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Reflections on the Relevance of the Principles of Scientific Management: An Analysis of McDonalds Corporation
2023, International Research Journal of Modernization in Engineering Technology and Science
The method of scientific management was developed by Frederic Taylor during the Second World War. With the commercial change came a rapidly expanding pool of people searching for opportunities that required a new management strategy. The first management theory to be used internationally was scientific management. It supports the efficient use of resources for maximum productivity, motivating employees to earn more money. According to Taylor, the main obstacle to improving the productivity of human labour is managers' ineptitude. Taylor decided to get into agreements with other businesses for the reorganisation of the manufacturing processes to make the jobs that each employee had to do simpler. The employees at the Taylorized factories perform the same straightforward duty and don't do a lot of other things. Although there are still several indications that the majority of companies are using scientific management for their commercial operations, the concepts of scientific management have a significant impact on society as a whole. This article examines how McDonald’s has utilised the scientific management principles to establish its dominance.
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The "Taylorism" method of scientific management was developed by Frederic Taylor during the Second World War. With the commercial change came a rapidly expanding pool of people searching for opportunities that required a new management strategy. The first management theory to be used internationally was scientific management. It supports the efficient use of resources for maximum productivity, motivating employees to earn more money. According to Taylor, the main obstacle to improving the productivity of human labour is managers' ineptitude. Taylor decided to get into agreements with other businesses for the reorganisation of the manufacturing processes to make the jobs that each employee had to do simpler. The employees at the Taylorized factories perform the same straightforward duty and don't do a lot of other things. Although there are still several indications that the majority of companies are using scientific management for their commercial operations, the concepts of scientific management have a significant impact on society as a whole. This article examines how McDonald’s has utilised the scientific management principles to establish its dominance.
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What is Scientific Management Theory? Scientific Management Theory In A Nutshell
Scientific Management Theory was created by Frederick Winslow Taylor in 1911 as a means of encouraging industrial companies to switch to mass production. With a background in mechanical engineering, he applied engineering principles to workplace productivity on the factory floor. Scientific Management Theory seeks to find the most efficient way of performing a job in the workplace.
Component | Description |
---|---|
Definition | Scientific Management Theory is a management approach that focuses on optimizing work processes by applying scientific methods to identify the most efficient way to perform tasks and allocate resources. It emphasizes the use of data and systematic analysis to improve productivity. |
Origin | Developed by Frederick Winslow Taylor in the late 19th and early 20th centuries, Scientific Management Theory emerged during the Industrial Revolution as a response to the need for increased efficiency in manufacturing and production processes. |
Principles | – : Scientific management involves breaking down tasks into smaller, measurable elements to determine the most efficient way to perform them. This often includes time and motion studies to identify optimal work methods. – : Taylor advocated for standardizing work methods and tools to eliminate variability and increase predictability. – : Employees should be selected based on their skills and abilities, matching them to specific job roles. – : Providing training and development to workers to ensure they can perform tasks optimally. – : Introducing performance-based incentive systems to motivate workers to achieve higher productivity. |
Importance | Scientific Management Theory played a significant role in shaping modern management practices by introducing systematic approaches to work processes, data-driven decision-making, and the concept of efficiency in organizations. |
Benefits | – : Scientific management aims to eliminate waste and inefficiency, leading to higher productivity. – : Improved efficiency often results in reduced production costs. – : The use of data and scientific analysis helps in making informed management decisions. – : It enhances the productivity and performance of both workers and organizations. |
Drawbacks | – : Critics argue that scientific management can lead to dehumanization of work and an excessive focus on efficiency at the expense of worker well-being. – : Workers may resist the rigid and highly standardized work methods imposed by scientific management. – : Some argue that scientific management is most suitable for repetitive, manual tasks and may not apply to knowledge work or creative industries. – : The rigid approach may not accommodate changing circumstances or evolving job roles. |
Contemporary Relevance | While some aspects of Scientific Management Theory have evolved, elements such as process optimization, data-driven decision-making, and the pursuit of efficiency continue to influence modern management practices. |
Applications | Scientific Management Theory has historically been applied in manufacturing and production industries, including automotive assembly lines and manufacturing plants. However, its principles have also been adapted and applied in service industries and healthcare to optimize processes and improve efficiency. |
Examples | – : Henry Ford applied principles of scientific management to automotive manufacturing, revolutionizing the production process and making cars more affordable. – : Fast food restaurants use standardized processes and workflows, influenced by scientific management principles, to ensure consistency and efficiency in food preparation. – : Call centers often employ time and motion studies and standardized scripts to improve the efficiency of customer service operations. – : Many manufacturing facilities continue to use scientific management principles to optimize production lines and reduce costs. |
Table of Contents
Understanding Scientific Management Theory
In the early 20th century, there was also a general belief that workers were lazy and inefficient.
Taylor argued that the remedy for inefficiency was to be found in systematic management – there was no use trying to recruit men who had extraordinary work ethics.
Taylor was one of the first to look at productivity from a scientific standpoint, believing in universal laws that governed labor productivity and efficiency.
For this reason, “Taylorism” is often referred to as one of the first forms of scientific management .
Taylor’s classic assumptions about workers
Taylor’s belief that workers were only motivated by money provides the basis for several classic assumptions:
- Workers find their work unenjoyable and have a natural tendency to slack off in a process he called natural soldiering. To counter this tendency, they must be closely monitored and controlled.
- To increase worker investment in their job, it should be broken down into bite-sized actions.
- Training should be provided to all employees to create a standardized way of working.
- Workers should be paid based on how much they produce (piece rate). Taylor argued that this would create a win-win scenario where the employee would earn more money and the business would maximize its profits.
The four core principles of Scientific Management Theory
Taylor was perhaps a product of his time, viewing employee labor as an extension of machine labor.
He was also a strong proponent of autocratic leadership , which an increasing number of modern companies are shying away from.
However, his principles of scientific management are still relevant today.
Here is a look at each principle:
- Select methods backed by science
Businesses should avoid giving workers the freedom to perform their jobs in any way they see fit.
The scientific method must be used to identify the single, most efficient way of doing the job.
- Assign workers to jobs that match their aptitude
Instead of assigning workers to jobs at random, assign them to roles where their unique capabilities will allow them to work at peak efficiency.
- Monitor worker performance
Monitor efficiency and ensure that necessary instruction is given on how to maintain productivity.
- Divide the workload between management and staff
Here, roles and responsibilities should clearly be defined.
Management should train workers and workers should implement lessons learned.
Examples of modern companies employing Scientific Management Theory
Although slightly outdated, scientific management theory is useful in highly competitive industries where labor costs need to be kept as low as possible.
Example organizations include:
- Amazon Case Study
where warehouse staff are paid on a piece-rate basis according to their level of productivity.
The company has also recently introduced patented wristbands that track employee performance in real-time.
McDonald’s Case Study
The homogenization of McDonald’s restaurants worldwide has meant that processes have had to become extremely refined.
The procedure for everything from making a burger to mopping the floor is the same – regardless of geographic location.
These processes are ultra-efficient and are broken down into actionable steps, which is a core component of Taylorism.
The aviation industry case study
Scientific management theory has played a pivotal role in the evolution of airport and airline management – a competitive, time-sensitive, and heavily regulated industry that requires companies to manage a multitude of different tasks.
Air New Zealand, for example, applied scientific management theory to its staff allocation and rostering systems over thirteen years between 1986 and 1999. Primarily, scientific management was used to address two core problems:
- The tours-of-duty (ToD) planning problem – where a sequence of flights must be constructed to crew the flight schedule. These sequences can comprise one-day periods of work but also encompass longer sequences spanning consecutive days with multiple flights and rest periods, and
- The rostering problem – where the airline has to match the ToD plan to individual employees to form a line of work (LoW) over a specific rostering period. In the process, airlines have to consider the employee’s skills or qualifications, employment contract conditions, operational rostering agreements, and any scheduled leave.
The role of management and crew
In aviation, the interaction of these problems can be considered from both the point of view of management and crew.
The management of Air New Zealand prefers maximum productivity and minimum-cost solutions that do not break laws and ensure all the work is performed.
They are also focused on the operational robustness of the schedule vis-à-vis sensitivity to disruptions.
For the Air New Zealand crew, on the other hand, the key concern is the quality of the solution.
What defines quality varies from one cohort to the next. Some consider the fair distribution of work to be important, while others hope to avoid arduous work patterns.
The importance of solving the aircrew-scheduling problem
Since aircraft and their associated crew are among the most expensive costs for an airline, their efficient utilization is vital to the company’s success and profitability.
Lured by the potential to reduce costs, history is littered with airlines who tried and failed to develop effective optimization methods.
But it was not until the 1980s that computational power became sufficiently advanced to solve the ToD problem.
Development of the model
In collaboration with the University of Auckland, Air New Zealand developed a total of 8 optimization-based systems. These systems, which were incorporated into the company’s database environment, solved all aspects of the planning and scheduling process across domestic and international routes.
One particular characteristic of these systems was that they presented solutions that exploited the rules. That is, the solutions were within the bounds of the law, made sense from a financial point of view, and were also beneficial for crew productivity and safety.
Air New Zealand also collaborated with NASA in its pioneering research on measuring fatigue, with the results subsequently added to the ToD systems as additional rules and constraints.
In dollar terms, scientific management theory allowed the airline to reduce the amount of money it spent on hotels, meals, and other expenses for crew who traveled overseas. The cost of constructing and maintaining the crewing system has also decreased over time.
Despite the company’s airline fleet and route structure increasing in size and complexity, the number of people Air New Zealand needed to employ to solve scheduling problems dropped from 27 in 1987 to just 15 in 2000.
At the time, conservative estimates put the total cost saving of the initiative at 15.655 million NZD per annum .
Key takeaways
- Scientific Management Theory is a theory of management that seeks to analyze and synthesize workflow to improve labor productivity.
- Scientific Management Theory was originally based on the assumption that workers were only motivated by money and is heavily geared toward autocratic leadership styles. Nevertheless, it is still relevant to modern organizations.
- Scientific Management Theory is particularly effective in industries with a high prevalence of menial or repetitive tasks where costs need to be minimized. Examples include Amazon and McDonald’s.
Key Highlights
- Origin and Background: Scientific Management Theory was developed by Frederick Winslow Taylor in 1911. It aimed to improve industrial productivity through the application of engineering principles to the workplace. Taylor believed in finding the most efficient ways of performing tasks.
- Worker Perceptions: In the early 20th century, there was a perception that workers were lazy and inefficient. Taylor’s theory aimed to address this by optimizing work processes.
- Efficiency and Systematic Management: Taylor believed that inefficiency could be addressed through systematic management rather than relying on recruiting individuals with extraordinary work ethics. He emphasized the need for scientific analysis to identify the most efficient ways of performing tasks.
- Taylorism: Taylor’s approach is often referred to as Taylorism. He believed in universal laws governing labor productivity and efficiency, and he introduced principles to optimize work processes.
- Assumptions About Workers: Taylor’s classic assumptions included that workers found work unenjoyable, had a tendency to slack off (natural soldiering), and needed close monitoring and control. He believed in breaking down tasks into manageable actions and providing standardized training.
- Piece-Rate Payment: Taylor advocated for paying workers based on their production, creating a win-win situation where employees earned more and businesses maximized profits.
- Core Principles: Taylor’s principles include selecting methods based on science, matching workers to suitable roles, monitoring worker performance, and clearly defining roles and responsibilities between management and staff.
- Modern Relevance: Although Taylorism is outdated in some aspects, its principles are still relevant, especially in industries where labor costs need to be minimized. Examples include Amazon and McDonald’s.
- Amazon Case Study: Amazon uses piece-rate payment for warehouse staff based on productivity and employs real-time performance tracking technology.
- McDonald’s Case Study: McDonald’s homogenized processes globally, ensuring consistency and efficiency in tasks like burger preparation and cleaning.
- Aviation Industry Case Study (Air New Zealand): The aviation industry has applied Scientific Management Theory to crew scheduling and planning, achieving cost savings and efficiency improvements.
- Air New Zealand’s Collaboration: Air New Zealand collaborated with the University of Auckland and NASA to develop optimization-based systems for crew scheduling, reducing costs and increasing efficiency.
- Benefits of Scientific Management: The theory has been successful in optimizing processes, reducing costs, improving efficiency, and aligning worker capabilities with tasks.
- Application and Limitations: Scientific Management Theory is effective in industries with repetitive tasks but may not fully accommodate the complexities of modern work environments.
- Autocratic Leadership: Taylor’s approach is associated with autocratic leadership , which may not align with modern leadership trends emphasizing empowerment and collaboration.
- Key Takeaways: Scientific Management Theory focuses on improving labor productivity through systematic analysis of work processes. It’s applicable in industries where repetitive tasks require optimization, and its principles are still relevant today.
Related Concepts | Description | When to Apply |
---|---|---|
by Frederick Taylor emphasizes improving labor productivity through systematic analysis of tasks, workflow optimization, and incentive systems. Key principles include time studies and standardization. | – When analyzing workflows to boost productivity. – When implementing performance measurement systems. – When designing incentive structures to motivate workers. – When fostering a culture of continuous improvement. – When optimizing resource allocation and cost management. – When aligning organizational structure with strategic objectives. – When addressing resistance to change and driving organizational reforms. – When improving operational efficiency and customer satisfaction. – When enhancing decision-making processes. – When preparing for career advancement or transitions. | |
advocates for scientific principles in management, aiming to maximize efficiency. It involves dividing tasks, standardizing processes, and hierarchical supervision. | – When streamlining operational processes. – When designing job roles to boost productivity. – When implementing performance measurement systems. – When training managers in scientific management principles. – When evaluating organizational structures. – When fostering a culture of accountability and transparency. – When aligning management practices with strategic objectives. – When addressing employee concerns related to change. – When benchmarking performance against industry standards. – When promoting a culture of continuous improvement. | |
analyze work processes to identify inefficiencies and improve productivity. It involves observing tasks, measuring time, and optimizing workflows. | – When analyzing work processes to identify bottlenecks. – When designing workstations or layouts. – When allocating resources efficiently. – When implementing new technologies or automation. – When evaluating the impact of changes in work procedures. – When fostering a culture of continuous improvement. – When aligning time and motion study findings with strategic objectives. – When benchmarking performance against industry standards. – When training employees in time management techniques. – When promoting a culture of accountability and transparency. | |
focuses on optimizing resource utilization and minimizing waste to enhance performance. It involves strategies like process optimization and automation. | – When analyzing workflows for streamlining. – When implementing performance measurement systems. – When training employees in lean principles. – When investing in technology solutions. – When conducting cost-benefit analyses. – When benchmarking performance against industry peers. – When fostering a culture of efficiency and productivity. – When aligning efficiency efforts with strategic objectives. – When addressing resistance to change. – When communicating the benefits of efficiency maximization. | |
defines best practices to ensure consistency and quality. It involves documenting procedures and implementing quality control measures. | – When documenting standard operating procedures (SOPs). – When training employees on standardized work processes. – When implementing quality control measures. – When conducting regular audits or inspections. – When communicating changes to work processes. – When integrating standardization with continuous improvement initiatives. – When benchmarking performance against industry benchmarks. – When fostering a culture of accountability. – When aligning standardization efforts with strategic objectives. – When addressing resistance to standardization initiatives. | |
emphasizes hierarchical structures, rules, and procedures to ensure organizational efficiency and stability. It focuses on clear division of labor, formalized communication channels, and adherence to established norms. | – When establishing clear roles and responsibilities within an organization. – When formalizing communication channels and decision-making processes. – When ensuring compliance with regulations and policies. – When promoting consistency and reliability in organizational operations. – When addressing issues related to accountability and transparency. – When managing complex projects or tasks with multiple stakeholders. – When implementing quality control measures and performance metrics. – When fostering a culture of discipline and adherence to established procedures. – When aligning organizational structure with strategic objectives and market demands. – When addressing resistance to change or challenges related to organizational inertia. | |
focuses on the psychological aspects of work and the importance of interpersonal relationships in organizational performance. It emphasizes employee satisfaction, motivation, and social needs fulfillment. | – When improving employee morale and job satisfaction. – When fostering teamwork and collaboration within teams or departments. – When addressing interpersonal conflicts or communication breakdowns. – When promoting a positive organizational culture and work environment. – When designing reward and recognition programs to motivate employees. – When conducting employee engagement surveys and feedback sessions. – When implementing leadership development programs to enhance managerial skills. – When promoting diversity and inclusion initiatives within the organization. – When aligning organizational goals with employee aspirations and values. – When addressing turnover or retention issues through improved people management practices. | |
focuses on minimizing waste and maximizing value in organizational processes. It involves principles such as continuous improvement, respect for people, and customer focus. | – When identifying and eliminating non-value-added activities in workflows. – When improving efficiency and reducing lead times in production or service delivery. – When empowering employees to contribute ideas for process improvement. – When implementing visual management tools to monitor performance and progress. – When fostering a culture of continuous learning and adaptation to change. – When aligning operations with customer needs and preferences. – When addressing quality issues or defects through root cause analysis and corrective actions. – When optimizing inventory management and supply chain operations. – When training employees in lean principles and problem-solving techniques. – When benchmarking performance against industry leaders and best practices in lean management. | |
focuses on continuous improvement and customer satisfaction through systematic approaches to quality assurance. It involves principles such as customer focus, process improvement, and employee involvement. | – When implementing quality control measures to meet customer expectations. – When conducting root cause analysis and corrective actions to address quality issues. – When fostering a culture of quality and excellence throughout the organization. – When training employees in quality management principles and techniques. – When establishing quality improvement teams to drive process optimization. – When implementing performance measurement systems to monitor quality metrics. – When aligning quality management efforts with strategic objectives and customer needs. – When promoting a culture of accountability and responsibility for quality outcomes. – When benchmarking performance against industry standards and best practices in quality management. – When addressing resistance to change or challenges related to organizational culture transformation. | |
explores the dynamics of individual and group behavior within organizations. It examines factors influencing employee motivation, job satisfaction, and performance. | – When analyzing factors contributing to employee motivation and engagement. – When assessing organizational culture and its impact on employee behavior. – When designing leadership development programs to enhance managerial effectiveness. – When addressing interpersonal conflicts or communication breakdowns in teams. – When implementing change management initiatives to support organizational transformation. – When conducting performance evaluations and feedback sessions. – When promoting diversity and inclusion initiatives within the organization. – When aligning organizational structure and processes with employee needs and preferences. – When addressing turnover or retention issues through improved people management practices. – When benchmarking organizational behavior metrics against industry benchmarks and best practices. | |
applies psychological principles to workplace settings to enhance employee well-being and organizational performance. It involves areas such as personnel selection, training, and job design. | – When designing recruitment and selection processes to identify top talent. – When conducting job analyses and designing work roles to maximize employee satisfaction and productivity. – When implementing training and development programs to enhance employee skills and competencies. – When assessing organizational culture and climate to identify areas for improvement. – When conducting performance appraisals and feedback sessions to support employee growth and development. – When addressing issues related to job stress, burnout, or work-life balance. – When promoting diversity and inclusion initiatives within the organization. – When aligning organizational policies and practices with legal and ethical standards. – When addressing employee grievances or concerns through effective conflict resolution techniques. – When benchmarking employee satisfaction and engagement metrics against industry standards and best practices. |
What are the 4 Principles of Scientific Management?
The core principles of Scientific Management are:
What is the example of scientific management theory?
Cases of scientific management comprise companies like Amazon and McDonald’s, which have made defined business processes for inventory and fulfillment (Amazon) and fast food (McDonald’s) the core strengths of their organizations.
- McDonald’s Case Study
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Identification of values relevant to business research, bleak house or bright prospect human resource management in australian smes, service quality and customer satisfaction in chinese fast food sector: a proposal for cffrserv, exploring surplus-based menu analysis in chinese-style fast food restaurants, the social legitimacy of disability inclusive human resource practices: the case of a large retail organisation, chinese consumer preference of chicken burgers cooked by sous-vide with korean-styled seasoning and available on the chinese fast food market, occurrence of heterocyclic amines in commercial fast-food meat products available on the chinese market and assessment of human exposure to these compounds., frequent adaptation and the mcdonald–kreitman test, the mcdonald-kreitman test and its extensions under frequent adaptation : problems and solutions, related papers.
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- Published: 03 September 2024
Unveiling spatial variations in atmospheric CO 2 sources: a case study of metropolitan area of Naples, Italy
- Roberto M. R. Di Martino ORCID: orcid.org/0000-0001-6435-2759 1 ,
- Sergio Gurrieri ORCID: orcid.org/0000-0003-4085-0440 1 ,
- Antonio Paonita ORCID: orcid.org/0000-0001-9124-5027 1 ,
- Stefano Caliro ORCID: orcid.org/0000-0002-8522-6695 2 &
- Alessandro Santi ORCID: orcid.org/0000-0002-1549-9786 2
Scientific Reports volume 14 , Article number: 20483 ( 2024 ) Cite this article
Metrics details
- Atmospheric chemistry
- Atmospheric science
- Climate sciences
- Environmental sciences
- Geochemistry
- Natural hazards
- Solid Earth sciences
- Volcanology
In the lower atmosphere, CO 2 emissions impact human health and ecosystems, making data at this level essential for addressing carbon-cycle and public-health questions. The atmospheric concentration of CO 2 is crucial in urban areas due to its connection with air quality, pollution, and climate change, becoming a pivotal parameter for environmental management and public safety. In volcanic zones, geogenic CO 2 profoundly affects the environment, although hydrocarbon combustion is the primary driver of increased atmospheric CO 2 and global warming. Distinguishing geogenic from anthropogenic emissions is challenging, especially through air CO 2 concentration measurements alone. This study presents survey results on the stable isotope composition of carbon and oxygen in CO 2 and airborne CO 2 concentration in Naples’ urban area, including the Campi Flegrei caldera, a widespread hydrothermal/volcanic zone in the metropolitan area. Over the past 50 years, two major volcanic unrests (1969–72 and 1982–84) were monitored using seismic, deformation, and geochemical data. Since 2005, this area has experienced ongoing unrest, involving the pressurization of the underlying hydrothermal system as a causal factor of the current uplift in the Pozzuoli area and the increased CO 2 emissions in the atmosphere. To better understand CO 2 emission dynamics and to quantify its volcanic origin a mobile laboratory was used. Results show that CO 2 levels in Naples’ urban area exceed background atmospheric levels, indicating an anthropogenic origin from fossil fuel combustion. Conversely, in Pozzuoli's urban area, the stable isotope composition reveals a volcanic origin of the airborne CO 2 . This study emphasizes the importance of monitoring stable isotopes of atmospheric CO 2 , especially in volcanic areas, contributing valuable insights for environmental and public health management.
Introduction
The equilibrium among natural CO 2 emissions, biotic uptake on land, and ocean absorption regulates long-term fluctuations in airborne CO 2 , establishing the greenhouse effect essential for the biosphere's existence on Earth. Human activities, particularly fossil fuel combustion, vehicle mobility, house heating, and waste management, disrupt the carbon cycle, leading to an increase in airborne CO 2 levels 1 , 2 , 3 , 4 , 5 . Disruption of this equilibrium worsens the effects of global warming and climate changes.
Global temperature data from Copernicus ( https://climate.copernicus.eu/ accessed on 2024, January 10), shows that the mean near-surface temperature in 2023 was ~ 1.4 ± 0.12 °C above the 1850–1900 average. This marked the warmest year in the 174-year observational record, surpassing the joint warmest years of 2016 and 2020. Notably, the last decade (2014–2023) encompasses the nine warmest years on record. Real-time data from specific locations reveals a continued increase in CO 2 levels in 2023, while consolidated concentration datasets of CO 2 , methane, and nitrous oxide reached their highest records in 2022.
Several causes contribute to global warming and climate change 6 . Since the eighteenth century the industrialization has led to the gradual abandonment of rural areas and the concentration of people in urbanized zones. Industries, mainly relying on electrical power generated by hydrocarbon combustion, settled in suburban areas contribute significantly to CO 2 emissions 5 , 7 . Urban growth, characterized by skyscrapers and increased vehicle mobility, results in continuous large-scale carbon dioxide release, predominantly concentrated in urban areas, significantly impacting the global atmospheric composition.
Earth degassing, driven by natural sources like soil respiration, volcanic degassing, and photosynthesis, contributes to atmospheric CO 2 concentrations 8 . Regions of active volcanism, responsible for a significant portion of natural gas emissions, release CO 2 of magmatic origin, particularly during eruptions, accounting for ~ 1% of global CO 2 emissions annually 9 , 10 , 11 . Although this percentage is modest on a global scale, locally, natural emissions may have a more substantial environmental impact, raising hazards for local populations 12 , 13 , 14 , 15 . For example, during the recent outgassing crisis at Vulcano, Italy 16 , 17 , gas hazards increased due to either diffuse degassing or crater plume emissions, though human health risk threshold value was not exceeded 18 , 19 , 20 .
Naples, with around 1 million residents, ranks third in population among Italian cities and is the most densely populated city in Europe. Its strategic location in Mediterranean shipping routes and heavy ship traffic in the harbour make it a potential major source of anthropogenic CO 2 . The city is located in a volcanic area with active volcanic and hydrothermal zones, making it an ideal study area to investigate the coexistence of human-related and natural CO 2 emissions.
This study presents the results of a spatial survey on airborne CO 2 in the metropolitan area of Naples. The survey aimed to collect measurements of airborne CO 2 concentration and stable isotopes of CO 2 to differentiate between volcanic and anthropogenic sources, identifying sources that elevate airborne CO 2 concentrations above the background. The study area includes Naples’ downtown and a broad urbanized zone extending from the western edge of Vesuvius volcano to Bacoli and Cuma in the east, and Agnano crater in the north, encompassing the active volcanic/hydrothermal zone of Campi Flegrei (Fig. 1 a). The Campi Flegrei area has experienced significant volcanic activity, including supereruptions, the oldest one dating back 40,000 years 21 , 22 . This area exhibits continuous degassing and seismic activity (i.e., Solfatara and Pisciarelli in the municipality of Pozzuoli). Anomalies in CO 2 emissions occur from soils via diffuse degassing and from fumaroles 23 , 24 , 25 , 26 , 27 , particularly in the Solfatara area (Zone A in Fig. 1 a). The most recent eruption originated from Monte Nuovo in 1538 A.D. Since then, this system has been in a state of persistent degassing and fluctuating seismic activity, leading to ground motion known as bradiseism. The study area has also increased the degassing since 2005 and is currently in unrest 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 . Human-related and geologic CO 2 emissions have distinct stable isotopic signatures, allowing differentiation in the air at the district scale through a combination of concentrations and isotopic measurements 12 , 13 , 18 , 19 , 20 , 37 , 38 , 39 . The results of the spatial survey enable a comparison between volcanic CO 2 emissions and those of anthropogenic origin in the urbanized area of Naples.
Study area, the route used during survey and dataset distribution. The survey was conducted in May 2023. ( a ) The blue line represents the route used during the survey. The selected subsets for Solfatara area (orange zone A), downtown Naples (green zone B), and airport (ice blue zone C) are shown. ( b ) Probability plot for concentration dataset. Global average value of airborne CO 2 concentration is reported as reference. (blue line indicates 423 ppm vol) for a comparison with the average CO 2 concentration over the target area (50% cumulative probability). ( c ) Histograms for both the oxygen isotope (δ 18 O–CO 2 ) and carbon isotope (δ 13 C–CO 2 ) compositions. ( d ) Three-dimensional view of the study area showing the atmospheric CO 2 concentration measurements at their respective locations. The height of the vertical bars is proportional to the concentration levels. The colour scale and bar height indicate that the highest CO 2 concentration was detected near the port. ( e ) Placement of the measurements within the study area. The colour scale is identical to that in subplot ( d ) and indicates the CO 2 concentration measured in the air. The maps ( a ), ( d ), and ( e ) were generated in Qgis 3.34 environment ( https://qgis.org/download/ ).
We developed a measurement program to detect and quantify the spatial variability of CO 2 concentration and its stable isotopes in the near-surface air of the Naples metropolitan area (Fig. 1 a). The dataset enables a better determination of the influence of meteorological factors and multiple greenhouse gas sources on the nature of the urban CO 2 dome 40 , 41 , 42 , 43 , which is considerably more challenging to identify than its mere presence. For this study, the wind direction was selected as the meteorological factor influencing CO 2 dispersal, while other meteorological factors (e.g., temperature, atmospheric pressure, relative humidity) can be averaged over the survey's completion time (11.4 h of acquisition during daytime over 24 h), as variations at the meteorological station from National Research Counsil (i.e., C.N.R. Long: 432,409; Lat: 4,520,399 UTM) are likely suitable for the entire Naples metropolitan area. Throughout the survey period, the weather remained consistently sunny. Table 1 presents the statistics of both environmental variables and atmospheric measurements.
Figure 1 b–c shows statistical distributions of measurements collected during the survey. The dataset collected in the Naples metropolitan area shows airborne CO 2 concentrations higher than 423 ppm vol (Fig. 1 b), which is the global reference for airborne CO 2 concentration for May 20, 2023 ( https://www.climate.gov/climatedashboard accessed on July 2, 2024). The probability plot 44 reveals three independent subsets of CO 2 concentrations. The 50% cumulative distribution indicates that the average value for the background CO 2 concentration in the urban area of Naples is 448.1 ± 1.0 ppm vol. The background population comprises more than 98.9% of the cumulative dataset, while the anomalous subset constitutes less than 0.1% of the cumulative dataset, with CO 2 concentrations exceeding 1300 ppm vol (Fig. 1 b).
Regarding stable isotopes, the carbon isotope composition of airborne CO 2 (reported in delta notation δ 13 C–CO 2 against the Vienna Pee Dee isotopic ratio-VPDB) shows values more 13 C-depleted than the theoretical background air (δ 13 C–CO 2 = − 8‰ vs VPDB). This result indicates that a source of CO 2 forces airborne CO 2 concentration above background values. This gas source has a 13 C-depleted isotopic signature and establishes an urban CO 2 dome in the Naples metropolitan area. Furthermore, the statistical parameters of the data distribution (skewness = − 2.27, kurtosis = 15.74) indicate that the dataset has a peak at δ 13 C–CO 2 = − 10.40‰, which is more 13 C-depleted than the theoretical atmospheric CO 2 value 45 .
The range of values for δ 18 O-CO 2 is wide compared to the spatial and temporal scales of the collected measurements. The oxygen isotope composition of airborne CO 2 depends on both the hydrology of the region and oxygen isotope fractionation in plant leaves during photosynthesis 46 , 47 , 48 . These factors change over spatial and temporal scales different from those of the measurement acquisition (i.e., ~ 10 4 –10 5 m and approximately 24 h, respectively). The oxygen isotope values are almost normally distributed (skewness = − 1.50, kurtosis = 7.3) throughout the study area (Fig. 2 b). Gaussian fitting of the oxygen values has a peak at δ 18 O–CO 2 = − 3.16‰ versus VPDB, which is more 18 O-depleted than the expected value for a coastal area of the Mediterranean region 49 , 50 , 51 , 52 .
Spatial variations of the CO 2 measurements collected during survey throughout the target area (cell size 10 m). The maps were generated in Qgis 3.34 environment ( https://qgis.org/download/ ) using Measurement interpolation generated in SAGA GIS environment ( https://saga-gis.sourceforge.io/en/index.html ). ( a ) Spatial variation of the airborne CO 2 concentration. ( b ) Spatial variation of the oxygen isotope composition of the airborne CO 2 . ( c ) Spatial variation of the carbon isotope composition of the airborne CO 2 . Traces of the concentration profiles are reported (black lines). See text for description.
The collected dataset was utilized to investigate the spatial variation of airborne CO 2 (Fig. 2 ). These data allow investigation of whether urban CO 2 sources affect atmospheric chemistry at a district scale or over the urban area (i.e., at the local scale, ~ 10 4 –10 5 m). The results illustrate a heterogeneous distribution of airborne CO 2 concentration over the Naples metropolitan area, with a concentration gradient from the coast to the inland, likely influenced by local atmospheric circulation. Granieri et al. 14 , who conducted detailed micrometeorological studies on atmospheric circulation in the Naples area for gas dispersal simulations, noted a diurnal sea breeze blowing from SW to NE, pushing clean air inland from the seaside during morning hours. A supply of clean air from the sea would dilute CO 2 concentration at relatively low levels. However, measured CO 2 concentrations in the urban area of Naples suggest that atmospheric circulation is insufficient to reduce atmospheric CO 2 concentrations to background levels, at least on days with similar weather conditions to the ones of the day of measurements. Further research should address the issue concerning the critical atmospheric circulation conditions that help to reduce the concentration level of CO 2 . The implementation of atmospheric CO 2 monitoring programs in urban areas, particularly when integrated with stable isotope composition analyses, is posited as an effective method for detecting anthropogenic or natural forcings influencing atmospheric CO 2 levels. Elevated atmospheric CO 2 concentrations are frequently correlated with increased levels of other pollutants, suggesting that these monitoring programs can significantly enhance public health management strategies. Additionally, in urbanized regions located within volcanic zones, atmospheric CO 2 monitoring is crucial for mitigating volcanic risks associated with gas emissions (i.e., the gas hazard) 19 . Examination of the dataset reveals areas with high airborne CO 2 concentrations, notably near Naples' harbour, where the highest CO 2 concentration was measured (Fig. 1 d,e), and the district of Museo square, among others (Zone B in Fig. 1 a).
Figure 2 illustrates additional zones with high concentrations of airborne CO 2 . The airborne CO 2 concentrations achieve 572 ppm vol in a zone situated in the northeastern sector of the investigated area. While this level does not surpass any established risk threshold for human health 53 , it exceeds the reference value recorded at NOAA Global Monitoring Laboratory for the investigated time frame (424 ppm by volume) by > 33%. A land use survey in the metropolitan area of Naples reveals the presence of the airport, particularly the runways and aircraft parking areas adjacent to the route used for data collection. Another zone exhibiting elevated concentrations of airborne CO 2 is identified on the western side of downtown Naples, within the municipality of Pozzuoli (i.e., transect B–B′ in Fig. 3 b). This area is renowned for its evidence of the underlying volcanic hydrothermal system of Campi Flegrei 26 , 27 , 28 , 29 , with airborne CO 2 concentrations reaching 567 ppm vol. The spatial distribution of airborne CO 2 concentrations in this zone appears more heterogeneous compared to other areas, attributable to the presence of several high concentration nuclei near Bagnoli and Baia (Figs. 2 a and 3 a), eastward and westward of Solfatara, respectively.
Transects through selected zones of the study area to inspect lateral variations of airborne CO 2 concentration (blue line), δ 18 O–CO 2 (red line), and δ 13 C-CO 2 (blue line). ( a ) A–A′ transect (Bacoli). ( b ) B–B′ transect (Solfatara). ( c ) C–C′ transect (Downtown). ( d ) D–D′ transect (Portici).
The δ 18 O–CO 2 has been recognized as a tracer of photosynthesis and the hydrologic cycle's effects on airborne CO 2 . These processes play a pivotal role in the fractionation of oxygen in airborne CO 2 at vastly different spatiotemporal scales. While the hydrologic cycle exhibits seasonal effects at the regional scale, notable changes in vegetation (e.g., transition from C3 to C4 or CAM plant dominant types) account for variations in the oxygen isotope composition due to differences in photosynthesis. Since the survey was completed in a few hours, the spatial variations in the oxygen isotope composition resulting from these processes are expected to have negligible effects on the spatial variations of δ 18 O–CO 2 , which constitutes an ancillary factor for identifying variations in the source of CO 2 at the district scale 12 , 13 , 18 , 20 , 51 , 54 .
The kriging interpolation of the δ 18 O-CO 2 dataset reveals a zone with slightly 18 O-depleted airborne CO 2 westward of downtown Naples, where the δ 18 O–CO 2 = ~ − 2‰. Near Baia, where high concentrations of CO 2 were measured (Fig. 2 a), the airborne CO 2 exhibits more 18 O-depleted values, reaching δ 18 O–CO 2 = − 5.38‰ through a steep isotopic gradient (e.g., transect A–A′ in Fig. 3 a). The δ 18 O–CO 2 abruptly increases to approximately − 2‰ northwestwardly along the transect A–A′ (Fig. 3 a). Airborne CO 2 shows less 18 O-depleted values near Solfatara. A concentration profile across the Pozzuoli area (Fig. 3 b) depicts the least 18 O-depleted CO 2 in the air, having δ 18 O–CO 2 = − 0.06‰ in the vicinity of Solfatara and toward the northeast (Fig. 3 b). The δ 18 O-CO 2 values decrease to an average of − 2.5‰ northeast of Astroni. Downtown Naples has been identified as an area where airborne CO 2 exhibits more 18 O-depleted values, although zones with δ 18 O–CO 2 < − 6.5‰ are heterogeneously distributed between Pianura and Capodimonte, where CO 2 exhibits more 18 O-depleted CO 2 (i.e., transect C–C′ in Fig. 3 c). In this zone, heavily 18 O-depleted CO 2 (δ 18 O–CO 2 < − 16.0‰) was measured in the harbour district.
Additionally, a wide zone elongated NW–SE exhibits δ 18 O–CO 2 < − 5.3‰, extending from the eastern edge of downtown Naples to the west of Torre del Greco, coinciding with a densely urbanized area and a widespread industrialized area (i.e., Area Est-Centro direzionale). Figure 3 d illustrates a step gradient of δ 18 O–CO 2 that separates the coastal zone where δ 18 O–CO 2 = ~ − 5.06‰ from the inland area where δ 18 O–CO 2 = ~ − 2.85‰. The ∆ 18 O–CO 2 = 2.21 represents an order of magnitude greater than the accuracy of the oxygen isotope determination (± 0.25‰). In summary, the spatial variations of the measurements show strong fluctuations of δ 18 O–CO 2 in different zones. Kriging interpolation of the δ 18 O–CO 2 dataset reveals areas with slightly 18 O-depleted airborne CO 2 westward of Naples' downtown, and more 18 O-depleted values eastwards of downtown Naples. Similarly, wide variations in δ 13 C–CO 2 values correspond to spatial variations in the carbon isotopic signature of airborne CO 2 (Fig. six). Dataset statistics indicate that airborne CO 2 is 13 C-depleted compared to standard air. Cross-sections show trends indicating potential CO 2 sources with 13 C-depleted or enriched signatures in different areas, with notable variations near Baia and downtown Naples. These results suggest considerable variability in emission sources at the scale of the urbanized zone, and a dominant source of CO 2 with a 13 C-depleted signature. This expectation arises because the carbon isotope signature of airborne CO 2 can track the source of the gas 55 , 56 .
The cross-section through the urbanized areas of Bacoli (Figs. 2 a and 3 a) shows an average value of δ 13 C–CO 2 = − 10.5‰, indicating airborne CO 2 to be more 13 C-depleted than theoretical air and global reference values recorded by NOAA ( https://www.climate.gov/climatedashboard accessed on July 2, 2024). A significant change in the carbon isotope composition of airborne CO 2 is evident at Baia, where a decrease to a value of δ 13 C–CO 2 < − 14‰ was measured, coinciding with concentration values higher than those measured at Bacoli (Fig. 2 c). Low values of δ 13 C-CO 2 indicates that a heavily 13 C-depleted source of CO 2 is responsible for forcing airborne CO 2 above background levels and is the main contributor to increased CO 2 concentration. The carbon isotope composition increases to less 13 C-depleted values north of Cuma and achieves δ 13 C–CO 2 = ~ − 9‰ in the northern zone of the target area. The B–B′ cross-section shows a different trend compared to the A–A′ profile (Fig. 3 a,b respectively). Specifically, δ 13 C–CO 2 decreases from approximately − 9 to − 11‰. Continuing along the Solfatara profile (Fig. 3 b), a sudden increase in δ 13 C–CO 2 value is observed, reaching values of approximately − 8‰ at the highest concentration values observed along the same profile.
This trend appears to be clearly opposite to that observed in the A–A′ profile, suggesting the presence of potential CO 2 sources with a less 13 C-depleted signature compared to those forcing airborne CO 2 concentration in adjacent areas. The alternative hypothesis, suggesting that clean air with δ 13 C–CO 2 = − 8‰ produces the observed values, can be dismissed based on the evidence that 13 C-enrichment correlates with an increase in CO₂ concentration. This trend contradicts the expectation of a decrease in CO₂ concentration, which would be consistent with the clean air hypothesis. Furthermore, the A–A′, C–C′, and D–D′ profiles demonstrate that CO₂ concentrations exhibit opposite trends in comparison with δ 13 C–CO 2 . Specifically, these transects reveal that increases in CO₂ concentration coincide spatially with decreases in δ 13 C–CO 2 , indicating that the effective source of CO₂ in these zones is more 13 C-depleted. In the surrounding area, δ 13 C–CO 2 values average around − 10‰ regardless of CO 2 concentration in the air. These 13 C-depleted values reduce evidences of spatial 13 C-enrichment in airborne CO 2 . Therefore, the gas source which causes rise in CO 2 concentration above background levels in the area of Solfatara has a carbon isotope composition only slightly 13 C-depleted compared to the VPDB standard. Accordingly, to the northeast of the Astroni crater, δ 13 C–CO 2 decrease sharply to values ranging between − 10 and − 11‰. Moreover, zones with high airborne CO 2 concentrations near both Bagnoli and Posillipo also show heavily 13 C-depleted isotopic composition (i.e., δ 13 C–CO 2 = − 14.69‰ and δ 13 C–CO 2 = − 13.85‰, respectively).
The C–C′ profile (Fig. 3 c) crosses Naples’ downtown (Fig. 2 ), which is busiest by vehicle during morning hours. The airborne CO 2 has δ 13 C–CO 2 values from − 17.65 to − 8.54‰ with an average δ 13 C–CO 2 = ~ − 11‰. High CO 2 concentrations along this profile occur at the harbour district (Fig. 3 c and Fig. 2 a), which coincides with the zone having the most 13 C-depleted values of airborne CO 2 (Fig. 2 c). A comparison with other profiles reveals that a 13 C-depleted source of CO 2 forces the airborne CO 2 concentration in downtown Naples more efficiently than in peripheral zones to the west (i.e., Bacoli, Baia, and Posillipo). This source is less effective in forcing CO 2 concentration in the zone near Pozzuoli (B–B′ profile), where the source of CO 2 has a less 13 C-depleted carbon isotope composition. This northwest-oriented profile shows a zone with less 13 C-depleted values of airborne CO 2 to northwest (Fig. 2 c), consistent with a decrease in airborne CO 2 concentrations (Fig. 2 a).
Remarkable variations in the stable isotope composition of airborne CO 2 can be identified east of the urban area of Naples (Fig. 2 c). In concordance with δ 18 O–CO 2 , δ 13 C–CO 2 shows remarkable variations along the seaside compared to the inland along the D–D′ profile (Fig. 3 d). Airborne CO 2 concentrations fluctuate, superimposed on a decrease from the seaside to the inland. According to this trend, the carbon isotope composition shows an opposite trend from the most 13 C-depleted values in the coastal zone to the less 13 C-depleted CO 2 inland, revealing that potential sources of CO 2 with heavily 13 C-depleted signatures force airborne CO 2 concentration in the coastal zone near Portici and Torre del Greco. These sources are less effective in forcing CO 2 concentration inland, near San Giorgio a Cremano.
Measurements of CO 2 concentration, combined with stable isotope compositions of airborne CO 2 , provide relevant data for distinguishing between natural and anthropogenic CO 2 emissions in the atmosphere, and potentially tracking the gas dispersal from various sources of greenhouse gases at the urban spatial scale (i.e., 10 4 –10 5 m). This method overcomes the inherent difficulty of studying CO 2 dispersion caused by its high background level and subtle spatial variations of airborne CO 2 concentration. Indeed, various sources of CO 2 have different isotopic signatures for both carbon and oxygen.
There are several methods for tracking the dispersion of gases emitted from a source into the atmosphere. The methods commonly used to track gas dispersion are based on models that require a priori knowledge of the source, the amount of gas emitted, and the geometry of the dispersion area. Isotopic studies combined with atmospheric chemistry follow a different paradigm. The data collected from field measurements underwent analysis utilizing the Keeling plot method mass balance models for oxygen and carbon isotopes 49 , 50 , 57 . The Keeling plot method facilitates the determination of the primary CO 2 source at the local level using observational data.
At the same time, the mass balance model for oxygen and carbon isotopes allows an assessment of the influences of the individual CO 2 sources on the local air composition. The mathematical expressions governing this model were developed within the framework of previous studies 20 and are expounded concisely upon in the method section dedicated to assessing additional CO 2 in the atmosphere. This method allows for detecting the forcing effects introduced by the gas sources on the composition of the atmosphere. The measurements utilized in the theoretical model results (see Eq. ( 11 ) in this study) furnish point-by-point estimates of additional CO 2 concentration (i.e., the C fs ) along the trajectory.
Subsequently, the interpolation of C fs values employing the Kriging algorithm model facilitates the simulation of CO 2 dispersion. This algorithm generates a predictive layer for δ 13 C–CO 2 , δ 18 O–CO 2 , CO 2 concentration, and C fs. This method has been successfully applied to detect chemical and isotopic effects on the air in the La Fossa caldera on the island of Vulcano, both during periods of quiescent outgassing and during the recent period of increased volcanic outgassing in 2021 20 .
The Keeling plot illustrates a correlation between the carbon isotope composition of CO 2 and the inverse of airborne CO 2 concentration. Figure 4 shows the concentration dataset normalized by the global reference for airborne CO 2 concentration (i.e., 423 ppm vol). Each straight line on this plot represents binary mixing between the atmospheric background and an additional CO 2 source. The intercept on the isotopic axis provides the carbon isotopic signatures, facilitating the identification of the CO 2 emission source.
The correlation between δ 13 C–CO 2 and the inverse of airborne CO 2 concentration (i.e., Keeling plot). Data were normalized against the Global reference values recorded by NOAA (a https://www.climate.gov/climatedashboard accessed on July 2, 2024. ( a ) Dataset collected over the target area. ( b ) Urbanized areas of Naples. Green circles distinguish the subset of measurement collected near the airport (zone C in Fig. 1 a) from those collected in downtown Naples (zone B in Fig. 1 a). ( c ) Pozzuoli–Solfatara–Agnano area (zone A in Fig. 1 a) Yellow circles distinguish the subset of magmatic origin from that of anthropogenic origin in the area (blue circles).
Figure 4 a displays several mixing lines between background air and various potential sources of CO 2 , including natural (e.g., soil and plant respiration or volcanic degassing) and anthropogenic origins (e.g., combustion of fossil fuels or natural gas and landfill CO 2 emissions), whose isotopic signatures were retrieved from previous studies 37 . A geometric mean regression is recommended for the analysis of a scattered dataset (i.e., R 2 < 0.980) in the Keeling plot due to the inherent bias associated with determining the carbon isotopic signature through the utilization of a linear regression model 58 . The line representing the isotopic signature of the forcing source can be derived by applying a standard regression and subsequently dividing by the r-coefficient. This corrective approach aims to approximate the geometric mean regression through the utilization of a standard estimate obtained from a linear regression model.
The dataset collected over the target area reveals a variety of mixing lines, highlighting the inherent complexity of identifying a single CO 2 source. The alignments of δ 13 C–CO 2 in the Keeling plot suggests that fossil fuel combustion is a significant source of greenhouse gases, resulting in airborne CO 2 concentrations ranging from > 600 to ~ 1410 ppm vol (i.e., normalized values are from 0.7 to 0.3, respectively). However, multiple CO 2 sources can influence airborne CO 2 concentrations in the target area, especially at low to intermediate values (i.e., from 423 to 600 ppm vol, corresponding to normalized values ranging 0.7–1). These results support findings that human-related activities, such as urban mobility by vehicles and household heating, predominantly based on the combustion of fossil fuels, contribute significantly to rise the airborne CO 2 concentration. Nonetheless, natural CO 2 emissions, such as those from volcanic outgassing, which is estimated on the synoptic scale to account for approximately 1% of total annual emissions, can locally play a pivotal role in the amount of CO 2 injected into the atmosphere.
A sector in Naples’ downtown (i.e., Zone B in Fig. 1 a), distinct from Zone A, which includes the Campi Flegrei volcanic/hydrothermal zone and the western suburbs of Naples (i.e., Bagnoli and Posillipo), can serve as a test site to quantify the specific contribution to increasing airborne CO 2 concentrations caused by human-related emissions. Figure 4 b illustrates δ 13 C–CO 2 against CO 2 concentrations, showing good agreement with the mixing line between background air and CO 2 produced by fossil fuel combustion, characterized by a heavily 13 C-depleted signature (i.e., δ 13 C–CO 2 = − 29.94‰). Furthermore, data collected in the airport zone (i.e., Zone C in Fig. 1 a), where high levels of airborne CO 2 concentrations have been measured, indicate that the CO 2 source affecting both concentration and isotope composition of airborne CO 2 is of anthropogenic origin (i.e., δ 13 C–CO 2 = − 29.31 ‰).
Figure 4 c illustrates the complex distribution of concentration and carbon isotope composition values detected in the study area, predominantly located in the urban area of Pozzuoli, in the western suburbs of Naples. Results of cluster analysis applied to a subset of measurements collected in the Zone A (Fig. 1 a) reveal that multiple CO 2 sources play an almost equivalent role in elevating the concentration of airborne CO 2 above background levels. One subset of measurements, with CO 2 concentrations in the range 423–700 ppm vol, exhibits an isotopic signature in good agreement with the mixing line between background air and CO 2 produced by the combustion of fossil fuels (i.e., δ 13 C–CO 2 = − 32.93‰). Another subset of measurements indicates that δ 13 C–CO 2 of the air increases as CO 2 concentrations rise due to the influence of a less 13 C-depleted CO 2 source, with δ 13 C–CO 2 ≈ − 1.97‰. Although slightly lower, this value aligns with the carbon isotopic signature of CO 2 emitted from Pisciarelli and Bocca Grande fumaroles.
Those data retrieved from application of laboratory techniques to condensed fumarolic fluids have accuracy ± 0.1‰ 26 . Differences in the range Δ 13 C < 0.4‰ can be neglected because of the accuracy of the measurements with Deltaray (i.e., ± 0.25‰ according to 12 , 13 , 18 , 37 , 58 ).
Equation ( 11 ), included in the method section, facilitates the calculation of additional CO 2 in the air owing to either natural (i.e., volcanic/hydrothermal CO 2 ) or anthropogenic (i.e., produced by the combustion of fossil fuels) emissions. This calculation is based on input parameters in a theoretical model and measurements of airborne CO 2 concentration, δ 13 C–CO 2 , and δ 18 O–CO 2 in the field. C fs provides the concentration of the forcing source of CO 2 , exceeding local background levels in the atmosphere. A combination of the positioning of the endogenous sources of CO 2 and results of the Keeling plot helps distinguish the application of the mass balance model to the dispersal of volcanic CO 2 in the zone Solfatara (i.e., Zone A in Fig. 1 a) and the dispersal of CO 2 produced by the combustion of fossil fuels downtown Naples (i.e., Zone B in Fig. 1 a).
Figure 5 shows dispersions of CO 2 from anthropogenic origin in Naples’ downtown. In particular, the excess CO 2 concentrations in air produced by hydrocarbon combustion, which has a 13 C-depleted isotope composition compared to standard air (Fig. 4 b). For the calculation of the additional amount of CO 2 in the air, an anthropogenic source of CO 2 with the isotopic signature δ 13 C = − 31.00‰ and δ 18 O = − 16.00‰ has been adopted as the model parameters. Figure 5 a shows the fossil fuel-derived CO 2 has a heterogeneous distribution across the target area. A CO 2 dome 40 , 41 , 42 , 43 appears irregular and has numerous lobulations. The dome encloses islands where hydrocarbon combustion forces the CO 2 above the atmospheric background and generates concentration peaks even greater than + 300 ppm above the airborne CO 2 background (Fig. 5 b). One such island of high CO 2 concentration is well delineated in the harbour area, which is renowned for being among the Mediterranean's major harbours. In fact, the burning of hydrocarbons sustains the majority of the ship traffic in these areas. Another area with high CO 2 concentrations is located in the western downtown, near one of the most densely populated areas of Naples.
Dispersal of anthropogenic CO 2 in downtown Naples (cell size 10 m). The maps were generated in Qgis 3.34 environment ( https://qgis.org/download/ ) using Measurement interpolation generated in SAGA GIS environment ( https://saga-gis.sourceforge.io/en/index.html ). ( a ) CO 2 concentration map that shows the CO 2 concentration excess above the reference background. The concentration excess value of 83 ppm vol has been set as the threshold for transparency. ( b ) Vertical profile (black line in subplot a) of the excess CO 2 concentration across downtown Naples. ( c ) Wind vectors and speed recorded at C.N.R. station. ( d ) Wind direction frequency during survey.
The results of isotopic investigations prove the anthropogenic origin of atmospheric CO 2 . It is reasonable to assume that most of the anthropogenic CO 2 found in downtown Naples is the result of hydrocarbon combustion produced by urban mobility, given that the average air temperature during measurement collection was 22 °C (with an air temperature range of 19–23 °C). Within the one-day measurement acquisition timescale, variations in wind intensity and direction affecting the dispersion of CO 2 cannot be ruled out. This is particularly expected in Zone B, where the wind can influence the dispersion of emitted plumes near Naples' harbour. However, the data on wind direction (Fig. 5 c) and speed indicate that during the acquisition time window, the atmospheric circulation brought in SSW air, which is generally less enriched in anthropogenic CO 2 . Given the morphology of the study area and the local effects of densely built environments (Fig. 5 d), it is reasonable to assume a dilution effect of anthropogenic CO 2 due to the influence of less CO 2 —rich air from the sea. Accordingly, the anthropogenic CO 2 concentration along the C–C′ profile (Fig. 5 c) shows a notable increase in airborne CO 2 near the harbour and above a pedestrian area, suggesting that proximal sources of greenhouse gas emissions in the nearby areas are responsible for the increase in CO 2 above background levels.
Measurements collected at Pozzuoli (Zone A in Fig. 1 a) reveal multiple origins for CO 2 present in the air, namely volcanic and anthropogenic. Although human-related activities cause high concentrations of airborne CO 2 , a comparison with downtown can be made concerning the dispersal of geogenic CO 2 in the Pozzuoli area because the Campi Flegrei volcanic/hydrothermal system was in a state of unrest at the time of measurement collection 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 . Results of the cluster analysis provide a subset for calculating the amount of geogenic CO 2 that the main degassing zones at Campi Flegrei discharge into the atmosphere. The isotope composition and the airborne CO 2 concentration values of this subset were used in Eq. ( 11 ), with the values for the isotopic signatures of both carbon and oxygen for CO 2 emitted at Solfatara and Pisciarelli in May 2023 serving as model parameters (i.e., δ 13 C–CO 2 = − 1.67‰ vs VPDB and δ 18 O–CO 2 = − 7.85‰ vs VPDB) to obtain point-to-point calculations of volcanic CO 2 dispersal. These values provide insight into the dispersal of volcanic CO 2 from the main degassing vents at Campi Flegrei based on direct measurements and model parameters (Fig. 6 a). A comparison with the downtown map (Fig. 5 a) shows a more homogeneous dispersal of volcanic CO 2 along an N–S oriented dispersal zone. Furthermore, the volcanic CO 2 concentration is higher than 124 ppm vol above background levels in the area lying between Pozzuoli and Pisciarelli alone. Considering a background air CO 2 concentration of 423 ppm vol and the volcanic input calculated in the present study, this result is in good agreement with dispersal simulations averaged over a whole diurnal cycle obtained using the DISGAS software 14 . Measurements of concentration, corroborated by isotopic determinations, reveal volcanic CO 2 dispersion in the area of Bagnoli and eastward towards the urbanized area of Naples. In this area, measurements of airborne CO 2 concentrations alone are not able to track the dispersal of volcanic CO 2 because comparable absolute concentration values are found throughout the urban areas, where the additional CO 2 has anthropogenic origins. According to Granieri et al. 14 , inland air circulation prevails during nighttime in the Gulf of Naples, when volcanic CO 2 dispersal occurs towards the sea.
Dispersal of volcanic CO 2 in Pozzuoli-Solfatara zone (cell size 10 m). The maps were generated in Qgis 3.34 environment ( https://qgis.org/download/ ) using Measurement interpolation generated in SAGA GIS environment ( https://saga-gis.sourceforge.io/en/index.html ). ( a ) CO 2 concentration map that shows the CO 2 concentration excess above the reference background. The concentration excess value of 75 ppm vol has been set as the threshold for transparency. ( b ) Wind vectors and speed recorded at C.N.R. station. ( c ) Wind direction frequency during survey. ( d ) Vertical profile (black line in subplot a) of the excess CO 2 concentration across Pozzuoli-Solfatara area.
At the time of the survey, weather datasets reveal that NW winds blew from Solfatara towards the sea, even during the early morning (i.e., by ~ 8:00 UTC), after which sea breeze dominated air circulation from the SE throughout the daytime hours (Figura 6b,c). Arguably, the dispersal of volcanic CO 2 results from a combination of volcanic CO 2 dispersal and the residual layer that develops during nighttime and has not yet been disrupted by diurnal atmospheric turbulence. These results show that spatial surveys for studying airborne CO 2 helps in identifying multiple sources of greenhouse gases at the district scale of urban areas. Furthermore, stable isotope measurements allow an assessment of the impact of either volcanic degassing or anthropogenic emissions on airborne CO 2 concentrations.
The results of this study illustrate that integrating measurements of carbon and oxygen isotopic composition with those of CO 2 concentration aids in elucidating the genesis and development of CO 2 dome in urbanized areas. This represents a step forward in evaluating the impact of specific carbon dioxide sources, whether anthropogenic or natural, on the progression of climate change, as it facilitates the discernment of the underlying causes of urban domes through direct investigations.
The findings of this study also suggest that surveys conducted in urban areas such as Naples can be utilized to identify the primary regions for continuous monitoring of both natural and anthropogenic CO 2 emissions against global warming. Climate change has reached a global scale and threatens the stability of various vital sectors, including infrastructure, the economy, electricity production, international relations, biodiversity, and freshwater and food resources. Climate change affects all regions of the world, and its macroscopic effects manifest through extreme weather events, producing vast damage in cities and rural areas.
The international community is implementing a series of measures to combat ongoing climate change, which significantly impacts economic and social systems globally. For instance, several ambitious plans aim to reduce greenhouse gas emissions by 2050, mainly CO 2 . To achieve such ambitious goals, it is crucial to estimate and monitor CO 2 emissions, especially in urban areas where most CO 2 is produced through hydrocarbon combustion. Currently, no monitoring tools are available to detect near-real-time CO 2 emissions for individual countries. Therefore, efforts to monitor CO 2 in the air on a regional scale (synoptic ~ 10 6 m) with low latency (through the publication of hourly, daily, weekly, and annual data) via networks of stations installed in densely urbanized areas are becoming increasingly relevant. However, monitoring CO₂ in the atmosphere is not straightforward due to the high background concentration (approximately 400 ppm vol), which limits the potential for spatial variability. Consequently, monitoring the concentration alone may not always provide sufficient data for real-time estimation. Various studies demonstrate that integrating isotopic and concentration data provides information on the origin of CO 2 emissions 12 , 13 , 16 , 18 , 20 , 37 , 38 , 39 , 58 , 59 , 60 .
The δ 18 O–CO 2 largely depends on the CO 2 partitioning among the atmosphere, hydrosphere, lithosphere, and biosphere and can be deciphered through isotopic fractionation processes. Recent studies 12 , 18 , 19 , 20 show that it is possible to quantify atmospheric CO 2 emissions from natural and anthropogenic sources, isotopically characterized by δ 13 C–CO 2 and δ 18 O–CO 2 values, through integrated monitoring of atmospheric CO 2 concentration, isotopic composition, and meteorological data (direct investigations).
Therefore, the implementation of an active monitoring system is urgent and represents a paradigm shift in quantifying atmospheric CO 2 emissions at the scale of individual urbanized areas, compared to the currently applied methods based on statistical data at the national level for countries that are signatories to the United Nations Framework Convention on Climate Change 61 .
Instrument setup
The instrument employed for data acquisition in this study is a Delta Ray–Thermo Fisher Scientific. It measures the concentration of the isotopologues 13 COO, 12 COO, and CO 18 O based on the adsorption strength of light in the mid-infrared region (~ 4.3 μm) following the Lambert–Beer law. The 13 C/ 12 C and 18 O/ 16 O ratios are calculated using different concentration ratios of the isotopologues, while the total CO 2 concentration is determined by summing the concentrations of the three CO 2 isotopologues. Stable isotope ratios are expressed in agreement with the VPDB scale using the δ-notation (i.e., δ 13 C–CO 2 and δ 18 O–CO 2 , respectively) within the CO 2 concentration range of 200–3500 ppm vol.
The Delta Ray instrument is equipped with the QTegra software. A specially designed template includes protocols for recording δ 13 C–CO 2 , δ 18 O–CO 2 , and CO 2 concentration values, along with information on the sample list, acquisition parameters, referencing, evaluation settings, and sample definition. Instrument calibration and referencing against two working standards ensure an accuracy of ± 0.25‰ for isotope determinations and ± 1 ppm vol for CO 2 concentration measurements.
The instrument records each measurement of δ 13 C–CO 2 and δ 18 O–CO 2 at a frequency of 1 Hz. Before data acquisition, the instrument conducts isotope ratio referencing on the working standards at a fixed CO 2 concentration (i.e., CO 2 = 400 ppm vol) approximating background airborne CO 2 . After purging the unknown air sample for 60 s, the instrument skips the purge and measures the concentration of CO 2 isotopologues in the air. Once the air has purged the gas inlet, the instrument calculates δ 13 C–CO 2 and δ 18 O–CO 2 , as well as CO 2 concentration.
Measurement strategies
An off-road vehicle housed the instrument, and the equipment for measuring δ 13 C–CO 2 , δ 18 O–CO 2 , and airborne CO 2 concentrations during the studies across the urbanized zone of Naples. The positioning of the vehicle was recorded by a global positioning system device (GARMIN GPSMAP® 64 s), time-synchronized with the Delta Ray's internal clock. In specific urban environments 12 , 37 , 38 , 39 , 55 , 56 , 62 and, more recently, in volcanic regions 18 , 20 , investigations have been conducted utilizing mobile laboratories to analyze the spatial variability of CO 2 .
An inverter (12 V input–output, pure sine wave) was connected to the car's electrical system, supplying power to the instrument (~ 300 W). A stainless-steel capillary (1/16 in.; Swagelok-typeTM, 3 m long) was connected to the instrument's inlet, with the other end attached to the front of the car roof (~ 2.3 m above the ground) to avoid potential contamination from the gasoline engine exhaust. The air passed through a filter (2 μm, 1/16 in, capillary aperture) to prevent contamination from dust on the roads. Considering the volume of the sampling capillary, the instrument's flow rate (approximately 100–110 ml min −1 ), and the average speed of the mobile laboratory (approximately 3.5 m s −1 ), the delay between measurements and their corresponding positions is approximately 25 m. This delay is comparable to the GPS positioning.
A route of approximately 170 km (Table 1 ) was designed in the laboratory to obtain a continuous, non-overlapping path, covering various environments in the wide urbanized area of Naples (Fig. 1 a). The route includes Miseno, Bacoli, Agnano, Campi Flegrei caldera, Pozzuoli, Capodimonte, Bagnoli, Posillipo to the east of Naples' downtown, and Portici, Ercolano, Torre del Greco, and San Giorgio a Cremano to the west, respectively. The route was planned to ensure that segments did not overlap, preventing an increase in the statistical weight of some route segments over others. The route was meticulously followed using a routing application (e.g., Google Maps). The survey was completed in thirteen hours at an average speed of 13 km h −1 , with the spatial density of measurements corresponding to the metric order (~ 4 m average distance between measurements). The dataset encompasses ~ 41,000 georeferenced measurements for δ 13 C–CO 2 , δ 18 O–CO 2 , and CO 2 concentration, respectively 63 . This method was already employed for a simultaneous airborne CO 2 spatial survey at Vulcano and revealed the dispersion of volcanic CO 2 through direct measurements 18 , 20 .
Data processing approach
The data acquired from onsite measurements underwent processing utilizing the Keeling plot approach and mass balance models for oxygen and carbon isotopes. The Keeling plot enables the identification of the predominant CO 2 source at the local scale through observational data. The mass balance model for oxygen and carbon isotopes aims to quantify the impact of the CO 2 source on the local air. The algebraic equations for the model were developed as part of a previous study 18 and are detailed in the following paragraph of this paper, addressing the assessment of either volcanic or anthropogenic CO 2 in the air at Naples’ urban area. This methodology integrates measurements of stable CO 2 isotopes in the air with isotopic signatures of both the local CO 2 source, determined through the Keeling plot method 49 , 50 , and CO 2 in the background air. The theoretical outcomes of the model facilitate the partitioning of CO 2 in the air between the local background air and the CO 2 source.
The Keeling plot 49 , 50 , is the method broadly used to identify the isotopic signature of the gas source that increases CO 2 concentrations at the atmospheric background. The Keeling plot method facilitates the examination of the primary origin of atmospheric CO 2 by analyzing the δ 13 C–CO 2 against the reciprocal of CO 2 concentration. This method relies on mass balance principles, wherein a local CO 2 source alters the concentration from the atmospheric baseline. Mathematically, this is expressed by equations:
where C and δ 13 C denote CO 2 concentration and δ 13 C–CO 2 , respectively. Subscripts denote measured values (m), atmospheric background (a), and local source (fs). The linear combination of these equations generates a straight line in the δ 13 C versus 1/C plot 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 as delineated by equation
Equation ( 3 ) provides insight into the carbon isotope composition of the local CO 2 source under constant background and CO 2 source conditions.
To identify the main CO 2 source downtown Naples, a subset of measurements was selected. This subset encompasses measurements collected in an area of 24.60 km 2 centred in the Plebiscito square district of Naples (436,676.0 E and 4,520,817.0 N). Another subset, with its centre in the Pozzuoli area (Lat: 428,058.0 E; Long: 4,520,131.0 N, UTM), was selected for comparative purposes with data collected in Naples’ downtown. Specifically focusing on Pozzuoli (Zone A), the assessment focused on volcanic CO 2 as the primary source of CO 2 in the air. The measurements used in the theoretical model results (Eq. ( 11 ) reported below in this study) provide the concentration of the isotopically marked CO 2 source (e.g., volcanic or anthropogenic), causing the airborne CO 2 concentration to exceed the background concentration, point by point within the area. In the case of the Solfatara-Pisciarelli degassing area (Zone A in Fig. 1 ), the circular area is 47.28 km 2 , and the theoretical model provides the concentration of volcanic CO 2 (C V ). Following this calculation, the interpolation of C V values using the Kriging algorithm generates simulations of CO 2 from the forcing source (volcanic or anthropogenic for Solfatara-Pisciarelli and Naples' downtown, respectively). This algorithm produces the prediction layer for δ 13 C–CO 2 , δ 18 O–CO 2 , CO 2 concentration, and concentration (C V or C F , respectively) based on the assumption that each interpolating variable changes linearly with the distance between adjacent measurements. This assumption aligns with the expected homogeneity of spatial variations in atmospheric variables at the local scale 7 . Kriging interpolation is a geostatistical method used to estimate unknown values of each spatial variable based on known measurements of δ 13 C–CO 2 , δ 18 O–CO 2 , and CO 2 concentration at specific measurement points. The spatial correlation of the data is modeled using a Gaussian variogram, a standard variogram model defined by the equation:
where γ(h) is the semivariance at lag distance h, C 0 is the nugget, C is the partial sill, and a is the range. The kriging system of equations is set up using the Gaussian variogram model to determine the weights assigned to each known data point. These weights are calculated to minimize the estimation variance for the unknown points. The Gaussian model ensures smooth interpolation with continuous and differentiable transitions between estimated values, reflecting the assumed autocorrelation structure of the data.
Based on variogram analysis, CO 2 concentration measurements (Supplementary Fig. S1 online) are spatially dependent up to 700 m (i.e., the range), beyond which they become substantially independent. The range for δ 13 C–CO 2 , indicating the distance at which spatial correlation between carbon isotope measurements becomes negligible, has also been set to 700 m for kriging interpolation. For δ 18 O–CO 2 measurements, the range was determined to be 800 m. The partial sill was calculated as 1780 for CO₂ concentration, 1.62 for δ 13 C–CO 2 , and 1.65 for δ 18 O–CO 2 , indicating the variance attributable to the spatial structure for each variable. Simulations of stable isotope variables, airborne CO 2 concentration, and volcanic CO 2 dispersion were executed using the SAGA GIS software package ( https://saga-gis.sourceforge.io/en/index.html ).
Quantification of the CO 2 input in the atmosphere
An appropriate mass balance model for airborne CO 2 incorporates both isotopic parameters and concentration. Utilizing literature values for δ 13 C–CO 2 and δ 18 O–CO 2 of standard air (e.g., δ 13 C–CO 2 = − 8‰ and δ 18 O–CO 2 = − 0.1‰ 50 ) alongside values specific to CO 2 of external sources (e.g., either volcanic/hydrothermal or fossil fuel derived CO 2 ), an isotopic mass balance model incorporates four unknowns: background air CO 2 concentration, CO 2 concentration in the forcing source of gas, air CO 2 mixing fraction, and volcanic CO 2 mixing fraction.
The model is expressed by Eq. ( 5 ), which represents the CO 2 concentration in the air:
where C represents the CO 2 concentration and X denotes the mixing fraction between forcing source and atmospheric CO 2 , with subscripts m, a, and fs referring to measured, background, and local forcing source of CO 2 , respectively. This model operates under the assumptions that external source (i.e., volcanic or fossil fuel derived CO 2 ) significantly elevates CO 2 concentration relative to background levels.
The binary mixing equation to determine the relative weights of CO 2 from volcanic and atmospheric sources is given by Eq. ( 6 ):
Similarly, Eqs. ( 7 )
describe the isotopic mass balance models for carbon and oxygen isotopes of CO 2 , respectively. The combination of Eq. ( 6 ) and ( 7 ) provides Eq. ( 9 ), which allows for the calculation of X a
Using Eq. ( 9 ) in Eq. ( 8 ) yields Eq. ( 10 ), enabling the determination of X FS
By employing both Eqs. ( 9 ) and ( 10 ) and rearranging Eq. ( 5 ), we derive Eq. ( 11 )
which provides the concentration of CO 2 produced by the local effective gas source in the air C fs .
Airborne CO 2 partitioning of between volcanic and human related components
Cluster analysis was conducted to explore the relationships between airborne CO 2 concentrations and carbon isotope composition. Cluster analysis facilitates the classification of observational datasets into distinct classes based on specified similarity criteria. The objective of this analysis is to discern several groups of data that exhibit internal homogeneity (i.e., similarity criteria) while displaying heterogeneity among themselves concerning both CO 2 concentration and stable isotope compositions (i.e., δ 13 C–CO 2 and δ 18 O–CO 2 values). Various clustering methods are available for partitioning datasets (e.g., k-means, hierarchical, and two-way clustering), each differing in the requirement of preselecting the number of clusters, statistical properties of the dataset, or computational complexity.
Hierarchical clustering enables the grouping of objects such that those within a group are similar to each other and distinct from objects in other groups. Hierarchical clustering holds an advantage over alternative methods as it obviates the necessity of specifying the number of clusters a priori. The hierarchical structure of clusters can be formed using partitioning algorithms, initially considering all objects as individual clusters. Subsequently, through an iterative process, objects are assigned to different clusters based on principles maximizing the inter-cluster distances. One variant of hierarchical clustering is agglomerative clustering, where each object begins as its own cluster, and pairs of smaller clusters are successively merged until all data is encompassed within a single cluster. Essentially, hierarchical clustering assesses object similarity (i.e., distance) to form new clusters. Cluster merging is predicated on the Euclidean distance metric, reflecting the sum of squares of object coordinates in Euclidean space. Calculation of Euclidean distances leads to the updating of the distance matrix, with the iterative process culminating in the merging of the last two clusters into a final cluster encompassing the entire dataset.
Multiple approaches exist for computing inter-cluster distances and updating the proximity matrix, with some (e.g., single linkage or complete linkage) assessing minimum or maximum distances between objects from different clusters. In the cluster analysis of our dataset, we employed the Ward approach, which evaluates cluster variance rather than directly measuring distances, aiming to minimize variance among clusters. In Ward's method, the distance between two clusters is contingent upon the increase in the sum of squares when the clusters are combined. Ward's method implementation seeks to minimize the sum of squares distances of points from cluster centroids. In contrast to other distance-based methods, Ward's method exhibits less susceptibility to noise and outliers. Hence, in this paper, the Ward method is preferred over alternative methods for clustering.
Conclusions
This study presents findings from a spatial survey conducted in the metropolitan area of Naples, Italy, aimed at examining potential variations in atmospheric CO 2 sources. The urban zone of Naples was chosen due to its diverse CO 2 sources, including those from both geological (e.g., volcanic/hydrothermal emissions) and anthropogenic (e.g., combustion-related) origins. Situated within the extensive volcanic zone hosting Vesuvius, Campi Flegrei, and active volcano on Ischia, Naples provides a compelling location for such investigation owing to its dense urban population compared to other urban areas in the European continent.
Identification of CO 2 sources was facilitated through a combination of stable isotopic analysis and concentration measurements. Stable isotopic composition (i.e., carbon and oxygen isotopic ratios) and airborne CO 2 concentration were measured using a high-precision laser-based analyzer installed in an SUV vehicle. Measurements, recorded at 1 Hz, were synchronized with GPS data to ascertain spatial positioning, achieving a spatial resolution on a metric scale.
Spatial variations in both isotopic composition and concentration were derived from the dataset using the kriging algorithm with Gaussian autocorrelation. Resulting maps delineated three zones characterized by elevated CO 2 concentrations exhibiting distinct stable isotopic signatures. The zone with the highest CO 2 concentration encompassed Naples’ downtown and harbour district, while intermediate concentrations were observed inland across the urban area. Spatial simulations indicated lower CO 2 concentrations along the seaside to the west of downtown, consistent with local morning atmospheric circulation patterns oriented from SW to NE. Additional zones of heightened CO 2 concentrations were identified near the airport, situated northeast of downtown, and in proximity to inhabited areas such as Pozzuoli and Pisciarelli, near Solfatara to the west. These last areas (Pozzuoli and Pisciarelli) exhibit manifestations of a broad hydrothermal/magmatic system beneath the Campi Flegrei, constituting a geological source of airborne CO 2 . Anthropogenic CO 2 emissions, primarily from vehicular engine combustion, were found to elevate CO 2 concentrations above background levels in downtown Naples, near the airport, and in the vicinity of Solfatara.
A mixing model incorporating stable isotope composition and airborne CO 2 concentration allowed quantification of CO 2 contributions from different sources. Geochemical modeling based on this approach revealed spatial dispersal patterns of additional CO 2 near Solfatara and downtown Naples, with volcanic CO 2 dispersing northeastward under prevailing morning winds northeast oriented. This volcanic CO 2 extends beyond the hydrothermal zone, supplementing anthropogenic CO 2 emissions from vehicular traffic.
This study underscores the utility of combining isotopic and CO 2 concentration data for discerning the dispersion of both endogenous greenhouse CO 2 and emissions from anthropogenic activities. Particularly relevant in densely populated volcanic/hydrothermal regions, this methodology effectively distinguishes between natural and anthropogenic gas emissions in the atmosphere, overcoming challenges associated with high background levels and subtle spatial variations of the airborne CO 2 . Measurements in Naples were collected within a single day, during the diurnal phase of the planetary boundary layer (PBL) evolution, under turbulent conditions and mixing of the atmospheric layer closest to the ground. Consequently, the CO 2 dispersal maps represent average conditions for the urban area of Naples. Establishing monitoring programs for the concentration and isotopic composition of airborne CO 2 in Naples and other cities is crucial for studying the impact of the daily evolution of the PBL on potential variations in airborne CO 2 . This is particularly important in areas where geogenic sources (i.e., volcanic or hydrothermal) coexist with anthropogenic CO 2 emissions (e.g., from fossil fuel combustion) resulting from high population density.
Data availability
The datasets generated during and/or analyzed during the current study are available in the ZENODO repository, https://zenodo.org/records/11300873 .
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Di Martino, R.M.R., Gurrieri, S., Paonita, A. et al. Unveiling spatial variations in atmospheric CO 2 sources: a case study of metropolitan area of Naples, Italy. Sci Rep 14 , 20483 (2024). https://doi.org/10.1038/s41598-024-71348-9
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Urban greening management arrangements between municipalities and citizens for effective climate adaptation pathways: four case studies from the netherlands.
1. Introduction
2. materials and methods, 2.1. theoretical framework: a social–ecological systems framework used to analyse collaborative management of green areas, 2.2. concepts of reference: “management”, “disembedding”, and “trust” in municipality–citizen relationships, 2.3. methodological framework: multiple-case-study and ethnographic fieldwork analysis, 3. case studies: urban green space configuration and organisational structure, 3.1. maximapark (utrecht): citizens’ ideas added to the park landscape, 3.2. dakpark (rotterdam): neighbours volunteering for green maintenance, 3.3. eva-lanxmeer (culemborg): 330 houses with public, private, and common gardens in a self-management eco-district, 3.4. groene mient (the hague): housing and green management through socratic decision-making principles, 4. results and discussion of case comparison, 4.1. similarities and differences in organisational structures and legal norms, 4.2. building trust and cooperative relationships through face-to-face interactions and resource transactions.
“First, because the designers, urban planners, and the architect who designed the district came from outside, they were not locals. Second, because the people who initially settled in EVA-Lanxmeer were also all from outside; individuals from all over the country were interested in this place, but they were considered peculiar. Third, because it is well-located land next to the station, and many people from Culemborg would have liked to live here” (Resident interviewed).
4.3. Considerations as to Monitoring and Sanctioning Rules
“There are citizen initiatives that aren’t that promising, you want to say goodbye, and people want to know it at a certain stage. He [the outsourced architecture designer of the park] does lots of quality control on the different initiatives when it’s something new, cause when you add something to the park, you add something to his own responsibility there to his design”.
5. Conclusions and Practical Implications
5.1. drivers for long-term effective collaborative management of urban green spaces, 5.2. practical implications potentially generalisable for effective adaptation pathways and further research, supplementary materials, author contributions, data availability statement, acknowledgments, conflicts of interest, appendix a. this appendix contains additional information about the fieldwork conducted, including photographs showcasing moments from the interviews and field visits.
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Name, Location, and Population | Surface Size (Hectares) | Year of Start | Urban Configuration | Governmental, Non-Governmental Organisations Involved (Legal Forms) | Property Rights Relevant to the Green Resource (% of Public, Private, and Common Land) |
---|---|---|---|---|---|
MaximaPark. Utrecht, Leidsche Rijn district (49,307 inhabitants) | 330 Ha. Forest park | 2007. Before, there was agricultural land. | Contact | Public Administration and Foundation of neighbours volunteering | Public land, 100% |
DakPark. Rotterdam, Delfshaven district (76,605 inhabitants) | 7 Ha. Park with vegetable gardens | 2014. Before, there were railways. | Contact | Public Administration and Foundation of neighbours volunteering | Public land, 100% |
EVA-Lanxmeer. Culemborg, eco-district (800 inhabitants) | 56 Ha. Eco-district with 330 houses | 1994. Before, there was agricultural land. | Contract at start, now contact | Owners’ Association and a network of foundations, associations, cooperatives, and corporations | Private lands, 68% (each house with private garden); public lands, 9% (green area on public land owned by the City Council of Culemborg); and common lands, 23% (owned by the owners’ community under the “mandeligheid” legal form) |
Groene Mient. The Hague, Segbroek district (60,054 inhabitants) | 0,76 Ha. Social–ecological housing with 33 houses | 2013. Before, there was a school. | Contract | Owners’ Association, a secondary association, and a cooperative | Private land, 44% (each house with private garden); and common lands, 56% (common gardens and roofs) under the Collective Private Ownership legal form |
Case Study | Data Source | Data Collection Period |
---|---|---|
Maxima Park, Utrecht. Forest park. | Three in-depth interviews: (1) Project Manager of MaximaPark, Department for Spatial Development, Utrecht City Council (2 interviews); (2) the designer of the Masterplan for the renovation of the neighbourhood where MaximaPark is located. | (1) 21 December 2023; 6 February 2024; (2) 14 December 2023 |
Two non-guided visits (tours are on request. There is a suggested itinerary on the website). | November and December 2023; February 2024 | |
Desk research with project documents. | September 2023–April 2024 | |
DakPark, Rotterdam. Park with vegetable gardens. | Two in-depth interviews: (1) volunteer group engaging in green maintenance; (2) Landscape Designer, Department of Planning, Landscape & Urban Development, Rotterdam City Council. | (1) 25 November 2023; 6 February 2024; (2) 28 December 2023 |
One Guided tour and one collective pruning session with volunteers and subsequent meal (participant observation). | 25 November and 2 December 2023 | |
Desk research with project documents. | September 2023–April 2024 | |
EVA-Lanxmeer, Culemborg. Eco-district. | Two in-depth interviews: (1) focal point of communication in EVA-Lanxmeer; (2) a resident of the dwellings (for 3 years). | (1) 21 December 2023; (2) 14 December 2023 |
One guided tour and one non-guided visit. | 4 and 22 November 2023 | |
Desk research with project documents. | September 2023–April 2024 | |
Groene Mient, The Hague. Social–ecological housing. | Two in-depth interviews: (1) a resident in the dwellings (for 3 years); (2) a resident in the dwellings (for 10 years). | (1) 28 October 2023; (2) 9 December 2023 |
One guided tour and one non-guided visit. | October and December 2023 | |
Desk research with project documents. | September 2023–April 2024 |
Case Study | Type of Resource Exchanged | Responsible Agent |
---|---|---|
EVA-Lanxmeer, Culemborg. | Salary and air travel for the eco-district architect for 7 months at the start of the project; | Municipality |
Monthly salary for the three resident owners maintaining the green spaces in the public district area (Terra Bella Foundation); | Municipality | |
Cleaning of interior streets; | Owners’ Association | |
Permaculture criteria added to the city’s green maintenance protocol; | Owners’ Association | |
Guided tours for researchers to learn about the eco-district (not for tourists). | Owners’ Association | |
DakPark, Rotterdam. | Training courses in pruning; | Municipality |
Part-time salary for two park activity coordinators and funding for purchasing tools and seeds; | Municipality | |
Labour for the green pruning of one-third of the park; | Volunteers’ Foundation | |
Guided tours in the park for tourists and researchers (paid and free, respectively); | Volunteers’ Foundation | |
Sheep grazing and chicken breeding for ecological composting. | Volunteers’ Foundation | |
Groene Mient, The Hague. | Sale of public land for housing; | Municipality |
Allocation of adjacent unused public land; | Municipality | |
Free guided tours for tourists and researchers. | Owners’ Association | |
MaximaPark, Utrecht. | Provision of tools and storage for volunteers on an ad-hoc basis; | Municipality |
Participation in occasional planting events. | Volunteers’ Foundation |
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Romero-Muñoz, S.; Sánchez-Chaparro, T.; Muñoz Sanz, V.; Tillie, N. Urban Greening Management Arrangements between Municipalities and Citizens for Effective Climate Adaptation Pathways: Four Case Studies from The Netherlands. Land 2024 , 13 , 1414. https://doi.org/10.3390/land13091414
Romero-Muñoz S, Sánchez-Chaparro T, Muñoz Sanz V, Tillie N. Urban Greening Management Arrangements between Municipalities and Citizens for Effective Climate Adaptation Pathways: Four Case Studies from The Netherlands. Land . 2024; 13(9):1414. https://doi.org/10.3390/land13091414
Romero-Muñoz, Sara, Teresa Sánchez-Chaparro, Víctor Muñoz Sanz, and Nico Tillie. 2024. "Urban Greening Management Arrangements between Municipalities and Citizens for Effective Climate Adaptation Pathways: Four Case Studies from The Netherlands" Land 13, no. 9: 1414. https://doi.org/10.3390/land13091414
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