 mg/L ( Lin et al., 2013 ).

Nitrates are a broader contaminant that is more closely associated with human cancers, especially colorectal cancer. A study in East Azerbaijan confirmed a significant association between colorectal cancer and nitrate in men, but not in women (Maleki et al., 2021). The carcinogenic risk of nitrates is concentration-dependent. The risk increases significantly when drinking water levels exceed 3.87 mg/L, well below the current drinking water standard of 50 mg/L. Drinking water with nitrate concentrations lower than current drinking water standards also increases the risk of colorectal cancer ( Schullehner et al., 2018 ).

Drinking water with high chromium content will bring high carcinogenicity caused by hexavalent chromium to residents. Drinking water intake of hexavalent chromium experiments showed that hexavalent chromium has the potential to cause human respiratory cancer. ( Zhitkovich, 2011 ). A case from Changhua County, Taiwan also showed that high levels of chromium pollution were associated with gastric cancer incidence ( Tseng et al., 2018 ).

There is a correlation between trihalomethane (THM) levels in drinking water and cancer mortality. Bladder and brain cancers in both men and women and non-Hodgkin’s lymphoma and kidney cancer in men were positively correlated with THM levels, and bladder cancer mortality had the strongest and most consistent association with THM exposure index ( Cantor et al., 1978 ).

From the perspective of water treatment process, carcinogens may be introduced during chlorine treatment, and drinking water is associated with all cancers, urinary cancers and gastrointestinal cancers ( Page et al., 1976 ). Chlorinated byproducts from the use of chlorine in water treatment are associated with an increased risk of bladder and rectal cancer, with perhaps 5,000 cases of bladder and 8,000 cases of rectal cancer occurring each year in the United States (Morris, 1995).

The impact of drinking water pollutants on cancer is complex. Epidemiological studies have shown that drinking water contaminants, such as chlorinated by-products, nitrates, arsenic, and radionuclides, are associated with cancer in humans ( Cantor, 1997 ). Pb, U, F- and no3- are the main groundwater pollutants and one of the potential causes of cancer ( Kaur et al., 2021 ). In addition, many other water pollutants are also considered carcinogenic, including herbicides and pesticides, and fertilizers that contain and release nitrates ( Marmot et al., 2007 ). A case from Hebei, China showed that the contamination of nitrogen compounds in well water was closely related to the use of nitrogen fertilizers in agriculture, and the levels of three nitrogen compounds in well water were significantly positively correlated with esophageal cancer mortality ( Zhang et al., 2003 ).

In addition, due to the time-lag effect, the impact of watershed water pollution on cancer is spatially heterogeneous. The mortality rate of esophageal cancer caused by water pollution is significantly higher downstream than in other regions due to the impact of historical water pollution ( Xu et al., 2019 ). A study based on changes in water quality in the watershed showed that a grade 6 deterioration in water quality resulted in a 9.3% increase in deaths from digestive cancer. ( Ebenstein, 2012 ).

Water Pollution and Child Health

Diarrhea is a common disease in children. Diarrhoeal diseases (including cholera) kill 1.8 million people each year, 90 per cent of them children under the age of five, mostly in developing countries. 88% of diarrhoeal diseases are caused by inadequate water supply, sanitation and hygiene (Team, 2004). A large proportion of these are caused by exposure to microbially infected water and food, and diarrhea in infants and young children can lead to malnutrition and reduced immune resistance, thereby increasing the likelihood of prolonged and recurrent diarrhea ( Marino, 2007 ). Pollution exposure experienced by children during critical periods of development is associated with height loss in adulthood ( Zaveri et al., 2020 ). Diseases directly related to water and sanitation, combined with malnutrition, also lead to other causes of death, such as measles and pneumonia. Child malnutrition and stunting due to inadequate water and sanitation will continue to affect more than one-third of children in the world ( Bartlett, 2003 ). A study from rural India showed that children living in households with tap water had significantly lower disease prevalence and duration ( Jalan and Ravallion, 2003 ).

In conclusion, water pollution is a significant cause of childhood diseases. Air, water, and soil pollution together killed 940,000 children worldwide in 2016, two-thirds of whom were under the age of 5, and the vast majority occurred in low- and middle-income countries ( Landrigan et al., 2018 ). The intensity of industrial organic water pollution is positively correlated with infant mortality and child mortality in less developed countries, and industrial water pollution is an important cause of infant and child mortality in less developed countries ( Jorgenson, 2009 ). In addition, arsenic in drinking water is a potential carcinogenic risk in children (García-Rico et al., 2018). Nitrate contamination in drinking water may cause goiter in children ( Vladeva et al.., 2000 ).

Discussions

This paper reviews the environmental science, health, and medical literature, with a particular focus on epidemiological studies linking water quality, water pollution, and human disease, as well as studies on water-related disease morbidity and mortality. At the same time, special attention is paid to publications from the United Nations and the World Health Organization on water and sanitation health research. The purpose of this paper is to clarify the relationship between water pollution and human health, including: The relationship between water pollution and diarrhea, the mechanism of action, and the research situation of meta-analysis; The relationship between water pollution and skin diseases, pathogenic factors, and meta-analysis research; The relationship between water pollution and cancer, carcinogenic factors, and types of cancer; The relationship between water pollution and Child health, and the major childhood diseases caused.

A study of more than 100 literatures found that although factors such as country, region, age, and gender may have different influences, in general, water pollution has a huge impact on human health. Water pollution is the cause of many human diseases, mainly diarrhoea, skin diseases, cancer and various childhood diseases. The impact of water pollution on different diseases is mainly reflected in the following aspects. Firstly, diarrhea is the most easily caused disease by water pollution, mainly transmitted by enterovirus existing in the aquatic environment. The transmission environment of enterovirus depends on includes groundwater, river, seawater, sewage, drinking water, etc. Therefore, it is necessary to prevent the transmission of enterovirus from the environment to people through drinking water intervention. Secondly, exposure to or use of heavily polluted water is associated with a risk of skin diseases. Excessive bacteria in seawater and heavy metals in drinking water are the main pathogenic factors of skin diseases. Thirdly, water pollution can pose health risks to humans through any of the three links: the source of water, the treatment of water, and the delivery of water. Arsenic, nitrate, chromium, and trihalomethane are major carcinogens in water sources. Carcinogens may be introduced during chlorine treatment from water treatment. The effects of drinking water pollution on cancer are complex, including chlorinated by-products, heavy metals, radionuclides, herbicides and pesticides left in water, etc., Finally, water pollution is an important cause of children’s diseases. Contact with microbiologically infected water can cause diarrhoeal disease in children. Malnutrition and weakened immunity from diarrhoeal diseases can lead to other diseases.

This study systematically analyzed the impact of water pollution on human health and the heterogeneity of diseases from the perspective of different diseases, focusing on a detailed review of the relationship, mechanism and influencing factors of water pollution and diseases. From the point of view of limitations, this paper mainly focuses on the research of environmental science and environmental management, and the research on pathology is less involved. Based on this, future research can strengthen research at medical and pathological levels.

In response to the above research conclusions, countries, especially developing countries, need to adopt corresponding water management policies to reduce the harm caused by water pollution to human health. Firstly, there is a focus on water quality at the point of use, with interventions to improve water quality, including chlorination and safe storage ( Gundry et al., 2004 ), and provision of treated and clean water ( Khan et al., 2013 ). Secondly, in order to reduce the impact of water pollution on skin diseases, countries should conduct epidemiological studies on their own in order to formulate health-friendly bathing water quality standards suitable for their specific conditions ( Cheung et al., 1990 ). Thirdly, in order to reduce the cancer caused by water pollution, the whole-process supervision of water quality should be strengthened, that is, the purity of water sources, the scientific nature of water treatment and the effectiveness of drinking water monitoring. Fourthly, each society should prevent and control source pollution from production, consumption, and transportation ( Landrigan et al., 2018 ). Fifthly, health education is widely carried out. Introduce environmental education, educate residents on sanitary water through newspapers, magazines, television, Internet and other media, and enhance public health awareness. Train farmers to avoid overuse of agricultural chemicals that contaminate drinking water.

Author Contributions

Conceptualization, XX|; methodology, LL; data curation, HY; writing and editing, LL; project administration, XX|.

This article is a phased achievement of The National Social Science Fund of China: Research on the blocking mechanism of the critical poor households returning to poverty due to illness, No: 20BJY057.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: water pollution, human health, disease heterogeneity, water intervention, health cost

Citation: Lin L, Yang H and Xu X (2022) Effects of Water Pollution on Human Health and Disease Heterogeneity: A Review. Front. Environ. Sci. 10:880246. doi: 10.3389/fenvs.2022.880246

Received: 21 February 2022; Accepted: 09 June 2022; Published: 30 June 2022.

Reviewed by:

Copyright © 2022 Lin, Yang and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Xiaocang Xu, [email protected]

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Assessment of Water Quality and Identification of Polluted Risky Regions Based on Field Observations & GIS in the Honghe River Watershed, China

Affiliations State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210093, P. R. China, Kunming Institute of Environmental Science, Kunming 650032, P.R. China

* E-mail: [email protected]

Affiliation Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), Beijing 100094, P. R. China

Affiliation College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210093, P.R. China

Affiliations State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210093, P. R. China, Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), Beijing 100094, P. R. China

  • Chang-An Yan, 
  • Wanchang Zhang, 
  • Zhijie Zhang, 
  • Yuanmin Liu, 
  • Cai Deng, 

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  • Published: March 13, 2015
  • https://doi.org/10.1371/journal.pone.0119130
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Fig 1

Water quality assessment at the watershed scale requires not only an investigation of water pollution and the recognition of main pollution factors, but also the identification of polluted risky regions resulted in polluted surrounding river sections. To realize this objective, we collected water samplings from 67 sampling sites in the Honghe River watershed of China with Grid GIS method to analyze six parameters including dissolved oxygen (DO), ammonia nitrogen (NH 3 -N), nitrate nitrogen (NO 3 -N), nitrite nitrogen (NO 2 -N), total nitrogen (TN) and total phosphorus (TP). Single factor pollution index and comprehensive pollution index were adopted to explore main water pollutants and evaluate water quality pollution level. Based on two evaluate methods, Geo-statistical analysis and Geographical Information System (GIS) were used to visualize the spatial pollution characteristics and identifying potential polluted risky regions. The results indicated that the general water quality in the watershed has been exposed to various pollutants, in which TP, NO 2 -N and TN were the main pollutants and seriously exceeded the standard of Category III. The zones of TP, TN, DO, NO 2 -N and NH 3 -N pollution covered 99.07%, 62.22%, 59.72%, 37.34% and 13.82% of the watershed respectively, and they were from medium to serious polluted. 83.27% of the watershed in total was polluted by comprehensive pollutants. These conclusions may provide useful and effective information for watershed water pollution control and management.

Citation: Yan C-A, Zhang W, Zhang Z, Liu Y, Deng C, Nie N (2015) Assessment of Water Quality and Identification of Polluted Risky Regions Based on Field Observations & GIS in the Honghe River Watershed, China. PLoS ONE 10(3): e0119130. https://doi.org/10.1371/journal.pone.0119130

Academic Editor: Yiguo Hong, CAS, CHINA

Received: October 20, 2014; Accepted: January 9, 2015; Published: March 13, 2015

Copyright: © 2015 Yan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: All relevant data are within the paper.

Funding: This work was supported by the National Basic Research Program of China (973, Grant No. 2010CB951404) and the National Natural Science Foundation of China (No. 41175088 and No.40971024) to W.C.Z. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

With the rapid economic and social development in recent decades, non-point source pollution to the environment from livestock and poultry industry, aquaculture industry, planting industry, and rural domestic sewage to our living space centered on the Earth has drawn much attention to the public and policy-makers. Among various pollutions, water environmental pollution, as a vital threat to human being health, also became the most remarkable issue for the sustainable development. Niemi GJ et al [ 1 ] reported that human activities mainly impact surface water quality through effluent discharges, using of agricultural chemicals, in addition to the increased exploitation of water resources. Many rivers in the developing countries are heavily polluted due to anthropogenic activities [ 2 ], especially in China. There are 426 of 532 rivers under monitoring that are undergoing different kinds and levels of pollutions, and 13 river sections of 7 main rivers in China flowing through 15 cities are highly polluted [ 3 ]. According to “Annual Report of Environment Quality in China, 2011” [ 4 ], Yangtze River and Zhujiang River were in good condition, Songhua River and Huaihe River were lightly polluted, Yellow River and Liaohe River were in medium contaminated, while Haihe River was heavily polluted. In general, the water quality monitoring for 204 rivers in 409 national river sections indicated that I-III, IV-V and poor V accounted for 59.9%、23.7% and 16.4%, respectively. The water pollution in China has become a serious issue to economic, social sustainable development, not only because the imbalance between available scant water resource and dense population, but also the inefficient of water resources regulation and management. As the secondary tributary on the upper reaches of the Huaihe River, the biggest river in the eastern China, the water quality of the Honghe River will definitely affect the Huai River. It is, therefore, essential to investigate and assess the present situation of water pollution along the Honghe River in the Honghe watershed, so as to understand the whole conditions of the Huaihe River Basin in Eastern China.

Water quality evaluation is considered as critical issue in recent years, especially when freshwater is becoming a scarce resource in the future [ 5 ]; the world-widely used principal methods for water quality assessment include single factor pollution index (SFPI) [ 6 ], complex pollution indices (CPI) [ 7 ], analytic hierarchy process (AHP) [ 8 ], fuzzy comprehensive evaluation (FCE) [ 9 ], gray evaluation (GE) [ 10 ], artificial neural network (ANN) [ 11 ], principal component analysis (PCA) [ 12 ], Fuzzy comprehensive-quantifying assessment (FCQA) [ 13 ], water quality identification (WQI) [ 14 , 15 ]…etc. However, these methods have a common disadvantage: they have to work with the spatial discontinuity of sampling data. This disadvantage directly leads to an obvious shortcoming of such methodology that they cannot identify hazardous and vulnerable regions resulted from polluted surrounding river sections. Water quality assessment at the basin scale requires not only a large number of variable and corresponding evaluation factors, but also a spatial distribution of pollution levels based on every variable and evaluation factor. GIS, as the most powerful tool for handling spatial data, performing spatial analysis and manipulating spatial outputs [ 16 ], becomes a unique tool for geo-statistical analysis and spatial interpolation utilizing measured samples with known values to estimate unknown values so as to visualize the pollution spatial patterns [ 17 ]. GIS and modeling have been specifically used in risk assessment and environmental pollution studies at a watershed scale [ 18 – 25 ].

Aiming at evaluating water quality spatially and identifying the potential polluted risky zones with GIS approach, this paper deals with the site observation data of water quality collected from a field campaign conducted within about 15 days in the Honghe River watershed located in the upper reaches of the Huaihe River Basin, Eastern China.

Methods and Materials

1 study area.

No specific permits were required for the study area. The location is not privately owned or protected, and the study studies did not involve endangered or protected species.

The study area, Honghe River watershed, is located between N32°25′-33°29′ and E113°19′-115°33′on the up-stream of the Huaihe River Basin, Eastern China ( Fig. 1 ). In Fig. 1 , we can see that the Honghe River watershed is situated on the north to Shayinhe River, south to Huaihe main stream, and west to Tangbai River of Yangtze Basin. It flows through Henan and Anhui provinces as well as another thirteen counties (cities) of China. The Honghe River, the secondary tributaries on up-stream of the Huaihe River Basin, originates from mount Nanao in Wugang, Henan province, and drains an extensive area with river channel about 312 km in length. It flows across Wugang city, Wuyang, Xiping, Shangcai, Xincai counties, discharges into the Huaihe River, and three main tributaries, Beiruhe, Zhentouhe, and Ruhe Rivers, constitute its river system. The total drainage of this watershed is about 12,380km 2 , in which mountainous area, hilly area and plain area occupied about 20%, 20% and 60%, respectively.

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With rapid economic development in late 1970s, Honghe stream began to experience increased pollution, especially in recent years the surface water of the drainage is polluted dramatically. The contaminants come mainly from domestic sewage, industrial wastewater, agriculture fertilizers, pesticides and human activity productions. The main pollution of the Honghe River watershed is the agricultural non-point source pollution. Enough evidences show that over the last 20 years, water quality in the Honghe River watershed has deteriorated significantly.

2 Sampling and Chemical Analysis

In order to ensure enough spatial water sampling representative in such a large watershed while decreasing the pressure of logistic support in the field to the minimum, the sampling strategy was designed to account for enough impacts being posed from the main tributary inputs upon downstream water quality by subdivided the watershed drainage area into 400 equal grids according to geographic location with GIS tool [ 26 ]. The sampling activity was conducted following “Technical Specification Requirements for Monitoring of Surface Water and Waste Water” (HJ/T91–2002) [ 27 ] in May 2011. Three water samples from each sampling site were taken and analyzed. Each sampling site was positioned by Global Positioning System (GPS) ( Table 1 ), and chemical analyses were carried out immediately after the water samples were brought back, the analyses procedure strictly obeys the guideline described in “Monitoring and Analysis Method of Water and Waste water” [ 28 ]. The measured chemical parameters include field DO, NH 3 -N, NO 3 -N, NO 2 -N, TN and TP. All the observed data was facilitated and visualized to perform spatial analysis with GIS software and achieved for further studies.

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3 Water quality assessment method

water pollution thesis

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water pollution thesis

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Results and Discussion

1 water quality assessment.

The water pollution level determined for 67 samples with 6 water quality parameters by single factor pollution index method are shown in Table 4 .

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https://doi.org/10.1371/journal.pone.0119130.t004

The data in the Table 4 indicates all the monitoring sections has undergone various pollutions differently, but mostly come from TP and NO 2 -N. According to the level III standard in “ Environmental Quality Standards for Surface Water ”, TP in all 63 monitoring sections all exceeded the standard limit of the category, reaching 94.03% in total, with 6.48 times higher than normal standard. The average pollution index was 7.09 in all monitoring sections, much higher than the limit value of serious polluted level ( Table 2 ). Similarly, the NO 2 -N pollution also reached the serious polluted level with the average pollution index of 8.44, specifically, there were 27 exceeding standard sections in total with 40.30% averaged exceeding standard rate and 0.98 averaged exceeding standard times. TN, compared with phosphorus and NO 2 -N, was in better situation, which had about 36 exceeding standard sections accounting for 53.73% in total, and the exceeding standard times and average single pollution index were 2.59 and 2.05, respectively, the pollution level was classified into heavily polluted. Both of DO and NH 3 -N were in slightly polluted level, with the average pollution index of 0.99 and 0.64, accordingly, 31 and 14 exceeding standard sections took up the averaged exceeding standard times of 0.39 and 1.27, respectively. The concentrations of NO 3 -N in all the monitoring sections were much lower than the standard limit of Category III. As far as what this paper was concerned, the TP, NO 2 -N and TN constituted of the main pollutants were far beyond the standard limit of Category III. Based on studies by Liu [ 32 ], it implied that non-point source caused by livestock and poultry industry, aquaculture industry, and planting industry was the major pollution source in Honghe River Watershed.

According to the water quality assessment results obtained by comprehensive pollution index method listed in Table 3 , the pie chart in Fig. 2 exhibited the water quality levels. Among 67 monitoring sections, 18 monitoring sections accounting for 26.87% in total were polluted seriously, water quality then was categorized in Poor V. 18 monitoring sections, about 26.87% of all, were heavily polluted, water quality was categorized in level V. 7 monitoring sections occupying about 10.45% in total were medium polluted and categorized in level IV. 19 monitoring sections accounting for 28.36% in total were slightly polluted and categorized in Category III. Only 5 monitoring sections accounting for 7.45% of all were categorized in sub-cleanness, where water quality was classified into level II. In summary, I-III water quality levels only took up 36%, and all the rest belonged to water quality Category IV and higher, therefore, the water quality in the Honghe river watershed was poorer in general.

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2 Identification of Polluted Risky Regions

In order to characterize the spatial pattern of the polluted and risky vulnerable zones in study area, the spatial distribution of single factor pollution index as well as comprehensive pollution indices for NH 3 -N, NO 2 -N, TP, TN, and DO were processed with GIS, geostatistical methods. Two evaluation criteria such as Mean Standardized Prediction Error (MSPF) and Root Mean Square Standardized Prediction Error (RMSS) were applied to recognize appropriate geostatistical method of spatial interpolation. The closer to 0 for MSPF and 1 for RMSS, the more precision it is. According to experimental results of different geostatistical methods of spatial interpolation, the most efficient and prominent method for observed data was the Ordinary Kriging (OrKrig) [ 33 ]. These evaluate results which were obtained by OrKrig interpolation method are best and acceptable, as the Table 5 shows here below:

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Fig. 3a illustrated the spatial variability of the single factor pollution index of NH 3 -N. From these maps, the dispersion of NH 3 -N pollution in the study area can be recognized and five major zones with NH 3 -N pollution over the watershed can be found. The first zone, defined as non-pollution area, located surrounding the Wugang, Queshan and Suiping counties, almost covered more than half of the study area (62.14%) as indicated in Table 6 . The second zone, defined as slightly polluted area, mainly concentrated in Zhentouhe and Honghe tributary river sections, covered about 24.04% of the study area. The third zone, defined as moderately polluted area, were separated one from another by miles of open land to the northwest, north-central, east and middle parts of the study area, covered 8.62% of the study area. The fourth zone was the heavily polluted area, covering only 5.2% and mainly distributed in the northwest part of the study area. The fifth zone, defined as seriously polluted area, was almost 0% in study area. So far, most parts of the watershed were polluted lightly by NH 3 -N, and the rest was under medium and heavy pollution risk.

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Concerning the single factor pollution index of NO 2 -N and the single factor pollution index of NO 3 -N, the spatial characteristics over the study area were interpolated with OrKrig interpolation method, and the result was shown in Fig. 3b . Five categories regarding to pollution levels were classified non-pollution, slightly polluted, moderately polluted, heavily polluted and seriously polluted to evaluate the spatial characteristics of each pollutant over the studied watershed. As summarized in Table 6 , non-pollution zone was about 42.86%, slightly polluted zone occupied about 19.80%, the moderately polluted zone took up 26.55% of the whole watershed, respectively, and mainly of them located in the northwest, middle, and easternmost parts of study area. The last 10.79% of the study area, distributed in the northwest and east part of the study area, was defined as the heavily polluted zone. Serious polluted zone based on this analysis didn’t appear up.

Fig. 3c exhibited the spatial characteristics of TN pollution and the classified pollution levels, obtained by the similar way as NO 2 -N. As Table 6 list below, the non-pollution, slightly polluted, moderately polluted, heavily polluted and seriously polluted zones were accounting for about 18.51%, 19.27%, 26.01%, 29.96% and 6.25% of the whole watershed, respectively. The pollution above the moderate polluted level predominated most of the watershed areas except the westernmost, south-central and north-central parts of the watershed.

The spatial distribution of TP pollution referred by single factor pollution index is displaying in Fig. 3d . According to the spatial analyses on TP pollution levels in Fig. 3d and the water parameters coverage percentage in Table 6 , about 57.7% of the watershed, mainly located in the northwest, was seriously polluted. Middle and south, approximately covered 27.62% and 13.75% of the watershed were heavily and moderately polluted. Only about 0.93% of the watershed was recognized as slightly polluted area. To wrap it up, the whole watershed was seriously polluted by the TP.

The TP pollution spatially interpolated with OrKrig approach for DO was mapped, spatial distributions for five pollution levels processed in similar way was exhibited in Fig. 3e . Spatial analyses on the results shown in Table 6 indicated that most of the watershed was moderately polluted accounting for about 59.72% of the total area, mainly distributed in the Midwest part of the watershed. The rest parts about 1.83% and 38.45% were non-pollution and slightly polluted zone, respectively.

Comprehensive pollution indices for each sampling site was spatially interpolated with OrKrig approach, and it was classified into 6 pollution levels according to classification standards on comprehensive pollution indices. The results were presented in Fig. 3f . Based on the statistics, the spatial characteristics of the 6 pollution levels were analyzed, and the following conclusions were drawed: (1) The cleanness level with comprehensive pollution indices less than 0.2, was almost non-existed in the studied watershed. (2) The sub-cleanness level with the comprehensive pollution indices varies from 0.2 to 0.4 only accounted for about 0.01% of the studied area. (3) The slightly polluted level with the comprehensive pollution indices varies from 0.4 to 0.7 covered about 16.72% of the studied area. (4) The moderately polluted level with the comprehensive pollution indices varies from 0.7 to 1.0 occupied about 13.69% of the studied area. (5) The heavily polluted level with the comprehensive pollution indices varies from 1.0 to 2.0 covered 42% of the studied area. (6) The seriously polluted level with the comprehensive pollution indices bigger than 2.0 took up about 27.58% of the rest. Those heavily even seriously polluted areas mainly located in the northeast, middle, south-central and easternmost parts of the watershed.

Liu [ 32 ] showed us that the pollution of surface water in Honghe River Watershed was mainly the agricultural non-point source pollution. It mainly included pollutions from livestock and poultry industry, aquaculture industry, planting industry, and rural domestic sewage. Six spatial patterns of related social-economic statistical indicators at county level of the Honghe River Watershed in 2011 [ 34 ] were used to verify reliability of the identified pollution risky regions, which were shown in Fig. 4 . Specifically, Fig. 3a displayed that Shangcai Area was most polluted by NH 3 -N, and the possible reasons for this were the largest population ( Fig. 4a ) and the largest consumption of chemical fertilizers ( Fig. 4d ). Likewise, the output of livestock and poultry in Xiping County ( Fig. 4e ) powerfully proved that this area was most polluted by TN ( Fig. 3c ). In other words, domestic sewage, livestock and poultry industry were the mainly polluted sources of NH 3 -N and TN in Honghe River Watershed. All these inferences had been confirmed by Liu [ 32 ].

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Conclusions

Therefore, in this paper, the evaluation method combining with single factor pollution index, comprehensive pollution index and GIS approach was successfully applied to evaluate water pollution variability of major water pollutants at monitoring sites and identify the potential polluted risky zones in Honghe River Watershed, upper stream of the Huaihe River Basin, eastern China. The results indicated: referring to the value standardized by Category III in “ Environmental Quality Standards for Surface Water ”, TP, NO 2 -N and TN were the main and serious excessive pollutants. According to the classification standards of pollution index, the whole water quality was comparatively poorer, with 22% of sections in Category I-III and 68% in Category IV-poor V. The main reasons to the watershed pollution were the discharge of industrial and agricultural wastes, domestic sewage such as people and livestock excrements around the watershed.

Geostatistical analysis and GIS helped to identify the polluted risky regions for each parameter. The zones of TP, TN, DO, NO 2 -N and NH 3 -N pollution, covering 99.07%, 62.22%, 59.72%, 37.34% and 13.82% of the watershed respectively and undergoing from medium to serious pollution, mainly distributed in northwest and middle of the watershed, and must be paid highly attention by water quality management department. Similarly, 83.27% of the watershed in total was polluted by comprehensive pollutants of medium, heavy and serious polluted level, which mainly lied on the northeast, middle, south-central and easternmost of the watershed. At the end of this paper, combined with spatial patterns of social-economic statistical indicators at county level of the Honghe River Watershed in 2011, this paper analyzed the major source of water pollution in Honghe River Watershed and verified the reliability of the identified polluted risky regions.

It is believed that these reliable results could be very useful and valuable to pollution control strategies, as well as future plan and management on the watershed; besides, they are also helpful to further research on water quality simulation and validate the simulation accuracy in watershed space.

Author Contributions

Conceived and designed the experiments: CAY WCZ. Performed the experiments: CAY WCZ ZJZ YML CD NN. Analyzed the data: CAY. Contributed reagents/materials/analysis tools: CAY. Wrote the paper: CAY WCZ.

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Water is a very important element for living organisms, and it is helpful in the circulation and transmission of nutrients in the biosphere. Due to industrialization, urbanization, and rapid increase in human population, the demand for water has increased sharply, and the quality has declined drastically. Although water has the ability to purify itself, when the concentration of pollutants generated from man-made sources becomes so high that it exceeds the self-purifying ability of water, then the water becomes polluted. Degradation of the physical, chemical, and biological characteristics of water by natural and man-made processes in such a way that it is unsuitable for humans and other biological communities. This is called water pollution.

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Dadsena, N., Nair, S.J. (2024). Water Pollution: A Review. In: Deka, J.K., Robi, P.S., Sharma, B. (eds) Emerging Technology for Sustainable Development. EGTET 2022. Lecture Notes in Electrical Engineering, vol 1061. Springer, Singapore. https://doi.org/10.1007/978-981-99-4362-3_11

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The Impacts of Water Pollution Emissions on Public Health in 30 Provinces of China

1 Department of International Trade and Economics, Business School, Hohai University, Changzhou 213022, China; nc.ude.uhh@62715002

Shijiong Qin

2 Department of Accounting, Business School, Hohai University, Changzhou 213022, China; nc.ude.uhh@0220133681

Chenjun Zhang

Yung-ho chiu.

3 Department of Economics, Soochow University, 56, Kueiyang St., Sec. 1, Taipei 10048, Taiwan; wt.ude.ucs@uihce

4 Department of Business Administration, Business School, Hohai University, Changzhou 213022, China; nc.ude.uhh@10019102

China’s economy in recent decades has developed at a very rapid speed, as evidenced by its GDP jumping to second place in the world. Although utilization of domestic water resources has helped spur economic development, sewage discharge as an undesirable output has unfortunately caused many negative effects on human health, causing concern from all walks of life. Therefore, governments in China at all levels are committed to urban sewage treatment policies in order to reduce the negative impact of water pollution on society. While most existing studies have targeted the macro-level modes of economic development and environmental pollution, their selection of research objects is too narrow by failing to adequately consider China’s water pollution and the consequential national health crisis. This study takes cities in 30 provinces of China as the research objects and applies various influencing factors of urban wastewater treatment and health (as two stages) to the modified two-stage dynamic Slacks-Based Measures (SBM) Data Envelopment Analysis (DEA) model. The results reveal that the overall efficiency of each province is increasing and that the efficiency of the wastewater treatment stage is greater, thus contributing to overall efficiency. Conversely, the health stage’s efficiency is far lower than the wastewater treatment stage’s efficiency, which has a notably adverse effect on overall efficiency. In addition, most input-output variables need much improvement. Based on the findings herein, we offer specific suggestions to each province for improving sewage treatment capacity, the level of medical care, and the quality of national health.

1. Introduction

China’s economy has developed very rapidly to become the second largest one in the world ever since it opened itself up and initiated widespread reforms. However, at the same time, an increasingly prominent conflict has arisen between its economic development and ecological environment. Especially in the rapid development of urbanization in recent years, domestic water resources are becoming heavily polluted and a major health hazard. The problem of urban water pollution is now gravely restricting the sustainable development of the country’s economy.

With the dramatic development of the urban economy and the rising urban population year by year, water pollution caused by manufacturing and basic living needs has turned increasingly serious. Industrial areas are typically concentrated in the suburbs, and the large-scale machinery and equipment are discharging high amounts of sewage. Moreover, the sewage treatment efficiency of enterprises is low, which leads to secondary water pollution. The daily lifestyles of urban residents also produce large amounts of domestic wastewater, and hence, urban water pollution problems need to be urgently solved. Considering the above situations, the aim of this paper is to improve China’s urban sewage treatment capacity and the health of its residents.

In the existing research on environmental pollution, most scholars study the macro-level perspective of the relationship between the environment and the economy, but the economic impacts of environmentally friendly innovation and its knowledge externalities on productivity have attracted increasing attention from the research community. Aldieri et al. (2019) [ 1 ] presented empirical evidence of public policy strategies that support the dissemination of environmentally friendly technologies. The results of a systematic literature review showed that innovation activities on environmental issues can produce important knowledge spillovers. Aldieri et al. (2019) [ 2 ] discussed the relationship between enterprises’ knowledge resource strategy and green innovation. The results showed that the emphasis of environmental innovation has shifted from internal knowledge to external knowledge. Government policies that promote complementary and coordinated knowledge in the environmental field are able to contribute to greater knowledge transfer and more sustainable development. Studies have thus demonstrated the role of innovation in sustainable development from various perspectives.

Data envelopment analysis (DEA) is an important and widely used analytical method. Its basic idea is to determine the best practice boundary of effective decision-making units (DMUs) to cover all inefficient DMUs. The greatest advantage of using DEA is that there is no need to specify a production function, and that DEA can consider multiple inputs and outputs at the same time. Based on a modified two-stage dynamic Slacks-Based Measures (SBM) model, we study 30 provincial-level administrative units in China (not including Hong Kong, Macao, Taiwan, and Tibet autonomous region) and their overall efficiency, two-stage efficiency, and the efficiencies of the variables wastewater treatment and health (as two stages) from 2014 to 2017, employing scientific data that reflect their sewage treatment and health situation.

The contributions of this paper are mainly the following two aspects. First, we target the national level for the first time to study completed investments into wastewater treatment projects, sewage treatment plants, municipal sewage treatment capacities, and other indicators of specific dynamic efficiency in the 30 provinces. Accordingly, the paper provides reference data for the country and the provinces from the macro-level and microlevel aspects. Second, the research’s innovation is evaluating “wastewater treatment” and “health” in two stages. In the first stage, wastewater treatment efficiency, completed investments into wastewater treatment projects, and sewage treatment plants are the input variables, while municipal sewage treatment capacity is the desirable output variable, and total wastewater discharge, chemical oxygen demand (COD) concentration, and heavy metal pollutants’ equivalent concentration are undesirable output variables. On this basis, we can measure the efficiency of health in the second stage. In this stage, the number of health technicians and local fiscal medical and health expenditures are taken as input variables, while average life expectancy and carcinogenic risk are desirable and undesirable output variables, respectively. By comparing overall efficiency, two-stage efficiency, each component’s efficiency of 30 provinces in China, and combining them with China’s specific national conditions and regional economic differences such as human geography, we are able to observe the variables’ volatility, analyze the input-output efficiency values in greater detail, and put forward corresponding proposals to the provinces, which should provide a scientific basis for urban sewage treatment in the country.

2. Literature Review

According to previous references, the majority of scholarly research on urban sewage generated by firms’ production and humans’ lifestyles and their treatment can be carried out from the following three aspects.

2.1. The Impact of Water Pollution Caused by Urban Production and Living

Water pollution has negative impacts on the environment. Using a drink-y reservoir and an irrigation-t reservoir as research subjects, Deng et al. (2020) [ 3 ] found that metals can precipitate from water into sediment in 10–15 days, and both reservoirs are heavily contaminated with heavy metals (chromium, manganese, copper, zinc, Cd, mercury, and lead), which can be harmful to human health. The major anthropogenic sources of pollution are fuel mix and industrial mills (6.4%) and agricultural activities (38%) used for drinking; and fuel mix and industrial mills (4.9%), agricultural activities (32.9%), and mines and quarries used for irrigation (62.1%). Therefore, to reduce human health risks, freshwater should be stored 10–15 days before drinking or irrigation. Wojtkowska and Bojanowski (2018) [ 4 ] analyzed the impact of sewage and sewage management on the water quality of rivers by evaluating their eutrophication level. The research objects were the waterways of the Dluga, Pisia Gagolina, and Utrata rivers and the Srebrna stream. The results showed that the total phosphorus concentration in Utrata’s water is the lowest (mean 0.38 mg P/L), and the total phosphorus concentration in Diuga’s water is the highest (mean 2.8 mg P/L). The average concentration of orthophosphate is between 0.23 mg P/L (Dluga) and 0.45 mg P/L (Pisia Gqgolina). Moreover, the degree of phosphate pollution in the four river channels and the degree of eutrophication in their water are relatively high because the main sources of pollution in all rivers are wastewater from sewage treatment plants, leakage (damage or deliberate leakage) from septic tanks, surface runoff from agricultural areas and roads, and landfill leachate. According to the regional environmental protection watchdog, all watercourses are in poor ecological condition. The object of municipal sewage treatment has an important influence on water quality, and its pollutants can be carried from the sewage discharge place for a long distance.

Shi et al. (2019) [ 5 ] used a two-stage dynamic DEA model to study the impact of water pollution on the environment and national health. The authors divided variables into two stages. In the first stage (production), labor, energy, and water consumption were the input variables, and GDP was the desired output variable, while COD, CO 2 , and chromium emissions were undesired output variables. In the second stage (health), the local financial health expenditure, and the number of health technicians were input variables. The health index and the population mortality rate were the desired output and the undesired output variables, respectively. Fixed asset investment was selected as the carryover indicator in both stages. The findings showed that urban sewage damaged the sustainable development of the environment and economy to a certain extent and also dragged down the degree of national health.

Domestic and foreign scholars have conducted extensive research on the negative effects of urban sewage. Ho and Goethals’s (2019) [ 6 ] critical analysis of the contributions of individuals and subsets of sustainable development goals (SDGs) points to the global problem of lake and pond eutrophication caused by massive sewage discharge. Looking at sewage indicators, benthic cover measurements, macroalgae biometrics, and pollution scoring tools, Abaya et al. (2018) [ 7 ] studied Hawaiian coral reefs and detected that effluent from production may have contributed to the decline. Xin et al. (2019) [ 8 ] studied the impact of complex pollution sources on water quality in the Dengsha River basin of the city of Dalian, pointing out that the deterioration of water quality caused by excessive nutrient emissions from various point and non-point sources has been a global challenge. Li et al. (2019) [ 9 ] used the green bias technology progress model derived on the directional distance function to measure technology progress and its determinants obtained on inputs and outputs in 30 provinces and regions of China from 1999 to 2015. The results demonstrated that most of China’s provinces and regions overuse water in industrial production, and that output-oriented technological advances exacerbate the discharge of water pollutants which affect the green and sustainable development of the economy prior to the implementation of the 11th Five-year Plan (2006–2010).

Many scholars have taken sewage treatment plants (WWTPs) as the research object, finding that the carbon dioxide (CO 2 ), methane (CH 4 ), nitrous oxide (N 2 O), hydrogen sulfide (H 2 S), and other gases generated by WWTPs in the sewage treatment process also have many negative effects. Hu et al. (2019) [ 10 ] took 344 centralized sewage treatment plants out of 152 typical national industrial parks (NIPs) as the research target and established a set of calculation methods to measure the three main greenhouse gases of CO 2 , CH 4 , and N 2 O emitted by WWTPs in NIPs. Their main results are as follows: 5.64 million tons of CO 2 equivalent (CO 2 -eq) were emitted, including 1.63 Mt CO 2 -eq on-site, 1.45 Mt CO 2 -eq off-site, and 2.56 Mt CO 2 -eq off-site related to sludge disposal. It can be seen that sewage treatment produced a large amount of greenhouse gases, causing a certain degree of impact on the environment and human health.

2.2. Municipal Sewage Treatment Methods

An et al. (2019) [ 11 ] discovered that national environmental laws and regulations to curb industrial wastewater came directly from the source and the structural impact because stringent environmental regulations can offset to some extent the inflow of the foreign direct energy-induced effect brought by the scale effect. In addition, the study also highlighted the increasing environmental investment and trade liberalization to improve the management of important industrial wastewater. Linge et al. (2012) [ 12 ] used datasets of 375 chemicals measured in reverse osmosis (RO) treated by WW (secondary wastewater), finding that dissolved organic carbon (DOC) in RO osmosis was between 3.7 and 10.7 mu g/L, attributable to at least one detected chemical, most of which was due to chemicals detected in less than 25% of samples. In conclusion, RO-treated WW is highly safe and can be regarded as an indirect source of drinking water.

Chen et al. (2020) [ 13 ] proposed a new method called water splitting coagulation (WSC), which synchronizes the treatment of wastewater containing both metal and organic pollutants. WSC uses water splitting in the bipolar membrane (BM) to constructively generate flocculation components (Ni (OH)(x)(2-x) +) by controlling the hydroxide transfer and cation transfer within BM and on the cation exchange membrane. Through using water cracking in BM, metal ion contaminants (M n+ , i.e., Ni 2+ , Fe 3+ , Cr 3+ /Cr 6+ , Co 2+ ) in electroplating wastewater are combined with free hydroxide ions and form a structure of controllable flocculation. Due to the water splitting in BM and the transition of metal ions on the cation exchange membrane, the water decomposition in BM and the transfer of metal ions across the cation exchange membrane is precisely controlled by adjusting the relevant parameters. Active ion migration during the WSC process follows a delivery mechanism, and it constructively results in a flocculating constituent (M(OH) x (n-x)+ ) by controlling the hydroxide delivery and cation delivery inside a BM and across a cation exchange membrane. Sure enough, the metal hydroxide is capable of absorbing textile dyes in (Dye) y M(OH) x (n-x)+ form following the interaction as that in an electro-coagulation process. Results manifesting this technology have great potential in complex industrial wastewater treatment. Membrane technology has become one of the important technologies for wastewater treatment in the printing and dyeing industry. Using literature metrology with National Knowledge Infrastructure (CNKI) and Web of Science (TM) (SCI), Liu et al. (2017) [ 14 ] studied the application status and prospect of membrane technology in wastewater treatment of printing and dyeing industry. The results showed that by 2015, the total capacity of the membrane technology in dyeing wastewater treatment in China was about 662,000 m(3).d(-1) and the number of applications was 128 (with capacity >= 500 m(3).d(-1)). Besides, “Ultrafiltration (UF) + ‘reverse osmosis’ (RO)” was the most widely applied process of membrane technologies in dyeing wastewater treatment, and the “membrane bioreactor (MBR) + RO” and “Continuous Membrane Filtration (CMF) + RO” were closely behind. Membrane technology is a promising and important technology in the wastewater treatment of the printing and dyeing industry.

The use of plants and natural processes to treat wastewater is an issue of interest to technicians and scientists around the world. Taking a southwestern sewage treatment plant in Poland as the research project, Bawiec et al. (2018) [ 15 ] analyzed the effects of temperature and sunlight on nitrate removal from hydroponic wastewater under greenhouse conditions. The findings denoted under mild climate conditions that the amount of solar radiation reaching the earth’s surface is not enough to ensure an effective year-round wastewater treatment process for hydroponic systems. Traditional wastewater treatment procedures are often insufficient to remove emerging contaminants such as PhACs (pharmaceuticals). Photocatalysis is an advanced oxidation process (AOP) that has been widely used in the removal of PhACs from wastewater due to its low operating cost. However, the problem of photocatalytic complete mineralization of PhACs is still a challenge. Based on the above background, Akpotu et al. (2019) [ 16 ] reviewed photocatalytic degradation, biodegradation, and the mechanism of degradation of phenolic compounds in wastewater and introduced the application of photocatalytic biodegradation system to degradation of PhACs in wastewater. The results deemed that a complete photocatalytic/biodegradation system is the key to complete mineralization of PhACs. Aerated wetland is an increasingly recognized natural wastewater treatment technology that relies heavily on mechanical aeration, but the relationship between volume oxygen mass transfer coefficient of wastewater in aerated wetland and organic carbon concentration remains unacquainted. Boog et al. (2020) [ 17 ] used clean water and pilot horizontal flow aerated wetland wastewater to treat domestic sewage and conducted oxygen migration experiments in laboratory-scale gravel columns. By increasing soluble CODs, the factor describing the ratio of volumetric oxygen transfer coefficient to clean water in wastewater was reduced. The derived regression equation alpha = 1.066 − 1.372 × 10 −3 mg CODs l-1 was incorporated into the numerical process model to simulate the effect of reduced oxygen migration on the hypothetical HF aerated wetland. Simulation results revealed that a high concentration of organic carbon will reduce oxygen migration in HF aerated wetland systems, thus reducing the treatment effect. Abbasi and Tauseef (2018) [ 18 ] reported a novel plate-flow-root horizontal bioreactor (SHEFROL (R)) on their own earlier development for the first time, hinting that the use of artemisoma annua can be used to treat wastewater quickly and efficiently. In addition to extensive primary and secondary treatments in the removal of suspended solids, chemical oxygen demand, and biological oxygen demand, E. prostrata is capable of substantially removing excess nutrients and heavy metals such as copper, nickel, and manganese leading to eutrophication (nitrogen and phosphorus); the system is expected to yield significant results in sewage treatment. Using the example of Cape Cod, Massachusetts, U.S.A., Perry et al. (2020) [ 19 ] detected that biofiltration and biofiltration systems can be used to treat sewage to reduce the pollutant load in sewage pipes and receiving water because they are highly efficient at removing pollutants and can adapt to different field conditions. Retained soil filters (RSFs) for a vertically flowing constructed wetland have been successfully tested as a form of continuous post-treatment of sewage from sewage treatment plants, however, RSFs cannot be used in dry weather conditions. Given that, Brunsch et al. (2020) [ 20 ] brought up a new method that uses a double retained soil filter. In dry weather, RSFs can be used to polish sewage from sewage treatment plants, and in overflow events can help retain soil filters to treat combined sewage overflow. The study was conducted in two pilot cities, which identified dual-use RSF is a promising approach to wastewater treatment that can be expanded and employed.

The difficulty of dewatering residual sludge is the main problem of sewage treatment. Zhang et al. (2019) [ 21 ] employed chitosan (CTS), an organic polymer flocculant widely used in water and sewage treatment, in sludge treatment. After CTS treatment, the moisture content of sludge cake decreased from 85.9% to 83.0%, SV30 to about 1/2, the volume of sludge decreased to 82.9%, and the precipitation and dehydration performance of sludge were greatly improved. Abu Qdais (2019) [ 22 ] also took an in-depth look at sludge treatment by using the multi-criteria analytic hierarchy process (AHP) to build an AHP model for optimal sludge management to help Jordan’s water authorities deal with sludge from sewage treatment plants. The AHP model included three main standards, nine sub-standards, and five sludge management alternatives. The analysis implicated that the priority of the sludge management scheme is as follows: recovery of energy from sludge is the highest priority option, followed by composting, untreated disposal, and evaporation tanks, and finally the least priority option is the production of building materials from sludge.

2.3. Health Effects of Municipal Sewage Treatment Residues

Volker et al. (2019) [ 23 ] quantitatively evaluated in vitro (100 species) and in vivo (20 species) data, respectively. To sum up, the results demonstrated that while traditional treatment methods can effectively reduce toxicity, residual effects in wastewater may pose a risk to the ecosystem based on effect trigger values. Lopes et al. (2020) [ 24 ] detected bacterial community structure by denatuated gel gradient electrophoresis (DGGE) and evaluated antibiotic resistance genes (ARG) by polymerase chain reaction (PCR). ARG has been detected in sludge samples after alkalization treatment, which may have an impact on human health. Current technologies used in sewage treatment plants (STPs) and WWTPs do not completely eliminate pollutants such as non-steroidal anti-inflammatory drugs (NSAIDs). Almeida et al. (2020) [ 25 ] indicated NSAIDs have been found in a variety of environmental water samples, with concentrations ranging from ng/L to mu g/L, causing serious environmental and public health problems. Assress et al. (2020) [ 26 ] conducted seasonal measurements of incoming and effluent water samples from three sewage treatment plants and one drinking water treatment plant in South Africa for eight commonly used azole antifungal agents. Moreover, the risk quotient (RQ) method was used to investigate human health risks associated with wastewater and drinking water. Human health risk assessments validated that fluconazole poses a high risk in wastewater and drinking water and may cause harm to human health and safety.

Metals and chemicals in wastewater undoubtedly have special toxicity. Bozecka and Sanak-Rydlewska (2018) [ 27 ] pointed out that metals interfering with the natural biological balance and inhibiting self-cleaning processes in water have particular toxic effects, such as cobalt, which enter the environment from industrial wastewater from electrochemical plants and metallurgical industries. Supporting this notion, Alharbi and El-Sorogy (2019) [ 28 ] collected 27 samples of coastal seawater and analyzed Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Cd, Hg, and Pb using an inductively coupled plasma mass spectrometer. The results exhibited that the concentration order of bb3 is: Sr > Ni > V > Cu > Zn > Al > Fe > Cr > Mn > Pb > 0 Co > 1 Cd > 2 Hg. This proved that the harmful substances in sewage do great harm to the human body. Ma et al. (2020) [ 29 ] validated that the heavy metal particles in acid industrial wastewater seriously harm the environment and public health. The effect of pH on the detection of toxic metals in wastewater was also studied by laser-induced breakdown spectroscopy and phase transition. The findings validated that the sensitivity of heavy metal elements in acidic wastewater could be significantly improved by optimizing the pH value of libs-pt solution. Wierzbicka (2020) [ 30 ] argued that nitrates and nitrites in sewage are harmful to human health when the concentration of them exceeds the safe level. In the end, the study provided a way to measure the concentration of these compounds by using electrochemical sensors to determine nitrates, thereby reducing the human impact of nitrates and nitrites in sewage.

Many studies have demonstrated that people who frequently touch wastewater or live near sewage treatment plants are susceptible to disease. Alawi et al. (2018) [ 31 ] measured the concentration of polycyclic aromatic hydrocarbons (PAHs) in inlet, outlet, and sludge samples from five sewage treatment plants in Jordan. They found that the total concentration of PAHs in the inlet samples is 1.163~2.866g/mL, the total concentration of PAHs in the outlet samples is 0.518~1.635g/mL, and the total concentration of PAHs in the sludge samples is 2.430~5.020g/g. In the studied sludge samples, the total cancer risk of exposure to PAHs is between 3.25×10(−5) and 7.43×10(−5). In Jordan, the number of people suspected of developing cancer from exposure to sewage treatment plant sludge ranges from 33 to 74 per million. This suggests that people exposed to wastewater have an increased risk of cancer.

Dehghani et al. (2018) [ 32 ] explored the concentration of bacteria and fungi in the air at a sewage treatment plant in southwestern Iran between September 2015 and May 2016. In total, 600 samples of bacteria and fungi were collected from around the operation unit and compared spatially and seasonally, indicating that bioaerosols produced by sewage treatment plants pose a threat to the health of factory workers and nearby residents. Brisebois et al. (2018) [ 33 ] assessed the presence of 11 viral pathogens in four wastewater treatment centers (WTCs) and used a metagenomic approach to describe the viral community in the air of one WTC. The presence of viruses in WTCs’ aerosols at different locations was evaluated, and the results of four common air samplers were compared. The study examined 4 of 11 viruses, including human adenovirus, rotavirus, hepatitis a virus, and herpes simplex virus type 1. The results of metagenic analysis revealed rare viral RNA sequences in the WTC aerosol, while the sequences from human DNA viruses are relatively much richer. WTC staff may be susceptible to viral diseases such as the common cold, influenza, and gastrointestinal infections.

3. Research Method

Efficiency mainly describes the relationship between input and output factors. Through efficiency measurement, we can understand the performance of a group of input factors in the output process. Based on the concept of Farrell (1957) [ 34 ], Charnes et al. (1978) [ 35 ] extended his theory to establish a generalized mathematical linear programming model, called the CCR (abbreviations of Charnes, A.C.; Cooper, W.W.; Rhodes, E.L.) model, that can measure multiple inputs and multiple outputs of fixed returns to scale. In 1984, Banker et al. (1984) [ 36 ] proposed the BCC model and revised variable return to scale (VRS) assumed by the CCR model to VRS. The CCR model and the BCC model measure radial efficiency—that is, they assume that the input or output terms could increase or decrease in equal proportion. In 2001, Tone (2001) [ 37 ] proposed the difference variable model (Slacks-Based Measure, SBM), which uses the difference variable as the measurement basis, while considering the slack between input and output and presenting SBM efficiency in a non-radial estimation and scalar value.

Färe et al. (2000) [ 38 ] came up with Network Data Envelopment Analysis (Network DEA), which states that the production process is composed of many secondary production technologies, and the secondary production technologies are regarded as Sub-DMUs. Aside from these, the optimal solution is obtained by using the traditional CCR and BCC models. Compared with the traditional DEA model, these secondary production technologies are identified as “black boxes”. Moreover, the Network DEA model applies these secondary production technologies to explore the impact of input allocation and intermediate wealth on the production process. Following Färe et al., Tone and Tsutsui (2009) [ 39 ] put forward the weighted SBM Network DEA model, whereby the linkage among various departments of the decision-making unit is taken as the analysis basis of the Network DEA model, and each department is regarded as a Sub-DMU. In the network DEA model, a dynamic approach is allowed, in which the DMU is evaluated at different time periods and cargos are introduced to connect the stages that make up the DMU in different periods (Tone and Tsutsui (2010) [ 40 ]). Dynamic DEA has developed because Kloop (1985) [ 41 ] proposed Window analysis in 1985. Using the dynamic analysis model in the first place, Färe and Grosskopf (1996) [ 42 ] were the first to put interlinked activities into dynamic analysis, with Kao and Hwang (2008) [ 43 ], Nemoto and Goto (1999, 2003) [ 44 , 45 ], Chang et al. (2009) [ 46 ], and other scholars publishing relevant analysis models successively.

Tone and Tsutsui (2014) [ 47 ] proposed the weighted SBM Dynamic Network DEA model with the linkage among various departments of the decision-making unit taken as the analysis basis of the Network DEA model and each department regarded as a Sub-DMU. Carryover activities are taken as the linkage, but Tone and Tsutsui’s dynamic network DEA model does not consider undesirable output. Because the dynamic network DEA model does not consider undesirable factors, in order to solve the problem of the undesirable factors and a multi-stage process, this paper proposes a modified two-stage dynamic data envelopment analysis model that combines the dynamic network DEA model and undesirable factors in order to evaluate the two stages of China’s urban sewage treatment and health from 2014–2017. The target is to avoid an underestimation or overestimation of efficiency value and improvement.

3.1. Modified Two-Stage Dynamic Data Envelopment Analysis Model

Suppose there are n DMU s ( j = 1,…, n ), with each having k divisions ( k = 1,…, K ), and T time periods ( t = 1,…, T ). Each DMU has an input and output at time period t and a carryover (link) to the next t +1 time period.

Set m k and r k to represent the inputs and outputs in each division K , with ( k,h ) i representing divisions k to h and L hk being the k and h division set. The inputs, outputs, links, and carryover definitions are outlined in the following paragraphs.

3.1.1. Wastewater Treatment Stage

X 1 t : Sewage treatment plants as input.

Y 1 g o o d t : Total wastewater discharge.

Y 1 b a d t : Municipal sewage treatment capacity and COD concentration.

Z ( 12 ) i n t (link between wastewater treatment stage and health stage): Heavy metal pollutant equivalent concentration.

3.1.2. Health Stage

X 2 t : Number of health technicians as input and local fiscal medical and health expenditure as input.

Y 2 g o o d t : Average life expectancy.

Y 2 b a d t : Carcinogenic risk.

Z o k l i n p u t ( t , ( t + 1 ) ) (Carryover): Completed investments in wastewater treatment projects.

The following is the non-oriented model:

(a) Objective function

θ 0 ∗ = min ∑ t = 1 T W t [ ∑ k = 1 K W k [ 1 − 1 m k + l i n k i n k + n i n p u t k ( ∑ i = 1 m k S i o k t − x i o k t + ∑ ( k h ) l = 1 l i n k i n k s o ( k h ) l i n t z o ( k h ) l i n t + ∑ k l n i n p u t k s o k l i n p u t ( t , t + 1 ) z o k l i n p u t ( t , t + 1 ) ) ] ] ∑ t = 1 T W t [ ∑ k = 1 K W k [ 1 + 1 r 1 k + r 2 k ( ∑ r = 1 r 1 k s r o k g o o d t + y r o k g o o d t + ∑ r = 1 r 2 k s r o k b a d t − y r o k b a d t ) ] ]

  • Subject to:

x o 1 t = X 1 t λ 1 t + s 1 o t − ( ∀ t )   ;

y o 1 g o o d t = Y 1 g o o d t λ 1 t − s 1 o g o o d t + ( ∀ t )

y o 1 b a d t = Y 1 b a d t λ 1 t + s 1 o b a d t − ( ∀ t )

λ 1 t ≥ 0 , s 1 o t − ≥ 0 , s 1 o g o o d t + ≥ 0 , ( ∀ t )

Z o ( 12 ) i n t = Z ( 12 ) i n t λ 1 t + S o ( 12 ) i n t ( ( 1 , 2 ) i n )

x o 2 t = X 2 t λ 2 t + s 2 o t − ( ∀ t )

y o 2 g o o d t = Y 2 g o o d t λ 2 t − s 2 o g o o d t + ( ∀ t )

y o 2 b a d t = Y 2 b a d t λ 2 t + s 2 o b a d t − ( ∀ t )

λ 2 t ≥ 0 , s 2 o t − ≥ 0 , s 2 o g o o d t + ≥ 0 , s 2 o b a d t − ≥ 0 , ( ∀ t )

e λ k t = 1 ( ∀ k , ∀ t )

Z o ( k h ) i n t = Z ( k h ) i n t λ k t + S o ( k h ) i n t ( ( k h ) i n = 1 , … , l i n k i n k ) ∑ j = 1 n z j k 1 α ( t , ( t + 1 ) ) λ j k t = ∑ j = 1 n z j k 1 α ( t , ( t + 1 ) ) λ j k t + 1

( ∀ k ; ∀ k l ; t = 1 , … , T − 1 )

(b) Period and division efficiencies

  • (b1) Period efficiency: ∂ 0 ∗ = min ∑ k = 1 K W k [ 1 − 1 m k + l i n k i n k + n g o o d k ( ∑ i = 1 m k S i o k t − x i o k t + ∑ ( k h ) l = 1 l i n k i n k s o ( k h ) l i n t z o ( k h ) l i n t + ∑ k l n g o o d k s o k l g o o d ( t , t + 1 ) z o k l g o o d ( t , t + 1 ) ) ] ∑ k = 1 K W k [ 1 + 1 r 1 k + r 2 k ( ∑ r = 1 r 1 k s r o k g o o d t + y r o k g o o d t + ∑ r = 1 r 2 k s r o k b a d t − y r o k b a d t ) ] (2)
  • (b2) Division efficiency: ϕ 0 ∗ = min ∑ t = 1 T W t [ 1 − 1 m k + l i n k i n k + n i n p u t k ( ∑ i = 1 m k S i o k t − x i o k t + ∑ ( k h ) l = 1 l i n k i n k s o ( k h ) l i n t z o ( k h ) l i n t + ∑ k l n i n p u t k s o k l i n p u t ( t , t + 1 ) z o k l i n p u t ( t , t + 1 ) ) ] ∑ t = 1 T W t   [ 1 + 1 r 1 k + r 2 k ( ∑ r = 1 r 1 k s r o k g o o d t + y r o k g o o d t + ∑ r = 1 r 2 k s r o k b a d t − y r o k b a d t ) ] (3)
  • (b3) Division period efficiency: ρ 0 ∗ = min 1 − 1 m k + l i n k i n k + n i n p u t k ( ∑ i = 1 m k S i o k t − x i o k t + ∑ ( k h ) l = 1 l i n k i n k s o ( k h ) l i n t z o ( k h ) l i n t ∑ k l n i n p u t k s o k l i n p u t i n p u t ( t , t + 1 ) z o k l i n p u t ( t , t + 1 ) ) 1 + 1 r 1 k + r 2 k ( ∑ r = 1 r 1 k s r o k g o o d t + y r o k g o o d t + ∑ r = 1 r 2 k s r o k b a d t − y r o k b a d t + ) (4)

3.2. Input, Desirable Output, and Undesirable Output Efficiency

Hu and Wang’s (2006) [ 48 ] total-factor energy efficiency index can be used to overcome any possible biases in the traditional energy efficiency indicators, for which there are eleven key efficiency models here in this present study: sewage treatment plants as input, total wastewater discharge, municipal sewage treatment capacity, municipal sewage treatment capacity, COD concentration, heavy metal pollutant equivalent concentration, number of health technicians as input, local fiscal medical and health expenditure as input, average life expectancy, carcinogenic risk, and investment in fixed assets.

The efficiency models are defined as formula (5)–(7):

If the target inputs equal the actual inputs, then the efficiencies are 1, which indicates overall efficiency; however, if the target inputs are less than the actual inputs, then the efficiencies are less than 1, which indicates overall inefficiency.

If the target desirable outputs are equal to the actual desirable outputs, then the efficiencies are 1, indicating overall efficiency; however, if the target desirable outputs are more than the actual desirable outputs, then the efficiencies are less than 1, indicating overall inefficiency.

If the target undesirable outputs are equal to the actual undesirable outputs, then the efficiencies are 1, indicating overall efficiency; however, if the target undesirable outputs are less than the actual undesirable outputs, then the efficiencies are less than 1, indicating overall inefficiency.

4. Empirical Analysis

4.1. data description, 4.1.1. explanation of variables.

This paper evaluates the wastewater treatment efficiency and health efficiency of 30 provincial administrative units based on the two-stage dynamic DEA model. As the focus of the study is on the provinces in China, Taiwan and Hong Kong and Macao special administrative regions are not analyzed. In addition, due to limited data of Tibet autonomous region, it is also not included.

In the wastewater treatment stage, completed investment in wastewater treatment project and sewage treatment plants are adopted as the input variables. Municipal sewage treatment capacity is the desirable output, while total wastewater discharge, COD concentration, and heavy metal pollutant equivalent concentration are undesirable output variables. Among them, completed investment in wastewater treatment project is selected as the carryover indicator, and heavy metal pollutant equivalent concentration is an intermediate variable. In the health stage, number of health technicians and local fiscal medical and health expenditure are taken as input variables. Average life expectancy and carcinogenic risk are agreed and not agreed outputs, respectively. See Table 1 for details.

Input and Output Variables.

The data on completed investment in wastewater treatment project, total wastewater discharge, COD concentration, number of health technicians, local fiscal medical and health expenditure, and average life expectancy are from the provincial annual data of the National Bureau of Statistics from 2014 to 2017. Data on sewage treatment plants and municipal sewage treatment capacity are obtained from China Environmental Statistics Yearbook 2014–2017. Heavy metal pollutant equivalent concentration and carcinogenic risk are calculated on the basis of different heavy metal concentrations from China Environmental Statistics Yearbook. The specific variables are described as follows.

① Completed investment in wastewater treatment project (investment). It refers to the investment that has been completed in a project to treat wastewater.

② Sewage treatment plants. It refers to the number of sewage treatment plants in a province (municipality directly under the central government, autonomous region).

③ Total wastewater discharge. It refers to the sum of industrial wastewater discharge and domestic sewage discharge.

④ Municipal sewage treatment capacity. It is defined as the total amount of sewage treated in a province (municipality directly under the central government, autonomous region) in a year.

⑤ COD concentration. It is defined as the concentration of oxygen required to oxidize organic pollutants in water with chemical oxidants. COD refers to the use of chemical oxidants (such as potassium dichromate) in water reducing substances (such as organic matter) and the oxidation decomposition of oxygen consumption, reflecting the extent of water pollution by reducing substances. The reducing substances can reduce the content of dissolved oxygen in the water, leading to the death of organisms in the water due to hypoxia and the deterioration of water quality. A higher COD denotes a higher content of reducing substances in the water and the more serious pollution. Since organic matter is the most common reducing substance in water, COD is an important parameter to measure organic pollution.

⑥ Heavy metal pollutant equivalent concentration. It is calculated on the basis of different heavy metal concentrations from China Environmental Statistics Yearbook. It refers to the degree of harm to the environment. The higher the equivalent concentration of pollution is, the greater is the degree of harm to the environment. According to China’s environmental quality standard for surface water GB3838-2002, the heavy metal index includes 6 items: cadmium (Cd), lead (Pb), chromium (Cr), nickel (Ni), zinc (Zn) and copper (Cu). However, there are some essential elements to support life, such as Zn, Cu and so on. No matter the lack or surplus of these elements, they will affect human health. There are other heavy metal elements, such as cadmium, chromium, etc., which have obvious toxic effects. No matter how they get into the body, they will cause poisoning, leading to serious illness and even death. Based on the existing literature [ 49 , 50 ] and the quality monitoring data of Dalian’s key drinking water sources [ 51 ], chemical carcinogens include hexavalent chromium, cadmium and arsenic. So in this paper, hexavalent chromium, cadmium and arsenic are used as the indexes affecting health.

⑦ Number of health technicians. Health Technicians includes practicing doctors, assistant practicing doctors, registered nurses, pharmacists (judges), test technicians (judges), image and trainee medical technicians, hygiene supervisors (medicine, nursing, skills) and other health professionals.

⑧ Local fiscal medical and health expenditure. It refers to the medical and health expenditure items in the general budget of the local government. It includes expenditure on medical and health management affairs, expenditure on medical services, expenditure on medical security, expenditure on disease prevention and control, expenditure on health supervision, expenditure on maternal and child health care, expenditure on rural health, etc.

⑨ Average life expectancy. It refers to the number of years that people can continue to live after the exact age of X at a certain age-specific mortality level. It is an indicator to measure the health level of residents in a country, a nation, or a region and can reflect the quality of life in a society.

⑩ Carcinogenic risk. It calculates the carcinogenic risk value of total chromium emission, arsenic emission, and cadmium emission. The health risks of individual carcinogenic pollutants in multiple exposure pathways are as follows:

In the formula, Ri represents the health risk value of a single pollutant under various exposure pathways, CDI represents the exposure dose, Sf represents the carcinogenic slope factor of the pollutant, and the unit is mg·kg −1 ·d −1 . The higher the R i value is, the greater is the health risk of a carcinogen—that is, the higher the cancer probability of the pollutant. In concrete analysis, the maximum acceptable risk level of the International Council on Cancer (ICRP), 5×10 −5 , is usually taken as a reference value, which is interpreted as no more than five people per 10,000 are affected by the chemical with a new disease or cancer. The formula for calculating the total risk of various carcinogens is shown below.

Here, R i t represents the total health risk of all pollutants in all exposure pathways.

Figure 1 illustrates the flow structure of this paper by using a flow chart. See Figure 1 for details.

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Network Dynamic Model.

4.1.2. Data Description

This study selects the input and output data of 30 provinces in China from 2014 to 2017 to calculate the average, the maximum, the minimum, and the standard values of completed investment in wastewater, treatment project, sewage treatment plants, total wastewater discharge, municipal sewage, treatment capacity, COD concentration, heavy metal pollutant equivalent concentration, number of health technicians, local fiscal medical and health expenditure, average life expectancy, and carcinogenic risk. See Table 2 for details.

Descriptive Statistics of Inputs and Outputs.

4.2. Overall Efficiency Analysis

This section calculates the overall efficiency of each province from 2014 to 2017 and ranks the 30 provinces in descending order according to their overall efficiency. From 2014 to 2017, the total efficiency values of DEA in the two stages from wastewater treatment input to health output of 30 provinces in China reveal that the overall efficiency of Ningxia and Qinghai is 1 for all four years, reaching the optimal state. See Table 3 for details.

Overall Efficiency by Provinces from 2014 to 2017.

The total efficiency of Hunan is 0.6261 in 2014, 0.7270 in 2015, and 1 in both 2016 and 2017, meaning the resource utilization efficiency is at the optimal state. On the contrary, Hainan, where the overall efficiency of the four years is the third highest, has an efficiency value of 1 in 2014 and 2015, but then the total efficiency value of the following two years falls to 0.6728 and 0.6566, indicating a deterioration of resource integration there. In total, the overall efficiencies of Gansu, Jiangsu, Xinjiang, Anhui, and Henan advance steadily in these four years, while Inner Mongolia displays a slow decline, and the overall efficiency of Fujian plummets to 0.3519 in 2017.

The highest value of overall efficiency for many provinces appears in 2016, such as Liaoning, Zhejiang, Heilongjiang, Shanxi, and Chongqing. The total efficiencies of Tianjin and Beijing increase steadily in the first three years and reach 1 in 2016, but then these two municipalities directly under central government control plummet to approximately 0.6 in 2017 and fail to maintain an optimal state. The highest value of Guangxi’s overall efficiency is 0.8034 in 2015, and then its overall efficiency in the other three years is about 0.4. The efficiency values of Shanghai, Guangdong, Yunnan, and Hubei change little in these four years, while those of Guizhou, Jilin, Shandong, Shaanxi, Sichuan, and Hebei change slightly, but their overall efficiency values are still at a low level.

Figure 1 compares the distribution of total efficiency in the 30 provinces from 2014 to 2017. The gap in total efficiency can be clearly seen through the radar chart. See Figure 2 for details.

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Four-year Overall Score and Total Efficiency of Provinces.

Figure 3 shows the geographical distribution of the overall efficiency of 30 provinces from 2014 to 2017. See Figure 3 for details.

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Geographical Distribution of Overall Score of Each Province in the 4 years. ( a ): 2014; ( b ): 2015; ( c ): 2016; ( d ): 2017.

4.3. Efficiency Comparison of the Two Stages

The efficiency of the wastewater treatment stage is visibly higher than that of the health stage, and many provinces reach the optimal state in the first stage. For example, the efficiencies of the wastewater treatment stage of Beijing, Guangdong, Hunan, and Shanghai are 1 from 2014 to 2017, and the efficiency values of Fujian and Gansu are 1 for three consecutive years. On the whole, the two stages illustrate a steady but slow growth trend, indicating that the five development concepts of “innovation, coordination, green development, openness, and sharing” have been deeply rooted in the hearts of the country’s citizens. As for the wastewater treatment stage, the efficiencies of Beijing, Guangdong, Hunan, Qinghai, Ningxia, and Shanghai are 1 from 2014 to 2017, and those of Gansu, Fujian, Inner Mongolia, and Zhejiang reach 1 for three years. However, there are still many provinces with low efficiency values. Guizhou, Hebei, Jilin, and Chongqing all have efficiency values below 0.5 in the four years. These provinces should take sewage treatment into account and make the best use of capital and personnel. See Table 4 for details.

Comparison of Two-stage Efficiency Scores from 2014–2017.

S_1 refers to wastewater treatment stage in DEA analysis; S_2 refers to health stage in DEA analysis.

The efficiency of the wastewater treatment stage has an obvious promoting effect on the total efficiency of each province, while the health stage to some extent inhibits the continuous growth of the total efficiency value of each province. For the four years, the efficiency of wastewater treatment in Beijing is 1. However, since the efficiency value of the second stage reaches an optimal state only in 2016, while it is around 0.3 in the other three years, bringing the total efficiency of Beijing to around 0.7 and ranking seventh in China. The efficiency of wastewater treatment in Guangdong is 1 for the four years, and that of the health stage is 0.1630, 0.1731, 0.1305, and 0.2297 from 2014 to 2017. The total efficiency is about 0.6, indicating that the health stage clearly is below total efficiency.

The efficiency of each province is closely related to geographical location, economic development, government policies, and other factors. The efficiencies of Ningxia and Qinghai in the two stages from 2014 to 2017 are 1, which is the top in China, thanks to their superior geographical location and the implementation of environmental protection concepts as well as due to the small number of factories and economic backwardness there. For Hunan and Shanghai, their efficiencies of wastewater treatment are 1 in each of the four years because of their developed economies and advanced wastewater treatment equipment. In these four years, the efficiencies of the two stages for Shaanxi and Chongqing are relatively low, rarely exceeding 0.5. In 2015, the efficiency of the health stage in Shaanxi is only 0.1531, or far behind other provinces. Both Shaanxi and Chongqing are heavily industrialized cities with severe pollution and have poor environmental protection awareness. Therefore, they must balance the relationship between economic development and environmental protection.

The average efficiency of the wastewater treatment stage is 0.6837, and that of the health stage is 0.4243 by calculation. We observe that the efficiency of the first stage is obviously higher than that of the second stage. Based on the average efficiency of each province, we divide the studied areas into four parts: high-high, low-low, high-low, and low-high. Among them, eight provinces including Beijing, Gansu, Hunan, Liaoning, Inner Mongolia, Ningxia, Qinghai, and Tianjin have higher values than the average efficiency in the two stages, while ten provinces including Hebei, Henan, Hubei, Jilin, Shandong, Shaanxi, Sichuan, Chongqing, Xinjiang, and Guizhou have lower values than the average efficiency in the two stages. Anhui, Guangxi, Hainan, Heilongjiang, Jiangxi, and Shanxi have efficiencies in the second stage that are higher than the average level, but their efficiencies in the first stage are lower than the average level. Fujian, Guangdong, Jiangsu, Shanghai, Yunnan, and Zhejiang have higher efficiencies than the average level in the first stage, but lower than average efficiencies in the second stage. Therefore, the health stage needs great improvement. See Figure 4 for details.

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Province Distribution by Stage Efficiency from 2014 to 2017.

4.4. Itemized Efficiency Analysis

4.4.1. sewage treatment plants’ efficiency analysis.

The efficiency value of many provinces reflects a trend of steadily increasing, with Gansu rising from 0.8586 in 2014 to 1 in 2015, 1 in 2016, and 1 in 2017. Jiangsu goes from 0.4809 in 2014 to 0.6838 in 2015, reaching 1 in both 2016 and 2017. Beijing, Guangdong, Hunan, Ningxia, Qinghai, and Shanghai all have an efficiency value of 1 for the four years, while Gansu, Zhejiang, Fujian, and Inner Mongolia have an efficiency value of 1 for three consecutive years. We see that these provinces attach great importance to the sewage treatment problem and have invested manpower, material resources, and financial resources to treat sewage and achieve outstanding results. However, in some provinces, the efficiencies do not increase significantly or even decline. The efficiency value of Guizhou is at a low level of 0.2–0.4. Shandong has a small range of 0.4–0.5. Jilin has a four-year efficiency value of about 0.5. Guangxi decreases from 0.9211 in 2014 to 0.7581 in 2017. Heilongjiang decreases from 0.6638 in 2014 to 0.3511 in 2017. See Table 5 for details.

Sewage Treatment Plants’ Efficiency of Each Province from 2014 to 2017.

4.4.2. Total Wastewater Discharge Efficiency Analysis

Beijing, Gansu, Guangdong, Hunan, Ningxia, Qinghai, and Shanghai have total wastewater discharge efficiencies of 1 for all four years. The efficiencies of Inner Mongolia, Fujian, and Zhejiang for 2014–2016 are 1. The efficiencies of Jiangsu, Tianjin, Heilongjiang, and Hainan are 1 for two consecutive years. Nonetheless, this efficiency variable generally presents a slight downward trend. The efficiencies of Inner Mongolia, Fujian, and Zhejiang in the first three years are 1, but then drop to 0.8934, 0.8082, and 0.6339 in 2017, respectively. Tianjin falls from 0.9004 in 2014 to 0.7884 in 2017, or down by 0.1120. Xinjiang falls from 0.8954 in 2014 to 0.6342 in 2017, or down by 0.2612. Hainan owns the biggest drop from 1 in 2014 and 2015 to 0.5048 in 2014, or down by 0.4952. Guizhou and Henan exhibit a slight change, fluctuating between 0.4 and 0.5 and ranking lower in efficiency. See Table 6 for details.

Total Wastewater Discharge’ Efficiency of Each Province from 2014 to 2017.

4.4.3. COD Concentration Efficiency Analysis

The efficiency value of the COD concentration variable is relatively high, reaching 1 in about 10% of the provinces every year, but showing a downward trend. Fujian drops from 1 in 2014 to 0.4024 in 2017, or down 0.5976; Hainan falls by 0.9411 from 1 in 2014 to 0.0589 in 2017, and Jiangsu decreases by 0.8115 from 0.9882 in 2014 to 0.1767 in 2017. The situation is improving, and the pollutants in the water gradually decrease. All provinces should still attach great importance to the harmful substances in the water to the human body and strengthen scientific and technological investment or introduce professional equipment to degrade harmful substances in water. See Table 7 for details.

COD Concentration Efficiency of Each Province from 2014 to 2017.

4.4.4. Number of Health Technicians’ Efficiency Analysis

The efficiency values in the four years for Hainan, Ningxia, and Qinghai are 1, reaching the optimal state. Numerous provinces register their highest efficiency in 2016, including Heilongjiang, Beijing, Chongqing, Shanghai, Shanxi, and Liaoning, while those hitting their lowest are Anhui, Henan, Guangxi, Inner Mongolia, Gansu, Jiangsu, and Guangdong. The efficiency values of most provinces decrease, including Fujian, Jiangxi, Hubei, Shandong, and Liaoning, which fall significantly from 0.8530, 0.7321, 0.7001, 0.5785, and 0.6105 in 2014 to 0.3135, 0.2042, 0.1299, 0.1001, and 0.2188 in 2017, respectively. A few provinces see slow or no distinct changes in efficiency. The efficiency values of Anhui and Henan are 0.9169 and 0.7007 in 2014, but they plunge in 2015 and 2016. Anhui drops to 0.6647 in 2015 and to 0.3336 in 2016, while Henan drops to 0.5977 in 2015 and to 0.1168 in 2016. In 2017, both provinces increase by 0.0831 and 0.2993, respectively. The efficiency of Hebei in the four years is about 0.1, while Shaanxi’s efficiency is about 0.2, with little change and always lower than the national average. See Table 8 for details.

Number of Health Technicians’ Efficiency of Each Province from 2014 to 2017.

4.4.5. Local Fiscal Medical and Health Expenditure Efficiency Analysis

The efficiency values of Hainan, Hunan, Ningxia, Qinghai, and Zhejiang in the four years are 1, and about 10% of the provinces reach the optimal state every year. This expenditure reveals a slow increasing trend. For example, Tianjin rises from 0.5638 in 2014 and 0.4998 in 2016 to 1 in 2016 and 2017, Heilongjiang increases from 0.3731 in 2014 to 0.5918 in 2017, and Xinjiang goes from 0.3322 in 2014 to 0.5897 in 2017. There is still great improvement in this variable. See Table 9 for details.

Local Fiscal Medical and Health Expenditure Efficiency of Each Province from 2014 to 2017.

4.4.6. Average Life Expectancy Efficiency Analysis

We note that the efficiency value of average life expectancy increases rapidly. Anhui, Beijing, Gansu, and Fujian rise to 1 in 2017 from 0.1921, 0.8226, 0.3367, and 0.2326 in 2014, respectively, increasing by 0.8079, 0.1774, 0.6633, and 0.7674. By the end of 2017, 26 provinces reach the optimal state. Guizhou, Hainan, Jilin, Heilongjiang, Ningxia, Qinghai, Shaanxi, Shanghai, Tianjin, Xinjiang, Chongqing, and other provinces all have an efficiency value of 1 in the four years, which hints that national health awareness has been enhanced and the happiness of urban residents has been improved. See Table 10 for details.

Average Life Expectancy Efficiency of Each Province from 2014 to 2017.

4.4.7. Carcinogenic Risk Efficiency Analysis

The efficiency value of the carcinogenic risk variable decreases on the whole. Jiangxi, Shanxi, Guizhou, and Heilongjiang decrease from 1 in 2014 to 0.3264, 0.3023, 0.5330, and 0.8218 in 2017, respectively, by falling in a range of 0.6736, 0.6977, 0.467, and 0.1782. Fujian decreases from 0.7877 in 2014 to 0.3487 in 2017, Hebei from 0.3306 in 2014 to 0.1492 in 2017, Hubei from 0.6554 in 2014 to 0.4459 in 2017, Jilin from 0.9362 in 2014 to 0.2030 in 2017, and Sichuan from 0.9168 in 2014 to 0.4633 in 2017. Among them, Jiangxi, Shanxi, and Jilin have a relatively large decline of about 0.7. To conclude, the medical treatment level has been enhanced. See Table 11 for details.

Carcinogenic Risk Efficiency of Each Province from 2014 to 2017.

5. Conclusions

According to the two-stage (wastewater treatment stage and health stage) dynamic SBM DEA model, this research analyzes the input and output efficiencies of 30 provinces in China, obtaining the following conclusions.

(1) The efficiency values of each province in China are influenced by geographical location, urban development, and pillar industries of each region’s economy. Ningxia, Qinghai, Beijing, and Hainan have higher efficiency values of various indicators that are close to or at the optimal state and are among the top in China. Located in the northwest inland arid region, Ningxia’s water environmental problems come mainly from agricultural water pollution, soil erosion, and water supply and demand imbalances, while its urban industrial and living wastewater is not serious. Moreover, the development of Ningxia’s urbanization is unbalanced with a smaller population and less domestic sewage, and so its efficiency is higher. Qinghai is located in the northeast of the Qinghai-Tibet Plateau, and due to its remote geographical location, its population is sparse. In addition, its economy is dominated by agriculture and animal husbandry, and so urban sewage is less. As the capital of China, Beijing is the political center, cultural center, and scientific research center, which is not based on the development of industry. Hainan is located in the southernmost part of China. Its economy is dominated by tourism, housing industry, agriculture, and low-carbon manufacturing industry. It also has a small resident population, and so it has less urban industrial wastewater and domestic sewage. Sichuan, Chongqing, Hebei, Shandong, Guizhou, and Shaanxi have lower efficiency values because the cities of Deyang and Panzhihua in Sichuan, Jinan, Weifang, and Zibo in Shandong, Handan and Tangshan in Hebei, Liupanshui in Guizhou, and Baoji in Shaanxi are all famous heavy industry cities with extremely serious industrial water pollution. Sichuan has a basin topography, Chongqing is mountainous, Guizhou is located in the southwest hinterland, and Hebei, Shandong, and Shaanxi are located in north China. Therefore, the pollutants are not easy to diffuse, and thus, the provinces mentioned above have low efficiency values.

(2) The efficiency value in the health stage is distinctly lower than that in the wastewater treatment stage, which puts a drag on the total efficiency of each province, and so there is more room to enhance efficiency. In the health stage, the efficiency of number of health technicians is significantly lower (by 0.2) than that of local fiscal medical and health expenditure. The efficiency values of number of health technicians in Hebei, Shaanxi, Jilin, Shanghai, and Xinjiang are relatively low at less than 0.5 in the four years. Shaanxi, Jilin, and Xinjiang have low efficiency values because of their remote geographical location and the gap between remuneration and workers’ treatment to the more developed areas of China. Hebei has low efficiency because of the siphon effect, thus presenting that high-quality resources are greatly concentrated in Beijing and Tianjin. Conversely, Shanghai has low efficiency values because of fierce competition and insufficient government input. The efficiency of carcinogenic risk is higher than the efficiency of average life expectancy, but the gap is narrowing. It means that carcinogenic risk efficiency is declining year by year and average life expectancy is increasing because 26 provinces in 2017 are at a level of 1, reflecting improved medical levels and the enhancement of national health consciousness. We can see that improving the efficiency of health stage mainly helps the efficiency of number of health technicians. One problem that every province should overcome is how to retain talents and give full play to the advantages of those talents.

(3) Each province should choose the best economic development mode according to its own situation to pursue a balance between economic development and environmental protection. Cities in Ningxia and Qinghai have relatively light water pollution, but the economic development of these two provinces is relatively backward. They can thus combine the original pillar industries, agriculture and animal husbandry, with “Internet +” to monitor the growth of crops or animals through artificial intelligence in real time. They may also consider simultaneously using the Internet to promote products more efficiently and cheaply, thus helping to boost sales and accelerate the development of their digital economy. At the same time, Ningxia and Qinghai could set up policies to attract investment (except for projects with high energy consumption and high pollution) and develop their own brand of special tourism. Sichuan, Chongqing, Hebei, Shandong, Guizhou, and Shaanxi should optimize their industrial structure. First, in response to the national call for mass entrepreneurship and innovation, they must gradually abandon heavy industry and develop high-tech enterprises to alleviate environmental problems such as water pollution. In addition, they can vigorously develop tourism and other service industries and go deeper into the excavation of the regional characteristics of specific investment projects. For example, Zunyi in Guizhou, Yan’an in Shaanxi, and Baiyang Lake in Hebei can develop the red tourism (taking the memorial sites and markers formed by the great achievements made by the people under the leadership of the communist party of China in the period of revolution and war as the carrier, and taking the revolutionary history, revolutionary deeds and revolutionary spirit as the connotation, we organize thematic tourism activities to remember and learn revolutionary martyr) industry and promote revolutionary traditional education.

(4) Provinces should retain health professionals in order to maintain the health of their citizens. For example, Hebei, Shaanxi, Jilin, and Xinjiang should establish a talent incentive model to improve the salary and welfare of health technicians, so that they are more willing to stay in their hometown and make contributions to medical and health care. Furthermore, each province could attract academic medical personnel to obtain employment and feasibly improve the level of local medical practices. In first-tier cities, like Shanghai, they should put people first, provide more jobs for health technicians, reduce the intensity of competition for jobs, and improve the happiness and sense of belonging of health technicians from all aspects so as to give take advantage of the personnel team.

(5) All provinces should place great importance to sewage treatment. First, enterprises and governments should target to increase technological input and introduce advanced sewage treatment equipment from abroad, or develop high-tech products independently, which would be beneficial for reaching the target of reducing the harm from chromium, arsenic, cadmium, and other substances in sewage to the human body. Second, another option is to set up efficient sewage treatment plants to prevent the secondary harm of sewage to humans and promote the recycling of water resources. Economically developed provinces such as Beijing, Shanghai, Guangdong, Zhejiang, and Jiangsu should make the best use of their economic, geographical, and talent advantages and take the lead in developing fruitful sewage treatment plants. Third, backed by national enforcement, laws and regulations should be enacted to curb the arbitrary discharge of urban production and domestic sewage and to reduce the quantity of sewage at the source. Finally, governments can strengthen environmental protection education, raises people’s environmental protection consciousness, and allow people to participate in social supervision.

(6) The central government can promote coordinated regional development. For instance, in terms of coordinated development in the Beijing-Tianjin-Hebei region, Beijing should gradually relieve itself of non-capital functions, optimize the urban layout, and expand the ecological space of environmental capacity. Hebei and Tianjin then can actively undertake the non-capital functions of Beijing, such as transforming Hebei from heavy industry to green coordinated development and initiating high-quality development of Tianjin’s economy. Coordinated development in the Yangtze River Delta can give full play to the leading role of Shanghai by sharing sewage treatment experience and technology with other cities. Jiangsu, Zhejiang, and Anhui should accept and actively learn advanced technology and give full play to their respective advantages, so as to achieve the goal of narrowing their economic development gap and to set up rational industrial division and green and sustainable economic development in the Yangtze River Delta. All provinces in China deserve to speed up the flow of factors, narrow the economic gap between regions, and finally, realize common prosperity.

The data of urban sewage treatment from 2014 to 2017 are selected in this paper. The research period is relatively short and the situation of sewage treatment in rural China isn’t taken into account. We will continue to follow up China’s sewage treatment situation in the following period.

Author Contributions

Conceptualization, Z.S.; data curation, S.Q.; formal analysis, Z.S. and Y.-h.C.; funding acquisition, Z.S. and C.Z.; methodology, Y.-h.C.; project administration, Z.S. and C.Z.; supervision, L.Z., and C.Z.; visualization, S.Q. and C.Z.; writing—original draft, L.Z., C.Z., and S.Q.; writing—review and editing, Z.S. and Y.-h.C. All authors have read and agreed to the published version of the manuscript.

This research was funded by the Ministry of Education Humanistic and Social Science Research Youth Funds, grant number 19YJC790112; Fundamental Research Funds for the Central Universities, grant number 2020QG1206; Ministry of Education Humanistic and Social Science Research Youth Funds, grant number 17YJC790194.

Conflicts of Interest

The authors declare no conflict of interest.

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Water’s ready availability in many locations makes it an easy choice for a variety of purposes, from cleaning to manufacturing to nuclear reactor cooling. However, many companies will then dump water, now mixed with waste, back into rivers or lakes without adequate cleaning, leading to significant environmental pollution.

However, there are other types of harm, such as noise pollution, which are less obvious but also dangerous to sea life. It is critical that you understand what you should and should not do during your writing process.

The stance that big manufacturing industries are the sole culprits of the damage done to the world’s rivers and oceans is a popular one. However, do not neglect the effects of other water pollution essay topics such as microorganisms.

Microbes can spread dangerous illnesses, making them a danger for both water inhabitants and the people who then use that water. Furthermore, they can eat up oxygen if left unchecked, starving fish and other water organisms and eventually making them die out.

Such situations usually result from agricultural practices, which can lead to powerful nutrients entering the water and enabling algae and other microorganisms to grow excessively. An overly lively environment can be as harmful as one where everything is threatened.

With that said, industrial manufacturers deserve much of the attention and blame they receive from various communities. Construction of dedicated waste-cleaning facilities is usually possible, but companies avoid doing so because the process will increase their costs.

You should advocate for green practices, but be mindful of the potential impact of a significant price increase on the global economy. Also, be sure to mention more exotic pollution variations in your types of water pollution essay.

Provide examples of noise pollution or suspended matter pollution to expand on the topic of the complexity of the harm humanity causes to the ecosphere.

You should show your understanding that there are many causes, and we should work on addressing all of them, a notion you should repeat in your water pollution essay conclusions.

However, you should try to avoid being sidetracked too much and focus on the titles of pollution and its immediate causes.

If you stretch far enough, you may connect the matter to topics such as the status of a woman in Islam. However, doing so contributes little to nothing to your point and deviates from the topic of ecology into social and religious studies.

Leave the search for connections to dedicated researchers and concentrate on discussing the major causes that are known nowadays. By doing this, you will be able to create an excellent and powerful work that will demonstrate your understanding of the topic.

Here are some tips for your writing:

  • Be sure to discuss the different types of pollution that is caused by the same source separately. Surface and groundwater pollution are different in their effects and deserve separate discussions.
  • Focus on the issues and not on solutions, as an essay does not provide enough space to discuss the latter in detail.
  • Be sure to discuss the effects of pollution on people and other land inhabitants as well as on water creatures.

Check IvyPanda to get more water pollution essay titles, paper ideas, and other useful samples!

  • Water Pollution: Causes, Effects and Possible Solutions This is why clean water is required in all the places to make sure the people and all the living creatures in the planet live a good and healthy life.
  • Air and Water Pollution in the Modern World The high number of vehicles in the city has greatly promoted air pollution in the area. Poor sewerage system, high pollution from industries and automobiles are among the major causes of air and water pollutions […]
  • Water Pollution: Causes, Effects, and Prevention Farmers should be encouraged to embrace this kind of farming which ensures that the manure used is biodegradable and do not end up accumulating in the water bodies once they are washed off by floods.
  • Water Pollution in the Philippines: Metropolitan Manila Area In this brief economic analysis of water pollution in Metro Manila, it is proposed to look at the industrial use of waters and the household use to understand the impact that the population growth and […]
  • Water Pollution and Management in the UAE The groundwater in UAE meets the needs of 51% of users in terms of quantity mainly for irrigation. Surface water is the source of groundwater and plays a major role in groundwater renewal.
  • Coca-Cola India and Water Pollution Issues The first difficulty that the representatives of the Coca-Cola Company happened to face due to their campaign in the territory of India was caused by the concerns of the local government.
  • Water Pollution in a Community: Mitigation Plan Though for the fact that planet earth is abundant with water and almost two-thirds of the planet is made up of water still it is viewed that in future years, a shortage of water may […]
  • Cashion Water Quality: Spatial Distribution of Water Pollution Incidents This essay discusses the quality of water as per the report of 2021 obtained from the municipality, the quality issue and the source of pollution, and how the pollution impacts human health and the environment […]
  • Water Pollution as a Crime Against the Environment In particular, water pollution is a widespread crime against the environment, even though it is a severe felony that can result in harm to many people and vast territories.
  • Importance of Mercury Water Pollution Problem Solutions The severity of the mercury contamination consequences depends on the age of the person exposed to the contamination, the way of contamination, the health condition, and many other factors.
  • Newark Water Crisis: Water Pollution Problem The main problem was rooted in the fact that lead levels in the drinking water were highly elevated, which is dangerous and detrimental to the population’s health.
  • Water Pollution: OIL Spills Aspects The effects of the oil spill on a species of ducks called the Harlequin ducks were formulated and the author attempted to trace out the immediate and residual effects of the oil on the birds.
  • Food Distribution and Water Pollution Therefore, food distribution is one of the central reasons for water pollution. According to Greenpeace, one of the ways to improve the ecology of the planet is by creating healthy food markets.
  • Water Pollution and Associated Health Risks The results of plenty of studies indicate the existence of the relation between the contamination of water by hazardous chemicals and the development of respiratory and cardiovascular diseases, cancer, asthma, allergies, as well as reproductive […]
  • Lake Erie Water Pollution There are worries among the members of the community that the lake could be facing another episode of high toxicity, and they have called for the authorities to investigate the main causes of the pollution […]
  • Storm Water Pollution Prevention Plan All players need to be trained in significant areas of business so as they can handle them with care and beware of the potential they have in causing damage.
  • Water Pollution in the US: Causes and Control Although water pollution can hardly be ceased entirely, the current rates of water pollution can be reduced by resorting to the sustainable principle of water use in both the industrial area and the realm of […]
  • Water Pollution and Its Challenges Water pollution refers to a situation where impurities find way into water bodies such as rivers, lakes, and ground water. This is a form of pollution where impurities enter water bodies through distinct sources such […]
  • Water Pollution Sources, Effects and Control Unfortunately, not all the users of water are responsible to ensure that proper disposal or treatment of the used water is done before the water is returned to the water bodies.
  • Water in Crisis: Public Health Concerns in Africa In the 21st century, the world faces a crisis of contaminated water, which is the result of industrialization and is a major problem in developing countries.
  • Air and Water Pollution Thus, it is classified as a primary pollutant because it is the most common pollutants in the environment. In the environment, the impact of carbon monoxide is felt overtime, since it leads to respiratory problems.
  • Causes of Water Pollution and the Present Environmental Solution Prolonged pollution of water has even caused some plants to grow in the water, which pose danger to the living entities that have their inhabitants in the water.
  • Water Pollution & Diseases (Undeveloped Nations) Restriction on movement and access to the affected area affects trade and the loss of human life and deteriorated health is a major blow on the economy and on the quality of human life.
  • Water and Water Pollution in Point of Economics’ View This research tries to explain the importance of water especially in an economist’s perspective by explaining the uses of water in various fields, pollution of water and the agents of pollution.
  • Environmental Justice Issues Affecting African Americans: Water Pollution Water pollution in the 1960s occurred due to poor sewage systems in the urban and rural areas. Unlike in the 1960s, there are reduced cases of water pollution today.
  • Water Pollution and Wind Energy Chemical pollution of water is one of the leading causes of death of aquatic life. It is thus evident that chemical pollution of water not only has negative effects on health, but it also substantially […]
  • Air and Water Pollution in Los Angeles One of the major problems facing major cities and towns in the world is pollution; wastes from firms and households are the major causes of pollution.
  • Water Pollution Causes and Climate Impacts The biggest percentage of sewage waste consists of water, treating the wastes for recycling would help in maintaining a constant supply of water.
  • Water Pollution Origins and Ways of Resolving The evidence provided by environmental agencies indicates that industrial agriculture is one of the factors that significantly contribute to the deterioration of water quality.
  • Mud Lick Creek Project – Fresh Water Pollution This potential source of pollutants poses significant risks to the quality of water at the creek in terms altering the temperature, pH, dissolved oxygen, and the turbidity of the water.
  • Water Pollution in the Jamaican Society
  • Water Pollution and Abstraction and Economic Instruments
  • Water Pollution and Individual Effects of Water Pollution
  • Understanding What Causes Water Pollution
  • An Analysis of Water Pollution as a Global Plague That Affects the People, Animals and Plants
  • Water Pollution Through Urban and Rural Land Use and Freshwater Allocation in New Zealand
  • Water Pollution: Globalization, One of the Causes and Part of the Solution
  • Voluntary Incentives for Reducing Agricultural Nonpoint Source Water Pollution
  • The Impact of Water Pollution on Public Health in Flint, Michigan
  • Understanding Water Pollution and Its Causes
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  • An Analysis of the Water Pollution and it’s Effects on the Environment
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  • What Are the Leading Factors of Water Pollution Around the World?
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  • How to Deal with the Big Problem of Deforestation and Water Pollution in Brazil and the Colombian Amazon?
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  • What Is the Harmonizing Model with Transfer Tax on Water Pollution Across Regional Boundaries in China’s Lake Basin?
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  • What Are the Kinds of Water Pollution Environmental Sciences?
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  • How Is Water Pollution Managed in Viet Nam’s Craft Villages?
  • What Should You Know About Water Pollution?
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Pollution in the Yellow River, Mongolia

Discharge from a Chinese fertilizer factory winds its way toward the Yellow River. Like many of the world's rivers, pollution remains an ongoing problem.

Water pollution is a rising global crisis. Here’s what you need to know.

The world's freshwater sources receive contaminants from a wide range of sectors, threatening human and wildlife health.

From big pieces of garbage to invisible chemicals, a wide range of pollutants ends up in our planet's lakes, rivers, streams, groundwater, and eventually the oceans. Water pollution—along with drought, inefficiency, and an exploding population—has contributed to a freshwater crisis , threatening the sources we rely on for drinking water and other critical needs.

Research has revealed that one pollutant in particular is more common in our tap water than anyone had previously thought: PFAS, short for poly and perfluoroalkyl substances. PFAS is used to make everyday items resistant to moisture, heat, and stains; some of these chemicals have such long half-lives that they are known as "the forever chemical."

Safeguarding water supplies is important because even though nearly 70 percent of the world is covered by water, only 2.5 percent of it is fresh. And just one percent of freshwater is easily accessible, with much of it trapped in remote glaciers and snowfields.

Water pollution causes

Water pollution can come from a variety of sources. Pollution can enter water directly, through both legal and illegal discharges from factories, for example, or imperfect water treatment plants. Spills and leaks from oil pipelines or hydraulic fracturing (fracking) operations can degrade water supplies. Wind, storms, and littering—especially of plastic waste —can also send debris into waterways.

Thanks largely to decades of regulation and legal action against big polluters, the main cause of U.S. water quality problems is now " nonpoint source pollution ," when pollutants are carried across or through the ground by rain or melted snow. Such runoff can contain fertilizers, pesticides, and herbicides from farms and homes; oil and toxic chemicals from roads and industry; sediment; bacteria from livestock; pet waste; and other pollutants .

Finally, drinking water pollution can happen via the pipes themselves if the water is not properly treated, as happened in the case of lead contamination in Flint, Michigan , and other towns. Another drinking water contaminant, arsenic , can come from naturally occurring deposits but also from industrial waste.

Freshwater pollution effects

the dry riverbed of the Colorado River

Water pollution can result in human health problems, poisoned wildlife, and long-term ecosystem damage. When agricultural and industrial runoff floods waterways with excess nutrients such as nitrogen and phosphorus, these nutrients often fuel algae blooms that then create dead zones , or low-oxygen areas where fish and other aquatic life can no longer thrive.

Algae blooms can create health and economic effects for humans, causing rashes and other ailments, while eroding tourism revenue for popular lake destinations thanks to their unpleasant looks and odors. High levels of nitrates in water from nutrient pollution can also be particularly harmful to infants , interfering with their ability to deliver oxygen to tissues and potentially causing " blue baby syndrome ." The United Nations Food and Agriculture Organization estimates that 38 percent of the European Union's water bodies are under pressure from agricultural pollution.

Globally, unsanitary water supplies also exact a health toll in the form of disease. At least 2 billion people drink water from sources contaminated by feces, according to the World Health Organization , and that water may transmit dangerous diseases such as cholera and typhoid.

Freshwater pollution solutions

In many countries, regulations have restricted industry and agricultural operations from pouring pollutants into lakes, streams, and rivers, while treatment plants make our drinking water safe to consume. Researchers are working on a variety of other ways to prevent and clean up pollution. National Geographic grantee Africa Flores , for example, has created an artificial intelligence algorithm to better predict when algae blooms will happen. A number of scientists are looking at ways to reduce and cleanup plastic pollution .

There have been setbacks, however. Regulation of pollutants is subject to changing political winds, as has been the case in the United States with the loosening of environmental protections that prevented landowners from polluting the country’s waterways.

Anyone can help protect watersheds by disposing of motor oil, paints, and other toxic products properly , keeping them off pavement and out of the drain. Be careful about what you flush or pour down the sink, as it may find its way into the water. The U.S. Environmental Protection Agency recommends using phosphate-free detergents and washing your car at a commercial car wash, which is required to properly dispose of wastewater. Green roofs and rain gardens can be another way for people in built environments to help restore some of the natural filtering that forests and plants usually provide.

For Hungry Minds

Related topics.

  • WATER POLLUTION
  • ENVIRONMENT AND CONSERVATION
  • FRESH WATER
  • GROUNDWATER
  • WATER QUALITY
  • WATER RESOURCES

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Water Pollution: Everything You Need to Know

Our rivers, reservoirs, lakes, and seas are drowning in chemicals, waste, plastic, and other pollutants. Here’s why—and what you can do to help.

Effluent pours out of a large pipe

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What is water pollution?

What are the causes of water pollution, categories of water pollution, what are the effects of water pollution, what can you do to prevent water pollution.

Water pollution occurs when harmful substances—often chemicals or microorganisms—contaminate a stream, river, lake, ocean, aquifer, or other body of water, degrading water quality and rendering it toxic to humans or the environment.

This widespread problem of water pollution is jeopardizing our health. Unsafe water kills more people each year than war and all other forms of violence combined. Meanwhile, our drinkable water sources are finite: Less than 1 percent of the earth’s freshwater is actually accessible to us. Without action, the challenges will only increase by 2050, when global demand for freshwater is expected to be one-third greater than it is now.

Water is uniquely vulnerable to pollution. Known as a “universal solvent,” water is able to dissolve more substances than any other liquid on earth. It’s the reason we have Kool-Aid and brilliant blue waterfalls. It’s also why water is so easily polluted. Toxic substances from farms, towns, and factories readily dissolve into and mix with it, causing water pollution.

Here are some of the major sources of water pollution worldwide:

Agricultural

A small boat in the middle of a body of water that is a deep, vibrant shade of green

Toxic green algae in Copco Reservoir, northern California

Aurora Photos/Alamy

Not only is the agricultural sector the biggest consumer of global freshwater resources, with farming and livestock production using about 70 percent of the earth’s surface water supplies , but it’s also a serious water polluter. Around the world, agriculture is the leading cause of water degradation. In the United States, agricultural pollution is the top source of contamination in rivers and streams, the second-biggest source in wetlands, and the third main source in lakes. It’s also a major contributor of contamination to estuaries and groundwater. Every time it rains, fertilizers, pesticides, and animal waste from farms and livestock operations wash nutrients and pathogens—such bacteria and viruses—into our waterways. Nutrient pollution , caused by excess nitrogen and phosphorus in water or air, is the number-one threat to water quality worldwide and can cause algal blooms , a toxic soup of blue-green algae that can be harmful to people and wildlife.

Sewage and wastewater

Used water is wastewater. It comes from our sinks, showers, and toilets (think sewage) and from commercial, industrial, and agricultural activities (think metals, solvents, and toxic sludge). The term also includes stormwater runoff , which occurs when rainfall carries road salts, oil, grease, chemicals, and debris from impermeable surfaces into our waterways

More than 80 percent of the world’s wastewater flows back into the environment without being treated or reused, according to the United Nations; in some least-developed countries, the figure tops 95 percent. In the United States, wastewater treatment facilities process about 34 billion gallons of wastewater per day . These facilities reduce the amount of pollutants such as pathogens, phosphorus, and nitrogen in sewage, as well as heavy metals and toxic chemicals in industrial waste, before discharging the treated waters back into waterways. That’s when all goes well. But according to EPA estimates, our nation’s aging and easily overwhelmed sewage treatment systems also release more than 850 billion gallons of untreated wastewater each year.

Oil pollution

Big spills may dominate headlines, but consumers account for the vast majority of oil pollution in our seas, including oil and gasoline that drips from millions of cars and trucks every day. Moreover, nearly half of the estimated 1 million tons of oil that makes its way into marine environments each year comes not from tanker spills but from land-based sources such as factories, farms, and cities. At sea, tanker spills account for about 10 percent of the oil in waters around the world, while regular operations of the shipping industry—through both legal and illegal discharges—contribute about one-third. Oil is also naturally released from under the ocean floor through fractures known as seeps.

Radioactive substances

Radioactive waste is any pollution that emits radiation beyond what is naturally released by the environment. It’s generated by uranium mining, nuclear power plants, and the production and testing of military weapons, as well as by universities and hospitals that use radioactive materials for research and medicine. Radioactive waste can persist in the environment for thousands of years, making disposal a major challenge. Consider the decommissioned Hanford nuclear weapons production site in Washington, where the cleanup of 56 million gallons of radioactive waste is expected to cost more than $100 billion and last through 2060. Accidentally released or improperly disposed of contaminants threaten groundwater, surface water, and marine resources.

To address pollution and protect water we need to understand where the pollution is coming from (point source or nonpoint source) and the type of water body its impacting (groundwater, surface water, or ocean water).

Where is the pollution coming from?

Point source pollution.

When contamination originates from a single source, it’s called point source pollution. Examples include wastewater (also called effluent) discharged legally or illegally by a manufacturer, oil refinery, or wastewater treatment facility, as well as contamination from leaking septic systems, chemical and oil spills, and illegal dumping. The EPA regulates point source pollution by establishing limits on what can be discharged by a facility directly into a body of water. While point source pollution originates from a specific place, it can affect miles of waterways and ocean.

Nonpoint source

Nonpoint source pollution is contamination derived from diffuse sources. These may include agricultural or stormwater runoff or debris blown into waterways from land. Nonpoint source pollution is the leading cause of water pollution in U.S. waters, but it’s difficult to regulate, since there’s no single, identifiable culprit.

Transboundary

It goes without saying that water pollution can’t be contained by a line on a map. Transboundary pollution is the result of contaminated water from one country spilling into the waters of another. Contamination can result from a disaster—like an oil spill—or the slow, downriver creep of industrial, agricultural, or municipal discharge.

What type of water is being impacted?

Groundwater pollution.

When rain falls and seeps deep into the earth, filling the cracks, crevices, and porous spaces of an aquifer (basically an underground storehouse of water), it becomes groundwater—one of our least visible but most important natural resources. Nearly 40 percent of Americans rely on groundwater, pumped to the earth’s surface, for drinking water. For some folks in rural areas, it’s their only freshwater source. Groundwater gets polluted when contaminants—from pesticides and fertilizers to waste leached from landfills and septic systems—make their way into an aquifer, rendering it unsafe for human use. Ridding groundwater of contaminants can be difficult to impossible, as well as costly. Once polluted, an aquifer may be unusable for decades, or even thousands of years. Groundwater can also spread contamination far from the original polluting source as it seeps into streams, lakes, and oceans.

Surface water pollution

Covering about 70 percent of the earth, surface water is what fills our oceans, lakes, rivers, and all those other blue bits on the world map. Surface water from freshwater sources (that is, from sources other than the ocean) accounts for more than 60 percent of the water delivered to American homes. But a significant pool of that water is in peril. According to the most recent surveys on national water quality from the U.S. Environmental Protection Agency, nearly half of our rivers and streams and more than one-third of our lakes are polluted and unfit for swimming, fishing, and drinking. Nutrient pollution, which includes nitrates and phosphates, is the leading type of contamination in these freshwater sources. While plants and animals need these nutrients to grow, they have become a major pollutant due to farm waste and fertilizer runoff. Municipal and industrial waste discharges contribute their fair share of toxins as well. There’s also all the random junk that industry and individuals dump directly into waterways.

Ocean water pollution

Eighty percent of ocean pollution (also called marine pollution) originates on land—whether along the coast or far inland. Contaminants such as chemicals, nutrients, and heavy metals are carried from farms, factories, and cities by streams and rivers into our bays and estuaries; from there they travel out to sea. Meanwhile, marine debris— particularly plastic —is blown in by the wind or washed in via storm drains and sewers. Our seas are also sometimes spoiled by oil spills and leaks—big and small—and are consistently soaking up carbon pollution from the air. The ocean absorbs as much as a quarter of man-made carbon emissions .

On human health

To put it bluntly: Water pollution kills. In fact, it caused 1.8 million deaths in 2015, according to a study published in The Lancet . Contaminated water can also make you ill. Every year, unsafe water sickens about 1 billion people. And low-income communities are disproportionately at risk because their homes are often closest to the most polluting industries.

Waterborne pathogens, in the form of disease-causing bacteria and viruses from human and animal waste, are a major cause of illness from contaminated drinking water . Diseases spread by unsafe water include cholera, giardia, and typhoid. Even in wealthy nations, accidental or illegal releases from sewage treatment facilities, as well as runoff from farms and urban areas, contribute harmful pathogens to waterways. Thousands of people across the United States are sickened every year by Legionnaires’ disease (a severe form of pneumonia contracted from water sources like cooling towers and piped water), with cases cropping up from California’s Disneyland to Manhattan’s Upper East Side.

A woman washes a baby in an infant bath seat in a kitchen sink, with empty water bottles in the foreground.

A woman using bottled water to wash her three-week-old son at their home in Flint, Michigan

Todd McInturf/The Detroit News/AP

Meanwhile, the plight of residents in Flint, Michigan —where cost-cutting measures and aging water infrastructure created a lead contamination crisis—offers a stark look at how dangerous chemical and other industrial pollutants in our water can be. The problem goes far beyond Flint and involves much more than lead, as a wide range of chemical pollutants—from heavy metals such as arsenic and mercury to pesticides and nitrate fertilizers —are getting into our water supplies. Once they’re ingested, these toxins can cause a host of health issues, from cancer to hormone disruption to altered brain function. Children and pregnant women are particularly at risk.

Even swimming can pose a risk. Every year, 3.5 million Americans contract health issues such as skin rashes, pinkeye, respiratory infections, and hepatitis from sewage-laden coastal waters, according to EPA estimates.

On the environment

In order to thrive, healthy ecosystems rely on a complex web of animals, plants, bacteria, and fungi—all of which interact, directly or indirectly, with each other. Harm to any of these organisms can create a chain effect, imperiling entire aquatic environments.

When water pollution causes an algal bloom in a lake or marine environment, the proliferation of newly introduced nutrients stimulates plant and algae growth, which in turn reduces oxygen levels in the water. This dearth of oxygen, known as eutrophication , suffocates plants and animals and can create “dead zones,” where waters are essentially devoid of life. In certain cases, these harmful algal blooms can also produce neurotoxins that affect wildlife, from whales to sea turtles.

Chemicals and heavy metals from industrial and municipal wastewater contaminate waterways as well. These contaminants are toxic to aquatic life—most often reducing an organism’s life span and ability to reproduce—and make their way up the food chain as predator eats prey. That’s how tuna and other big fish accumulate high quantities of toxins, such as mercury.

Marine ecosystems are also threatened by marine debris , which can strangle, suffocate, and starve animals. Much of this solid debris, such as plastic bags and soda cans, gets swept into sewers and storm drains and eventually out to sea, turning our oceans into trash soup and sometimes consolidating to form floating garbage patches. Discarded fishing gear and other types of debris are responsible for harming more than 200 different species of marine life.

Meanwhile, ocean acidification is making it tougher for shellfish and coral to survive. Though they absorb about a quarter of the carbon pollution created each year by burning fossil fuels, oceans are becoming more acidic. This process makes it harder for shellfish and other species to build shells and may impact the nervous systems of sharks, clownfish, and other marine life.

With your actions

We’re all accountable to some degree for today’s water pollution problem. Fortunately, there are some simple ways you can prevent water contamination or at least limit your contribution to it:

  • Learn about the unique qualities of water where you live . Where does your water come from? Is the wastewater from your home treated? Where does stormwater flow to? Is your area in a drought? Start building a picture of the situation so you can discover where your actions will have the most impact—and see if your neighbors would be interested in joining in!
  • Reduce your plastic consumption and reuse or recycle plastic when you can.
  • Properly dispose of chemical cleaners, oils, and nonbiodegradable items to keep them from going down the drain.
  • Maintain your car so it doesn’t leak oil, antifreeze, or coolant.
  • If you have a yard, consider landscaping that reduces runoff and avoid applying pesticides and herbicides .
  • Don’t flush your old medications! Dispose of them in the trash to prevent them from entering local waterways.
  • Be mindful of anything you pour into storm sewers, since that waste often won’t be treated before being released into local waterways. If you notice a storm sewer blocked by litter, clean it up to keep that trash out of the water. (You’ll also help prevent troublesome street floods in a heavy storm.)
  • If you have a pup, be sure to pick up its poop .

With your voice

One of the most effective ways to stand up for our waters is to speak out in support of the Clean Water Act, which has helped hold polluters accountable for five decades—despite attempts by destructive industries to gut its authority. But we also need regulations that keep pace with modern-day challenges, including microplastics, PFAS , pharmaceuticals, and other contaminants our wastewater treatment plants weren’t built to handle, not to mention polluted water that’s dumped untreated.

Tell the federal government, the U.S. Army Corps of Engineers, and your local elected officials that you support water protections and investments in infrastructure, like wastewater treatment, lead-pipe removal programs, and stormwater-abating green infrastructure. Also, learn how you and those around you can get involved in the policymaking process . Our public waterways serve every one of us. We should all have a say in how they’re protected.

This story was originally published on May 14, 2018, and has been updated with new information and links.

This NRDC.org story is available for online republication by news media outlets or nonprofits under these conditions: The writer(s) must be credited with a byline; you must note prominently that the story was originally published by NRDC.org and link to the original; the story cannot be edited (beyond simple things such as grammar); you can’t resell the story in any form or grant republishing rights to other outlets; you can’t republish our material wholesale or automatically—you need to select stories individually; you can’t republish the photos or graphics on our site without specific permission; you should drop us a note to let us know when you’ve used one of our stories.

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Can ‘forever’ chemicals become less so this senior thesis works toward smarter cleanup of pfas..

By Molly Sharlach

May 20, 2024

Student and professor having a discussion while student points at information in a notebook.

For her senior thesis, Amélie Lemay worked with Ian Bourg, an associate professor of civil and environmental engineering and the High Meadows Environmental Institute. She developed complex simulations of how PFAS molecules, a critical class of environmental contaminants, move and interact at the interface of water and air. Photos by Sameer A. Khan/Fotobuddy

The class of chemicals known as PFAS — used in firefighting foams, some nonstick cookware, and many other products — can resist heat and repel water. Their chemical bonds are hard to break, and they persist in water sources for decades.

Exposure to them has been associated with cancers, “impacts to the liver and heart, and immune and developmental damage to infants and children,” according to the Environmental Protection Agency, which recently set national limits for PFAS in drinking water.

For her thesis research, Princeton senior Amélie Lemay has crafted computer simulations that could one day help lead the way to removing PFAS pollution from the environment.

Lemay, a civil and environmental engineering major, used simulations to investigate how seven types of molecules behave above bodies of water, where the water meets the air. She modeled their tendencies to mix with water or stick to the water-air boundary, and probed how mixtures of PFAS molecules interact — mimicking the messy reality of contaminated water.

Detailed knowledge of this chemistry could be key to understanding how remediation methods will work in settings like water treatment plants. Over the next few years, utilities across the United States will need to find effective ways to remove PFAS (per- and polyfluoroalkyl substances) from drinking water to comply with the EPA limits.

“Most of our drinking water treatment plants are not set up to deal with these compounds,” said Lemay. “This type of research can eventually lead to better ways to be able to take PFAS out of water.”

Lemay, of Wynnewood, Pennsylvania, came to Princeton with aspirations of using engineering to address environmental challenges. But using computer simulations to understand pollution was not part of her plan.

The summer after her first year, in 2021, Lemay secured internship support from Princeton’s High Meadows Environmental Institute to conduct field work with associate professor Ian Bourg on how rocks weather in the Princeton area and in the French Alps — research with implications for soil nutrients and atmospheric CO2 forecasting.

But COVID-19 travel restrictions were still in place that summer, so Bourg worked remotely with Lemay and several other students to set up simulations exploring the behavior of pollutants ranging from PFAS to anti-inflammatory drugs to insecticides.

“I actually ended up really liking this alternative project, and I think it’s even better suited for me than the original project would have been,” said Lemay, who earned certificates in statistics and machine learning and sustainable energy .

Portrait of Amélie Lemay in an academic office with a laptop computer.

The research was an excellent opportunity for Lemay to build her computer coding skills and learn the intricacies of molecular dynamics simulation software.

“When I first started with Professor Bourg, he had to walk me through step by step how to create a file” simulating a single chemical compound, Lemay said. Over time, she learned to add more complexity, accounting for variables like salinity and surface tension. Now the work is “like second nature.”

The summer project was a new direction in the lab’s research. Bourg, an associate professor of civil and environmental engineering and the High Meadows Environmental Institute, said he was learning along with the students. He quickly realized that he could rely on Lemay: “She’s been thinking like a grad student since the very beginning, in terms of being super conscientious and questioning the way we do things,” said Bourg.

Lemay and Ethan Sontarp, a geosciences major, continued the project as research assistants in Bourg’s group for the next two years. Eventually, they modeled the behavior of more than 80 organic pollutants at the water-air interface.

Lemay and Sontarp were co-first authors of a 2023 paper reporting the results in the journal Environmental Science and Technology. The article has been downloaded more than 2,000 times and is Bourg’s most-read research paper from last year — a testament to its value as a resource for researchers looking to improve the tracking and remediation of pollutants, said Bourg.

In her junior year, Lemay conducted independent work with Professor Barry Rand , who studies the properties of new materials for solar cells, analyzing factors that influence the adoption of rooftop solar energy. She published this analysis last year in the journal Energy Policy.

For her senior thesis with Bourg, she developed complex simulations of how multiple PFAS molecules move and interact at the interface of water and air. Her results have revealed that the contaminants’ movements are not limited only by physical space but also by complex charge interactions among neighboring PFAS molecules.

Space-filling 3D models of two types of molecules; water molecules shown in red and white and PFAS molecules shown in pink and aqua.

Lemay is now submitting this work for scientific publication. The simulations are a powerful way to understand how pollutants move in the environment, potentially helping to explain how rain interacts with contaminants, and why sea spray and lake spray aerosols are an important source of PFAS exposure in coastal communities. Lemay hopes this understanding can inform strategies to clean up PFAS pollution.

Lemay turned to engineering in high school, when she took part in a summer research program on biomolecular engineering. “In science, you’re seeking to uncover the unknown, which is very important,” she said. “But I found that the problem-solving and design aspects of engineering really appealed to me. I loved how practical and pragmatic the applications were.”

After nearly three years of research at Princeton, Lemay has gained comfort with the uncertainties of the process. “If you pursue something, and you don’t fully understand what the data are showing you at first — that used to be distressing to me,” she said. “But I’ve come to realize that it’s part of the process. You’re trying to do something that’s never been done before. No one has the right answer.”

This summer, Lemay will pursue a project advised by Professor Mark Zondlo analyzing electric vehicle use and neighborhood-level air pollution.

In the fall, she will begin a Ph.D. program in civil and environmental engineering at the Massachusetts Institute of Technology. She’s interested in using computational methods to design chemicals for programmed degradation, to prevent problems with environmental contamination in the future.

“I think Princeton’s focus on undergraduate research really sets this institution apart,” said Lemay. “I’m grateful to have had the chance to work with multiple mentors who have shown me … how to design solutions and search for knowledge, and then share that with the greater community.”

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Yale Climate Connections

Yale Climate Connections

Climate change is affecting mental health literally everywhere

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Farmers who can’t sleep, worrying they’ll lose everything amid increasing drought. Youth struggling with depression over a future that feels hopeless. Indigenous people grief-stricken over devastated ecosystems. For all these people and more, climate change is taking a clear toll on mental health — in every part of the world.  

Experts shared these examples and others during a recent summit organized by the Connecting Climate Minds network that brought together hundreds of scientists, doctors, community leaders, and other experts from dozens of countries who have spent the past year studying how climate change is harming mental health in their regions. 

Although mental illnesses are often viewed as an individual problem, the experts made clear that climate change is contributing to mental health challenges everywhere. 

The Connecting Climate Minds youth ambassador from Borneo, Jhonatan Yuditya Pratama, said his Indigenous community views nature as a sacred extension of being. Seeing the devastation of climate change on ancestral lands has brought his community “a profound sense of grief and loss,” he said.

“For us, mental health isn’t just about individuals,” he said. “It’s about the collective well-being of our communities and the land itself. When nature suffers, so do we.”  

Extreme weather and air pollution are taking a toll 

In her keynote, Marina Romanello, executive director of the Lancet Countdown and a Connecting Climate Minds advisory board member, explained the key ways that climate change threatens mental health. 

  • Extreme heat is associated with increased self-harm and violence as well as more general feelings of negativity. It also leads to feelings of isolation when people feel trapped inside their relatively cooler homes.
  • Wildfire or extreme weather stokes anxiety leading up to an event — and afterward — that can lead to PTSD or depression for survivors who have seen cherished places or lives lost.
  • Farmers, fisherpeople, and others whose livelihoods are tied to the environment experience chronic stress, worry, and depression over things they can’t control, like extreme weather, habitat loss, and drought.
  • Water scarcity increases stress for people in charge of seeking and transporting household water. Water scarcity also makes it hard for people to stay clean, potentially leading to isolation, loneliness, and depression. 
  • Air pollution can keep kids out of school, leading to social isolation and, over time, a sense of hopelessness about the future. 

What’s more, people are experiencing the compounding effects of multiple disasters, said Emma Lawrance, who leads the Climate Cares Centre, a U.K.-based team that researches and supports mental health in the face of environmental crises: “With more frequent disasters, people can no longer recover psychologically from one before another occurs,” Lawrance said.  

And these escalating hazards are exacerbating social inequality, said Alaa Abelgawad, the Connecting Climate Minds youth ambassador representing northern Africa and western Asia. “[It’s] manifesting as anxiety, depression, and a profound sense of disempowerment among marginalized populations.”

Who is most vulnerable to climate change and mental health challenges? 

Many Indigenous communities have already been facing intergenerational trauma and a sense of deep disconnect from land and culture. Recurring climate devastation can intensify feelings of grief, stress, and disillusionment about the future, contributing to increased rates of addiction and suicide, participants said. 

Farmers, too, are among the most vulnerable. Changing seasonal norms, increasing drought, and a higher risk of severe weather are directly affecting their livelihoods. 

Sacha Wright, head of research at the youth-focused organization Force of Nature and part of Connecting Climate Minds’s “lived experience” working group, said that in Kenya, many small farmers are struggling with declining harvests and out of desperation have resorted to cutting down trees for charcoal. Though they felt they had no choice, some said cutting down the trees made the whole situation feel even worse. She spoke of high rates of depression, hopelessness, trauma, and a widespread feeling of “not knowing what to do.” 

For young people, climate change can also evoke a sense of hopelessness and powerlessness. In the Yucatan, one young person Wright interviewed said the only choices in life there are to migrate or enter the military. 

“When I see drought, I see my community leaving school and going to the military,” the person interviewed said. 

Mercy Njeru, a member of Connecting Climate Mind’s sub-Saharan Africa working group, said extreme heat is often leading to school closures across the region, setting youth up for failure and a sense of hopelessness. 

“When it’s so hot and you’re so anxious you can’t work, you can’t do anything because you’re feeling anxious or you’re feeling so sad from all the heat around you,” she said. 

In addition to environmental impacts, generational inequity and a sense of moral distress also contribute to anxiety for many youth. Britt Wray, director of Stanford Medicine’s Special Initiative on Climate Change and Mental Health, said she hears from many young people that power holders aren’t taking sufficient action, instead depending entirely on their generation to solve climate change. 

“This offloading of responsibility — without adequate partnership from the elder and more powerful contingents among us — can make burdensome climate anxiety and distress much worse,” she said.

Read: What baby boomers can do about climate change, according to Bill McKibben

What can be done to protect mental health as the climate changes? 

To help address the rising tide of mental health challenges, governments and public health leaders need to know exactly what kinds of impacts people are experiencing in their own communities.

First step: looking at experiences in every region. 

“We will only be successful if we can continue to connect and engage people from very different sectors, from neighborhoods all the way to multilateral organizations,” said Pamela Collins, chair of the department of mental health at the Johns Hopkins Bloomberg School of Public Health. 

Other examples of ways forward include everything from expanding health insurance to include climate-related mental health impacts to ensuring government policy supports people whose work has been affected by climate change to improve their job prospects. Several participants also spoke of the importance of returning to the wisdom of ancestral knowledge to address climate change in general, including mental health impacts. 

Other specific solutions offered by Connecting Climate Minds participants include:

  • More public green space. Collins, the Hopkins professor, cited a study highlighting the need for more accessible green space in cities, a move that could have multiple positive outcomes, including on mental health. Forest bathing , AKA spending dedicated time in nature, reduces stress and anxiety, increases serotonin production, and improves mood regulation and overall mental health — all while being low-intensity and low-cost, said Niaya Harper Igarashi, part of Connecting Climate Mind’s eastern and southeastern Asia working group. 
  • Focusing on reducing inequity. Making sure everyone has access to nutritious food, clean air and water, and sustainable energy sources is good for the climate and community. 
  • Talking helps. In many communities, mental health is a taboo topic. By talking more openly about it on a personal level, in social or spiritual settings, at the dinner table, or in your doctor’s office, individuals can combat stigma and contribute to a growing understanding of these issues. 
  • Meeting people where they are. From using vocabulary that makes sense for different communities to meeting people’s basic needs, solutions are most effective when they’re tailored for what real people are actually going through. For example, Wray, the Stanford expert, said meeting kids where they are includes screening for climate distress where many of them are every day: at school.

Lawrance, the Climate Cares lead who helped organize the summit, said it was heartening to see solutions being advanced around the world. 

“The dialogue showed this really strongly: that many solutions do already exist,” she said. “And it’s by learning from each other’s ways of knowing and doing that we can best find the ones that work for our context, and ensure people experiencing the worst climate impacts have a future where they cannot just survive, but thrive.”

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  • MyU : For Students, Faculty, and Staff

News Roundup Spring 2024

The Class of 2024 spring graduation celebration

CEGE Spring Graduation Celebration and Order of the Engineer

Forty-seven graduates of the undergraduate and grad student programs (pictured above) in the Department of Civil, Environmental, and Geo- Engineering took part in the Order of the Engineer on graduation day. Distinguished Speakers at this departmental event included Katrina Kessler (MS EnvE 2021), Commissioner of the Minnesota Pollution Control Agency, and student Brian Balquist. Following this event, students participated in the college-wide Commencement Ceremony at 3M Arena at Mariucci. 

UNIVERSITY & DEPARTMENT

The University of Minnesota’s Crookston, Duluth, and Rochester campuses have been awarded the Carnegie Elective Classification for Community Engagement, joining the Twin Cities (2006, 2015) and Morris campuses (2015), and making the U of M the country’s first and only university system at which every individual campus has received this selective designation. Only 368 from nearly 4,000 qualifying U.S. universities and colleges have been granted this designation.

CEGE contributed strongly to the College of Science and Engineering’s efforts toward sustainability research. CEGE researchers are bringing in over $35 million in funded research to study carbon mineralization, nature and urban areas, circularity of water resources, and global snowfall patterns. This news was highlighted in the Fall 2023 issue of  Inventing Tomorrow  (pages 10-11). https://issuu.com/inventingtomorrow/docs/fall_2023_inventing_tomorrow-web

CEGE’s new program for a one-year master’s degree in structural engineering is now accepting applicants for Fall 2024. We owe a big thanks to DAN MURPHY and LAURA AMUNDSON for their volunteer work to help curate the program with Professor JIA-LIANG LE and EBRAHIM SHEMSHADIAN, the program director. Potential students and companies interested in hosting a summer intern can contact Ebrahim Shemshadian ( [email protected] ).

BERNIE BULLERT , CEGE benefactor and MN Water Research Fund founder, was profiled on the website of the University of Minnesota Foundation (UMF). There you can read more about his mission to share clean water technologies with smaller communities in Minnesota. Many have joined Bullert in this mission. MWRF Recognizes their Generous 2024 Partners. Gold Partners: Bernie Bullert, Hawkins, Inc., Minnesota Department of Health, Minnesota Pollution Control Agency, and SL-serco. Silver Partners: ISG, Karl and Pam Streed, Kasco, Kelly Lange-Haider and Mark Haider, ME Simpson, Naeem Qureshi, Dr. Paul H. Boening, TKDA, and Waterous. Bronze Partners: Bruce R. Bullert; Brenda Lenz, Ph.D., APRN FNP-C, CNE; CDM Smith; Central States Water Environment Association (CSWEA MN); Heidi and Steve Hamilton; Jim “Bulldog” Sadler; Lisa and Del Cerney; Magney Construction; Sambatek; Shannon and John Wolkerstorfer; Stantec; and Tenon Systems.

After retiring from Baker-Tilly,  NICK DRAGISICH  (BCE 1977) has taken on a new role: City Council member in Lake Elmo, Minnesota. After earning his BCE from the University of Minnesota, Dragisich earned a master’s degree in business administration from the University of St. Thomas. Dragisich retired in May from his position as managing director at Baker Tilly, where he had previously served as firm director. Prior to that, he served as assistant city manager in Spokane, Washington, was the city administrator and city engineer in Virginia, Minnesota, and was mayor of Chisholm, Minnesota—all adding up to more than 40 years of experience in local government. Dragisich was selected by a unanimous vote. His current term expires in December 2024.

PAUL F. GNIRK  (Ph.D. 1966) passed away January 29, 2024, at the age of 86. A memorial service was held Saturday, February 24, at the South Dakota School of Mines and Technology (SDSM&T), where he started and ended his teaching career, though he had many other positions, professional and voluntary. In 2018 Paul was inducted into the SDSM&T Hardrocker Hall of Fame, and in 2022, he was inducted into the South Dakota Hall of Fame, joining his mother Adeline S. Gnirk, who had been inducted in 1987 for her work authoring nine books on the history of south central South Dakota.

ROGER M. HILL  (BCE 1957) passed away on January 13, 2024, at the age of 90. His daughter, Kelly Robinson, wrote to CEGE that Roger was “a dedicated Gopher fan until the end, and we enjoyed many football games together in recent years. Thank you for everything.”

KAUSER JAHAN  (Ph.D. 1993, advised by Walter Maier), PE, is now a civil and environmental engineering professor and department head at Henry M. Rowan College of Engineering. Jahan was awarded a 3-year (2022- 2025), $500,000 grant from the U.S. Department of Environmental Protection Agency (USEPA). The grant supports her project, “WaterWorks: Developing the New Generation of Workforce for Water/Wastewater Utilities,” for the development of educational tools that will expose and prepare today’s students for careers in water and wastewater utilities.

SAURA JOST  (BCE 2010, advised by Timothy LaPara) was elected to the St. Paul City Council for Ward 3. She is part of the historic group of women that make up the nation’s first all-female city council in a large city.

The 2024 ASCE Western Great Lakes Student Symposium combines several competitions for students involved in ASCE. CEGE sent a large contingent of competitors to Chicago. Each of the competition groups won awards: Ethics Paper 1st place Hans Lagerquist; Sustainable Solutions team 1st place overall in (qualifying them for the National competition in Utah in June); GeoWall 2nd place overall; Men’s Sprint for Concrete Canoe with rowers Sakthi Sundaram Saravanan and Owen McDonald 2nd place; Product Prototype for Concrete Canoe 2nd place; Steel Bridge (200 lb bridge weight) 2nd place in lightness; Scavenger Hunt 3rd place; and Aesthetics and Structural Efficiency for Steel Bridge 4th place.

Students competing on the Minnesota Environmental Engineers, Scientists, and Enthusiasts (MEESE) team earned second place in the Conference on the Environment undergraduate student design competition in November 2023. Erin Surdo is the MEESE Faculty Adviser. Pictured are NIKO DESHPANDE, ANNA RETTLER, and SYDNEY OLSON.

The CEGE CLASS OF 2023 raised money to help reduce the financial barrier for fellow students taking the Fundamentals of Engineering exam, a cost of $175 per test taker. As a result of this gift, they were able to make the exam more affordable for 15 current CEGE seniors. CEGE students who take the FE exam pass the first time at a rate well above national averages, demonstrating that CEGE does a great job of teaching engineering fundamentals. In 2023, 46 of 50 students passed the challenging exam on the first try.

This winter break, four CEGE students joined 10 other students from the College of Science and Engineering for the global seminar, Design for Life: Water in Tanzania. The students visited numerous sites in Tanzania, collected water source samples, designed rural water systems, and went on safari. Read the trip blog: http://globalblogs.cse.umn.edu/search/label/Tanzania%202024

Undergraduate Honor Student  MALIK KHADAR  (advised by Dr. Paul Capel) received honorable mention for the Computing Research Association (CRA) Outstanding Undergraduate Research Award for undergraduate students who show outstanding research potential in an area of computing research.

GRADUATE STUDENTS

AKASH BHAT  (advised by William Arnold) presented his Ph.D. defense on Friday, October 27, 2023. Bhat’s thesis is “Photolysis of fluorochemicals: Tracking fluorine, use of UV-LEDs, and computational insights.” Bhat’s work investigating the degradation of fluorinated compounds will assist in the future design of fluorinated chemicals such that persistent and/or toxic byproducts are not formed in the environment.

ETHAN BOTMEN  (advised by Bill Arnold) completed his Master of Science Final Exam February 28, 2024. His research topic was Degradation of Fluorinated Compounds by Nucleophilic Attack of Organo-fluorine Functional Groups.

XIATING CHEN , Ph.D. Candidate in Water Resources Engineering at the Saint Anthony Falls Laboratory is the recipient of the 2023 Nels Nelson Memorial Fellowship Award. Chen (advised by Xue Feng) is researching eco-hydrological functions of urban trees and other green infrastructure at both the local and watershed scale, through combined field observations and modeling approaches.

ALICE PRATES BISSO DAMBROZ  has been a Visiting Student Researcher at the University of Minnesota since last August, on a Doctoral Dissertation Research Award from Fulbright. Her CEGE advisor is Dr. Paul Capel. Dambroz is a fourth year Ph.D. student in Soil Science at Universidade Federal de Santa Maria in Brazil, where she studies with her adviser Jean Minella. Her research focuses on the hydrological monitoring of a small agricultural watershed in Southern Brazil, which is located on a transition area between volcanic and sedimentary rocks. Its topography, shallow soils, and land use make it prone to runoff and erosion processes.

Yielding to people in crosswalks should be a very pedestrian topic. Yet graduate student researchers  TIANYI LI, JOSHUA KLAVINS, TE XU, NIAZ MAHMUD ZAFRI  (Dept.of Urban and Regional Planning at Bangladesh University of Engineering and Technology), and Professor Raphael Stern found that drivers often do not yield to pedestrians, but they are influenced by the markings around a crosswalk. Their work was picked up by the  Minnesota Reformer.

TIANYI LI  (Ph.D. student advised by Raphael Stern) also won the Dwight David Eisenhower Transportation (DDET) Fellowship for the third time! Li (center) and Stern (right) are pictured at the Federal Highway Administration with Latoya Jones, the program manager for the DDET Fellowship.

The Three Minute Thesis Contest and the Minnesota Nice trophy has become an annual tradition in CEGE. 2023’s winner was  EHSANUR RAHMAN , a Ph.D. student advised by Boya Xiong.

GUANJU (WILLIAM) WEI , a Ph.D. student advised by Judy Yang, is the recipient of the 2023 Heinz G. Stefan Fellowship. He presented his research entitled Microfluidic Investigation of the Biofilm Growth under Dynamic Fluid Environments and received his award at the St. Anthony Falls Research Laboratory April 9. The results of Wei's research can be used in industrial, medical, and scientific fields to control biofilm growth.

BILL ARNOLD  stars in an award-winning video about prairie potholes. The Prairie Potholes Project film was made with the University of Delaware and highlights Arnold’s NSF research. The official winners of the 2024 Environmental Communications Awards Competition Grand Prize are Jon Cox and Ben Hemmings who produced and directed the film. Graduate student Marcia Pacheco (CFANS/LAAS) and Bill Arnold are the on-screen stars.

Four faculty from CEGE join the Center for Transportation Studies Faculty and Research Scholars for FY24–25:  SEONGJIN CHOI, KETSON ROBERTO MAXIMIANO DOS SANTOS, PEDRAM MORTAZAVI,  and  BENJAMIN WORSFOLD . CTS Scholars are drawn from diverse fields including engineering, planning, computer science, environmental studies, and public policy.

XUE FENG  is coauthor on an article in  Nature Reviews Earth and Environment . The authors evaluate global plant responses to changing rainfall regimes that are now characterized by fewer and larger rainfall events. A news release written at Univ. of Maryland can be found here: https://webhost.essic. umd.edu/april-showers-bring-mayflowers- but-with-drizzles-or-downpours/ A long-running series of U of M research projects aimed at improving stormwater quality are beginning to see practical application by stormwater specialists from the Twin Cities metro area and beyond. JOHN GULLIVER has been studying best practices for stormwater management for about 16 years. Lately, he has focused specifically on mitigating phosphorous contamination. His research was highlighted by the Center for Transportation Studies.

JIAQI LI, BILL ARNOLD,  and  RAYMOND HOZALSKI  published a paper on N-nitrosodimethylamine (NDMA) precursors in Minnesota rivers. “Animal Feedlots and Domestic Wastewater Discharges are Likely Sources of N-Nitrosodimethylamine (NDMA) Precursors in Midwestern Watersheds,” Environmental Science and Technology (January 2024) doi: 10.1021/acs. est.3c09251

ALIREZA KHANI  contributed to MnDOT research on Optimizing Charging Infrastructure for Electric Trucks. Electric options for medium- and heavy-duty electric trucks (e-trucks) are still largely in development. These trucks account for a substantial percentage of transportation greenhouse gas emissions. They have greater power needs and different charging needs than personal EVs. Proactively planning for e-truck charging stations will support MnDOT in helping to achieve the state’s greenhouse gas reduction goals. This research was featured in the webinar “Electrification of the Freight System in Minnesota,” hosted by the University of Minnesota’s Center for Transportation Studies. A recording of the event is now available online.

MICHAEL LEVIN  has developed a unique course for CEGE students on Air Transportation Systems. It is the only class at UMN studying air transportation systems from an infrastructure design and management perspective. Spring 2024 saw the third offering of this course, which is offered for juniors, seniors, and graduate students.

Research Professor  SOFIA (SONIA) MOGILEVSKAYA  has been developing international connections. She visited the University of Seville, Spain, November 13–26, 2023, where she taught a short course titled “Fundamentals of Homogenization in Composites.” She also met with the graduate students to discuss collaborative research with Prof. Vladislav Mantic, from the Group of Continuum Mechanics and Structural Analysis at the University of Seville. Her visit was a part of planned activities within the DIAGONAL Consortium funded by the European Commission. CEGE UMN is a partner organization within DIAGONAL, represented by CEGE professors Mogilevskaya and Joseph Labuz. Mantic will visit CEGE summer 2024 to follow up on research developments and discuss plans for future collaboration and organization of short-term exchange visits for the graduate students from each institution. 

DAVID NEWCOMB  passed away in March. He was a professor in CEGE from 1989–99 in the area of pavement engineering. Newcomb led the research program on asphalt materials characterization. He was the technical director of Mn/ROAD pavement research facility, and he started an enduring collaboration with MnDOT that continues today. In 2000, he moved from Minnesota to become vice-president for Research and Technology at the National Asphalt Pavement Association. Later he moved to his native Texas, where he was appointed to the division head of Materials and Pavement at the Texas A&M Transportation Institute, a position from which he recently retired. He will be greatly missed.

PAIGE NOVAK  won Minnesota ASCE’s 2023 Distinguished Engineer of the Year Award for her contributions to society through her engineering achievements and professional experiences.

The National Science Foundation (NSF) announced ten inaugural (NSF) Regional Innovation Engines awards, with a potential $1.6 billion investment nationally over the next decade. Great Lakes ReNEW is led by the Chicago-based water innovation hub,  Current,  and includes a team from the University of Minnesota, including PAIGE NOVAK. Current will receive $15 mil for the first two years, and up to $160 million over ten years to develop and grow a water-focused innovation engine in the Great Lakes region. The project’s ambitious plan is to create a decarbonized circular “blue economy” to leverage the region’s extraordinary water resources to transform the upper Midwest—Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin. Brewing one pint of beer generates seven pints of wastewater, on average. So what can you do with that wastewater?  PAIGE NOVAK  and her team are exploring the possibilities of capturing pollutants in wastewater and using bacteria to transform them into energy.

BOYA XIONG  has been selected as a recipient of the 2024 40 Under 40 Recognition Program by the American Academy of Environmental Engineers and Scientists. The award was presented at the 2024 AAEES Awards Ceremony, April 11, 2024, at the historic Howard University in Washington, D.C. 

JUDY Q. YANG  received a McKnight Land-Grant Professorship Award. This two-year award recognizes promising assistant professors and is intended to advance the careers of individuals who have the potential to make significant contributions to their departments and their scholarly fields. 

Professor Emeritus CHARLES FAIRHURST , his son CHARLES EDWARD FAIRHURST , and his daughter MARGARET FAIRHURST DURENBERGER were on campus recently to present Department Head Paige Novak with a check for $25,000 for the Charles Fairhurst Fellowship in Earth Resources Engineering in support of graduate students studying geomechanics. The life of Charles Fairhurst through a discussion with his children is featured on the Engineering and Technology History Wiki at https://ethw.org/Oral-History:Charles_Fairhurst#00:00:14_INTRODUCTION

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IMAGES

  1. (DOC) Research Proposal on Water Pollution.docx

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  2. (PDF) Water pollution by heavy metal and organic pollutants: Brief

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  3. ⇉Water Pollution and Wastage Essay Example

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COMMENTS

  1. (PDF) Water Pollution: Sources and Its Impact on Human ...

    Water pollution is a major problem in the global environment. This necessitates continuing assessment and review of water resource policy at all levels. ... Thesis. Water Pollution: Sources of ...

  2. (Pdf) Researches in Water Pollution: a Review

    This makes the river water unsa fe for drinking and bathing. About 1500 substances have. been listed as pollutants in freshwater ecosystems and a generalised list of pollutants includes. acids and ...

  3. Urbanization: an increasing source of multiple pollutants to rivers in

    Our multi-pollutant approach could support effective water pollution assessment in urban areas. ... (PhD thesis). Wageningen University, Wageningen, The Netherlands 235 pp, (2016). ...

  4. Frontiers

    Background: More than 80% of sewage generated by human activities is discharged into rivers and oceans without any treatment, which results in environmental pollution and more than 50 diseases. 80% of diseases and 50% of child deaths worldwide are related to poor water quality.Methods: This paper selected 85 relevant papers finally based on the keywords of water pollution, water quality ...

  5. (PDF) WATER POLLUTION-SOURCES,EFFECTS AND CONTROL

    WATER POLLUTION -SOURCES,EFFECTS AND CONTROL. M. Romeo Singh 1* and Asha Gupta 2. 1* Centre for Biodiversity, Department of Botany. Nagaland University, Lumami-798627, India. 2 Centre of Advance ...

  6. The impact of water infrastructure inequality on marginalized communities

    Water infrastructure inequality acknowledges that. certain communities lack access to resources and outdated infrastructure perpetuates inequality. and environmental injustice.4 The longer water systems remain compromised, the more difficult. it will be to confront the ramifications.

  7. Assessment of Water Quality and Identification of Polluted Risky ...

    Water quality assessment at the watershed scale requires not only an investigation of water pollution and the recognition of main pollution factors, but also the identification of polluted risky regions resulted in polluted surrounding river sections. To realize this objective, we collected water samplings from 67 sampling sites in the Honghe River watershed of China with Grid GIS method to ...

  8. Water Quality Assessment and Pollution Source Identification of the

    Water scarcity is a growing threat to economic and social development and widespread water pollution in recent decades further complicates the threat, especially in developing countries [1,2,3].Water pollution caused both by anthropogenic activities such as urbanization [], industrial accidents [5,6,7], dam construction [], and natural phenomena like soil erosion [] and climate change [], is a ...

  9. Water Pollution: A Review

    The economic impact of water pollution is very visible. It is expensive to clean polluted water in comparison with the cost of clean water. Water pollution affects fish and other aquatic organisms. It also has a negative impact on tourism. According to an estimate, there is a direct and indirect loss of $50 billion annually due to water pollution.

  10. Water pollution Its causes and effects

    Abstract. The topic of water contamination is one of the significant studies that, because of its great effect on the lives of humans, animals and plants alike, has attracted the attention of researchers and those interested in the environment. It is not less harmful than contamination of the air and soil, but more closely linked to them.

  11. The Impacts of Water Pollution Emissions on Public Health in 30

    2.1. The Impact of Water Pollution Caused by Urban Production and Living. Water pollution has negative impacts on the environment. Using a drink-y reservoir and an irrigation-t reservoir as research subjects, Deng et al. (2020) [] found that metals can precipitate from water into sediment in 10-15 days, and both reservoirs are heavily contaminated with heavy metals (chromium, manganese ...

  12. IOP Conference Series: Earth and Environmental Science PAPER OPEN

    Water pollution leads to a change in its specifications since pollution reduces its ability to perform its natural role. So, it becomes inappropriate for human, agricultural or industrial uses allocated to it. There was a belief argues that rivers, seas, oceans and other watercourses are the most appropriate places for dumping production and ...

  13. 102 Water Pollution Essay Topic Ideas & Examples

    102 Water Pollution Essay Topic Ideas & Examples. Water pollution essays are an excellent way to demonstrate your awareness of the topic and your position on the solutions to the issue. To help you ease the writing process, we prepared some tips, essay topics, and research questions about water pollution.

  14. PDF Thesis Produced Water Quality Characterization and Prediction For

    The assessment of produced water quality for the Wattenberg field was conducted in four phases: (1) field work and sampling (2) water sample analysis (3) statistical and spatial analysis overview of produced water data (4) development of spatial prediction methods. 3.1 Water Sampling.

  15. Water Pollution: Effects, Prevention, and Climatic Impact

    PDF | On Mar 21, 2018, Inyinbor Adejumoke A. and others published Water Pollution: Effects, Prevention, and Climatic Impact | Find, read and cite all the research you need on ResearchGate

  16. Water pollution

    Jerry A. Nathanson. Water pollution is the release of substances (such as chemicals or microorganisms) or energy (in the form of radioactivity or heat) into surface and subsurface waters to the point that the substances interfere with beneficial use of the water or with the natural functioning of ecosystems.

  17. Water pollution facts and information

    Pollution can enter water directly, through both legal and illegal discharges from factories, for example, or imperfect water treatment plants. Spills and leaks from oil pipelines or hydraulic ...

  18. Urban Water Management (Final Thesis)

    The goal of urban water management thesis to investigate components of urban water system and careful, economic use handling of the water in urban. The first goal of this ... 1972 passage of the Federal Water Pollution Control Act Amendments, which established policies for controlling wastewater discharges in an effort to protect water

  19. Full article: Perceptions about water pollution among university

    4.4. The awareness of water pollution in general. There is excellent awareness of the real cause of water pollution by the students in Iraq. 50% replied that Iraq causes the highest part of the pollution. The student's water pollution concern was 95%, where they answered that the concern should be sometimes permanent.

  20. Water Pollution Definition

    What is water pollution? Water pollution occurs when harmful substances—often chemicals or microorganisms—contaminate a stream, river, lake, ocean, aquifer, or other body of water, degrading ...

  21. Shodhganga@INFLIBNET: WATER POLLUTION EFFECTS AND LEGAL CONTROLS IN

    Summary: 172-186, References: i-ix, Appendix: x-xv. Shodhganga: a reservoir of Indian theses @ INFLIBNET The Shodhganga@INFLIBNET Centre provides a platform for research students to deposit their Ph.D. theses and make it available to the entire scholarly community in open access.

  22. (PDF) Water pollution Its causes and effects

    2- Acid rain and its effect on water pollution: Acid rain are a result of the formation of sulfuric and. nitric acids and the interaction of sulfur and nitrogen oxides in raindrops. Its pH is ...

  23. Can 'forever' chemicals become less so? This senior thesis works toward

    For her thesis research, Princeton senior Amélie Lemay has crafted computer simulations that could one day help lead the way to removing PFAS pollution from the environment. Lemay, a civil and environmental engineering major, used simulations to investigate how seven types of molecules behave above bodies of water, where the water meets the air.

  24. Climate change is affecting mental health literally everywhere

    Water scarcity increases stress for people in charge of seeking and transporting household water. Water scarcity also makes it hard for people to stay clean, potentially leading to isolation, loneliness, and depression. Air pollution can keep kids out of school, leading to social isolation and, over time, a sense of hopelessness about the future.

  25. Causes, negative effects, and preventive methods of water pollution in

    4 Amare (2020) Thesis Ethiopia Meta-analysis To inv estigate the causes . and effects of water . ... Environmental pollution in water resources, soil, air [13], food [14-17, 18,19, 20,21, 22,23 ...

  26. News Roundup Spring 2024

    CEGE Spring Graduation Celebration and Order of the EngineerForty-seven graduates of the undergraduate and grad student programs (pictured above) in the Department of Civil, Environmental, and Geo- Engineering took part in the Order of the Engineer on graduation day. Distinguished Speakers at this departmental event included Katrina Kessler (MS EnvE 2021), Commissioner of the Minnesota ...