Effective Air Quality Management Strategies Research Articles

Enhancing spatial modeling and risk mapping of six air pollutants using synthetic data integration with convolutional neural networks

Air pollution poses significant risks to human health and the environment, necessitating effective air quality management strategies. This study presents a novel approach to air quality management by integrating an autoencoder (AE) with a convolutional neural network (CNN) algorithm in Tehran city of Iran. One of the primary and vital problems in deep learning is model complexity, and the complexity of a model is affected by data distribution, data complexity, and information volume. AE provide a helpful way to denoise input data and make building deep learning models much more efficient. The proposed methodology enables spatial modeling and risk mapping of six air pollutants, namely, particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). For air pollution modelling, data from a spatial database containing the annual average of six pollutants from 2012 to 2022 was utilized. The model considered various parameters influencing air pollution: altitude, humidity, distance to industrial areas, NDVI (normalized difference vegetation index), population density, rainfall, distance to the street, temperature, traffic volume, wind direction, and wind speed. The risk map accuracy was assessed using the area under the receiver operating characteristic (ROC) curve for six pollutants. Among them, NO2, PM10, CO, PM2.5, O3, and SO2 exhibited the highest accuracy with values of 0.964, 0.95, 0.896, 0.878, 0.877, and 0.811, respectively, in the risk map generated by the CNN-AE model. The findings demonstrated the CNN-AE model’s impressive precision when generating the pollution risk map.

Enhancing spatial modeling and risk mapping of six air pollutants using synthetic data integration with convolutional neural networks

Air pollution poses significant risks to human health and the environment, necessitating effective air quality management strategies. This study presents a novel approach to air quality management by integrating an autoencoder (AE) with a convolutional neural network (CNN) algorithm in Tehran city of Iran. One of the primary and vital problems in deep learning is model complexity, and the complexity of a model is affected by data distribution, data complexity, and information volume. AE provide a helpful way to denoise input data and make building deep learning models much more efficient. The proposed methodology enables spatial modeling and risk mapping of six air pollutants, namely, particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). For air pollution modelling, data from a spatial database containing the annual average of six pollutants from 2012 to 2022 was utilized. The model considered various parameters influencing air pollution: altitude, humidity, distance to industrial areas, NDVI (normalized difference vegetation index), population density, rainfall, distance to the street, temperature, traffic volume, wind direction, and wind speed. The risk map accuracy was assessed using the area under the receiver operating characteristic (ROC) curve for six pollutants. Among them, NO2, PM10, CO, PM2.5, O3, and SO2 exhibited the highest accuracy with values of 0.964, 0.95, 0.896, 0.878, 0.877, and 0.811, respectively, in the risk map generated by the CNN-AE model. The findings demonstrated the CNN-AE model’s impressive precision when generating the pollution risk map.

Environmental and geographical factors influencing the spread of SARS-CoV-2 over 2 years: a fine-scale spatiotemporal analysis.

Since its emergence in late 2019, the SARS-CoV-2 virus has led to a global health crisis, affecting millions and reshaping societies and economies worldwide. Investigating the determinants of SARS-CoV-2 diffusion and their spatiotemporal dynamics at high spatial resolution is critical for public health and policymaking. This study analyses 194,682 georeferenced SARS-CoV-2 RT-PCR tests from March 2020 and April 2022 in the canton of Vaud, Switzerland. We characterized five distinct pandemic periods using metrics of spatial and temporal clustering like inverse Shannon entropy, the Hoover index, Lloyd's index of mean crowding, and the modified space-time DBSCAN algorithm. We assessed the demographic, socioeconomic, and environmental factors contributing to cluster persistence during each period using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), to consider non-linear and spatial effects. Our findings reveal important variations in the spatial and temporal clustering of cases. Notably, areas with flatter epidemics had higher total attack rate. Air pollution emerged as a factor showing a consistent positive association with higher cluster persistence, substantiated by both immission models and, to a lesser extent, tropospheric NO2 estimations. Factors including population density, testing rates, and geographical coordinates, also showed important positive associations with higher cluster persistence. The socioeconomic index showed no significant contribution to cluster persistence, suggesting its limited role in the observed dynamics, which warrants further research. Overall, the determinants of cluster persistence remained across the study periods. These findings highlight the need for effective air quality management strategies to mitigate air pollution's adverse impacts on public health, particularly in the context of respiratory viral diseases like COVID-19.

Air Quality Monitoring Using Low-Cost Sensors in Urban Areas of Jodhpur, Rajasthan.

Air pollution poses a significant health hazard in urban areas across the globe, with India being one of the most affected countries. This paper presents environmental monitoring study conducted in Jodhpur, Rajasthan, India, to assess air quality in diverse urban environments. The study involved continuous indoor and outdoor air quality monitoring, focusing on particulate matter (PM2.5) levels, bioaerosols, and associated meteorological parameters. Laser sensor-based low-cost air quality monitors were utilized to monitor air quality and Anderson 6-stage Cascade Impactor & Petri Dish methods for bioaerosol monitoring. The study revealed that PM2.5 levels were consistently high throughout the year, highlighting the severity of air pollution in the region. Notably, indoor PM2.5 levels were often higher than outdoor levels, challenging the common notion of staying indoors during peak pollution. The study explored the spatial and temporal diversity of air pollution across various land-use patterns within the city, emphasizing the need for tailored interventions in different urban areas. Additionally, bioaerosol assessments unveiled the presence of pathogenic organisms in indoor and outdoor environments, posing health risks to residents. These findings underscore the importance of addressing particulate matter and bioaerosols in air quality management strategies. Despite the study's valuable insights, limitations, such as using low-cost air quality sensors and the need for long-term data collection, are acknowledged. Nevertheless, this research contributes to a better understanding of urban air quality dynamics and the importance of public awareness in mitigating the adverse effects of air pollution. In conclusion, this study underscores the urgent need for effective air quality management strategies in urban areas. The findings provide valuable insights for policymakers and researchers striving to address air pollution in rapidly urbanizing regions.

Air pollution concentration fuzzy evaluation based on evidence theory and the K-nearest neighbor algorithm

Background: Air pollution, characterized by complex spatiotemporal dynamics and inherent uncertainty, poses significant challenges in accurate air quality prediction, and current methodologies often fail to adequately address these complexities.Objective: This study presents a novel fuzzy modeling approach for estimating air pollution concentrations.Methods: This fuzzy evaluation method integrates an improved evidence theory with comprehensive weighting and the K-nearest neighbor (KNN) interval distance within the framework of the matter-element extension model. This involves generating the basic probability assignment (BPA) based on interval similarity, performing sequential fusion using the Dempster–Shafer evidence theory, enhancing the fusion results via comprehensive weighting, and conducting fuzzy evaluation of air pollution concentrations using the matter-element extension KNN interval distance.Results: Our method achieved significant improvements in monitoring air pollution concentrations, incorporating spatiotemporal factors and pollutant concentrations more effectively than existing methods. Implementing sequential fusion and subjective–objective weighting reduced the error rate by 38% relative to alternative methods.Discussion: Fusion of multi-source air pollution data via this method effectively mitigates inherent uncertainty and enhances the accuracy of the KNN method. It produces more comprehensive air pollution concentration fusion results, improving accuracy by considering spatiotemporal correlation, toxicity, and pollution levels. Compared to traditional air-quality indices, our approach achieves greater accuracy and better interpretability, making it possible to develop more effective air quality management strategies. Future research should focus on expanding the dataset to include more diverse geographical and meteorological conditions, further refining the model to integrate external factors like meteorological data and regional industrial activity, and improving computational efficiency for real-time applications.

Air Quality Index Prediction Using Machine Learning

Air pollution poses a significant threat to public health, particularly in rapidly developing countries like India, where urbanization and industrialization contribute to diverse sources of pollutants. This research employs machine learning techniques to comprehensively analyse and predict air quality levels across different regions of India. Extensive air quality data, encompassing various pollutants and temporal variations, is collected and compiled. State-ofthe-art machine learning models are developed to identify key influencing factors, including industrial emissions, vehicular traffic, agricultural practices, and meteorological conditions. The models are evaluated for accuracy and reliability in predicting air quality levels. The study aims to provide valuable insights into the complex dynamics of air pollution in the Indian context. By understanding the contributing factors and predicting future air quality levels, this research contributes to the development of targeted and effective air quality management strategies. Keywords: Machine Learning, Air Pollution, AQI.

Identifying urban emission sources and their contribution to the oxidative potential of fine particulate matter (PM2.5) in Kuwait.

In this study, we investigated the seasonal variations, chemical composition, sources, and oxidative potential of ambient PM2.5 (particles with a diameter of less than 2.5 μm) in Kuwait City. The sampling campaign was conducted within the premises of Kuwait Institute for Scientific Research from June 2022 to May 2023, covering different seasons throughout the year. The personal cascade impactor sampler (PCIS) operated at flow rate of 9 L/min was employed to collect weekly PM2.5 samples on PTFE and quarts filters. These collected samples were analyzed for carbonaceous species (i.e., elemental and organic carbon), metals and transition elements, inorganic ions, and DTT (dithiothreitol) redox activity. Furthermore, principal component analysis (PCA) and multi-linear regression (MLR) were used to identify the predominant emission sources and their percentage contribution to the redox activity of PM2.5 in Kuwait. The results of this study highlighted that the annual-averaged ambient PM2.5 mass concentrations in Kuwait (59.9 μg/m3) substantially exceeded the World Health Organization (WHO) guideline of 10 μg/m3. Additionally, the summer season displayed the highest PM2.5 mass concentration (75.2 μg/m3) compared to other seasons, primarily due to frequent dust events exacerbated by high-speed winds. The PCA identified four primary PM2.5 sources: mineral dust, fossil fuel combustion, road traffic, and secondary aerosols. The mineral dust was found to be the predominant source, contributing 36.1% to the PM2.5 mass, followed by fossil fuel combustion and traffic emissions with contributions of 23.7% and 20.3%, respectively. The findings of MLR revealed that road traffic was the most significant contributor to PM2.5 oxidative potential, accounting for 47% of the total DTT activity. In conclusion, this comprehensive investigation provides essential insights into the sources and health implications of PM2.5 in Kuwait, underscoring the critical need for effective air quality management strategies to mitigate the impacts of particulate pollution in the region.

A comprehensive approach combining positive matrix factorization modeling, meteorology, and machine learning for source apportionment of surface ozone precursors: Underlying factors contributing to ozone formation in Houston, Texas

Ozone concentrations in Houston, Texas, are among the highest in the United States, posing significant risks to human health. This study aimed to evaluate the impact of various emissions sources and meteorological factors on ozone formation in Houston from 2017 to 2021 using a comprehensive PMF-SHAP approach. First, we distinguished the unique sources of VOCs in each area and identified differences in the local chemistry that affect ozone production. At the urban station, the primary sources were n_decane, biogenic/industrial/fuel evaporation, oil and gas flaring/production, industrial emissions/evaporation, and ethylene/propylene/aromatics. At the industrial site, the main sources were industrial emissions/evaporation, fuel evaporation, vehicle-related sources, oil and gas flaring/production, biogenic, aromatic, and ethylene and propylene. And then, we performed SHAP analysis to determine the importance and impact of each emissions factor and meteorological variables. Shortwave radiation (SHAP values are ∼5.74 and ∼6.3 for Milby Park and Lynchburg, respectively) and humidity (∼4.87 and ∼4.71, respectively) were the most important variables for both sites. For the urban station, the most important emissions sources were n_decane (∼2.96), industrial emissions/evaporation (∼1.89), and ethylene/propylene/aromatics (∼1.57), while for the industrial site, they were oil and gas flaring/production (∼1.38), ethylene/propylene (∼1.26), and industrial emissions/evaporation (∼0.95). NOx had a negative impact on ozone production at the urban station due to the NOx-rich chemical regime, whereas NOx had positive impacts at the industrial site. The study's findings suggest that the PMF-SHAP approach is efficient, inexpensive, and can be applied to other similar applications to identify factors contributing to ozone-exceedance events. The study's results can be used to develop more effective air quality management strategies for Houston and other cities with high levels of ozone.

Spatial variability of PM10, PM2.5 and PM chemical components in an industrialised rural area within a mountainous terrain

We describe the measurement and spatial variability of particulate matter (PM) chemical composition, PM10 and PM2.5 in the Greater Tubatse Municipality, South Africa. Monthly samples were collected over 12 months (July 2015 to June 2016) using the inexpensive and easy to operate passive samplers of the University of North Carolina. Sites for sample collection were located at private residences, a church, a hospital and a school. Concentrations of PM10, PM2.5 and PM chemical components were determined using computer-controlled scanning electron microscopy with energy-dispersive X-ray spectroscopy. The annual observed concentrations at all sites were below the South African National Ambient Air Quality Standards of 40 μg/m3 for PM10 and 25 μg/m3 for PM2.5. The Cr-rich and CrFe-rich particles showed substantial heterogeneity with high concentrations observed near the chrome smelters, and Si-rich particles were highest near the silicon mine. SiAl-rich particles were highest at sites close to busy roads, while SiAlFe-rich particles were less spatially distributed. The low spatial variability of SiAlFe-rich particles indicates that these elements are mainly found in crustal material. Using the synoptic meteorological parameters of The Air Pollution Model, we were unable to effectively determine correlations between PM10 and mixing height, Monin–Obukhov length, air pollution potential, or coefficient of divergence.
 Significance:
 
 We have shown that the use of University of North Carolina passive samplers coupled with computer-controlled scanning electron microscopy is effective in determining the chemical composition of PM.
 The use of passive samplers is a cheap and effective method to collect data in remote areas of South Africa which have limited or no electricity supply.
 Assessment of the spatial distribution of PM and PM chemical components can assist in the development of effective air quality management strategies.

Anatomy of the winter 2017 air quality emergency in Delhi

The Indian capital Delhi experienced an environmental emergency in early November 2017 when levels of toxic PM2.5 particles surpassed WHO guidelines by 25 times (11 times by Indian Standards) for a prolonged period of a week (peak 24 h average ~650 μg/m3). We hereby demonstrate the role that monsoon dynamics played in linking and mixing dust emitted from a large natural dust storm, 3000 km away in the Middle East, with smoke from agriculture fires in northwest India. This dust and smoke rich air was then transported to Delhi where, under stagnant conditions, it mixed with local emissions resulting in very high pollution levels. The heavy aerosol-laden air altered the land-skin surface air temperature difference resulting in increased surface wind speeds, favouring faster dispersion and an unusual sharp decline in PM2.5 (PM2.5–110 μg/m3). Understanding the multi-scale nature of such events is important in improving our abilities to forecast these events and in developing effective air quality management strategies for the mega cities.

Development of risk-based air quality management strategies under impacts of climate change

Climate change is forecast to adversely affect air quality through perturbations in meteorological conditions, photochemical reactions, and precursor emissions. To protect the environment and human health from air pollution, there is an increasing recognition of the necessity of developing effective air quality management strategies under the impacts of climate change. This paper presents a framework for developing risk-based air quality management strategies that can help policy makers improve their decision-making processes in response to current and future climate change about 30–50 years from now. Development of air quality management strategies under the impacts of climate change is fundamentally a risk assessment and risk management process involving four steps: (1) assessment of the impacts of climate change and associated uncertainties; (2) determination of air quality targets; (3) selections of potential air quality management options; and (4) identification of preferred air quality management strategies that minimize control costs, maximize benefits, or limit the adverse effects of climate change on air quality when considering the scarcity of resources. The main challenge relates to the level of uncertainties associated with climate change forecasts and advancements in future control measures, since they will significantly affect the risk assessment results and development of effective air quality management plans. The concept presented in this paper can help decision makers make appropriate responses to climate change, since it provides an integrated approach for climate risk assessment and management when developing air quality management strategies. Implications: Development of climate-responsive air quality management strategies is fundamentally a risk assessment and risk management process. The risk assessment process includes quantification of climate change impacts on air quality and associated uncertainties. Risk management for air quality under the impacts of climate change includes determination of air quality targets, selections of potential management options, and identification of effective air quality management strategies through decision-making models. The risk-based decision-making framework can also be applied to develop climate-responsive management strategies for the other environmental dimensions and assess costs and benefits of future environmental management policies.

Sources and transport of black carbon at the California–Mexico border

At international border areas that suffer from poor air quality, assessment of pollutant sources and transport across the border is important for designing effective air quality management strategies. As part of the Cal-Mex 2010 field campaign at the US–Mexico border in San Diego and Tijuana, we measured black carbon (BC) concentrations at three locations in Mexico and one in the United States. The measurements were intended to support the following objectives: to characterize the spatial and temporal variability in BC, to estimate the BC emission inventory, to identify potential source areas of BC emissions, and to assess the cross-border transport of BC. Concentrations at Parque Morelos, the campaign's supersite, averaged 2.2 μg m−3 and reached a maximum value of 55.9 μg m−3 (1-min average). Sharp, regularly occurring peaks around midnight were suggestive of clandestine industrial activity. BC concentrations were more than two times higher, on average, in Tijuana compared to San Diego. BC and carbon monoxide (CO) were strongly correlated at the three sites in Mexico. The ΔBC/ΔCO ratio of 5.6 ± 0.5 μg m−3 ppm−1 in Tijuana, or 4.7 ± 0.5 μg m−3 ppm−1 when adjusted for seasonal temperature effects to represent an annual average, was comparable to that in other urban areas. Tijuana's emissions of BC were estimated to be 230–890 metric tons per year, 6–23% of those estimated for San Diego. Large uncertainties in this estimate stem mainly from uncertainties in the CO emission inventory, and the lower end of the estimate is more likely to be accurate. Patterns in concentrations and winds suggest that BC in Tijuana was usually of local origin. Under typical summertime conditions such as those observed during the study, transport from Tijuana into the US was common, crossing the border in a northeasterly direction, sometimes as far east as Imperial County at the eastern edge of California.

Investigation of Air Pollution from Automobiles at Intersections on Some Selected Major Roads in Ogbomoso, South Western, Nigeria

This paper reports the results of the investigation of air pollution from automobiles at intersections on some selected major roads in Ogbomoso, South Western, Nigeria. Four sampling routes were considered with ten sampling points (SP1 - SP10) placed 2.0m away from the edge of the road. Priority parameters: SO2, NOx, CO were monitored. The results of SO2, NOx, CO were in the range of 0.02 - 0.09ppm, 0.009-0.039ppm, 1.79-51.38ppm respectively. The concentration of SO2 was highest at SP 6 with value 0.5103ppm. Emission concentration for NOx was found to be highest at SP 6 where traffic congestion and traffic intersection is observed. The highest level obtained for the air pollution indicator of CO was at: SP 9, 6, 3and 10 due to traffic congestion and traffic intersection, where long waiting time for vehicles was observed. All the three monitored air pollutants when compared with AQI level (Air quality index) were in the range of: SO2 - was from very poor to good, NOx- from good to very good, CO - very good to moderate and moderate to poor in different locations. The study concludes that, it has become quite important to understand the role of mobile source emissions on air quality through well-designed studies. This information can provide important input for the formulation of effective air quality management strategies.

The assessment of emission-source contributions to air quality by using a coupled MM5-ARPS-CMAQ modeling system: A case study in the Beijing metropolitan region, China

A coupled MM5-ARPS-CMAQ air quality modeling system was applied for investigating the contributions of various emission sources to the ambient concentrations of PM 10 in the Beijing metropolitan region of China. A three-level nested simulation domain was established for facilitating the modeling study, and a GIS-based atmospheric pollutant emission database was constructed to provide important emission information for the Models-3/CMAQ model. After verification, the coupled modeling system was implemented to predict the hourly contribution ratios of various PM 10 emission sources to Beijing's air quality during four representative months of January, April, August and October in the base year of 2002. It was found from the modeling results that three major sources (including fugitive industrial emissions, construction sites, and road dusts) presented a relatively high contribution to the PM 10 pollution in Beijing, with the contribution ratios of 28.67%, 42.88%, 42.46% and 35.67% for spring, summer, autumn and winter, respectively. In addition, the impacts of emissions from two major industrial zones within Beijing on its air quality were quantified. Moreover, the coupled modeling system was applied to explore possible future PM 10 concentration distributions in 2008 through two proposed emission sources reduction strategies. It was found that Beijing needs to take continual emission control efforts to comprehensively improve its air quality to the Class II level by 2008. The modeling results could provide sound decision making basis for developing effective air quality management strategies in the planning region.

A modelling system for air quality management

To develop effective air quality management strategies at regional and urban levels requires the preparation of several actions belonging to different scientific areas and analysis of their complex interactions. A well-structured management system must be based on efficient tools that integrate diverse sets of information - emissions, traffic, meteorological variables and air quality data. Ideally, such a system should include appropriate modelling tools that could be used to perform spatial interpolation and air quality forecasts. This paper will describe the modelling system developed for predicting the emissions (emission pre-processor), dispersion and chemical transformation of pollutants in urban areas and its application for two sites in Portugal (Lisbon and Oporto). For predicting the concentration fields, all categories of sources are taken into account, namely stationary point and area sources and traffic. This allows the construction of an urban air quality management system for decision support.