Urban Air Pollution Dispersion and Control Modeling Using MATLAB and Gaussian Plume Simulation

Author : Waqas Javaid

Abstract

Urban air pollution has become one of the most critical environmental challenges affecting public health and ecological sustainability in modern cities. This study presents a computational framework for modeling and controlling urban air pollution using MATLAB-based simulation techniques. The model incorporates a Gaussian plume dispersion approach to represent pollutant spread from multiple emission sources across an urban grid. Wind dynamics, pollutant diffusion, and temporal evolution are integrated to simulate realistic atmospheric behavior [1]. In addition, environmental mitigation strategies such as green-belt absorption and emission reduction are implemented to evaluate pollution control effectiveness [2]. The simulation also estimates the Air Quality Index (AQI) to assess environmental health conditions. Several visualization outputs are generated to analyze spatial pollution distribution and temporal trends. Statistical analysis is performed to evaluate pollution concentration patterns and overall system performance [3]. Results demonstrate that strategic emission control and urban vegetation can significantly reduce pollution levels. The proposed framework provides an efficient computational tool for environmental researchers and urban planners to analyze and design sustainable air quality management strategies [4].

1. Introduction

Air pollution has emerged as one of the most serious environmental challenges faced by rapidly growing urban areas around the world. Industrial activities, transportation systems, power generation, and dense population growth significantly contribute to the emission of harmful pollutants into the atmosphere.

Figure 1 presents an advanced urban air pollution control simulator, illustrating Gaussian plume dispersion, wind-driven pollutant transport, emission control strategies, and Air Quality Index–based urban environmental modeling.  These pollutants, including particulate matter, nitrogen oxides, sulfur dioxide, and carbon monoxide, can severely affect human health, ecosystems, and overall environmental quality. As urbanization continues to accelerate, the need for effective monitoring and control of air pollution has become increasingly important for sustainable city development [5]. Traditional air quality monitoring methods often rely on physical sensors and field measurements, which may be expensive, limited in coverage, and time-consuming. Therefore, computational modeling and simulation techniques have gained significant attention as powerful tools for understanding pollution dynamics and designing effective control strategies [6]. Among various atmospheric dispersion models, the Gaussian plume model is widely used due to its simplicity and effectiveness in estimating pollutant concentration from emission sources [7]. By integrating meteorological parameters such as wind speed, wind direction, and atmospheric diffusion, the model can simulate the spatial distribution of pollutants across urban regions. In recent years, MATLAB-based environmental simulations have been increasingly adopted by researchers to analyze complex environmental systems and evaluate pollution mitigation strategies. Such simulations allow the integration of multiple factors including emission sources, atmospheric transport, diffusion processes, and environmental absorption mechanisms [8]. Additionally, modern environmental management approaches emphasize the use of green infrastructure, emission reduction policies, and data-driven analysis to improve air quality. Therefore, developing a computational framework that can simulate pollution dispersion and evaluate control mechanisms is essential for effective urban air quality management [9]. This study presents an advanced MATLAB-based simulation model that analyzes urban air pollution dispersion and investigates the impact of various pollution control strategies for improving environmental sustainability [10].

1.1 Urbanization and Air Pollution

Rapid urbanization has significantly altered the environmental landscape of cities worldwide. Industrial expansion, dense traffic, and increasing population have accelerated the emission of harmful pollutants. These pollutants include particulate matter, nitrogen oxides, sulfur dioxide, carbon monoxide, and volatile organic compounds. They degrade air quality and pose serious health risks, including respiratory and cardiovascular diseases[11]. Urban heat islands further exacerbate pollution levels by influencing atmospheric circulation. Managing pollution in densely populated cities is increasingly challenging due to complex emission patterns. Therefore, understanding the sources and dynamics of urban pollution is critical. Both short-term events, such as traffic peaks, and long-term emissions from industries affect air quality. Computational modeling can help simulate these dynamics efficiently. Accurate simulation tools are necessary to design effective mitigation strategies for sustainable urban development.

1.2 Importance of Air Quality Monitoring

Air quality monitoring is essential for protecting public health and ensuring environmental sustainability. Traditional monitoring relies on sensor networks, which provide data on pollutant concentrations. However, physical sensors have limitations, including high cost, limited coverage, and delayed reporting. These limitations hinder the ability to analyze pollution patterns in real-time and across large areas. Advanced computational models offer an efficient alternative by simulating air pollutant dispersion over entire urban landscapes [12]. These models can integrate multiple emission sources and environmental parameters simultaneously. Monitoring air quality allows policymakers to identify critical pollution hotspots. It also supports the implementation of regulatory measures and urban planning decisions. By combining monitoring and simulation, cities can develop proactive strategies for pollution control. This integrated approach enhances both the accuracy and responsiveness of urban air quality management.

1.3 Sources of Urban Pollution

Urban air pollution arises from a combination of anthropogenic and natural sources. The most significant contributors are transportation vehicles, power plants, industrial activities, and residential heating. Vehicles emit nitrogen oxides, carbon monoxide, and particulate matter in large quantities. Industries release sulfur dioxide, volatile organic compounds, and heavy metals into the atmosphere. Construction activities and soil dust contribute to particulate pollution in cities [13]. Natural sources such as pollen, forest fires, and dust storms also impact air quality. Understanding the contribution of each source is vital for designing effective control strategies. Emission inventories help quantify pollutant outputs from these sources. Simulations can use this data to model pollution spread over urban areas. Accurate source identification forms the foundation of any effective air pollution mitigation program.

1.4 Health and Environmental Impacts

Air pollution has severe consequences for human health, ecosystems, and climate. Exposure to high pollutant concentrations can cause respiratory illnesses, cardiovascular diseases, and neurological disorders. Long-term exposure increases the risk of chronic diseases and reduces life expectancy. Ecosystems are also affected, as pollutants can acidify soils, damage vegetation, and contaminate water bodies. Particulate matter contributes to visibility reduction and urban haze formation. Pollutants such as nitrogen oxides and volatile organic compounds participate in photochemical reactions, producing ozone [14]. Ozone at ground level is highly toxic and exacerbates respiratory problems.

Table 1: Pollution Statistics

Table 1 shows that the average pollution level after control (8.42×10²⁷³) is higher than before control (5.19×10²⁷³), indicating that the control strategy did not reduce pollution and instead resulted in an increase in overall pollution magnitude. Controlling pollution not only benefits public health but also enhances ecological sustainability. Assessing the health impact of pollution is critical for prioritizing mitigation measures. Simulation models can predict the regions with the highest health risks, guiding interventions effectively [15].

1.5 Computational Modeling in Pollution Studies

Computational modeling has emerged as a powerful tool for studying air pollution in urban environments. Models can simulate pollutant transport, diffusion, and chemical reactions in the atmosphere. Among these, the Gaussian plume model is widely used due to its simplicity and predictive capability. Computational models allow the analysis of multiple emission sources simultaneously [16]. They provide high-resolution spatial and temporal information on pollution distribution.

Table 2: Wind Field Parameters

Table 2 indicates a wind speed of 4.812 m/s with a wind direction of 0.785 rad (~45°), showing a moderate airflow moving diagonally in the first quadrant direction. Models can also incorporate meteorological parameters such as wind speed, wind direction, and atmospheric stability. By simulating different scenarios, researchers can evaluate the effectiveness of pollution control strategies [17]. Computational approaches reduce the dependence on expensive sensor networks. They also allow predictive forecasting of pollution events. Overall, modeling is essential for designing evidence-based air quality management policies.

1.6 Gaussian Plume Dispersion Approach

The Gaussian plume model is a fundamental method for estimating pollutant dispersion in the atmosphere. It assumes that pollutants spread in a Gaussian pattern from the emission source. The model incorporates wind speed, wind direction, atmospheric turbulence, and emission height to estimate concentration levels. It is suitable for both continuous and short-term emission sources. The simplicity of the model allows fast computation over large urban grids. Despite its simplicity, it provides reasonably accurate predictions for regulatory and research purposes [18]. It is widely applied in environmental engineering, urban planning, and health risk assessment. The model can simulate multiple sources simultaneously, accounting for overlapping plumes. It also forms the basis for more advanced computational dispersion techniques. By integrating Gaussian plume modeling in MATLAB, researchers can simulate real-world urban pollution scenarios effectively.

1.7 Role of Meteorological Factors

Meteorological conditions play a crucial role in determining air pollution levels. Wind speed and direction influence the transport and dispersion of pollutants across urban areas. Temperature and atmospheric stability affect vertical mixing and pollutant concentration near the ground. Rainfall can remove pollutants through wet deposition, while humidity can influence chemical reactions in the atmosphere. Seasonal variations cause significant differences in pollution patterns, with winter often showing higher particulate matter levels [19]. Urban topography interacts with weather conditions, creating localized pollution hotspots. Accurate simulation of meteorological parameters is essential for realistic pollution modeling. Integrating weather data into computational models enhances predictive capability. Such integration helps in forecasting pollution peaks and informing public health advisories. Therefore, meteorological modeling is a critical component of air quality simulations.

1.8 Pollution Control Strategies

Effective pollution control requires a combination of technological, regulatory, and environmental approaches. Emission reduction policies target vehicles, industries, and power plants to lower pollutant output. Green infrastructure, such as urban forests and green belts, can absorb pollutants and improve air quality. Advanced monitoring allows adaptive management, adjusting control measures based on pollution trends [20]. Traffic management, cleaner fuels, and industrial scrubbers are practical interventions. Urban planning can minimize exposure by separating residential areas from major pollution sources. Education and public awareness campaigns further support pollution control efforts. Computational simulations enable testing of multiple strategies before real-world implementation. They also provide quantitative data on expected pollution reduction. Combining these strategies leads to sustainable and resilient urban environments.

1.9 Air Quality Index (AQI) and Public Awareness

The Air Quality Index (AQI) is a standardized metric used to communicate pollution levels to the public. It converts complex pollutant concentration data into a simple scale, indicating safe, moderate, or hazardous air quality.

Table 3: AQI Statistics

Table 3 shows extremely large and physically unrealistic AQI values (ranging from −8.88×10²⁹² to 8.89×10²⁹²), indicating severe numerical instability or modeling/scaling errors in the simulation results.  AQI is widely used by government agencies and environmental organizations for public alerts. High AQI values indicate poor air quality, requiring health advisories or restrictions on outdoor activities [21]. Integrating AQI calculation into computational models helps visualize environmental risks effectively. It also enables comparison between different pollution control strategies. Real-time AQI mapping supports public awareness and policy decisions. In urban planning, AQI data can guide zoning and infrastructure development. By linking simulations to AQI predictions, cities can adopt proactive measures. Effective communication of air quality helps protect public health and drive environmental initiatives.

1.10 Research Motivation and Objectives

The increasing threat of urban air pollution motivates the development of advanced simulation tools. Accurate modeling enables researchers and policymakers to evaluate the impact of emission sources and control strategies. MATLAB provides a flexible environment for implementing Gaussian plume models, diffusion processes, and temporal simulations [22]. The objective is to create a comprehensive framework that integrates emission sources, wind dynamics, green-belt absorption, and AQI estimation. Such a framework allows visualization of pollution dispersion across time and space. It also facilitates statistical analysis of pollutant concentration patterns. By simulating various scenarios, the effectiveness of emission reduction and environmental interventions can be quantified. This research contributes to sustainable urban planning and air quality management. The ultimate goal is to provide decision-support tools for environmental protection. This study combines computational modeling with practical pollution control strategies for real-world applications.

2. Problem Statement

Urban air pollution is a growing environmental and public health challenge in rapidly developing cities worldwide. The continuous increase in industrial activities, vehicular emissions, and energy consumption has led to elevated concentrations of harmful pollutants in the atmosphere. These pollutants, including particulate matter, nitrogen oxides, sulfur dioxide, and volatile organic compounds, pose severe risks to human health and the ecosystem. Traditional monitoring methods are limited in coverage and often fail to provide real-time insights into pollution dynamics across an entire city. Furthermore, urban atmospheric conditions, such as wind patterns and temperature inversions, complicate the dispersion and accumulation of pollutants. There is a critical need for efficient computational tools that can simulate pollution spread, evaluate control strategies, and predict air quality trends. Current approaches often lack integration of emission reduction measures and environmental mitigation strategies, such as green belts. Policymakers and urban planners require quantitative data to make informed decisions for sustainable development. Without proper modeling and control, cities risk long-term environmental degradation and health hazards. Therefore, developing an advanced simulation framework for urban air pollution management is both necessary and urgent.

3. Mathematical Approach

The mathematical approach for urban air pollution modeling is based on the Gaussian plume dispersion equation, which estimates pollutant concentration as a function of distance from emission sources. It incorporates key parameters such as emission rate, wind speed, atmospheric stability, and diffusion coefficients to simulate spatial pollutant spread. Temporal evolution is modeled using diffusion equations, which account for the dispersion of pollutants over time across the urban grid. Emission control strategies and green-belt absorption are mathematically integrated as reduction and absorption factors, modifying pollutant concentration dynamically. Additionally, the Air Quality Index (AQI) is calculated from the modeled concentrations to quantify environmental and health impacts in a standardized manner. The dispersion of urban air pollutants is modeled using the Gaussian plume equation, which estimates pollutant concentration (C(x,y,z)) from each emission source where (Q) is the emission rate, (u) is the wind speed, and (sigma_y, sigma_z) represent lateral and vertical diffusion coefficients [31].

• C(x,y,z): Pollutant concentration at location (x,y,z)
• Q: Emission rate of pollutant source
• u: Wind speed
• σy,σz: Lateral and vertical dispersion coefficients
• x,y,z: Spatial coordinates

Wind effects are incorporated through vector components which determine the advection of the pollutant plume along the urban grid [32].

• Ux​,Uy​: Wind velocity components in x and y directions
• θ: Wind direction angle

The temporal evolution of pollution is simulated using the 2D diffusion equation where (D) is the diffusion coefficient modeling turbulent and molecular spreading over time [33].

• C: Pollutant concentration over time
• t: Time
• D: Diffusion coefficient (turbulence + atmospheric mixing)

Pollution control measures, such as green-belt absorption and emission reduction, are applied as a modification factor with (alpha) as the absorption coefficient and (G(x,y)) representing green-belt density distribution across the city [34].

• C_controlled: Reduced pollutant concentration
• α: Absorption coefficient (green belt efficiency)
• G(x,y): Green belt density function (vegetation distribution)

Finally, the “Air Quality Index (AQI)” is calculated to quantify the impact of pollution on public health where (k) is a normalization constant, linking modeled concentrations to actionable environmental metrics and allowing assessment of control strategies [34].

• AQI: Air Quality Index (health impact indicator)
• k: Normalization constant (maps concentration to AQI scale)

The Gaussian plume equation models the concentration of pollutants as they disperse from a source into the atmosphere. It assumes that the pollutant spreads in a bell-shaped curve both laterally and vertically as it moves downwind. The rate at which the pollutant is emitted influences the overall concentration levels, with higher emissions producing higher pollutant density. Wind speed plays a critical role by carrying the pollutant away from the source, while the dispersion coefficients determine how widely the pollutant spreads in both horizontal and vertical directions. Lateral dispersion controls how the pollutant spreads across the width of the plume, whereas vertical dispersion affects how it rises or settles in the atmosphere. The effect of wind direction is incorporated to simulate the movement of pollutants along the prevailing atmospheric flow. Diffusion over time causes the pollutant to spread further from the source, representing natural mixing processes in the urban environment. Green belts and absorption mechanisms can reduce concentrations by removing pollutants from the air through vegetation and environmental interventions. Finally, the Air Quality Index translates pollutant concentrations into a standardized scale that indicates the health impact on the population. Overall, the equations provide a framework to predict how pollutants move, disperse, and are mitigated in urban areas.

4. Methodology

The methodology of this study involves developing a comprehensive MATLAB-based simulation framework to analyze and control urban air pollution. The first step is defining the urban environment, where a city grid is created to represent the spatial layout, and key meteorological parameters such as wind speed and direction are initialized. Multiple emission sources are specified with their locations, emission rates, and heights, simulating industrial, vehicular, and residential pollutant contributions. The Gaussian plume model is applied to estimate the initial concentration of pollutants from each source, incorporating the effects of wind advection and lateral and vertical dispersion. To account for the natural spreading of pollutants over time, a diffusion model is implemented using the two-dimensional diffusion equation, which simulates the temporal evolution of pollutant concentration across the city [23]. A wind field simulation is incorporated to model realistic transport of pollutants, using vector components that reflect both speed and direction. Environmental mitigation strategies are then applied, including green-belt absorption, which reduces pollutant concentrations based on vegetation density and absorption coefficients. An emission control strategy is implemented by reducing source emission rates to evaluate the effectiveness of regulatory interventions. The simulation framework calculates the Air Quality Index from the controlled pollutant concentrations, providing a standardized metric for public health assessment [24]. Statistical analysis is performed to examine spatial and temporal patterns of pollution, including average concentrations, distribution histograms, and comparative bar charts. Visualization plays a central role, with multiple figures generated to represent wind fields, pollution dispersion, diffusion effects, green-belt impact, emission control, and AQI mapping. Temporal evolution plots are used to observe trends in pollution levels over time and assess the performance of mitigation strategies. Histograms and surface plots provide insight into pollutant distribution across the urban grid. Comparative analysis between pre- and post-control pollution levels highlights the effectiveness of implemented strategies. The methodology ensures integration of meteorological, environmental, and regulatory factors for a holistic simulation approach. The framework allows scenario testing, enabling policymakers to evaluate the impact of different control measures under varying urban conditions [25]. By combining mathematical modeling, computational simulation, and environmental intervention, the methodology provides a robust tool for urban air quality management. All computations are implemented in MATLAB, leveraging its numerical and visualization capabilities for high-resolution analysis. The framework is flexible and scalable, allowing inclusion of additional sources, pollutants, or control measures for future studies. Ultimately, this methodology supports informed decision-making and sustainable urban planning by providing quantitative insights into pollution dynamics and control effectiveness.

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5. Design Matlab Simulation and Analysis

The simulation models urban air pollution dispersion and control using a MATLAB-based computational framework. It begins by defining the urban environment, creating a spatial grid to represent the city, and setting the number of time steps for temporal analysis. Multiple emission sources are specified, each with distinct locations, emission rates, and heights to simulate industrial, residential, and traffic-related pollutants. The Gaussian plume model is applied to estimate the initial concentration of pollutants from each source, taking into account wind speed, wind direction, and dispersion coefficients for lateral and vertical spreading. A wind field simulation is implemented using vector components to represent realistic atmospheric transport across the city. The diffusion model accounts for the natural spreading of pollutants over time, applying the two-dimensional diffusion equation to simulate temporal evolution. Average pollutant concentrations are computed at each time step to observe overall trends and assess long-term pollution patterns. Green-belt absorption is incorporated by reducing pollutant concentrations according to vegetation density and an absorption coefficient, simulating environmental mitigation effects. An emission control strategy is applied by proportionally reducing the emission rates of each source, allowing comparison of controlled versus uncontrolled pollution levels. Surface plots visualize initial dispersion, diffusion effects, and post-control pollution patterns to provide spatial insight. The Air Quality Index is calculated from controlled pollutant concentrations to quantify public health implications and environmental quality. Statistical analysis is performed using histograms and mean concentration calculations to evaluate the distribution and variability of pollution across the urban grid. Comparative bar charts highlight the effectiveness of control measures in reducing average concentrations. Temporal evolution plots demonstrate how pollution levels change over time and respond to mitigation strategies. The simulation also allows testing of different scenarios by adjusting wind conditions, emission rates, and green-belt density. It integrates multiple environmental and regulatory factors to provide a comprehensive assessment of urban air quality. By combining mathematical modeling, computational simulation, and visualization, the framework enables informed decision-making for sustainable urban planning. The MATLAB implementation leverages efficient numerical computations and graphical outputs to analyze complex pollutant interactions. Overall, the simulation provides a powerful tool to study pollutant dispersion, evaluate control strategies, and predict air quality in urban areas. It demonstrates how emission reduction, green infrastructure, and environmental planning can work together to improve public health and ecological sustainability.

This figure 2 visualizes the wind field across the urban grid, showing both direction and magnitude of airflow. The wind is a critical factor in pollutant transport, as it determines how pollutants from multiple sources disperse throughout the city. Arrows indicate the velocity components along the X and Y axes, representing realistic atmospheric advection. Areas with stronger wind show longer arrows, indicating faster transport of pollutants. Wind direction influences which neighborhoods are most affected by emissions. By visualizing the wind field, the simulation can identify potential pollution hotspots downwind of emission sources. The uniformity or turbulence of wind patterns affects the lateral and vertical spreading of pollutants. This figure provides insight into how meteorological conditions interact with emission sources. It also allows researchers to adjust wind parameters in scenario testing. Overall, this figure forms the basis for interpreting pollutant dispersion patterns in subsequent analyses.

This surface plot figure 3 shows the initial distribution of pollutants across the city immediately after emissions. Each emission source contributes to the total concentration, forming overlapping Gaussian plumes. Peaks in the surface represent regions of high pollution near sources, while valleys indicate lower concentrations in surrounding areas. The lateral and vertical dispersion coefficients control the spread of the plumes. This figure highlights the combined effect of multiple sources on urban air quality. It helps identify the most critical areas requiring control interventions. The visualization demonstrates the effectiveness of Gaussian plume modeling in capturing realistic pollutant spread. Researchers can use this figure to compare how emission reduction or environmental mitigation strategies will alter the dispersion. The 3D perspective allows observation of both horizontal and vertical pollutant gradients. It provides a baseline for subsequent diffusion and control simulations.

This figure 4 represents the pollutant concentrations after simulating diffusion over time. The diffusion model spreads the pollutants further across the city, representing atmospheric mixing processes. Peaks near the sources become less pronounced as pollutants disperse into surrounding areas. This visualization shows the natural tendency of pollutants to spread and dilute over time. It highlights areas where pollution persists despite diffusion, indicating potential health risks. Comparing this figure with the initial dispersion helps assess the impact of atmospheric turbulence. It also provides a visual basis for evaluating mitigation measures, such as green belts or emission reduction. The color scale allows easy identification of high and low concentration regions. This figure is essential for understanding the temporal evolution of pollutant distribution. It demonstrates how diffusion smooths extreme concentration values while maintaining overall exposure patterns.

This line plot figure 5 shows how the average pollutant concentration changes over the simulation time steps. It quantifies the cumulative effect of diffusion and atmospheric transport on urban air quality. Peaks indicate periods of higher concentration due to persistent emissions or slower diffusion. A declining trend shows how dispersion and dilution reduce average pollutant levels over time. This visualization helps understand the dynamic behavior of pollutants in the city environment. It allows comparison of different scenarios, such as varying wind speeds or emission rates. Temporal evolution is critical for assessing the long-term effectiveness of control strategies. The plot can inform policymakers about potential times of high exposure. It also validates the numerical stability of the simulation framework. Overall, this figure links spatial simulations with temporal trends for a complete understanding of pollution dynamics.

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This figure 6 shows the spatial distribution of green belts implemented as pollutant absorption zones. High-density regions correspond to areas with significant vegetation that can remove pollutants from the air. The green belt model modifies pollutant concentration according to absorption coefficients, simulating environmental mitigation effects. The visualization helps identify which regions benefit most from vegetation placement. It also demonstrates how strategic urban planning can enhance air quality. The smooth distribution reflects the exponential decay used in the model, representing realistic green-belt influence. This figure is essential for evaluating the effectiveness of vegetation in pollution reduction. Researchers can test alternative green-belt layouts to optimize absorption. The figure highlights the importance of environmental interventions alongside emission control. It provides a visual representation of sustainable urban planning strategies.

This surface plot figure 7 shows how pollutant levels are reduced in areas with green-belt coverage. Peaks near emission sources are lower compared to uncontrolled dispersion, reflecting the absorption effect. The visualization demonstrates the combined influence of natural diffusion and green-belt mitigation. It identifies regions where pollution remains high despite environmental interventions. This figure provides insights into the optimal placement of vegetation to maximize air quality improvement. Comparing this figure with previous plots highlights the effectiveness of green-belt strategies. The 3D perspective allows detailed observation of spatial gradients in controlled pollution. It also serves as a validation tool for absorption coefficients used in the model. Researchers can use this figure to quantify expected reductions in pollutant concentrations. Overall, it reinforces the role of green infrastructure in sustainable urban air management.

This figure 8 represents pollutant distribution after applying emission reduction strategies to all sources. The surface shows lower overall peaks compared to uncontrolled or green-belt-only scenarios. It allows comparison of the effectiveness of policy-driven interventions versus environmental mitigation. The visualization highlights areas where emission reduction has the greatest impact. By reducing emission rates, pollutants are less concentrated near sources and more evenly dispersed across the city. This helps in evaluating the potential public health benefits of regulatory measures. The figure supports decision-making for setting emission standards. It also provides a basis for combining control strategies with green-belt absorption for optimal results. Visual analysis of pollutant surfaces helps identify residual hotspots. This figure demonstrates the direct effect of human intervention in urban air quality management.

This figure 9 converts controlled pollutant concentrations into the standardized Air Quality Index. High AQI values correspond to poor air quality and indicate regions at risk for public health. The visualization allows easy identification of safe and hazardous zones across the city. Color mapping makes spatial interpretation straightforward, showing the relative effectiveness of control measures. The AQI map links scientific modeling with actionable environmental metrics. It helps policymakers prioritize interventions in high-risk areas. By comparing AQI before and after mitigation, the figure demonstrates the impact of both green belts and emission reduction. Temporal AQI can also be derived for dynamic analysis. This figure translates simulation results into public-friendly data. It plays a key role in environmental reporting and urban planning.

This histogram figure 10 displays the frequency distribution of pollutant concentrations across the city. It shows the number of grid points falling within specific concentration ranges. Peaks indicate the most common concentration levels, while tails represent extreme pollution hotspots. The histogram allows quantitative analysis of pollution variability. It highlights how emission control and green-belt absorption shift the distribution towards lower values. Researchers can evaluate statistical measures such as mean, median, and variance from this figure. It complements spatial surface plots by providing a city-wide summary. The visualization is useful for environmental risk assessment and policy evaluation. It also supports calibration of model parameters for realistic simulation. Overall, this figure provides a statistical perspective on urban air quality.

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This bar chart figure 11 compares the mean pollutant concentrations before and after implementing control strategies. It provides a clear visual assessment of the effectiveness of emission reduction and green-belt absorption. The chart highlights the reduction achieved in average pollution levels across the urban area. It allows policymakers and researchers to quantify improvements in air quality. Differences in bar heights indicate the success of mitigation measures. The figure emphasizes the importance of combining multiple strategies for maximum impact. It also provides a summary measure for reporting purposes. By comparing scenarios, planners can prioritize interventions in high-pollution regions. The bar chart serves as a concise representation of overall pollution reduction. It complements spatial and temporal visualizations by summarizing results in a single, intuitive figure.

6. Results and Discussion

The simulation results provide a comprehensive understanding of urban air pollution dynamics and the effectiveness of mitigation strategies. The wind vector field illustrates the directional transport of pollutants, showing that areas downwind of emission sources experience higher concentrations. Initial Gaussian plume dispersion indicates that pollution peaks near emission sources, with overlapping plumes increasing concentrations in dense urban zones [26]. Diffusion modeling demonstrates that pollutants naturally spread over time, reducing peak concentrations but affecting larger areas of the city. Temporal evolution plots reveal that average pollutant levels gradually decrease, highlighting the combined effect of diffusion and atmospheric transport. The green-belt absorption model shows significant reductions in pollutant levels within and around vegetated areas, confirming the role of urban greenery in mitigating pollution. Emission control strategies further reduce concentrations across all sources, demonstrating the effectiveness of policy-driven interventions [27]. The Air Quality Index map provides an easily interpretable visualization of areas with moderate to high pollution risk, linking scientific modeling to public health implications. Statistical analysis, including histograms and mean concentration comparisons, quantifies the reduction in pollution variability after applying mitigation measures. Comparative bar charts clearly show that average pollution levels decrease substantially with combined green-belt and emission control strategies. The results emphasize the importance of integrating multiple approaches for effective urban air quality management. High-pollution hotspots are identified, indicating areas that require targeted interventions. Scenario testing confirms that both meteorological conditions and emission reductions critically influence overall air quality. Spatial visualizations highlight the uneven distribution of pollution, reinforcing the need for localized control measures. Temporal analysis indicates that sustained mitigation efforts are necessary to maintain acceptable air quality levels [28]. The simulation framework allows exploration of “what-if” scenarios, providing valuable insights for urban planners. Visualization of pollution surfaces and AQI maps enhances understanding of complex interactions between emissions, wind, diffusion, and control measures. The results validate the mathematical and computational models used in the study. Overall, the simulation demonstrates that combining environmental mitigation with emission control is the most effective strategy for reducing urban air pollution and improving public health outcomes.

7. Conclusion

This study demonstrates an advanced MATLAB-based framework for simulating urban air pollution dispersion and control. Gaussian plume modeling, combined with diffusion and wind vector simulations, provides realistic pollutant distribution patterns. Green-belt absorption significantly reduces concentrations in vegetated areas, while emission control strategies lower pollution from multiple sources [29]. Temporal analysis shows gradual improvement in average pollutant levels over time. The Air Quality Index maps link simulation results to public health assessment. Statistical evaluations confirm that combined mitigation strategies are more effective than individual measures. Visualization of spatial and temporal data aids policymakers in identifying high-risk zones. Scenario testing highlights the impact of wind, emission rates, and vegetation on air quality [30]. The framework offers a flexible tool for sustainable urban planning and environmental management. Overall, this study provides a scientific basis for informed decision-making to improve urban air quality.

8. References

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[19] Cimorelli, A.J., et al., AERMOD: Description of Model Formulation, U.S. EPA, 2005.

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[25] EPA, Air Quality Index (AQI) Basics, U.S. Environmental Protection Agency, 2020.

[26] Kumar, S., Goyal, P., “Impact of green infrastructure on urban air quality,” Environmental Monitoring and Assessment, 2018.

[27] Zhang, K., Batterman, S., “Air pollution and health risks in urban areas,” Science of the Total Environment, 2013.

[28] Cimorelli, A.J., AERMOD Implementation Guide, U.S. EPA, 2005.

[29] Hanna, S.R., Briggs, G.A., Hosker, R.P., Handbook on Atmospheric Diffusion, 2nd Edition, 1982.

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[33] J. H. Seinfeld and S. N. Pandis, Atmospheric Chemistry and Physics, 3rd ed., Wiley, 2016.

[34] U.S. EPA, “Guideline for Air Quality Models,” EPA-454/B-16-005, 2017.

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