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The Congo has long been a site of contestation for global environmental governance strategies, with Indigenous Batwa, Bambuti, and Baaka groups at the center of transnational climate discourse. One such strategy, the REDD+ initiative (Reducing Emissions from Deforestation and Forest Degradation), has come to the fore as a carbon-trading-based solution to the environmental challenges facing the Congo Basin. This article critiques the REDD+ initiative's deterritorialized approach, which favors the voices of international stakeholders over forest-dependent peoples Indigenous to the region. Taking a political ethnographical approach to the area of Mai-Ndombe, this research is based on formal and informal interactions with international actors, local communities, and Indigenous peoples. The strengths and weaknesses of mitigation methods like community forestry, institutionalized participation, and participatory mapping are explored. Ultimately, it is asserted that Indigenous decision-making, rather than institutionally imposed hegemony, must be integrated into the REDD+ initiative. Keywords: REDD+ initiative, Congo Basin, Indigenous peoples, forest conservation, climate change, carbon trading, Batwa, Bambuti, Baaka groups, deterritorialized approach, political ethnography, Mai-Ndombe, community forestry, participatory mapping, Indigenous decision-making, Introduction Over the past twelve years, Indigenous peoples in the Global South, particularly in the Central African Basin, have increasingly found themselves at the center of international environmental governance strategies [...]

Author(s): Nandita Narayanasamy (corresponding author) [1]; Lalita Josyula [2] INTRODUCTION Air pollution is the degradation of air quality by anthropogenic or natural causes due to the introduction of harmful quantities [...], Introduction: Construction sites generate high levels of air pollution, contributing to more than 4% of particulate matter in the atmosphere. Literature indicates that on-site pollution is an important factor that contributes to lung impairments in construction workers. Chronic obstructive pulmonary disease (COPD) and acute respiratory distress syndrome (ADRS) are known to be exacerbated because of exposure to a variety of construction pollutants mainly particulate matter (PM10, PM2.5). Methods: We conducted a systematic review and meta-analysis to assess whether exposure to construction site pollutants is associated with impairment of pulmonary parameters like Forced vital capacity (FVC), Forced expiration volume in 1 sec (FEV1) and FEV1/FVC. A total of 221 publications of observational studies were reviewed and 18 were selected; the data of which were meta-analyzed. Results and Discussion: In all studies the test subjects (construction workers) exposed to occupational air pollutants showed significantly lower FVC and FEV1 as compared expected levels (p≤ 0.01). The calculated Odds ratio indicates that there is positive association between COPD as compared to ARDS and exposure to pollutants. Keywords: Pulmonary health, construction workers, Pollution

Silica serves as the primary filler in the fabrication of eco-friendly tires, and achieving an optimal dispersion of polar silica within the natural rubber matrix is crucial for crafting high-performance rubber composites. In this study, biodegradable surfactants polyethylene glycol (PEG) with molecular weights of 200, 400, and 800 were employed to modiiy silica. The modified silica was characterized by Fourier-transform infrared spectroscopy; PEG-modified silica with different molecular weights was compounded with Si69, a conventional silane coupling agent, in the formulation. This aimed to reduce Si69 dosage and mitigate the emission of volatile organic gases, such as ethanol, generated during the silanization reaction between Si69 and silica. Experimental findings revealed that compared with natural rubber composites containing six parts of Si69, the addition of PEG-modified silica enhanced filler dispersion in the composite while reducing Si69 dosage by three parts. This led to accelerated vulcanization rates, effectively decreased energy consumption during production, and significantly improved wet slip resistance, while maintaining optimal rolling resistance. Rubber composites prepared with PEG800-modified silica exhibited a 10% increase in elongation at break, a 12% increase in tensile product coefficient, and a 19% enhancement in wet slip resistance. Highlights * Silica is modified by polyethylene glycol with molecular weight of 200. 400, and 800. * The amount of silane coupling agent and VOC emissions are reduced. * The interfacial bonding between silica and rubber matrix is enhanced. * The tensile product coefficient and wet slip resistance are improved by 12% and 19%. KEYWORDS modification, natural rubber, polyethylene slycol, silica, wet slip resistance, 1 | INTRODUCTION Natural rubber (NR) stands out as a renewable, bio-based polymer composed of cis-l,4-polyisoprene, obtained from the latex of rubber trees through coagulation and drying processes. In addition [...]

The main reason that deteriorates air quality in mega cities is the increase in concentrations of air pollutant parameters. Meteorological parameters and atmospheric conditions play an important role in the increase of pollutant concentrations. This study provides insights into temperature inversions (TIs) during polluted days (PDs) and severe polluted days (SPDs) in Istanbul. Key findings include higher inversion frequencies during SPDs, particularly at 0000 UTC, along with a positive relationship between inversion frequencies and pollutant concentrations, notably with a 99% occurrence of inversions at 0000 UTC along SPDs. Analysis of inversion subgroups reveals surface-based inversions (SBIs) dominating at 0000 UTC, while elevated (EIs) and lower-troposphere inversions (LTIs) prevail at 1200 UTC. Winter months exhibit increased frequency and intensity of SBIs, aligning with expectations of subsidence motion under high-pressure systems. Inversion strengths and depths are higher during SPDs, with the highest strengths observed in winter at 0000 UTC and the deepest inversions occurring in winter for SPDs. Generally, the highest inversion strengths and shallowest inversion depths were observed in SBIs. EIs had the lowest frequency during the winter months, while LTIs occurred more often in the spring months. These findings underscore the importance of understanding TI patterns for effective air quality management in Istanbul. [ABSTRACT FROM AUTHOR]

The paper analyzes the processes of emission and dispersion of particulate contaminants from a large point source emitter: a hard coal-fired power plant. Reference is made to the European Green Deal and its main objective of reducing anthropogenic particulate and greenhouse gas emissions. CHPP, Krakow Combined Heat and Power Plant, Poland, as described in the article, has a strong impact on the mechanisms that shape the microclimatic factors of the Krakow agglomeration. This combined heat and power plant provides heat and electricity for the city, while simultaneously emitting significant amounts of suspended particulate matter into the atmosphere. Due to the adverse impact of non-conventional energy sources on the natural environment and the increasing effects of climate warming, radical changes need to be implemented. The HYSPLIT (Hybrid Single-Particles Lagrangian Integrated Trajectories) model was used to track the movement of contaminated air masses. A 5-day episode of increased hourly concentrations of PM2.5 particulate matter contamination was selected to analyze the backward trajectories of air mass displacement. From 15 August 2022 to 19 August 2022, high 24-h particulate matter concentrations were recorded, measuring around 20 µg/m3. The HYSPLIT model, a unique tool in the precise identification of point sources of pollution and their impact on the air quality of the region, was used to analyze the influx of polluted air masses. A 5-day episode of increased hourly concentrations of PM2.5 pollutants was selected for the study, with values of approximately 20 µg/m3. It was found that low-pressure systems over the North Atlantic brought wet and variable weather conditions, while high-pressure systems in southern and eastern Europe, including Poland, provided stable and dry weather conditions. The simulation results were verified by analyzing synoptic maps of the study area. The image of the displacement of contaminated air masses obtained from the HYSPLIT model was found to be consistent with the synoptic maps, confirming the accuracy of the applied model. This means that the HYSPLIT model can be used to create maps of contaminant dispersion directions. Consequently, it was confirmed that modeling using the HYSPLIT model is an effective method for predicting the displacement directions of particulate contamination originating from coal combustion in a combined heat and power plant. Identifying circulation patterns and front zones during episodes of increased contaminant concentrations is strategic for effective weather monitoring, air quality management, and alerting the public to episodes of increased health risk in a large agglomeration. [ABSTRACT FROM AUTHOR]

This study explores a hybrid forecasting framework for air pollutant concentrations (PM10, PM2.5, and NO2) that integrates Seasonal Autoregressive Integrated Moving Average (SARIMA) models with Bidirectional Long Short-Term Memory (BiLSTM) networks. By leveraging SARIMA's strength in linear and seasonal trend modeling and addressing nonlinear dependencies using BiLSTM, the framework incorporates Box-Cox transformations and Fourier terms to enhance variance stabilization and seasonal representation. Additionally, attention mechanisms are employed to prioritize temporal features, refining forecast accuracy. Using five years of daily pollutant data from Romania's National Air Quality Monitoring Network, the models were rigorously evaluated across short-term (1-day), medium-term (7-day), and long-term (30-day) horizons. Metrics such as RMSE, MAE, and MAPE revealed the hybrid models' superior performance in capturing complex pollutant dynamics, particularly for PM2.5 and PM10. The SARIMA combined with BiLSTM, Fourier, and Attention configuration demonstrated consistent improvements in predictive accuracy and interpretability, with attention mechanisms proving effective for extreme values and long-term dependencies. This study highlights the benefits of combining statistical preprocessing with advanced neural architectures, offering a robust and scalable solution for air quality forecasting. The findings provide valuable insights for environmental policymakers and urban planners, emphasizing the potential of hybrid models for improving air quality management and decision-making in dynamic urban environments. [ABSTRACT FROM AUTHOR]

In this comprehensive analysis of Chile's air quality dynamics spanning 2016 to 2021, the utilization of data from the National Air Quality Information System (SINCA) and its network of monitoring stations was undertaken. Quintero, Puchuncaví, and Coyhaique were the focal points of this study, with the primary objective being the construction of predictive models for sulfur dioxide (SO2), fine particulate matter (PM2.5), and coarse particulate matter (PM10). A hybrid forecasting strategy was employed, integrating Autoregressive Integrated Moving Average (ARIMA) models with Artificial Neural Networks (ANN), incorporating external covariates such as wind speed and direction to enhance prediction accuracy. Vital monitoring stations, including Quintero, Ventanas, Coyhaique I, and Coyhaique II, played a pivotal role in data collection and model development. Emphasis on industrial and residential zones highlighted the significance of discerning pollutant origins and the influence of wind direction on concentration measurements. Geographical and climatic factors, notably in Coyhaique, revealed a seasonal stagnation effect due to topography and low winter temperatures, contributing to heightened pollution levels. Model performance underwent meticulous evaluation, utilizing metrics such as the Akaike Information Criterion (AIC), Ljung-Box statistical tests, and diverse statistical indicators. The hybrid ARIMA-ANN models demonstrated strong predictive capabilities, boasting an R2 exceeding 0.90. The outcomes underscored the imperative for tailored strategies in air quality management, recognizing the intricate interplay of environmental factors. Additionally, the adaptability and precision of neural network models were highlighted, showcasing the potential of advanced technologies in refining air quality forecasts. The findings reveal that geographical and climatic factors, especially in Coyhaique, contribute to elevated pollution levels due to seasonal stagnation and low winter temperatures. These results underscore the need for tailored air quality management strategies and highlight the potential of advanced modeling techniques to improve future air quality forecasts and deepen the understanding of environmental challenges in Chile. [ABSTRACT FROM AUTHOR]

Climate change poses direct and indirect threats to public health, including exacerbating air pollution. However, the influence of rising temperature on air quality remains highly uncertain in the United States, particularly under rapid reduction in anthropogenic emissions. Here, we examined the sensitivity of surface-level fine particulate matter (PM2.5) and ozone (O3) to summer temperature anomalies in the contiguous US as well as their decadal changes using high-resolution datasets generated by machine learning. Our findings demonstrate that in the eastern US, stringent emission control strategies have significantly reduced the positive responses of PM2.5 and O3 to summer temperature, thereby lowering the population exposure associated with warming-induced air quality deterioration. In contrast, PM2.5 in the western US became more sensitive to temperature, highlighting the urgent need to manage and mitigate the impact of worsening wildfires. Our results have important implications for air quality management and risk assessments of future climate change. [ABSTRACT FROM AUTHOR]

In response to the demand for advanced tools in environmental monitoring and policy formulation, this work leverages modern software and big data technologies to enhance novel road transport emissions research. This is achieved by making data and analysis tools more widely available and customisable so users can tailor outputs to their requirements. Through the novel combination of vehicle emissions remote sensing and cloud computing methodologies, these developments aim to reduce the barriers to understanding real-driving emissions (RDE) across urban environments. The platform demonstrates the practical application of modern cloud-computing resources in overcoming the complex demands of air quality management and policy monitoring. This paper shows the potential of modern technological solutions to improve the accessibility of environmental data for policy-making and the broader pursuit of sustainable urban development. The web-application is publicly and freely available at https://cares-public-app.azurewebsites.net. [ABSTRACT FROM AUTHOR]

Complex mixtures of air pollutants, including ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), black carbon (BC), and fine particulate matter (PM2.5), present significant health risks. To understand the factors influencing air pollution levels and their temporal variations, comprehensive high-resolution long-term air pollution data are essential. This study analyzed the characteristics, lagged meteorological effects, and temporal patterns of six air pollutant concentrations over a one-year period at an urban residential site in Kenitra, Morocco. The results reveal pronounced seasonal and diurnal variations in pollutant levels, shaped by meteorological factors, emission sources, and local geographic conditions. PM2.5, SO2, and CO concentrations peaked during winter, while NO2 and CO exhibited consistent diurnal peaks during morning and evening rush hours across all seasons, driven by traffic emissions and nocturnal pollutant accumulation. In contrast, O3 concentrations were highest during summer afternoons due to photochemical reactions fueled by strong UV radiation, while winter levels were the lowest due to reduced sunlight. Lagged meteorological effects further highlighted the complexity of air pollutant dynamics. Meteorological factors, including temperature, wind speed, humidity, and pressure, significantly influenced pollutant levels, with both immediate and lagged effects observed. Lag analyses revealed that PM2.5 and BC levels responded to wind speed, temperature, and humidity over time, highlighting the temporal dynamics of dispersion and accumulation. CO is sensitive to temperature and pressure changes, with delayed impacts, while O3 formation was primarily influenced by temperature and wind speed, reflecting complex photochemical processes. SO2 concentrations were shaped by both immediate and lagged meteorological effects, with wind direction playing a key role in pollutant transport. These findings emphasize the importance of considering both immediate and lagged meteorological effects, as well as seasonal and diurnal variations, in developing air quality management strategies. [ABSTRACT FROM AUTHOR]

Air pollution in Jakarta, one of the most polluted cities globally, has reached critical levels, with PM2.5 concentrations exceeding the WHO guidelines and posing significant health risks. Accurate forecasting of PM2.5 is crucial for effective air quality management and public health interventions. PM2.5 exhibits significant nonlinear fluctuations; thus, this study employed two machine learning approaches: self-exciting threshold autoregressive tree (SETAR-Tree) and long short-term memory (LSTM). The SETAR-Tree model integrates regime-switching capabilities with decision tree principles to capture nonlinear patterns, while LSTM models long-term dependencies in time-series data. The results showed that: (1) SETAR-Tree outperformed LSTM, achieving lower RMSE (0.1691 in-sample, 0.2159 out-sample) and MAPE (2.83% in-sample, 2.98% out-sample) compared to LSTM's RMSE (0.2038 in-sample, 0.2399 out-sample) and MAPE (3.48% in-sample, 4.05% out-sample); (2) SETAR-Tree demonstrated better responsiveness to sudden regime changes, capturing complex pollution patterns influenced by meteorological and anthropogenic factors; (3) PM2.5 in Jakarta often exceeds the WHO limits, highlighting this study's importance in supporting strategic planning and providing an early warning system to reduce outdoor activity during extreme pollution. [ABSTRACT FROM AUTHOR]

Accurate and timely air quality forecasting is crucial for mitigating pollution-related hazards and protecting public health. Recently, there has been a growing interest in integrating visual data for air quality prediction. However, some limitations remain in existing literature, such as their focus on coarse-grained classification, single-moment estimation, or reliance on indirect and unintuitive information from visual images. Here we present a dual-channel deep learning model, integrating surveillance images and multi-source numerical data for air quality forecasting. Our model, which combines a single-channel hybrid network consisting of VGG16 and LSTM (named VGG16-LSTM) with a single-channel Long Short-Term Memory (LSTM) network, efficiently captures detailed spatiotemporal features from surveillance image sequences and temporal features from atmospheric, meteorological, and temporal data, enabling accurate time-series forecasting of PM2.5 and PM10 concentrations. Experiments conducted on the 2021 Shanghai dataset demonstrate that the proposed model significantly outperforms traditional machine learning methods in terms of accuracy and robustness for time-series forecasting, achieving R2 values of 0.9459 and 0.9045 and RMSE values of 4.79 μg/m3 and 11.51 μg/m3 for PM2.5 and PM10, respectively. Furthermore, validation results on the datasets from two stations in Kaohsiung, Taiwan, with average R2 values of 0.9728 and 0.9365 and average RMSE values of 1.89 μg/m3 and 5.69 μg/m3 for PM2.5 and PM10 using a pretrain–finetune training strategy, confirm the model's adaptability across diverse geographical contexts. These findings highlight the potential of integrating surveillance images to enhance air quality prediction, offering an effective supplement to ground-level environmental monitoring. Future work will focus on expanding datasets and optimizing network architectures to further improve forecasting accuracy and computational efficiency, enhancing the model's scalability for broader regional air quality management. [ABSTRACT FROM AUTHOR]

NH3 is the most important alkaline gas in the atmosphere and functions as a precursor to secondary ammonium salts. Therefore, identifying its sources and quantifying its emissions is imperative. NH4+ represents a principal component of atmospheric particulate pollutants. As particulate matter acts as a condensation nucleus for wet deposition, the NH4+ concentrations in precipitation are typically elevated. During the evaporation process of wet deposition, NH4+ is likely to be re-emitted as a gas (NH3). In Northeast China, heavy and frequent snowfall occurs, making it crucial to elucidate the NH3 release flux and the factors influencing this release during the snow evaporation process in representative cities of this region. This study collected snow samples from five notable snowfall events in Changchun, Jilin Province that occurred from November 2023 to March 2024, to conduct indoor simulated evaporation experiments (among them, the actual phase changes of two snowfall events consisted mainly of evaporation, whereas those of the other three events consisted mainly of sublimation). The results indicated that during the snow evaporation process, the average ratio (R) of NH4+ re-emitted as NH3 was 39.28 ± 12.07%. The average release flux (F) was 1624.58 ± 2064.5 μg/m2, and the average release rate (V) was 361.33 ± 465.31 ng/(m2·s). Within the 100-m atmospheric boundary layer, the total NH3 flux that was released subsequently during the evaporation of all snowfall events in Changchun during the study period was 46.97 mg/m2. The average release flux for individual snowfall events was 688.66 μg/m2, which constituted approximately 2.44% of the NH3 concentration documented in the winter atmosphere of Changchun. During the snow evaporation or sublimation process, R was positively correlated with the environmental temperature (P < 0.05) and negatively correlated with both the environmental wind speed and snowfall amount (P < 0.05). Significant variations in R were observed across multiple functional areas, which were ranked in descending order as follows: commercial areas (R = 26.16%), residential areas (R = 22.01%), cultural and educational areas (R = 20.7%), and industrial areas (R = 18.01%). The influence of a snow-melting agent (NaCl) on the conversion rate of NH3 initially increased but then subsequently decreased as the dose increased. The lowest measured value (R = 22.02%) was 0 mg/L, whereas the peak measured value (R = 30.31%) was attained at a concentration of 200 mg/L. The findings of this research are important for illuminating the cycling process of NH3 in the atmosphere and for the comprehensive management of air quality. [ABSTRACT FROM AUTHOR]

Globally, industrial developments have continued to affect people's health with limited research in this space. The South Durban Industrial Basin in KwaZulu-Natal, South Africa, is an area where industrial activities are undertaken close to residential areas and continues to be a challenge since the democratic transition. Drawing on democratic and good governance frameworks, this paper aims to understand why addressing air pollution is still a challenge in the democratic dispensation and the role of civil society and good governance in addressing air quality. Semi-structured interviews were conducted with key stakeholders to identify practices employed since the advent of democracy to address air pollution and understand challenges that prevent air pollution reduction. Despite the efforts of civil society, air pollution continues to be a challenge due to poor local governance to enforce and monitor air pollution and establish democratic platforms to genuinely include and address citizen concerns. [ABSTRACT FROM AUTHOR]

This study reviews particulate matter (PM) research in Indonesia, focusing on current trends, health impacts, challenges, and future research directions. As the largest archipelago country, Indonesia faces severe pollution annually due to rapid urbanization, industrial activities, vehicle emissions, and forest fires. PM levels often exceed WHO and NAAQS standards, especially in urban areas and during forest fire seasons, posing significant health risks to vulnerable populations. Most PM studies have been conducted in major cities, primarily concentrated on Java Island. While there are several studies in Sumatra and Borneo, they commonly focus on the effects of peatland fires, and research in the eastern part of Indonesia remains limited. Substantial gaps in PM studies have been highlighted, including limited monitoring infrastructure, technology, data inconsistencies, and socio-economic challenges. Recent studies emphasize the need for more research on size-segregated PM, including ultrafine particles (UFPs), to fully understand their behavior in the atmosphere, sources, distribution, and health impacts. Chemical analysis and source apportionment studies are also crucial but currently limited due to equipment and analytical challenges. To improve PM management, the study proposes strategic options, including adopting advanced monitoring technologies along with low-cost samplers, increasing funding and technical training, enhancing coordination among stakeholders, and fostering international collaboration. Furthermore, public awareness campaigns and community-based monitoring are essential for effective air quality management. [ABSTRACT FROM AUTHOR]

Biological contamination of fresh produce by fungi in storage is becoming a serious problem. Gaseous chlorine dioxide (ClO2) has been used to prevent fungal growth on fresh produce; however, the specific effects of gaseous ClO2 at concentrations low enough to be safe for the human body on fungal growth remain unknown. Therefore, in this study, we aimed to investigate the effect of low-concentration gaseous ClO2 on fungal growth in sweet potatoes over 1 month. Here, a mechanochemical reaction involving the collision of two types of powders was used to produce low concentrations of gaseous ClO2. The experiment was conducted in a container and chlorine dioxide gas was diffused by a circulator to verify its microbiostatic effect in a large space. A clear microbiostatic effect was observed in potatoes without skin when exposed to low-concentration ClO2 for 3 days. Notably, low concentrations (< 1.0 ppm) of ClO2 reduced Rhizopus stolonifer growth in sweet potatoes with skin over 1 month. Therefore, low concentrations of gaseous ClO2 are sufficient to inhibit fungal growth via gas diffusion. [ABSTRACT FROM AUTHOR]

Polycyclic aromatic hydrocarbons (PAH) are key components of particulate matter (PM) in terms of the toxicological risk of polluted air. Although commonly monitored in ambient air, PAHs are also present in indoor air, making the measurement of indoor PAH content essential for understanding the health risks associated with indoor environments. This study presents findings from measurements conducted across 37 households where children resided, using 7-day sampling campaigns to collect PM1. The health risk assessment methods are detailed herein, along with a source apportionment analysis to explore the associations with potential sources and differences from ambient air concentrations. Additionally, the incremental lifetime cancer risk (ILCR) was calculated to assess long-term health risks associated with exposure to indoor PAHs. The results showed consistently higher PAH concentrations in outdoor environments (from 0.079 ng m−3 for dibenzo(a,h)anthracene to 1.638 ng m−3 for benzo(b)fluoranthene) compared to indoor environments (from 0.029 ng m−3 for dibenzo(a,h)anthracene to 0.772 ng m−3 for indeno(1,2,3-cd)pyrene), suggesting significant transfer of PAHs from outdoor to indoor air. The source apportionment analysis indicated that traffic emissions, fossil fuel combustion, and residential heating were the predominant sources of PAHs in both environments, with the concentration of indoor PAHs largely influenced by gasoline and liquid fossil fuel combustion. The diagnostic ratios supported these findings, with coal and biomass as additional sources impacting outdoor PAH levels. The ILCR analysis revealed that the exposure levels for both children (indoors at 1.78 × 10−5, outdoors at 1.92 × 10−6) and adults (indoors at 1.15 × 10−5, outdoors at 1.24 × 10−6) remained below the U.S. EPA's risk threshold, suggesting limited carcinogenic risk under typical household conditions in this study. These findings emphasize the complexity of PAH distribution between indoor and outdoor environments, illustrating how urban outdoor pollution sources contribute to indoor air quality and highlighting the relevance of effective air quality management strategies. [ABSTRACT FROM AUTHOR]

This study investigates the temporal and spatial variations in PM2.5 concentrations in Dhaka, Bangladesh, from 2001 to 2023 and evaluates the impact of meteorological factors and the effectiveness of mitigation strategies on air pollution. Using satellite and ground-based data, this study analyzed the seasonal trends, daily fluctuations, and the influence of COVID-19 lockdown measures on air quality. Our findings reveal a persistent increase in PM2.5 levels, particularly during winter, with concentrations frequently exceeding WHO guidelines. Our analysis suggests significant correlations between meteorological conditions and PM2.5 concentration, highlighting the significant role of meteorological conditions, such as rainfall, humidity, and temperature, in modulating PM2.5 levels. Our analysis found that PM2.5 levels exhibited a significant inverse correlation with relative humidity (r = −0.72), rainfall (r = −0.69), and temperatures (r = −0.79), highlighting the role of meteorological conditions in mitigating pollution levels. Additionally, the study underscores the temporary improvements in air quality during lockdown periods, demonstrating the potential benefits of sustained emission control measures. The research emphasizes the need for comprehensive and multi-faceted air quality management strategies, including stringent vehicular and industrial emissions regulations, enhancement of urban green spaces, and public awareness campaigns to mitigate the adverse health impacts of PM2.5 pollution in Dhaka. [ABSTRACT FROM AUTHOR]

Watersheds are spatial units where air pollution, along with other environmental issues, is observed. The Meriç-Ergene Basin, selected as the study area, encompasses a part of the industrial belt of the Marmara Region due to its status as an economic attraction center. This situation has led to an increase in population and transportation density, with anthropogenic pressures significantly contributing to the levels of atmospheric pollution in the region. The main sources of air pollution in the watershed generally include fuels used for domestic heating, industrial activities, and motor vehicles. The aim of this study is to statistically investigate the air quality levels in the Meriç-Ergene Basin, which, in addition to its high agricultural characteristics, is an area where agriculture-based industries and other industrial activities are heavily concentrated, based on various pollutants. In the watersheld, along with air pollutants from domestic heating sources, emissions from urban and industrial activities increase pollution levels, further exacerbated by unfavorable topographical and meteorological conditions. Air pollution in the watershed has reached levels that are detrimental to the entire ecosystem, impacting human health and other living organisms. The concentrations of particulate matter (PM10) and sulfur dioxide (SO2), which have been regularly monitored in the watershed, were analyzed in 10-year intervals from 1990 to 2020. In 2022, measurements of PM2.5, NO2, NO, NOx, and O3 were also included. Data from monitoring stations in Edirne, Karaağaç, Keşan, Kırklareli, Vize, Lüleburgaz, Tekirdağ, Çerkezköy, and Çorlu were utilized. In 2022, especially the concentrations of particulate matter and nitrogen oxides consistently exceeded the established limit values. This study is anticipated to support the management of air quality in the region by identifying the temporal variations of pollution levels. [ABSTRACT FROM AUTHOR]

Air pollution is a pervasive global issue affecting human health, ecosystems, and the environment. This paper reviews the evolution and implementation of air quality legislation in Australia and Canada, providing a comparative analysis of their approaches to managing air pollution. Both countries have established robust legal frameworks to address air quality issues, involving governmental policies, regulatory mechanisms, and collaborative efforts among stakeholders. Australia began its air quality regulation at the state level in the 1950s, evolving into a national framework with the National Environment Protection Measure for Ambient Air Quality in 1998. In contrast, Canada centralized its efforts with the Clean Air Act in 1971, complemented by the National Air Pollution Surveillance program. Key findings reveal that, while both nations have achieved significant improvements in air quality, challenges remain in addressing the broader impacts of air pollution, such as climate change and health-related economic burdens. Australia's air quality standards are generally more stringent than Canada's for particulate matter but more relaxed for sulfur dioxide, nitrogen dioxide, and ozone. Both countries employ advanced air quality monitoring and reporting systems, with Australia's Air Quality Index and Canada's Air Quality Health Index providing critical public health information. The study highlights the need for continuous improvement and a more integrated approach to air quality management. By examining the legislative and regulatory landscapes of Australia and Canada, this paper offers valuable insights for other countries striving to enhance their air quality governance and mitigate the adverse effects of air pollution on a global scale. [ABSTRACT FROM AUTHOR]

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Harsh winters, aging infrastructure, and the demand for modern amenities are major factors contributing to the deteriorating air quality in Bishkek. The city meets its winter heating energy needs through coal combustion at the central heating plant, heat-only boilers, and in situ heating equipment, while diesel and petrol fuel its transportation. Additional pollution sources include 30 km2 of industrial area, 16 large open combustion brick kilns, a vehicle fleet with an average age of more than 10 years, 7.5 km2 of quarries, and a landfill. The annual PM2.5 emission load for the airshed is approximately 5500 tons, resulting in an annual average concentration of 48 μg/m3. Wintertime daily averages range from 200 to 300 μg/m3. The meteorological and pollution modeling was conducted using a WRF–CAMx system to evaluate PM2.5 source contributions and to support scenario analysis. Proposed emissions management policies include shifting to clean fuels like gas and electricity for heating, restricting secondhand vehicle imports while promoting newer standard vehicles, enhancing public transport with newer buses, doubling waste collection efficiency, improving landfill management, encouraging greening, and maintaining road infrastructure to control dust emissions. Implementing these measures is expected to reduce PM2.5 levels by 50–70% in the mid- to long-term. A comprehensive plan for Bishkek should expand the ambient monitoring network with reference-grade and low-cost sensors to track air quality management progress and enhance public awareness. [ABSTRACT FROM AUTHOR]

Particulate matter smaller than 2.5 μm (PM2.5) in Madrid is a critical concern due to its impacts on public health. This study employs advanced methodologies, including the CRISP-DM model and hybrid Prophet–Long Short-Term Memory (LSTM), to analyze historical data from monitoring stations and predict future PM2.5 levels. The results reveal a decreasing trend in PM2.5 levels from 2019 to mid-2024, suggesting the effectiveness of policies implemented by the Madrid City Council. However, the observed interannual fluctuations and peaks indicate the need for continuous policy adjustments to address specific events and seasonal variations. The comparison of local policies and those of the European Union underscores the importance of greater coherence and alignment to optimize the outcomes. Predictions made with the Prophet–LSTM model provide a solid foundation for planning and decision making, enabling urban managers to design more effective strategies. This study not only provides a detailed understanding of pollution patterns, but also emphasizes the need for adaptive environmental policies and citizen participation to improve air quality. The findings of this work can be of great assistance to environmental policymakers, providing a basis for future research and actions to improve air quality in Madrid. The hybrid Prophet–LSTM model effectively captured both seasonal trends and pollution spikes in PM2.5 levels. The predictions indicated a general downward trend in PM2.5 concentrations across most districts in Madrid, with significant reductions observed in areas such as Chamartín and Arganzuela. This hybrid approach improves the accuracy of long-term PM2.5 predictions by effectively capturing both short-term and long-term dependencies, making it a robust solution for air quality management in complex urban environments, like Madrid. The results suggest that the environmental policies implemented by the Madrid City Council are having a positive impact on air quality. [ABSTRACT FROM AUTHOR]

This study explores energy consumption, thermal performance, and indoor environmental quality (IEQ) in terminal buildings. Through detailed thermal analysis, this research identifies key sources of heat loss, such as thermal bridges in walls and windows, which significantly increase energy demands for heating. IEQ measurements show that the lack of mechanical ventilation, combined with high passenger densities, frequently leads to CO2 levels exceeding recommended thresholds, highlighting the urgent need for improved ventilation systems. Energy requirements were calculated based on the TS 825 standard and compared to actual consumption data, showing that optimizing boiler settings could save 22% of heating energy without any additional investment. Simulations and economic analyses further showed that adding thermal insulation to the building envelope and installing double-glazed windows with improved U-values could achieve significant energy savings and reduce CO2 emissions, all with favorable payback periods. A life cycle assessment (LCA) was conducted to evaluate the environmental impact of these interventions, demonstrating significant reductions in the airport's carbon footprint. The findings underscore the importance of aligning operational standards with international guidelines, such as ASHRAE and CIBSE, to ensure thermal comfort and optimize energy use. [ABSTRACT FROM AUTHOR]

Air pollution is a global issue that demands urgent attention due to its detrimental effects on human health and the environment. Land Use and Land Cover (LULC) change is an essential factor that significantly impacts ambient air quality through alterations in emission sources, vegetation cover, natural processes, and urban design. This study investigates the spatio-temporal variation of key air pollutants resulting from urban LULC changes in the Delhi region. Findings reveal a notable increase in pollutant concentrations, particularly particulate matter, in 2019 (PM10: 318.65 ± 45.80 µg/m3) and 2023 (PM10: 383.70 ± 61.49 µg/m3), compared to 2008 (PM10: 246.76 ± 30.66). LULC change analysis demonstrates a rise in built-up areas 24.59%(2008 to 2019), 33.62% (2008 to 2023) and a decline in vegetation cover 27.49% (2008 to 2019),32.37% (2008 to 2023). Correlation analysis indicates a positive correlation between PM10 and urban indices (+ 0.63) and a negative correlation between PM10 and vegetation indices (− 0.61), highlighting the impact of LULC on air quality deterioration. Subsequently, a fuzzy inference system model integrates LULC information to develop an air quality index (AQI). Incorporating LULC changes in AQI assessment offers a realistic approach to address the complexity arising from combined air pollutant effects, surpassing conventional AQI calculation methods. The findings underscore the significance of understanding the impact of Land Use and Land Cover (LULC) change on ambient air quality in formulating effective air quality management programs and policies. Integrating this knowledge into policymaking is crucial for the successful abatement of air pollution in urbanized areas. [ABSTRACT FROM AUTHOR]

Atmospheric elements interact with pollutants in complex, multidimensional ways, affecting air quality. Understanding these relationships requires a comprehensive analysis of time-series weather and pollutant data which has a negative or positive impact on the ecosystem and human health. This study examines the relationships between meteorological factors and air pollutants in different landscapes (agricultural, park, desert and industrial) of Karbala, Iraq, using data from January 2021 to June 2024. Principal component analysis (PCA) revealed that photochemical smog (45-46%), particulate matter (20-22%), and meteorological effects on particulates (14-16%) are the main factors influencing air pollution. PM2.5 was the dominant pollutant, impacting air quality on 84-88% of days, followed by ozone on 12-16%. Winter showed the best air quality, while summer had no "Good" days. Among the four areas studied, the desert suburb had the cleanest air, and the industrial area the most polluted. These findings offer crucial insights for air quality management in the region. [ABSTRACT FROM AUTHOR]

This study innovatively employs drones equipped with air quality sensors to collect three-dimensional air quality data in a campus microenvironment. Data are accurately corrected using a BP neural network, and a cubic model is constructed using three-dimensional interpolation. Combining photogrammetry technology, this study analyzes air quality patterns, finding significant differences from macro trends. Construction activities and large electronic experimental equipment significantly increase PM2.5 levels in the air. In rainy weather, the respiration of vegetation is enhanced, leading to higher CO2 concentrations, while water bodies exhibit higher temperatures in rainy weather due to their high specific heat capacity. This research not only provides a new perspective for microenvironment air quality monitoring but also offers a scientific basis for future air quality monitoring and management. [ABSTRACT FROM AUTHOR]

There are significant challenges to indoor air quality (IAQ) in indoor ice arenas due to fossil fuel-powered equipment. This research explores the air quality management practices in Ontario’s ice arenas, focusing on pollutants like carbon monoxide (CO) and nitrogen dioxide (NO2), which pose health risks to arena workers and the public. A survey was conducted among directors/managers/operators of ice arenas in Ontario, revealing key concerns such as aging ventilation systems, inadequate monitoring, and budget constraints. The findings highlight that while many arenas are transitioning to electric equipment and implementing regular maintenance, significant gaps remain in IAQ monitoring, particularly for NO2. This research advocates for enhanced training, consistent inspection practices, and better resource allocation to ensure the safety of arena users. [ABSTRACT FROM AUTHOR]

Analyzing the fluctuations of particulate matter (PM) concentrations and their scaling correlation structures are useful for air quality management. Multifractal characterization of PM2.5 and PM10 of three cities in India wase considered using the detrended fluctuation procedure from 2018 to 2021. The cross-correlation of PM concentration in a multifractal viewpoint using the multifractal cross-correlation analysis (MFCCA) framework is proposed in this study. It was observed that PM2.5 was more multifractal and complex than PM10 at all the locations. The PM–gaseous pollutant (GP) and PM–meteorological variable (MV) correlations across the scales were found to be weak to moderate in different cities. There was no definite pattern in the correlation of PM with different meteorological and gaseous pollutants variables. The nature of correlation in the pairwise associations was found to be of diverse and mixed nature across the time scales and locations. All the time series exhibited multifractality when analyzed pairwise using multifractal cross-correlation analysis. However, there was a reduction in multifractality in individual cases during PM–GP and PM–MV paired analyses. The insights gained into the scaling behavior and cross-correlation structure from this study are valuable for developing prediction models for PMs by integrating them with machine learning techniques. [ABSTRACT FROM AUTHOR]

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Roadside vegetation helps to retain air pollutants emitted by road traffic. On the other hand, its presence makes it difficult to ventilate street canyons. The paper examines the influence of vegetation on the dispersion of air pollution generated by road traffic, using the example of two street canyons—both-sided and one-sided street canyons. The study was conducted taking into account the actual emission conditions occurring on the analyzed road sections estimated using the HBEFA methodology. Subsequently, a three-dimensional pollution dispersion model named MISKAM was employed to simulate the air pollutant dispersion conditions in the analyzed street canyons. The modelling results were compared with the measurement data from air quality monitoring stations located in these canyons. The obtained results indicated that the presence of vegetation can significantly impact on the air dispersion of traffic-related exhaust and non-exhaust emissions. The impact of vegetation is more pronounced in the case of a street canyon with dense, high-rise development on both sides than in the case of a street canyon with such development on only one side. The results for the both-sided street canyon demonstrate that the discrepancy between the scenario devoid of vegetation and the scenario with vegetation was approximately 5 µg/m3 (10%) for PM10 and approximately 54 µg/m3 (45%) for NOx, with the former scenario showing lower values than the latter. Nevertheless, the scenario with the vegetation exhibited a lesser discrepancy with the air quality measurements. Vegetation functions as a natural barrier, reducing wind speed in the street canyon, which in turn limits the spread of pollutants in the air, leading to pollutant accumulation near the building walls that form the canyon. Consequently, atmospheric dispersion modelling must consider the presence of vegetation to accurately evaluate the effects of road traffic emissions on air quality in urban areas, particularly in street canyons. The results of this study may hold importance for urban planning and decision-making regarding environmental management in cities aimed at improving air quality and public health. [ABSTRACT FROM AUTHOR]

Air pollution has serious and long-standing negative effects on human health. The primary focus of research on air pollution in the United States since the enactment of the Clean Air [...]

Purpose: This study aims to assess the status of sustainable performance, given the significance of indoor air quality related to health and the environment. This research focus on the current status of indoor air quality management in these universities and simplifies its relevance and criticality in safeguarding the well-being of the academic community and the environment. Design/methodology/approach: This study used the Unit-based Sustainability Assessment Tool (USAT) as a comprehensive instrument to assess sustainability performance across various modules: Operations Management, Research, Education and Social/Community. In-depth interviews were conducted across the 11 public universities in Thailand to gain a comprehensive understanding of the current practices, challenges and initiatives related to indoor air quality and sustainability management. The approach provides a foundation for future research to identify causal factors and potential solutions for the observed indoor air quality management gaps. Findings: The research outcomes underscore the outstanding efforts in academic research, with many institutions showcasing advanced measures and a solid dedication to mitigating air pollution. However, there is a noticeable disparity in the practical management of indoor air quality, with many universities presenting unsatisfactory conditions for professors, academic staff and students. Practical implications: Recognizing that proficient indoor air quality (IAQ) management strengthens the practical and scholarly intersection, this document highlights a crucial alignment with the Sustainable Development Goals (SDGs) and health implications. It advocates for carefully implementing pragmatic IAQ strategies within academic institutions, guiding the pathway towards sustainable, health-conscious environments. Social implications: This research addressed indoor air quality (IAQ) within universities. The document intertwines health implications and Sustainable Development Goals (SDGs), revealing a significant gap between academic research and practical IAQ management. While universities are committed to sustainability and community engagement, inconsistencies in IAQ management practices impact staff and student well-being and productivity. This exploration underscores the universal applicability of IAQ management strategies, driving educational institutions towards cultivating healthier, sustainable indoor environments globally. Originality/value: This research introduces a new approach integrating air quality assessment and sustainability management in Thai universities. It aims to bridge the gap between environmental health and education. The Unit-based Sustainability Assessment Tool is developed as a part of this research, which provides new insights into improving indoor environments. This tool is fundamental for health and learning. The significance of this research lies in guiding policy and campus management towards sustainable, health-promoting practices, thereby adding value to the discourse on educational sustainability. This work can pave the way for enhanced well-being in academic settings, marking a significant step forward in sustainable educational practices. [ABSTRACT FROM AUTHOR]

Air quality management commonly aims to mitigate nitrogen oxide (NOx) emissions from combustion, reducing ozone (O3) and particulate matter (PM) pollution. Despite such ongoing efforts, regulations have recently proven ineffective in rural areas like the Salton Sea Air Basin of Southern California, which routinely violates O3 and PM air quality standards. With over $2 billion in annual agricultural sales and low population density, air quality in the region is likely influenced by the year-round farming activity. We conducted a source apportionment of NOx (an important precursor to both O3 and PM) using nitrogen stable isotopes of ambient NO2, which revealed a significant contribution from soil-emitted NOx to the regional budget. The soil source strength was estimated based on the mean δ15N-NOx from each emission category in the California Air Resources Board's NOx inventory. Our annual average soil emission estimate for the air basin was 11.4 ± 4 tons/d, representing ~ 30% of the extant NOx inventory, 10× larger than the state's inventory for soil emissions. Unconstrained environmental factors such as nutrient availability, soil moisture, and temperature have a first-order impact on soil NOx production in this agriculturally intensive region, with fertilization and irrigation practices likely driving most of the emissions variability. Without spatially and temporally accurate data on fertilizer application rates and irrigation schedules, it is difficult to determine the direct impacts that these variations have on our observations. Nevertheless, comparative analysis with previous studies indicates that soil NOx emissions in the Imperial Valley are likely underrepresented in current inventories, highlighting the need for more detailed and localized observational data to constrain the sizeable and variable emissions from these arid, agricultural soils. [ABSTRACT FROM AUTHOR]

Chemical transport models (CTM) have wide and profound applications in air quality simulations and managements. However, its applications are often constrained by high computational burdens. In this study, we developed a neural network based CTM model (FastCTM) to efficiently simulate ten air pollutant composition variables, including major PM2.5 species of SO42-, NO3-, NH4+, organic matters and other inorganic components, coarse part of PM10, SO2, NO2, CO and O3. The FastCTM has a principle-informed structure by explicitly encoding atmospheric physical and chemical processes in a basic simulator. Specifically, in the simulator, five neural network modules are proposed to respectively represent five major atmospheric processes of primary emissions, transport, diffusion, chemical reactions and depositions. Given 1-hour initial condition data, the FastCTM is able to simulate future 24-hour concentrations of the ten air pollutants with corresponding meteorology fields and emissions as input. The FastCTM is trained with operational forecast data from a numerical CTM model named Community Multiscale Air Quality (CMAQ) in 2018–2022. The well-trained FastCTM is evaluated comparing to the long-term CMAQ forecast in an independent year 2023, and achieves high agreements with mean RMSE values of 9.1, 11.9, 4.4, 4.0, 48.9 and 10.9 μg/m3 and R2 values of 0.8, 0.81, 0.8, 0.83, 0.9 and 0.7 for PM2.5, PM10, SO2, NO2, CO, and O3. Besides, assessed against hourly site observations of six criteria pollutants, the RMSE values of FastCTM have small relative differences of 4.3 %, 4.2 %, -2.8 %, -1.7 %, -0.3 % and -3.2 % compared to that of CMAQ. The FastCTM model also exhibited reasonable responses of air quality to meteorological variables of air temperature, wind speed and planetary boundary layer height, as well as to input pollutant emissions. Furthermore, due to the principles-oriented structure, internal process analysis could be performed by FastCTM to quantify the specific contribution from each of the five processes for hourly air pollutant concentration changes. In a nutshell, FastCTM has multi-functional advantages in air pollutant concentration simulations, sensitivity analysis and internal process analysis with high computation efficiencies on GPU and accuracy. [ABSTRACT FROM AUTHOR]

Air pollution, particularly fine particulate matter (PM), poses significant health risks to vulnerable populations such as children, older adults, and individuals with chronic illnesses. Understanding the spatial distribution of these populations and their access to air quality information is crucial for effective interventions. In urban areas like Gangseo District, the distribution of essential facilities and accessibility varies greatly. While studies have highlighted the health impacts of PM, research on optimizing air quality monitoring for at-risk groups remains limited. This study aims to identify optimal locations for air quality monitoring by analyzing the spatial distribution of vulnerable populations and facility accessibility. Using Geographic Information Systems (GIS) and isochrone maps, we identified areas with high concentrations of vulnerable groups and poor access to healthcare facilities. Our findings revealed significant disparities in access to air quality information, with some high-risk areas underserved by current monitoring systems. This study integrated demographic data and spatial analysis to propose strategic monitoring placements. The methodology can be applied to other urban settings and offers a framework for improving air quality management. This study underscores the importance of targeted air quality monitoring to protect vulnerable populations and suggests practical steps for policymakers to enhance public health. [ABSTRACT FROM AUTHOR]

Air quality management is crucial in the Philippines due to its profound implications for public health, ecosystems, and sustainable development. Project AiRMoVE, short for the Ambient Air Remote Sensing, Modeling, and Visualization Environment project, aims to enhance air quality management through engineering solutions and geospatial technologies such as remote sensing (RS), geographic information systems (GIS) and numerical modeling. Implemented by the University of the Philippines Training Center for Applied Geodesy and Photogrammetry (UP TCAGP), Project AiRMoVE collaborates with key stakeholders to identify attainment and non-attainment areas in the National Capital Region (NCR) for air quality monitoring. This paper provides an overview of Project AiRMoVE's objectives, methodologies, and outcomes, emphasizing the significance of RS and GIS in revolutionizing air quality monitoring and management. It underscores a systematic approach adopted in enhancing government capacity for air quality management -- a combination of workshops, technical trainings, and stakeholders' forums. [ABSTRACT FROM AUTHOR]

Given the increasing importance of effectively identifying synergistic changes between PM2.5 and O3 and comprehensively analyzing their impact on air quality management in China, we employ the Sen+Mann–Kendall (Sen+M-K) trend test in this study to examine the temporal and spatial variation trends of PM2.5 and O3 in the Yangtze River Delta (YRD), from 2003 to 2020. We identified the regions where these pollutants exhibited synergistic changes and established the pathways between the pollutants and their potential drivers, using geographically weighted random forest algorithms and structural equation modeling. The study results revealed as follows: (1) Overall, the PM2.5 concentrations show a decreasing trend, while the O3 concentrations exhibit an increasing trend, in the YRD. Analysis of the combined trends indicates that approximately 95% of the area displays opposing trends for PM2.5 and O3, with only about 4% in the southern region showing synergistic trends for both pollutants. (2) Drought and the average temperature are the main drivers of the changes in PM2.5 and O3 concentrations in areas experiencing synergistic changes. Their combined effects alleviate the aggregation of PM2.5 and reduce the formation of VOCs, indirectly reducing the generation of pollutants. The negative effect of the average temperature on the O3 concentration may indicate the existence of nonlinear effects and complex interaction effects between the drivers. NOx and VOCs play important dual roles in the generation and conversion of pollutants, although their overall impact is smaller than meteorological factors. They produce significant indirect effects through their interaction with meteorological and other human factors, further affecting the concentrations of PM2.5 and O3. In areas without coordinated changes, the main impact of meteorological factors remains unchanged, and the relationship between the two anthropogenic emission sources and their effects on PM2.5 and O3 are complex, with different directions and levels involved. This study provides detailed insights into the drivers of air quality changes in the YRD and offers a scientific basis for environmental management authorities to develop more comprehensive and targeted strategies for balancing the control of PM2.5 and O3 pollution. [ABSTRACT FROM AUTHOR]

The article discusses the significant environmental health challenges faced by the WHO Eastern Mediterranean Region (EMR), with air pollution alone causing more than 560,000 premature deaths annually. The region's air quality is severely impacted by natural and anthropogenic sources, leading to high levels of particulate matter and nitrogen dioxide. Despite some countries having national ambient air quality standards, there has been a lack of significant reduction in air pollution levels, indicating inadequate air quality management strategies. The health sector is urged to take strategic actions to advocate for reducing air pollution, address root causes of disease, build capacities, and enhance surveillance systems to mitigate the impact of air pollution on public health in the region. [Extracted from the article]

In developing countries, vehicle emissions are a major source of atmospheric pollution, worsened by aging vehicle fleets and less stringent emissions regulations. This results in elevated levels of particulate matter, contributing to the degradation of urban air quality and increasing concerns over the broader effects of atmospheric emissions on human health. This study proposes a Hybrid Explainable Boosting Machine (EBM) framework, optimized using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), to predict vehicle-related PM2.5 concentrations and analyze contributing factors. Air quality data were collected from Open-Seneca sensors installed along the Nairobi Expressway, alongside meteorological and traffic data. The CMA-ES-tuned EBM model achieved a Mean Absolute Error (MAE) of 2.033 and an R2 of 0.843, outperforming other models. A key strength of the EBM is its interpretability, revealing that the location was the most critical factor influencing PM2.5 concentrations, followed by humidity and temperature. Elevated PM2.5 levels were observed near the Westlands roundabout, and medium to high humidity correlated with higher PM2.5 levels. Furthermore, the interaction between humidity and traffic volume played a significant role in determining PM2.5 concentrations. By combining CMA-ES for hyperparameter optimization and EBM for prediction and interpretation, this study provides both high predictive accuracy and valuable insights into the environmental drivers of urban air pollution, providing practical guidance for air quality management. [ABSTRACT FROM AUTHOR]

In response to the COVID-19 pandemic, the Iranian government swiftly implemented immediate and decisive measures to control the spread of the infection. This study aims to demonstrate the impact of restriction measure on air pollution, also to highlight the potential variability in results that can arias from different methodological approach. A comprehensive dual-approach assessment was conducted to evaluate the effect of the lockdown measures on criteria air pollutants. Firstly, a traditional approach compared air quality during the pandemic period with baseline conditions from 2013 to 2019. Secondly, observed air pollution values during different periods with varying restrictions in 2020 were compared with expected values. This comprehensive analysis allows for a robust comparison and quantification of the impact of different lockdown measures in Ahvaz. The study revealed significant changes in air pollutant concentrations in Ahvaz during 2020, with variations observed across different pollutants. Notable reductions were observed in O3 levels, particularly in November (-54.44% compared to the baseline) and December (-63.58% compared to expected values). Decreases in CO levels were observed in multiple months, while substantial reductions in PM10 and PM2.5 were observed during various periods. Inconsistencies in the magnitudes and directions of changes were found when comparing baseline and forecasted values. The overall stringency index showed an inverse association with changes in O3, NO2, and CO, with international travel controls and restrictions on internal movement having significant impacts. This study provides valuable insights into the impact of COVID-19 lockdown measures on air pollution in Ahvaz, Iran, using a comprehensive dual-approach assessment. The findings highlight the effectiveness of these measures in reducing specific criteria air pollutants and emphasize the importance of implementing appropriate strategies for air quality management during similar public health emergencies. [ABSTRACT FROM AUTHOR]

This study investigated the anomalous seasonal variations in particulate matter (PM) concentrations—specifically PM2.5 and PM10—in Padang City, Indonesia, situated within the Equatorial climate zone. A one-year dataset of half-hourly PM measurements from January to December 2023, collected by the Air Quality Monitoring System (AQMS) managed by the Environmental Agency of West Sumatra (DLH), was utilized. Maps of hotspots and air mass backward trajectories were used to identify possible transboundary emissions affecting Padang City. Despite the region experiencing nearly continuous rainfall, significant elevations in PM levels were observed during the typically drier months of August to October. Specifically, PM2.5 levels peaked at 36.57 µg/m3 and PM10 at 39.58 µg/m3 in October, significantly higher than in other months and indicating a substantial deviation from the typical expectations for equatorial climates. These results suggest that the high PM concentrations are not solely due to local urban emissions or normal seasonal variations but are also significantly influenced by transboundary smoke from peatland fires and agricultural burning in neighboring provinces such as Bengkulu, Riau, Jambi, and South Sumatra. Backward trajectory analysis further confirmed the substantial impact of regional activities on degradation of air quality in Padang City. The study underscores the need for integrated air quality management that includes both local and transboundary pollution sources. Enhanced monitoring, public engagement, and inter-regional collaboration are emphasized as crucial strategies for mitigating the adverse effects of PM pollution in equatorial regions like Padang City. [ABSTRACT FROM AUTHOR]

This study examines the validation and spatial–temporal variability of simulated PM10 and PM2.5 concentrations over Ahmedabad city using the WRF-Chem model with EDGAR global emissions and locally developed PM emissions as inputs. The validation process involves comparing simulated PM10, PM2.5, and meteorological parameters with in-situ measurements from stations at Pirana and S P Stadium during 16th-17th May and 16th-17th December 2018. The analysis focuses on six-hourly averaged data, highlighting the significant role of Wind Speed, Wind Direction, and Planetary Boundary Layer Height (PBLH) in the dispersion of air pollutants. The results show that the model using locally developed PM emissions significantly reduces bias and provides better spatial distribution patterns compared to EDGAR emissions. For instance, in May, the average simulated PM10 using EDGAR emissions was 85 µg/m3, while the locally developed emissions yielded an average of 183 µg/m3, closer to the observed in-situ average of 178 µg/m3. Similarly, in December, the simulated PM10 using EDGAR emissions was 97 µg/m3 compared to 232 µg/m3 from local emissions, with an in-situ average of 147 µg/m3. The spatial analysis reveals that during May, 52% of the western areas experienced 'Good' air quality in the morning, decreasing to 40% by the evening. In contrast, December showed more severe pollution, with 45% of the North–North Western city experiencing 'Moderately Polluted' air quality by evening. The correlation coefficients (R2) for PM10 validation at Pirana and S P Stadium were 0.73 and 0.93 respectively in May using EDGAR emissions, improving to 0.77 and 0.91 with local emissions. This study underscores the need for improved emission inventories and mitigation strategies to enhance air quality in Ahmedabad. The significant seasonal variations and the impact of meteorological parameters on pollutant dispersion highlight the importance of localized data and targeted interventions for effective air quality management. [ABSTRACT FROM AUTHOR]

This study examines the impact of air pollution on Jordan's cultural heritage sites, focusing on key pollutants (SO2, HNO3, O3, PM10) and climate conditions. Using 2019 data and future projections for 2040–2059 and 2080–2099, the research reveals significant material corrosion in urban areas like Amman and Irbid, driven by pollutants such as SO₂ and PM10. Random Forest Analysis identifies these pollutants as primary contributors to material degradation. Future scenarios indicate corrosion rates exceeding safe limits across Jordan, underscoring the need for proactive conservation strategies. This work highlights the critical role of air quality management in protecting cultural heritage, especially under climate change pressures, and provides guidance for national policy development. [ABSTRACT FROM AUTHOR]

Accurate and fine‐scaled prediction of ozone concentrations across space and time, as well as the assessment of associated human risks, is crucial for protecting public health and promoting environmental conservation. This paper introduces NetGBM, an innovative machine‐learning model designed to comprehensively model ozone levels across China's diverse topography and analyze the spatiotemporal distribution of ozone and exposure. Our model focuses on daily, weekly, and monthly predictions, achieving commendable R2 ${\mathrm{R}}^{2}$ coefficients of 0.83, 0.77, and 0.79, respectively. By constructing a gridded map of ozone and incorporating both land use and meteorological features into each grid, we achieved ozone prediction at a high spatiotemporal resolution, outperforming previous research in terms of performance and scale, particularly in regions with limited monitoring stations. The results can be further improved when applied to regional research using meteorological and ozone data from regional stations. Additionally, our research revealed that temperature is the most significant factor affecting ozone concentrations across China. In health risk assessment, we retrieved a high‐resolution spatial distribution of ozone‐attributed mortality for 5‐COD and daily ozone inhalation distributions during our study period. We concluded that ozone‐attributed mortality is predominantly caused by stroke and IHD, accounting for more than 70% of the total deaths in 2021, with the highest mortality rates in developed urban areas such as the NCP and the YRD. Our experiment demonstrated the potential of NetGBM in robustly modeling ozone across China with high spatiotemporal resolution and its applicability in measuring associated health risks. Plain Language Summary: This study introduces NetGBM, an innovative machine‐learning model designed to forecast ozone levels and assess ozone‐attributed public health risks. This model is crucial for post‐pandemic air quality management, providing high‐resolution predictions that are essential for targeted health interventions and informed environmental policies. By integrating feature engineering with predictive analytics, NetGBM enhances its performance, particularly in regions with limited monitoring. This makes it a robust tool for developing sustainable environmental strategies. Key Points: We introduce NetGBM, a machine‐learning model for high spatiotemporal resolution ozone prediction across ChinaWe perform ozone inhalation and GEMM analysis, which identifies regions facing elevated health risks due to high ozone exposureThe NetGBM model has superior performance compared to prior research and performs robustly in regions with limited monitoring resources [ABSTRACT FROM AUTHOR]