Public Attention Index of Air Pollution Exposure from PM2.5 in Thailand

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.

Air quality monitoring in low- and middle-income countries (LMICs) is often challenged by data scarcity and limited resources. In this study, we employ low-cost BlueSky (TSI incorporation, USA) sensors to assess the seasonal trends and variations of PM2.5 concentrations in both the northwestern (Panchagarh and Rajshahi) and southern (Bhola) regions of Bangladesh. Our research focuses on understanding transboundary air pollution dynamics, an issue critical for effective air quality management in the region. We utilize backward airmass trajectory analysis to identify potential sources influencing air quality in the specified regions. By tracing the origins of air masses, we aim to pinpoint the contributing factors to the observed pollution levels. To further quantify the contribution of transboundary air pollution, we employ the Weather Research and Forecasting with Chemistry (WRF Chem) modeling system. By integrating observational data from the low-cost sensors and meteorological information into the model, we aim to enhance our understanding of the spatial and temporal distribution of transboundary pollution in Bangladesh. Our findings not only contribute to the development of effective air quality management strategies in the studied regions but also demonstrate the feasibility of utilizing low-cost sensor networks for comprehensive air quality assessments in data-scarce LMICs.

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.

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.

The study evaluates the ambient air quality in Akure South Local Government Area, Ondo State, Nigeria, focusing on particulate matter (PM2.5 and PM10) and its health implications on residents. Primary data were collected using Purple Air Monitor devices at 28 ground stations and structured questionnaires, base maps. The study utilized QGIS for mapping, SPSS for statistical analysis and WHO’s AirQ software for health risk assessment. Findings indicated exceedances of WHO and USEPA recommended limits for PM2.5 and PM10, with areas near major highways showing elevated PM levels. The health risk assessment revealed potential increases in hospitalizations and mortality due to air pollution. This research contributes uniquely by applying geospatial evaluation and health risk assessment tools for comprehensive air quality analysis. It highlights the urgent need for effective air quality management strategies, including the deployment of air quality monitoring stations and public education campaigns. The study provides crucial insights for policy makers, emphasizing the importance of mitigating air pollution’s adverse effects on urban settings. Received: 15 September 2024 / Accepted: 8 November 2024 / Published: 20 November 2024

Abstract. Urban air quality poses a significant public health challenge, particularly in complex urban areas like street canyons, where traffic emissions intensify the issue. The limitations of traditional monitoring methods, marked by a narrow scope and poor visualisation, hinder our understanding of pollutant dispersion dynamics, impeding effective air quality management. Consequently, this study explores the synergy of 3D city models and Computational Fluid Dynamics (CFD) simulation to analyse air pollutant dispersion in complex urban settings. To enhance geo-visualisation, the research employs the next generation of Open Geospatial Consortium (OGC) APIs for data delivery. The integration of 3D city models with CFD simulations and geo-visualisation techniques aims to develop an urban digital twin. This digital twin is envisioned to offer valuable insights into the dynamics of air pollutant dispersion within street canyons, thereby informing more effective air quality management strategies.

The Dallas-Fort Worth (DFW) metroplex is one of the fastest-growing metropolitan regions in the United States and serves as the largest economic hub in the Southern United States. Despite extensive regulatory efforts, the region is classified as an ozone non-attainment area based on the National Ambient Air Quality Standards (NAAQS), posing significant public health risks due to prolonged exposure to elevated ozone levels. Ozone, a secondary pollutant, is formed through complex photochemical reactions involving precursors such as volatile organic compounds (VOCs) and nitrogen oxides (NOx) rather than directly emitted from sources. The non-linear interaction of ozone precursors in the atmosphere presents substantial challenges in developing effective ozone reduction strategies. This dissertation analyzes air pollution measurements from 2000 to 2023, collected from multiple air quality monitoring stations across the DFW metroplex, using data mining, statistical analysis, source apportionment techniques, and machine learning (ML). By leveraging advanced techniques, this study aims to enhance the understanding of spatiotemporal pollution trends and improve air quality management within the region. Since 2000, concentrations of oxides of nitrogen (NOx) and carbon monoxide (CO) – key pollutants primarily emitted from traffic and other combustion-related sources – have shown a significant decline across the DFW metroplex. However, despite this reduction in conventional urban emissions, ozone concentrations at Denton Airport South (DEN), an exurban site, Fort Worth Northwest (FWNW), a semiurban site, and Dallas Hinton (DAL), a highly urbanized site in the DFW, have exhibited only a minor reduction, yet none of the three sites consistently met the NAAQS for ozone attainment. DAL intermittently achieved attainment status, but DEN and FWNW remained in non-attainment throughout the study period. A major contributing factor to this persistent ozone issue is the Barnett Shale, a large shale gas formation adjacent to DFW, which has been a significant source of unconventional total non-methane hydrocarbons (NMHC) emissions. The mean NMHC concentration at DEN (207.33 ± 317.23 ppb-C) – located within an active shale gas region (SGR) – was found to be more than twice the levels observed at DAL (60.54 ± 49.71 ppb-C) and FWNW (80.95 ± 65.37 ppb-C). These findings indicate that emissions from shale gas activities have contributed substantially to the atmospheric VOC levels in the region, potentially contributing to elevated ozone levels despite reductions in NOx and CO emissions from urban sources. The ozone formation potential (OFP) of NMHC at DEN was overwhelmingly dominated by slow-reacting alkanes primarily emitted from natural gas sources. In contrast, DAL was impacted by alkanes, alkenes, and aromatics from conventional urban sources, such as traffic emissions. While FWNW was impacted by NMHC from a mix of urban and natural gas sources. Using the Dispersion Normalized Positive Matrix Factorization (DN-PMF) technique, a source apportionment analysis of NMHC concentrations at DEN identified eight distinct source factors, with oil and gas activities accounting for over 94% of the total measured NMHC concentrations. The top contributing sources included natural gas extraction (71.5%), a mixed source consisting of natural gas and aviation fuel combustion (8.3%), condensate production (7.7%), and crude oil extraction (6.5%). At FWNW, NMHC concentrations were influenced by seven source factors, with natural gas extraction (37.8%) and traffic emissions (21.1%) as the dominant contributors. In DAL, NMHC levels were primarily driven by traffic-related emissions, where diesel and gasoline sources combined accounted for 32.1%, while natural gas sources contributed 31%. These findings underscore the significant role of natural gas extraction and production activities affecting the measured VOC concentrations at DEN, whereas urban traffic emissions played a more prominent role along with natural gas in NMHC profiles at DAL. Meanwhile, FWNW was impacted by a mixed composition of natural gas and urban emission sources. This analysis underscores the spatiotemporal heterogeneity of air emissions over the urban and exurban areas of North Texas. Given the variations in air emissions over any region, coupled with non-linear interactions of air pollutants in the atmosphere, real-time or near-real-time forecasting of air pollution levels remains quite challenging. Air pollutant concentration forecasting models were developed using machine learning (ML) algorithms, including Artificial Neural Networks (ANN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). RF and XGBoost models were specifically applied for VOC concentration prediction at the Denton Airport South (DEN) site, utilizing oil and natural gas (ONG) production activity data. The RF model demonstrated superior performance, achieving an R² > 0.89, indicating strong predictive accuracy for VOC trends. For 8-hour ozone concentration forecasting, a novel hybrid Recurrent Neural Network (RNN) was developed using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. The model was initially trained on data from FWNW, where it exhibited excellent predictive accuracy with R² > 0.97. When applied to DAL and DEN for the years 2021 through 2023, the model maintained high performance, achieving R² > 0.94 for DAL and R² > 0.91 for DEN. Future improvements to these models could involve the integration of additional domain-specific variables, such as more detailed emission inventories, to further enhance predictive capabilities. Additionally, the development of an advanced machine learning model incorporating a comprehensive source-chemical fingerprint database would facilitate automated and highly accurate source attribution, reducing the complexity associated with manual identification in source apportionment studies. Training ML models on such extensive datasets would improve source characterization and contribute to more 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

Inequities in air pollution exposure and gaps in air quality monitoring

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.

Air pollution poses significant risks to human health, ecosystems, and socio-economic stability. Accurately predicting PM2.5 concentration and Air Quality Index (AQI) is crucial for understanding pollution factors and devising effective control measures. This paper addresses Question C of the 2023 Huazhong Cup, focusing on predicting air quality using a multivariate hybrid prediction model, Prophet-XGBoost, which combines the Prophet time series decomposition algorithm and the XGBoost machine learning model. To address problem 1, this study performed KNN interpolation and IQR outlier removal on the data in Annex 1 and Annex 2 to eliminate missing and outliers in the meteorological data, and then standardised the data. Then, Random Forest Regression (RFR) was used to filter out the features related to the changes of PM2.5 concentration. Firstly, the Random Forest model was trained to determine the decision tree and the optimal number of leaves of the model, and then regression analysis was performed to find out the importance of these features to PM2.5 concentration, and the three main features screened out were PM10, the average temperature and CO, with the scores of 0.7742, 0.1075 and 0.0910, respectively. Comparison with the multiple linear regression model in the model evaluation demonstrated the accuracy of the model, and the calculation of Pearson's correlation coefficient confirmed the reasonableness of the model. In order to solve problems 2 and 3, when constructing the multi-step model, this study divides the training set and test set according to the ratio of 8:2, and firstly trains the prediction results of the known data based on the Prophet timedecomposition algorithm and the XGBoost machine learning model respectively. The results show that the two models have their own advantages and disadvantages, in order to obtain the prediction results that can meet the cyclical and seasonal changes of meteorological features, as well as its unstable and nonlinear characteristics, this study combines the two models together and adopts the Prophet-XGBoost combined prediction model model. The indexes of the combined model are greatly improved compared with the single model, which proves the reasonableness of the hybrid model prediction. Finally, the Prophet-XGBoost model was used to predict the PM2.5 concentration and AQI at the given times in Annex 3, and the air quality warning levels were determined based on the prediction results, which provided a useful reference for the formulation of more effective air quality management strategies.[1]

Source apportionment of PM10 particles in the urban atmosphere using PMF and LPO-XGBoost.

Climate change can affect the dispersion of air pollutants, and it is important to investigate how the emission sources would contribute to air pollution in the context of future climate scenarios. The main objective of this study is to examine the impact of climate-induced changes on dispersion and concentration of local air pollutants related with aircraft operations in airport. The dispersion modelling was implemented considering current and future climate conditions (Copernicus data for 2050). The emission inventory for NO2 from aircraft was compiled for winter and summer periods using current publicly available flight-tracking data and emission factors provided by the European Environment Agency. The methodology is applied to Lisbon International Airport allowing quantification of climate-induced changes on local air pollution near the airport. Although the highest NO2 levels related to aircraft activities occur in winter, the most pronounced difference between the two climate scenarios is obtained for summer, with an increase up to 73% in the daily average concentration under future climate conditions. The area exceeding the EU daily average limit value expands by 59% in summer, while a slight 4% decrease is obtained for winter. Additionally, the data were analysed considering modelling receptor point co-located with the current measurements and showing that at some hours the airport may contribute up to 25% of NO2 observed in densely populated area. This study highlights the importance of climate change in shaping future airport-related air pollution, emphasizing the need for effective air quality management strategies in the context of climate change.

The development of effective air quality management strategies at regional and urban level requires the preparation of several actions belonging to different scientific areas and analysis of its complex interactions. A well structured management system must be based on efficient tools that integrate a diverse set of informations 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. The present work describes the modelling system developed for the air quality management of the city of Lisbon. This system is incorporated in a visual environment and coupled with the modelling package allowing a fast analysis of the air quality situation over the city and the consequent development of on-line control measures. Transactions on Ecology and the Environment vol 15, © 1997 WIT Press, www.witpress.com, ISSN 1743-3541

In this work, the level of inhalable coarse and fine particles (inhalable particulate matter with a diameter of 10 µm, PM10) in Riyadh, Saudi Arabia, was measured in connection to various climatic variables. PM10 is a significant air pollutant that adversely affects human health and the environment. Understanding the effect of meteorological conditions on PM10 levels is crucial for developing effective air quality management strategies. This research employed statistical analysis to identify the key meteorological variables influencing PM10 concentrations in Riyadh, providing valuable insights for air quality improvement initiatives in the region. Data collected from January to December of 2023 is used in this study to investigate multiple PM10‐related issues, including correlations to meteorological metrics. The study results revealed that the average PM10 level in Al Muruj and Al Ghurabi was 75.73–346.00 μg/m3. There was a negative correlation (r = −0.56) between temperature and wind speed (WS) and a positive correlation (r = 0.46) between relative humidity (RH) and temperature. Polar plots over the winter months showed that winds coming from the west and southwest had the highest value at low WSs (< 2 m s−1) and the lowest value at high WSs (>3 m s−1). The relationship between various weather variables and PM10 levels was successfully illustrated using polar plots. Additional research on PM source distribution and modeling is necessary to support the establishment of an air quality index (AQI) and the development of a successful Riyadh air quality plan.