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This study investigated the sources and distribution characteristics of PM2.5 and its chemical components (ions, carbons, elements) at five locations within the Banwal and Sihwa National Industrial Complexes in Ansan and Siheung. These large-scale industrial clusters, comprising 7642 businesses across sectors such as petrochemicals, steel, machinery, and electronics, operate throughout the year. From 2020 to 2023, the average PM2.5 concentration in the study area was 28.66 ± 16.72 μg/m3, with notable seasonal differences observed across the five measurement points. Ionic components were the primary contributors to PM2.5, while carbon and trace element concentrations fluctuated with the seasons. The coefficient of divergence (COD) analysis indicated that emission source differences between sites were insignificant, with COD values consistently below the threshold of 0.3. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) identified secondary aerosols and vehicle emissions as the main sources of PM2.5, alongside additional contributions from Asian dust, industrial emissions, road dust, coal combustion, metal processing, biomass burning, and soil dust. These results highlight the need for systematic and economical air pollution control strategies in complex industrial areas, using COD to identify source differences and quantify contributions at different sites. [ABSTRACT FROM AUTHOR]

Air pollution is defined as an adverse event that negatively affects ecosystems and standard conditions necessary for human survival and progress, manifested by certain substances in the atmosphere exceeding specific concentration levels. The control of air pollution is a significant strategic task related to the national economies and the well-being of the people. In the face of increasingly severe environmental pollution problems, accurately predicting air pollution indicators becomes crucial. Among the popular air pollution prediction methods, the K-nearest neighbors (KNN) appears to be one of most promising approaches. In this paper, we develop a novel KNN rule that combines the ridge estimators called KNN-ridge regression (KNN-RR). The proposed KNN-RR is motivated by the sensitivity problem that multi-collinearity exists in the current KNN regression, aiming to enhance the prediction performance. Our theoretical result shows that under some mild assumptions, there exists a penalty parameter such that the mean square prediction error of ridge regression is smaller than that of ordinary least square regression. We examine the empirical performances of KNN-RR and other methods on real-world datasets, such as the AQI and PM2.5 prediction, and the results indicate that our method has some advantages in improving prediction accuracy. To a certain extent, this paper paves a new way to improve some supervised machine learning methods. [ABSTRACT FROM AUTHOR]

One of the Sustainable Development Goals is to enable cities to thrive, improve resource efficiency, and reduce pollution. However, balancing economic development with pollution control and fostering green growth remains a major challenge for developing countries. This study, using panel data from 284 prefecture-level and above cities in China from 2006 to 2020, evaluates urban green economy efficiency (GEE). Then, adopting implementation of "12th Five-Year Plan for Key Regional Air Pollution Prevention and Control" in China as a quasi-natural experiment, this study investigates the impact of air pollution prevention and control (APPC) on urban GEE. Findings reveal an overall upward trajectory in urban GEE across China, marked by discernible regional differentiations following implementation of national Plan. Considering the "hysteresis effect" and spatially "pollution halo effect", APPC notably improves urban GEE, particularly within cities of eastern zone, high-population density cities, large-sized cities, non resource-based cities and major-APPC cities. Importantly, APPC acts as a catalyst for both enhancing green innovation quality and optimizing industrial structures, ultimately fostering urban GEE in China. These findings are significant and also provide a valuable supplement to the existing study. [ABSTRACT FROM AUTHOR]

Environmentally persistent free radicals (EPFRs) and polycyclic aromatic hydrocarbons (PAHs) are persistent pollutants in atmospheric particulate matter that are detrimental to human health. This study collected atmospheric particulate matter during and after the spring festival travel season in Tainan, Taiwan, from various locations and analyzed the carbon composition and PAH isomeric ratios to identify the sources. In this study, EPFR concentrations were measured using electron paramagnetic resonance spectroscopy, with the highest concentration found to be 3.04 × 10(12) spins/m3. EPFRs contained predominantly oxygen-centered radicals in PM2.5, which are mainly existed in PM1. The results show that EPFR concentrations on PM, measured per unit volume (spins/m3) or mass (spins/g), were highest during the spring festival travel season. The daily inhalation exposure to the sum of EPFRs and PAHs in PM2.5 was estimated to be equivalent to inhaling 0.11–0.15 cigarette tar EPFRs per day. This report is the first to document EPFRs in environmental atmospheric particulate matters in Taiwan, which has significantly contributed to local air pollution control and reduced exposure risks to public health in Tainan. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) emissions from anthropogenic sources contribute substantially to air pollution. The unequal adverse health effects caused by source-emitted PM emphasize the need to consider the discrepancy of PM-bound chemicals rather than solely focusing on the mass concentration of PM when making air pollution control strategies. Here, we present a dataset about chemical compositions of real-world PM emissions from typical anthropogenic sources in China, including industrial (power, industrial boiler, iron & steel, cement, and other industrial process), residential (coal/biomass burning, and cooking), and transportation sectors (on-road vehicle, ship, and non-exhaust emission). The data was obtained under the same strict quality control condition on field measurements and chemical analysis, minimizing the uncertainty caused by different study approaches. The concentrations of PM-bound chemical components, including toxic elements and PAHs, exhibit substantial discrepancies among different emission sectors. This dataset provides experimental data with informative inputs to emission inventories, air quality simulation models, and health risk estimation. The obtained results can gain insight into understanding on source-specific PMs and tailoring effective control strategies. [ABSTRACT FROM AUTHOR]

This research study investigates hourly data on concentrations of five major air pollutants such as particulate matter (PM10, PM2.5) and gaseous pollutants (SO2, NO2, CO) measured during 2022 at four hotspot sites (industrial site, traffic site, commercial site, harbour, and one residential site) in Chennai, India. The analysis encompasses temporal variations spanning annual, seasonal, and diurnal variations in the pollutants. Notably, PM10 and CO emerge as the predominant pollutants, with the highest concentrations at industrial and traffic sites (PM10: 67.64 ± 40.77 µg/m3, CO: 1.41 ± 0.84 mg/m3; traffic site: PM10: 58.67 ± 20.05 µg/m3, CO: 0.99 ± 0.57 mg/m3). Seasonal dynamics reveal prominent winter spikes in particulate matter (PM10, PM2.5) and carbon monoxide (CO) concentrations, while nitrogen dioxide (NO2) and sulphur dioxide (SO2) levels peak during the summer season, particularly in the harbour area. The proximity to roadways exerts a discernible influence on diurnal patterns, with traffic sites showcasing broader rush hour peaks compared to sharper spikes observed at other sites. Furthermore, distinct bimodal patterns are evident for PM10 and PM2.5 concentrations in residential and harbour areas. A common lognormal distribution pattern is identified across the studied sites, suggesting consistent air quality trends despite contrasting locations. The conditional probability function (CPF) is used in conjunction with local meteorological conditions for identifying key pollution sources in each location. The implementation of polar plots emphasizes industries as principal local sources of pollution, at industrial sites significantly contributing to PM10, SO2, and NO2 concentrations under specific wind conditions. The main objective of the present study is to facilitate a good understanding of pollutant dynamics, pollution sources, and their intricate interplay with meteorological factors, thereby contributing to the formulation and implementation of effective air pollution control and mitigation strategies. [ABSTRACT FROM AUTHOR]

In last few years, air pollution has caused many issues in China. With the flourishing of education, the relationship between advanced human capital structure (AHCS) and air pollution deserves thoughtful consideration. Therefore, this article uses a dynamic panel model to analyze the effect of AHCS on air pollution with data from 31 Chinese provinces from 2014 to 2020. Considering the potential endogenous problem of AHCS affecting air pollution, the system generalized method of moments method is employed to overcome endogenous bias. Empirical results reveal that, in China, AHCS significantly worsens air pollution. By comparing the dynamic panel model with the traditional static panel model, traditional mean regression was found to overestimate the impact of AHCS on air pollution. Then we explored the mechanism by which AHCS affects air pollution and found that industry structure, urbanization, per capita disposable income, and industry cluster mediate the effect of AHCS on air pollution. Finally, some policy implications are proposed to improve AHCS and air pollution control. [ABSTRACT FROM AUTHOR]

This study investigates the projected impact of air pollution on mortality and Disability-Adjusted Life Years (DALYs) across SAARC countries. Utilizing Time Series and Machine Learning methodologies such as Autoregressive Integrated Moving Average, Exponential Smoothing, and Neural Network, the research aims to accurately forecast the mortality and DALYs attributed to air pollution from 2020 to 2030. Statistical analyses reveal a consistent upward trend in deaths and DALYs during the forecasting period, primarily driven by Ambient Particulate Matter Pollution (APM) and Ambient Ozone Pollution (AOP). Comparing the predictive accuracy of the models, Neural Network outperformed other methods, as indicated by Root Mean Square Error (RMSE) values. Specifically, the study finds that deaths and DALYs due to Ambient Particulate Matter pollution are least prevalent in the Maldives, while India and Pakistan exhibit the highest rates, and deaths and DALYs due to Ambient Ozone pollution are lowest in the Maldives and highest in Bangladesh and Pakistan. Moreover, deaths and DALYs attributed to Household Air Pollution (HAP) are lowest in Pakistan and highest in Nepal. These findings underscore the urgent need for air pollution control measures and informed policymaking in SAARC countries to mitigate the escalating health burden associated with air pollution. [ABSTRACT FROM AUTHOR]

Amid escalating tension between environmental conservation and economic development, the imperative to enhance air quality has become increasingly urgent. This study elucidates a sophisticated approach for the assessment and remediation of air pollution issues through the integration of an enhanced particle swarm optimization algorithm and a differential gravitational fireworks algorithm-optimized support vector machine (SVM). In the initial phase of this research, a series of intricate data preprocessing and augmentation procedures were conducted, and the differential evolution algorithm played a pivotal role. The differential gravitational fireworks algorithm was subsequently introduced to optimize the SVM parameter settings, thereby bolstering classification accuracy and mitigating issues such as overfitting. Through rigorous and meticulous empirical testing, the augmented SVM model demonstrated notable performance in terms of classification accuracy and sequential and nonsequential data fusion, surpassing conventional SVM techniques. Notably, our sequential fusion method achieved an accuracy of up to 91%, at least 3% higher than that of nonsequential techniques. In conclusion, this study reveals an innovative and enhanced technological approach that is highly effective for the precise measurement and control of air pollution levels. [ABSTRACT FROM AUTHOR]

The study used the AirQ + software developed by the World Health Organization (WHO) to evaluate the health impacts associated with long-term exposure to PM2.5 in Mashhad, Iran. For this purpose, we analyzed the daily average concentrations of PM2.5 (with a diameter of 2.5 micrometers or less) registered by the air quality monitoring stations from 2016 to 2021. The levels of PM2.5 surpassed the Air Quality Guidelines (AQG) limit value of 5 µg/m3 (annual value) established by WHO. The findings revealed that the burden of mortality (from all-natural causes) at people above 30 years old associated with PM2.5 exposures was 2093 [95% confidence interval [CI]: 1627–2314] deaths in 2016 and 2750 [95% CI: 2139–3038] deaths in 2021. In general, the attributable mortality from specific causes of deaths (e.g., COPD (chronic obstructive pulmonary diseases), IHD (ischemic heart diseases) and stroke) in people above 25 years old increased between the years, but the mortality from lung cancer was stable at 46 [95% CI: 33–59] deaths in 2016 and 48 [95% CI: 34–61] deaths in 2021. The attributable mortality from ALRI (Acute Lower Respiratory Infection) in children below 5 years old increased between the years. We also found differences in mortality cases from IHD and stroke among the age groups and between the years 2016 and 2021. It was concluded that burden of disease methodologies are suitable tools for regional and national policymakers, who must take decisions to prevent and to control air pollution and to analyze the cost-effectiveness of interventions. [ABSTRACT FROM AUTHOR]

The escalation of PM2.5 pollution in the Zhengzhou Metropolitan Area (ZMA) underscores the pressing need for air pollution mitigation measures. We utilized high-resolution PM2.5 data from 2020, with a 1 km spatial resolution, to identify and comprehend the factors influencing PM2.5 concentration across the urban–rural continuum. This extensive dataset, inclusive of terrain, meteorological data, vegetation cover, population density, GDP, nighttime light data, and land use categories, facilitated our analysis of PM2.5 trends across the urban–rural spectrum in nine ZMA cities. Our results demonstrate that there is no consistent correlation between city size and classification with PM2.5 pollution levels. However, urban and suburban regions demonstrated higher pollution levels compared to rural areas. The PM2.5 concentrations exhibited considerable variance along the urban–rural continuum. Spring and summer exhibited rising concentrations along the continuum, while autumn witnessed a decrease. Spatially, the PM2.5 concentration demonstrated higher trends in the eastern and southern regions compared to the western and northern areas, indicating distinctive urban–rural gradient patterns. The influencing factors displayed a scale effect: metropolitan areas showed a stronger correlation with natural elements such as elevation and wind speed; suburban regions correlated with meteorological factors; urban areas were notably impacted by socio-economic factors. Effective collaboration among cities for emission reduction and pollution control is crucial, irrespective of meteorological conditions. [ABSTRACT FROM AUTHOR]

Establishing a reasonable cost-sharing and compensation mechanism for air pollution control is a prerequisite for realizing inter-regional cooperative treatment. Taking inter-provincial sulfur dioxide (SO2) emissions in China from 2005 to 2019 as the research object, this paper proposes a data-driven approach to establish a cost-sharing index system of regional SO2 treatment in four dimensions and construct a cost-sharing and compensation scheme using the entropy-TOPSIS method. The results revealed that there are significant spatial and temporal differences in the treatment cost of SO2 emission, and the total SO2 treatment costs at the national level increased first and then decreased during the study period, meanwhile, the regional SO2 treatment costs are much higher in the less economically developed regions such as the central and western regions than in economically developed eastern coastal regions. The design of the cost-sharing and compensation mechanism of SO2 treatment should consider the regional differences in abatement capacity, abatement potential, abatement responsibility, and development demands. The economically developed regions should share higher treatment costs according to their historical cumulative abatement responsibilities, and provide economic compensation and technical support to the less economically developed regions. Specifically, the marginal abatement cost in the more economically developed eastern region is much higher than that in the less economically developed central and western areas due to their large abatement responsibility and strong reduction capacity but insufficient abatement potential, so the eastern regions can transfer part of their abatement responsibility to the central and western regions using economic compensation. Reasonable cost sharing and horizontal compensation can help promote regional cooperation and synergistic management in air pollution abatement. Finally, corresponding policy recommendations are given to provide a decision basis for cross-regional cooperation in air pollution control. [ABSTRACT FROM AUTHOR]

This study presents a GIS-based on-road vehicular emission inventory from the tailpipe, brake wear, tire wear and road dust resuspension due to vehicles plying on the road in the city of Lucknow. From the tailpipe, carbon monoxide (CO), sulphur dioxide (SO2), Particulate Matter (PM), oxides of Nitrogen (NOx), 1,3 Butadiene, Total aldehyde and Total PAH emissions were estimated. From the non-exhaust (brake wear and tire wear) and road dust resuspension PM10, PM2.5, and heavy metals were estimated. PM, SO2, NOx, and CO, emissions from the tailpipe are estimated to be 5.8, 0.2, 58, and 141 tons/day respectively. 1,3 Butadiene, Formaldehyde, Acetaldehyde, Total aldehyde, and Total PAH emission are estimated to be 575, 853, 113, 1468, and 57,542 gm/day respectively. PM10 and PM2.5 emission from road dust resuspension in Lucknow city was estimated to be 155 tons/day and 37 tons/day respectively. The results show that PM10 emission from road dust resuspension is 27 times higher as compared to tailpipe emission. This indicates inadequate road conditions or high silt loads in the city of Lucknow. Priority should be given to reducing silt load on roads while developing a PM mitigation plan or policy for air pollution control. [ABSTRACT FROM AUTHOR]

Odor pollution is the biggest source of complaints from citizens concerning environmental issues after noise. Often, the need for corrective actions is evaluated through simulations performed with atmospheric dispersion models. To save resources, air pollution control institutions perform a first-level odor impact assessment, for screening purposes. This is often based on Gaussian dispersion models (GDM), which does not need high computational power. However, their outputs tend to be conservative regarding the analyzed situation, rather than representative of the real in-site conditions. Hence, regulations and guidelines adopted at an institutional level for authorization/control purposes are based on Lagrangian particle dispersion models (LPDM). These models grant a more accurate simulation of the pollutants' dispersion even if they are more demanding regarding both technical skills and computing power. The present study aims to increase the accuracy of screening odor impact assessment by identifying the correlation function of the outputs derived from the two simulation models. The case study is placed in northern Italy, where a single-point source, with various stack heights, was considered. The case study is placed in northern Italy, where a single-point source, with various stack heights, was considered. The obtained correlation functions allow the practitioner to have a more accurate first-level odor impact assessment, to save time for training, and to reduce the site-specific meteorological data before proceeding with the simulation. The identified functions could allow institutions to estimate the results that would have been forecasted with the application of the more complex LPDM, applying, however, the much simpler GDM. This solution grants an accurate tool which can be used to address citizens' concerns while saving workforce and technical resources. Limitations are related to the specificity of the method regarding type sources, orography, and meteorological conditions. Comparison with other screening tools is also presented and discussed. [ABSTRACT FROM AUTHOR]

The pollutant emissions of diesel-powered heavy-duty trucks (HDTs) seriously damage the air quality. The promotion of hydrogen fuel cell HDTs through purchase subsidy policy to reduce emissions has become an important approach to control air pollution. This study focuses on the impact of hydrogen fuel cell HDT purchase subsidies on air quality in the context of China, covering the panel data of 31 Chinese cities from 2014 to 2021 and applying a two-way fixed effects model to analyze the contribution of purchase subsidies and hydrogen refueling station construction subsidies to air quality. Results show that (1) the increase in purchase subsidies could improve the air quality by around 6.1% and there is a lag effect. (2) Purchase subsidies make a larger contribution to air quality compared with construction subsidies. (3) Purchase subsidies can improve air quality by reducing carbon emissions in transport industry. In sight of these results, policy makers should emphasize the implementation of purchase subsidies and hydrogen refueling station construction subsidies and stimulate manufacturers to improve the performance of hydrogen fuel cell so as to contribute more to the environment. [ABSTRACT FROM AUTHOR]

Accurate prediction of air quality change is essential for air pollution control and human daily mobility. Due to the strong spatial and temporal correlation of air quality changes, existing air quality prediction methods often face the problem of low prediction accuracy due to insufficient extraction of spatio-temporal features. In this paper, we proposed a self-tuning spatio-temporal neural network (ST2NN) to enhance air quality prediction. ST2NN model consisted of four modules. First, ST2NN model constructed a temporal feature extraction module and a spatial feature extraction module based on gated recurrent unit (GRU) and graph convolutional neural network (GCN), respectively, and the two feature extraction modules adopted a parallel structure, which could effectively extract the spatio-temporal features in data. Additionally, ST2NN model constructed a feature fusion module based on gating mechanism to delineate the contribution of spatio-temporal features to the predicted values. Further, ST2NN model constructed a Hyperband hyperparameter optimization module based on Hyperband optimization algorithm to automatically adjust the network hyperparameters. The structure of ST2NN model endowed it with excellent spatio-temporal feature extraction and parameter adaptability. ST2NN model was evaluated and compared with existing models, including convolutional long short-term memory neural network (ConvLSTM), GRU, combined convolutional neural network and long short-term memory neural network (CNN-LSTM), and GCN-LSTM for air quality index (AQI) prediction using data from twelve monitoring stations in Beijing, China. Across all four evaluation indexes, ST2NN model outperformed the comparative models, improving prediction accuracy by 0.51%-10.18% (measured using R 2 ). From the experimental results, it can be seen that ST2NN model constructed from the perspective of spatio-temporal feature extraction has better prediction performance compared with the existing air quality prediction model, which provides a new method for air quality prediction and has certain application value. [ABSTRACT FROM AUTHOR]

Synergizing air pollution control and carbon emission reduction has been widely proposed and highlighted. Evaluating the synergy of air pollution and carbon emissions has been the primary concern and essential support for synergistic control. Current research and works have attempted to assess synergy from multiple perspectives, but the informativeness and comprehensiveness of the synergy of air pollution and carbon emissions have been limited. This study develops a framework evaluating the synergy of PM2.5, ozone, and CO2 emission from the correlation and aggregate perspectives based on the large-scale and deep exploitation of the correlation and additivity of the data samples. A case study on the monthly synergy of air pollution and CO2 emission has been performed in major Chinese urban agglomerations at the city level. The results informatively present the seasonal and city-level characteristics and heterogeneity of synergy for PM2.5-ozone-CO2 while providing partitioned and classified recommendations for synergistic control. A comprehensive synergy typology of synergy, bare, aggregate, and correlation for air pollution and CO2 emission provides a reference for planning short-period synergistic control strategies. [ABSTRACT FROM AUTHOR]

Demonstrating the temporal changes in PM2.5 pollution risk in regions facing serious PM2.5 pollution problems can provide scientific evidence for the air pollution control of the region. However, research on the variation of PM2.5 pollution risk on a fine temporal scale is very limited. Therefore, we developed a method for quantitative characterizing PM2.5 pollution risk based on the supply and demand of PM2.5 removal services, analyzed the time series characteristics of PM2.5 pollution risk, and explored the reasons for the temporal changes using the urban areas of Beijing as the case study area. The results show that the PM2.5 pollution risk in the urban areas of Beijing was close between 2008 and 2012, decreased by approximately 16.3% in 2016 compared to 2012, and further decreased by approximately 13.2% in 2021 compared to 2016. The temporal variation pattern of the PM2.5 pollution risk in 2016 and 2021 showed significant differences, including an increase in the number of risk-free days, a decrease in the number of heavily polluted days, and an increase in the stability of the risk day sequence. The significant reduction in risk level was mainly attributed to Beijing's air pollution control measures, supplemented by the impact of COVID-19 control measures in 2021. The results of PM2.5 pollution risk decomposition indicate that compared to the previous 2 years, the stability and predictability of the risk variation in 2016 increased, but the overall characteristics of high risk from November to February and low risk from April to September did not change. The high risk from November to February was mainly due to the demand for coal heating during this period, a decrease in PM2.5 removal service supply caused by plant leaf fall, and the common occurrence of temperature inversions in winter, which hinders the diffusion of air pollutants. This study provides a method for the analysis of PM2.5 pollution risk on fine temporal scales and may provide a reference for the PM2.5 pollution control in the urban areas of Beijing. [ABSTRACT FROM AUTHOR]

The effectiveness of national policies for air pollution control has been demonstrated, but the relative effectiveness of short-term emission reduction measures in comparison with national policies has not. Here we show that short-term abatement measures during important international events substantially reduced PM2.5 concentrations, but air quality rebounded to pre-event levels after the measures ceased. Long-term adherence to strict emission reduction policies led to successful decreases of 54% in PM2.5 concentrations in Beijing, and 23% in atmospheric nitrogen deposition in China from 2012 to 2020. Incentivized by "blue skies" type campaigns, economic development and reactive nitrogen pollution are quickly decoupled, showing that a combination of inspiring but aggressive short-term measures and effective but durable long-term policies delivers sustainable air quality improvement. However, increased ammonia concentrations, transboundary pollutant flows, and the complexity to achieving reduction targets under climate change scenarios, underscore the need for the synergistic control of multiple pollutants and inter-regional action. In response to the rebound of air pollution during large events in China, the authors propose that a combination of ambitious short-term measures and enduring long-term pollution control policies is a sustainable solution for improving air quality. [ABSTRACT FROM AUTHOR]

In recent years, the combined pollution of PM2.5 and O3 in China, particularly in economically developed regions such as the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), has garnered significant attention due to its potential implications. This study systematically investigated the changes of PM2.5 and O3 and their associated human health effects in the GBA, utilizing observational data spanning from 2015 to 2019. The findings revealed a spatial trend indicating a gradual decrease in PM2.5 levels from the northwest to the southeast, while the spatial distribution of MDA8 O3 demonstrated an opposing pattern to that of PM2.5. The monthly fluctuations of PM2.5 and MDA8 O3 exhibited V-shaped and M-shaped patterns, respectively. Higher MDA8 O3 concentrations were observed in autumn, followed by summer and spring. Over the five-year period, PM2.5 concentrations exhibited a general decline, with an annual reduction rate of 1.7 μg m−3/year, while MDA8 O3 concentrations displayed an annual increase of 3.2 μg m−3. Among the GBA regions, Macao, Foshan, Guangzhou, and Jiangmen demonstrated notable decreases in PM2.5, whereas Jiangmen, Zhongshan, and Guangzhou experienced substantial increases in MDA8 O3 levels. Long-term exposure to PM2.5 in 2019 was associated with 21,113 (95% CI 4968–31,048) all-cause deaths (AD), 1333 (95% CI 762–1714) cardiovascular deaths (CD), and 1424 (95% CI 0–2848) respiratory deaths (RD), respectively, reflecting declines of 27.6%, 28.0%, and 28.4%, respectively, compared to 2015. Conversely, in 2019, estimated AD, CD, and RD attributable to O3 were 16,286 (95% CI 8143–32,572), 7321 (95% CI 2440–14,155), and 6314 (95% CI 0–13,576), respectively, representing increases of 45.9%, 46.2%, and 44.2% over 2015, respectively. Taken together, these findings underscored a shifting focus in air pollution control in the GBA, emphasizing the imperative for coordinated control strategies targeting both PM2.5 and O3. [ABSTRACT FROM AUTHOR]

Air quality forecasting is an important application area of the time series forecasting problem. The successful prediction of the air quality of a place well in advance can able to help administrators to take the necessary steps to control air pollution. The administrator can also warn the citizens about the adverse effect of air pollution in advance. In this study, an air quality forecasting method is proposed to successfully forecast the air quality of a place. Here the type-2 fuzzy time series (FTS) forecasting method is applied to predict air quality. The performance of any FTS heavily depends on the selection of its hyperparameters. In this letter, a fuzzy time series optimization (FTSBO) algorithm is proposed to optimize all the hyperparameters of the FTS forecasting method. The proposed FTSBO algorithm originated from the butterfly optimization technique. In this work, the performance of the proposed forecasting method is also compared to the well-known forecasting methods. The simulation results established that the proposed forecasting method produces satisfactory performance, and its performance is better in comparison to other well-known forecasting methods. [ABSTRACT FROM AUTHOR]

Long-term trends in PM2.5-attributable cardiovascular diseases (CVDs) and subtypes mortality remain unclear between China and India, facing dual challenges of aging and severe air pollution. We aim to compare long-term trends and age-, period-, and cohort-effects changes in PM2.5-attributable CVD and subtypes mortality in China and India from 1990 to 2019. Data are derived from Global burden of disease 2019. Long-term trends and age-, period-, and cohort-effects changes were evaluated by join-point regression and age-period-cohort models. We found that in China, the age-standardized mortality rate in CVD potentially increased in 1990–2012, followed by a significant downwards trend in 2013–2019, while the rate was obviously upward in India from 1990 to 2019. The impact of PM2.5 on CVD and subtypes mortality increased sharply with age in both countries. The period risk ratios (RRs) began to significantly decline in 2010–2014 in China, while India revealed a noticeable increase during the 30 years. The cohort RRs are still increasing among the younger generation in China, while the impact in India is not as pronounced. These findings suggest that the older adults are more susceptible to the effects of PM2.5. And compared with India, the PM2.5 hazards in China have begun to reduce because of the early implementation of strict air pollution control policies, suggesting that it is more necessary for India to conduct strict and efficient air pollution control policies to increase the health benefits. [ABSTRACT FROM AUTHOR]

This study assesses the health benefits of better air quality by examining the causal impact of China's stringent " 2 + 26 " regional air pollution control policy on local air quality and population health. Employing a spatial regression discontinuity design that capitalizes on the policy's location-specific features, we present compelling evidence that the 2 + 26 policy results in an average reduction of 12.2 units in the local Air Quality Index (AQI) and a 47.0% decrease in per capita medical expenditure from 2014 to 2018. A one-unit reduction in AQI corresponds to a 0.88% reduction in per capita annual medical spending, equivalent to RMB 30.2 (US$4.6). These health gains stem from reduced chronic disease prevalence and improved subjective well-being. Nationally, air quality improvement during 2014–2018 could save RMB 674 billion (US$104 billion) annually in national direct medical costs, constituting 11.6% of national medical expenditure in 2018. Our findings underscore the substantial health and welfare gains achievable through pollution controls in developing countries. [ABSTRACT FROM AUTHOR]

As one of China's national urban agglomerations, the middle reaches of the Yangtze River urban agglomeration (MRYRA) has suffered from serious PM2.5 pollution for a considerable period. Using deep learning methods to fit the quantitative relationship between aerosol optical depth (AOD) and PM2.5 (AOD-PM2.5) has received extensive attention. In this study, based on the correlation between AOD and PM2.5 data, a new network model BiCNN is proposed by using the satellite-ground synergy method, aiming to obtain the spatial distribution of PM2.5 concentration in the MRYRA. Data from MRYRA 2019 is used for model training and testing. The results show that BiCNN exhibits the best estimation on the annual scale, with a fit coefficient R2 of 0.836 and the smallest error, RMSE of 6.746 and MAPE of 12.497 compared to the benchmark model. The model effectively improves the relationship between AOD-PM2.5. The spatial distribution of PM2.5 in the MRYRA from 2019 to 2022 by applying the BiCNN model inversion shows that the PM2.5 concentration is higher in 2020, but the overall air quality improves year by year. Seasonal evaluation results show that BiCNN has a significant advantage in PM2.5 estimation in autumn, with R2 reaching 0.883. The spatial distribution of PM2.5 shows a spatial pattern of higher in spring and winter, lower in summer and autumn, and transition in autumn. The BiCNN model integrates the advantages of Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory Neural Network (BiLSTM), makes full use of historical and future data, and can effectively capture the temporal and spatial characteristics between cofactors and PM2.5 data. It has achieved ideal results in the PM2.5 estimation of the MRYRA and provides an effective scientific method for air pollution control in urban agglomerations. [ABSTRACT FROM AUTHOR]

Atmosphere pollution is a burning issue in Pakistan and therefore getting substantial attention at both the national and international levels. It is not only confined to the cities, but the small towns in Pakistan also suffer from this problem. However, very little information exists regarding public awareness and willingness to pay (WTP) for atmosphere pollution, particularly among the inhabitants of towns. This study aims to investigate the role of public awareness and WTP in the elimination of atmosphere pollution via a well-structured interview schedule. The interval regression model is used to analyze the data. The analysis shows that radio, television, and newspapers are the prime sources of information regarding atmosphere pollution in the study area. The attitude of the respondents concerning the severity of atmosphere pollution, only 46.0 percent declares pollution in towns as 'moderately severe.' Regarding the sources of pollution, 48 percent of the respondents attribute vehicular emissions besides industrial emissions to atmosphere pollution. The environment-friendly measures that respondents favored include the use of public transportation (37.7%) and solar equipment (27.7%) followed by the carbon tax and personal car sharing. There exists a significant difference between the WTP for self-protective measures before and after the dissemination of information. The analysis further demonstrates that the WTP to support the control and prevention of air pollution after the dissemination of information is significantly higher than before the dissemination of such information. However, respondents tend to pay more for self-protective measures than to support the control and prevention measures. Alternatively, this may indicate that inhabitants of the study area want the government to play a dominant role in controlling atmosphere pollution and preventive actions. Among the various socio-economic factors, the level of education and income significantly increase the WTP to eliminate atmosphere pollution. This study is important to better understand public awareness and WTP to prevent atmosphere pollution in Pakistan. It could also contribute to activating positive public participation to control atmosphere pollution and its management to move toward sustainable development. [ABSTRACT FROM AUTHOR]

The United Nations' Sustainable Development Goal (SDG) 3.9 calls for a substantial reduction in deaths attributable to PM2.5 pollution (DAPP). However, DAPP projections vary greatly and the likelihood of meeting SDG3.9 depends on complex interactions among environmental, socio-economic, and healthcare parameters. We project potential future trends in global DAPP considering the joint effects of each driver (PM2.5 concentration, death rate of diseases, population size, and age structure) and assess the likelihood of achieving SDG3.9 under the Shared Socioeconomic Pathways (SSPs) as quantified by the Scenario Model Intercomparison Project (ScenarioMIP) framework with simulated PM2.5 concentrations from 11 models. We find that a substantial reduction in DAPP would not be achieved under all but the most optimistic scenario settings. Even the development aligned with the Sustainability scenario (SSP1-2.6), in which DAPP was reduced by 19%, still falls just short of achieving a substantial (≥20%) reduction by 2030. Meeting SDG3.9 calls for additional efforts in air pollution control and healthcare to more aggressively reduce DAPP. Reducing PM2.5 air pollution from biomass burning, transport, energy, and manufacturing, in combination with improvements in healthcare, especially in emerging economies like India and China, will be crucial to meeting SDG3.9 [ABSTRACT FROM AUTHOR]

The design of air pollution control policies is a complex decision-making process. Public participation can enhance the legitimacy and effectiveness of environmental urban regulations but does not always occur naturally. This article presents the experience of the use of role-playing in six mid-sized cities in southern Chile to evaluate how residents affected by high concentrations of fine particulate matter perceive the problem and debate possible solutions. For this, the participants were asked to assume the role of advisors. On that premise, they had to prioritize between a series of mitigation measures and reach a consensus with other advisors. The results show the potential of role-playing games, on the one hand, to promote critical thinking and argument skills among the general public and, on the other, to help policymakers in the design of more operative and fair plans of action. [ABSTRACT FROM AUTHOR]

This "Policy Brief" explores the potential integration of Aerosol Optical Depth (AOD) into the United Kingdom's air quality and public health monitoring frameworks, highlighting its potential to enhance existing air pollution control strategies. Amid growing concerns over air pollution's impact on health, particularly the link between particulate matter and lung cancer, this brief presents a focused investigation into how AOD can be leveraged alongside traditional monitoring methods to provide a more nuanced understanding of air quality trends. By correlating AOD data with lung cancer incidence rates within the UK, the brief aims to uncover potential associations and inform public health decisions. Furthermore, it discusses the advantages and limitations of employing AOD in air pollution and respiratory disease monitoring, advocating for a strategic enhancement of the UK's air pollution monitoring efforts. This approach seeks to complement and refine current monitoring practices with advanced remote sensing techniques, aiming to inform policy innovations that prioritize environmental health and public welfare. Through a comprehensive review of existing data and policies, the brief underscores the urgency of adopting multidimensional air quality management strategies that respond to technological advancements and emerging public health needs. [ABSTRACT FROM AUTHOR]

Coal-fired power plants (CFPPs) are one of the most significant sources of mercury (Hg) emissions certified by the Minamata Convention, which has attracted much attention in recent years. In this study, we used the Web of Science and CiteSpace to analyze the knowledge structure of this field from 2000 to 2022 and then reviewed it systematically. The field of Hg emissions from coal-fired power plants has developed steadily. The research hotspots can be divided into three categories: (1) emission characterization research focused on speciation changes and emission calculations; (2) emission control research focused on control technologies; (3) environmental impact research focused on environmental pollution and health risk. In conclusion, using an oxygen-rich atmosphere for combustion and installing high-efficiency air pollution control devices (APCDs) helped to reduce the formation of Hg0. The average Hg removal rates of APCDs and modified adsorbents after ultra-low emission retrofit were distributed in the range of 82–93% and 41–100%, respectively. The risk level of Hg in combustion by-products was highest in desulfurization sludge (RAC > 10%) followed by fly ash (10% < RAC < 30%) and desulfurization gypsum (1% < RAC < 10%). Additionally, we found that the implementation of pollution and carbon reduction policies in China had reduced Hg emissions from CFPPs by 45% from 2007 to 2015, increased the efficiency of Hg removal from APCDs to a maximum of 96%, and reduced global transport and health risk of atmospheric Hg. The results conjunctively achieved by CiteSpace, and the literature review will enhance understanding of CFPP Hg emission research and provide new perspectives for future research. [ABSTRACT FROM AUTHOR]

Over the past few years, there has been a significant focus on air pollution due to its various detrimental effects on human health. However, its influence on people's tendency to have children remains uncertain, as only a few studies have examined the correlation between public perception of air pollution and the desire to start a family. This article introduces a theoretical framework utilizing a two-stage interval iteration model to explore the connection between children's relative utility and the perception of air pollution. Data for this study were gathered from the "Chinese General Social Survey" (CGSS 2013). The CGSS 2013 project employed a four-stage stratified random sampling technique and conducted household interviews using questionnaires. The sample covered 28 provincial-level cities across China. The hypothesis was tested using a Probit regression model. The findings indicate that individuals considering air pollution a significant issue are 8.62% less likely to have more than one child. The variation in fertility desire sensitivity to air pollution points to heterogeneity among residents, such as registered residents and those living in various residential areas, as well as individuals with different characteristics like education levels. The study concludes that air quality significantly influences human fertility desire, highlighting the urgent necessity to raise awareness of environmental protection issues among both the public and authorities. In particular, there are two key steps to address this issue. Firstly, the government should establish clear air pollution control objectives and refine policies to enhance governance efficiency. Secondly, there is a need to encourage environmentally friendly behaviours among the public, promote more significant involvement in public environmental matters, and ensure effective oversight of the government's responsibilities in managing air pollution. [ABSTRACT FROM AUTHOR]

This study aims to generate a satellite-based qualitative emission source characterization for the heavily polluted eastern part of China in the 2010–2016 time period. The applied source identification technique relies on satellite-based NOx and SO2 emission estimates by OMI, their SO2:NOx ratio, and the MIX anthropogenic emission inventory to distinguish emissions from different emission categories (urban, industrial, natural) and characterize the dominant source per 0.25° × 0.25° grid cell in East China. Overall, we find good agreement between the satellite- and emission inventory–based spatiotemporal distribution and characterization of the dominant emission sources in East China in 2010–2016. In 2010, the satellite measurements suggest an emission distribution less dominated by industrial areas, a somewhat larger role for urban/transportation areas and agricultural activities, and more natural emissions in the southern part compared to the bottom-up emission categorization. In 2016, more than half of the classified emission categories over East China have remained the same. At the same time, there is a notable increase of agricultural lands and decrease of areas dominated by industry/transportation in 2016, suggestive of an overall decrease in heavy air pollution in East China over the course of 7 years. This is likely attributed to the sustained efforts of the Chinese government to drastically improve the air quality, especially since 2013 when the National Air Pollution Prevention and Control Action Plan was enacted. However, signs of urban expansion (urbanization) and rural–urban migration ("Go West" motion) stemmed from China's rapid economic growth and labour demand are evident; escalating industrialization (even with cleaner means) and the urban population growth in East China resulted in stronger emissions from sources representing consumption and transportation which are strongly related to NO2 and PM10 pollution (rather than SO2) and are directly influenced by the population size. This resulted to a shift of the emissions from the east mainly to the north and northwest of East China. Overall, although the effectiveness of the Chinese environmental control policies has been successful, the air pollution problem remains an important concern. [ABSTRACT FROM AUTHOR]

The widespread nitrogen oxides (NOx, mainly in NO) in the atmosphere have threatened human health and ecological environment. The dilute NO (ppb) is difficult to efficiently remove via the traditional process due to its characteristics of low concentration, wide range, large total amount, etc. Photocatalysis can utilize solar energy to purify NO pollutants under mild conditions, but its application is limited due to the low selectivity of nitrate and poor activity of NO removal. The underlying reason is that the interface mechanism of NO oxidation is not clearly understood, which leads to the inability to accurately regulate the NO oxidation process. Herein, the recent advances in the photocatalytic oxidation of NO are summarized. Firstly, the common strategies to effectively regulate carrier dynamics such as morphology control, facet engineering, defect engineering, plasma coupling, heterojunction and single-atom catalysts are discussed. Secondly, the progress of enhancing the adsorption and activation of reactants such as NO and O2 during NO oxidation is described in detail, and the corresponding NO oxidation mechanisms are enumerated. Finally, the challenges and prospects of photocatalytic NO oxidation are presented in term of nanotechnology for air pollution control. This review can shed light on the interface mechanism of NO oxidation and provide illuminating information on designing novel catalysts for efficient NOx control. [ABSTRACT FROM AUTHOR]

The chronology of China's legislation on controlling air pollution and water pollution is not equivalent, and firms may change their pollution inputs under environmental regulations. By examining a nationwide policy, China's Key Cities for Air Pollution Control, which aims to mitigate air pollution emissions, we find that although firms in regulated cities do manage to reduce their level of air pollution, they do so by installing "end-of-pipe" abatement equipment, which consumes a large amount of water and eventually increases water pollution. Thus, the ratio of water to air emissions increases by up to 15%. As pollution transfers from the air to water, this regulation-induced pollution substitution hinders the improvement of the overall environmental quality. Our results suggest that when evaluating the effectiveness of environmental regulation, abatement methods, especially "end-of-pipe" equipment, should also be taken into account to consider the pollution substitution effect. [ABSTRACT FROM AUTHOR]

With a large number of new energy vehicles being put into use, it is the general trend for traditional fuel vehicles to withdraw from the market in an orderly manner. Determining the optimal ratio between them in this process is of great significance to the low-carbon sustainable development of cities. Therefore, considering the constraints of urban automobile development planning and air pollution prevention and control policies, a multi-objective model to minimize pollutants and costs is constructed in this paper. Through model calculation and sensitivity analysis of dynamic impact relationship of different types of vehicles, it is determined that when new energy vehicles account for around 36% in Beijing, 57% in Shanghai and 46% in Guangzhou, the pollutant emissions can be minimized without causing a significant increase in social costs. Additionally, compared with 2030, Beijing, Shanghai and Guangzhou can achieve emission reductions of 320,000 tons, 200,000 tons and 250,000 tons, respectively, in 2050 if they implement the policy of banning the sale and delisting of fuel vehicles, which could provide suggestions for the guidance of the low-carbon development plan of the automobile industry. [ABSTRACT FROM AUTHOR]

Protecting people from air pollution is an important task for developing countries. Over the past several decades, different types of policies have been enacted in developing countries to improve air quality, with policy impact analysis being conducted as well. As for China, while there is some existing literature on the impact of short-term action on air quality, there is much less research to investigate the roles of long-term air quality control mechanism on air quality change. This paper uses the "2 + 26" city cluster in China as a case study area to particularly analyze the roles played by short-term campaigns that compose the Winter Action Plan and a long-term mechanism referred to as the environmental accountability system in pollution control. Based on econometric and descriptive statistical analyses, it is found that the implementation of the Winter Action Plan has significantly improved the air quality, and the environmental accountability system seems to have exerted an impact, as cities with better air quality appear to establish a more stringent environmental accountability system. The paper provides the following insights for other developing countries: short-term campaigns can quickly reduce pollution and protect human health, while long-term mechanisms are needed to ensure sustainable environmental protection. [ABSTRACT FROM AUTHOR]

Innovation is the first power to drive the county's green and low-carbon. It is crucial to explore the impact of innovation on air pollution from the perspective of counties at the bottom of the administrative division hierarchy. The article is aimed at exploring the direct impact effects, spatial spillover effects, impact mechanism pathways, non-linear relationships, and cost-benefits of innovation drive on air pollution in counties. To this end, based on the collection of county-level data from 2007 to 2020 in mainland China, the article constructs a fixed-effects model, a dynamic panel model, and a spatial Durbin model for analysis. For every 1% increase in the quantity of innovation, the county SO2 emission concentration decreases by 0.2% on average; for every 1% increase in the quality of innovation, the county SO2 emission concentration decreases by 0.3% on average. When the county innovation quantity driver increases by one standard deviation, the county SO2 concentration decreases by an average of 0.29%; when the county innovation quality driver each standard deviation increases, the county SO2 concentration is reduced by 0.33% on average. The significant entry of high-end factors, the increased frequency of regulation by the environmental protection department, and the increasing efficiency of energy use are the important mechanism pathways for innovation-driven reduction of air pollution in counties. There is no significant "(inverted) U-shaped" relationship between innovation-driven air pollution in the county samples. There is a negative spatial spillover effect of the innovation quality drive on air pollution control in all Chinese county samples. Innovation to drive the declining size of the county's sulfur dioxide can bring about one billion yuan (about 139.81 million U.S. dollars) in comprehensive economic benefits. In the coming period, county governments should build a new pattern of "blue sky and white clouds" with neighboring regions in terms of spatial agglomeration of high-end elements, green transformation and utilization of energy, and intelligent monitoring and supervision of pollution. [ABSTRACT FROM AUTHOR]

Heavy metal pollutants such as Hg, As, Pb, Cr, and Cd emitted from coal and waste combustion have received widespread attention. In this study, we systematically investigated the emission characteristics of heavy metals in waste incineration and coal-fired flue gases, focused on testing the removal effect of self-made cold electrode electrostatic precipitator (CE-ESP) on heavy metals in flue gas, and made a comparative analysis with the existing air pollution control devices (APCDs). Test results from waste incineration power plant showed that each APCD showed a certain effect on the removal of heavy metals in condensable particulate matter (CPM), with an average removal efficiency of bag filter was 86%, but its effect on Hg removal was slightly worse. Under the coupled field with electrified cold electrode plate operation mode, the average removal efficiency of CE-ESP on heavy metals in CPM was as high as 93%, including 76% for Hg. The removal efficiency of heavy metals (especially Hg) in CPM increased with the increase of flue gas temperature difference between inlet and outlet of CE-ESP. Test results from this coal-fired power plant showed that heavy metals were enriched in fly ash to a higher degree than in slag, the synergistic control of heavy metals in submicron particulate matter by the dust remover was not obvious, and there was a significant correlation between each heavy metal emission factor and its content in coal. Under the temperature field with non-electric cold electrode plate operation mode, the overall effect of CE-ESP on the removal of gaseous heavy metals was better than that of particulate heavy metals. Under the conventional electric field operation mode, CE-ESP was less effective in removing particulate Cr and gaseous Hg0. Under the coupled field with electrified cold electrode plate operation mode, the average removal efficiencies of CE-ESP for particulate and gaseous heavy metals were 82.37% and 76.16%, respectively. [ABSTRACT FROM AUTHOR]

This study investigates a circulating fluidised bed (CFB) incineration plant to examine the concentrations and fingerprints of polychlorinated dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs), and biphenyls (PCBs) at five locations downstream of the post-combustion zone. Sampling encompassed both flue gas and ash, spanning from the high-temperature superheater to the outlet of the baghouse filter, thus covering a wide range of flue gas temperatures. The analysis reveals a continuous increase in PCDD/F and PCB concentrations in the flue gas from the superheater to the inlet of the air pollution control system (APCS). The maximum concentrations observed were 75.8 ng/Nm3 for PCDDs, 219 ng/Nm3 for PCDFs, and 763 ng/Nm3 for PCBs. These values represent 9.14, 11.5, and 6.37 times their respective concentrations at the outlet of the high-temperature superheater. Concurrently, the levels of PCDD/Fs and dioxin-like PCBs (dl-PCBs) in the ash steadily increased along the cooling path of the flue gas within the plant. Comparing dl-PCBs to the total amount of 209 PCB congeners, it was evident that dl-PCBs exhibited a trend more akin to that of PCDD/Fs. A robust linear correlation was observed between dl-PCBs and PCDD/Fs (R2 = 0.99, p < 0.001), surpassing that between PCBs and PCDD/Fs (R2 = 0.92, p < 0.01), suggesting that dl-PCBs share closer formation pathways with PCDD/Fs. Additionally, elemental composition analysis of fly ash samples aimed to explore potential links between fly ash characteristics and PCDD/F and PCB formation. The Cl/S ratio increased from 1.58 to 5.13 with decreasing flue gas temperature. Principal component analysis (PCA) was employed to visualise the concentrations of PCDD/Fs and PCBs in the flue gas alongside elemental contents in the fly ash. With the exception of PCBs in ash, all other PCDD/Fs and PCBs in fly ash exhibited positive correlations with both carbon (C) and chlorine (Cl). Furthermore, a positive relationship between C/Cl and PCDD/Fs-PCBs in fly ash implies that fly ash serves as the primary reaction surface for dioxin generation during low-temperature heterogeneous catalytic reactions. [ABSTRACT FROM AUTHOR]

With increasing levels of air pollution, air quality prediction has attracted more attention. Mathematical models are being developed by researchers to achieve precise predictions. Monitoring and prediction of atmospheric PM2.5 levels, as a predominant pollutant, is essential in emission mitigation programs. In this study, meteorological datasets from 9 years in Isfahan city, a large metropolis of Iran, were applied to predict the PM2.5 levels, using four machine learning algorithms including Artificial Neural |Networks (ANNs), K-Nearest-Neighbors (KNN), Support Vector |Machines (SVMs) and ensembles of classification trees Random Forest (RF). The data from 7 air quality monitoring stations located in Isfahan City were taken into consideration. The Confusion Matrix and Cross-Entropy Loss were used to analyze the performance of classification models. Several parameters, including sensitivity, specificity, accuracy, F1 score, precision, and the area under the curve (AUC), are computed to assess model performance. Finally, by introducing the predicted data for 2020 into ArcGIS software and using the IDW (Inverse Distance Weighting) method, interpolation was conducted for the area of Isfahan city and the pollution map was illustrated for each month of the year. The results showed that, based on the accuracy percentage, the ANN model has a better performance (90.1%) in predicting PM2.5 grades compared to the other models for the applied meteorological dataset, followed by RF (86.1%), SVM (84.6%) and KNN (82.2%) models, respectively. Therefore, ANN modelling provides a feasible procedure for the managerial planning of air pollution control. [ABSTRACT FROM AUTHOR]

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Air pollution has emerged as a substantial threat to China's sustainable development. What remains overlooked is the resource dependence on the control of air pollution. At the same time, the resource curse may be broken by the growing digital economy, which will in turn reduce air pollution. To examine this problem, a total of 225 Chinese prefecture-level cities were selected from 2011 to 2018 to investigate the associations among resource dependence, the digital economy, and air pollution, and further, explore the mediating effect of income inequality and industrial upgrading. The primary findings are enumerated as follows: First, resource dependence is positively correlated to air pollution; second, the digital economy negatively regulates the effect of resource dependence on air pollution, that is, as the digital economy grows, air pollution decreases via resource dependence as a whole. However, a noteworthy phenomenon is that the negative moderating effect is evidently heterogeneous and is especially prominent in central China and non-resource-based cities; third, income inequality and industrial upgrading play a mediating role between resource dependence and air pollution; fourth, the digital economy can significantly influence the process where resource dependence affects air pollution via income inequality and industrial upgrading. These findings indicate the direction in the development of new strategies for different regions. Alleviating resource dependence is an important way of air pollution control. In particular, the main focus on mitigating air pollution should be adjusted to the level of income inequality and industrial structure. And the empirical results call for a strong emphasis on the digital economy for higher air quality in resource-based cities. [ABSTRACT FROM AUTHOR]

This study assessed the regional differences of PM2.5 (fine particulate matter) concentration and secondary components in China. Through chemical analysis (Thermal/Optical Carbon Analyzer and ion chromatography) and statistical tools (SPSS and Origin software), PM2.5 samples' concentration and main components were estimated and compared in Tianjin and Guangzhou in December 2016. The average PM2.5 mass concentration was 148 μg/m3 in Tianjin and 62 μg/m3 in Guangzhou, indicating heavy air pollution in both cities. Organic carbon constituted 15% of PM2.5 in Tianjin, while that accounted for 25% in Guangzhou. The concentration of the secondary organic carbon accounted for 40% and 33% of organic carbon in Tianjin and Guangzhou, respectively. The ionic components in Tianjin made up 49% of PM2.5, while those of Guangzhou were responsible for 36% locally. The sulfate oxidation ratio reached 0.41 in Tianjin and 0.53 in Guangzhou while the nitrate oxidation ratio was 0.23 and 0.14 in the two cities, respectively. These results indicated high secondary conversion rate in PM2.5 pollution in both cities and suggested that PM2.5 mass concentration could not fully reflect its environmental and health effects, and so more attentions should be paid to the effects of its components, especially secondary components. In addition, the results could provide a scientific basis for making policies concerning air pollution control and developing strategies of air quality improvement. [ABSTRACT FROM AUTHOR]

This paper examined the effect of air pollution joint prevention and control on pollution emissions in China. Specifically, based on the panel data of 290 cities from 2007 to 2021, taking the implementation of the "Air Pollution Prevention and Control Action Plan" as a natural experiment, the difference-in-difference-in-difference (DDD) model was used to explore the effect of air pollution joint prevention and control on haze pollution. Results show that air pollution joint prevention has a significant impact on pollutant emissions either as a whole or as a single pollutant. In terms of individual urban agglomeration, whether the Yangtze River Delta or the Pearl River Delta urban agglomerations, the air pollution joint prevention and control policy has a significant impact not only on the overall reduction of pollutant emissions but also on the reduction of single PM2.5 or industrial sulfur dioxide emissions alone. Environmental regulations have also achieved the effect of haze control in general and have a significant impact on the reduction of PM2.5 or industrial sulfur dioxide emissions. Environmental regulations also significantly reduced PM2.5 emissions in these three urban agglomerations. These findings provide a scientific basis and essential reference for understanding the implementation effect of regional joint prevention and control policies comprehensively and objectively. [ABSTRACT FROM AUTHOR]

Catalytic destruction of nitrogen oxides (NOx) combined with dust removal technique has attracted much attention, yet the application in the solid waste incineration air pollution control process is still lacking due to the complex flue gas atmosphere. In this work, the Mn-Ce-Co-Ox catalyst–coated polyphenylene sulfide (PPS) filter fiber with efficient dust removal and low-temperature polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) destruction has been prepared with a redox-precipitation method. The catalyst was uniformly grown around the PPS fiber with appropriate catalyst loading. The effects of several key operating parameters (e.g., reaction temperature, catalyst loading amount, and filtration velocity) on the catalytic efficiency were comprehensively investigated. The results show that the Mn-Ce-Co-Ox/PPS has a decomposition yield of 78.0% in PCDD/Fs and 96% in nitric oxide (NO) conversion at 200 °C. The poisoned catalytic filter exhibits a removal efficiency of 88.6% for PCDD/Fs. In addition, the catalytic filter can completely reject particles smaller than 1.0 μm with a low filtration resistance. Therefore, this efficient and energy-conserving catalytic filter shows promising applications in flue gas pollution treatments. [ABSTRACT FROM AUTHOR]

Accurate prediction of PM2.5 concentrations can provide a solid foundation for preventing and controlling air pollution. When the Long Short-Term Memory (LSTM) is applied to predict PM2.5 concentration, the influential factors strongly correlated with PM2.5 concentration are directly fed into the LSTM network. However, the influence of these factors on PM2.5 concentration is different. To address this issue, a weighted Random Forest (RF)-LSTM model was proposed to predict PM2.5 concentration for the next six hours in this study. This model first uses the RF to select the factors that are more important for predicting PM2.5 concentration and then uses a fully connected neural network to learn the weight value of each factor. Finally, the weighted data is fed into the LSTM network. The model is trained, validated, and tested using hourly air pollutant and meteorological data collected from four monitoring stations in Beijing, China, from November 1, 2019 to February 28, 2022. The prediction performance of the weighted RF-LSTM model was compared to the RF-LSTM and LSTM models. The results show that the RMSE and MAE of the weighted RF-LSTM model are the smallest, and the R2 is the largest for the next six hours' prediction of PM2.5 concentration at four stations. Compared to the LSTM model, the weighted RF-LSTM model decreases RMSE by 2.3%-5.3%, MAE by 5.6%-9.6%, and improves R2 by 2.0%-4.8%, showing that the weighted RF-LSTM model proposed in this study can achieve better prediction performance and has strong generalization ability. [ABSTRACT FROM AUTHOR]

Fine particulate matter ( P M 2.5 ) poses a significant threat to human life and health, and therefore, accurately predicting P M 2.5 concentration is critical for controlling air pollution. Two improved types of recurrent neural networks (RNNs), the long short-term memory (LSTM) and gated recurrent unit (GRU), have been widely used in time series data prediction due to their ability to capture temporal features. However, both degrade into random guessing as the time length increases. In order to enhance the accuracy of P M 2.5 concentration prediction and address the issue of random guessing in RNNs neural networks, this study introduces a TCN-biGRU neural network model. This model is a hybrid prediction approach based on combining temporal convolutional networks (TCN) and bidirectional gated recurrent units (bi-GRU). TCN extracts higher-level feature information from longer time series data of P M 2.5 concentrations, while bi-GRU captures features from past and future data to achieve more accurate predictive outcomes. This case study utilizes data from monitoring stations in Beijing in 2021 for conducting P M 2.5 prediction experiments. The TCN-biGRU model achieves an average absolute error, root mean square error, and R 2 of 4.20, 7.71, and 0.961 in its predictive outcomes. When compared to the predictive outcomes of individual LSTM, GRU, and bi-GRU models, it is evident that the TCN-biGRU model exhibits smaller errors and superior predictive performance. [ABSTRACT FROM AUTHOR]

The topic of air pollution and its effect on public health has become a hot policy issue that has attracted worldwide attention, but this attention has seldom been extended to the causal relationship between atmospheric environmental policy (AEP), air pollution, and public health. This paper uses panel data from 30 provinces in China to construct spatial econometric models that analyze the impact of AEP on air pollution, the impact of air pollution on public health, and the mediation effect that air pollution may have between AEP and public health. The results demonstrate that there is a significant positive spatial spillover effect of soot and dust (SD) emission intensity and the overall air pollution level as measured by the Air Pollution Index (API). The AEP has significant inhibitory effects on the intensity of sulfur dioxide and SD emissions, as well as on overall air pollution. An increase in the overall air pollution level has a significant detrimental effect on public health as measured by average life expectancy. Air pollution as measured by API is a mediating factor in the relationship between AEP and public health. The study results could help to effectively control air pollution and promote public health by leading to improvements in regional pollution prevention and control mechanisms and strengthening of the central government's policy formulation and local governments' policy implementation process. [ABSTRACT FROM AUTHOR]

To explain the duration and dose effects of pollutant exposure on public health and provide scientific data for air pollution prevention and control and disease prevention by examining the influence of PM2.5 concentration and exposure duration on daily outpatient visits among patients with cardiovascular, cerebrovascular, and respiratory diseases in a typical heavy industrial city in China. Daily outpatient data on cardiovascular, cerebrovascular, and respiratory diseases and regional PM2.5 exposure duration and concentration were collected from a provincial hospital in Taiyuan, China, from 2016 to 2021. The correlations of numeric variables were analyzed using the Pearson correlation method. A generalized additive model (GAMs) was also established to investigate the effects of PM2.5 concentration and exposure duration on outpatient visits. Correlation analysis showed that the outpatient visits in Taiyuan was significantly correlated with the PM2.5 concentration and exposure duration. The longer the exposure time of PM2.5 pollution, the stronger the correlation of PM2.5 with outpatient visits showed. Cardiovascular outpatient visits were extremely significant related with medium to long-term exposure of PM2.5 (exposure with more than 30 days) (p < 0.001). In addition, outpatient visits of cerebrovascular and respiratory disease were extremely significant correlated with PM2.5 (exposures within 0–360 days) (p < 0.001). The results of GAMs showed the linear or the nonlinear relationships between outpatient visits and exposure of PM2.5. Among the linear relationships, when average concentration of PM2.5 (exposure within less than 15 days) increased by 1 mg/m3, the cardiovascular outpatient visits increased most dramatically (by about 440 people). For nonlinear relationships, when the average PM2.5 concentration (exposure with over 30 days or more) increased by 1 mg/m3, the most dramatic increase occurred in cardiovascular outpatient visits (with a maximum increase of 7000), followed by cerebrovascular outpatient visits (with a maximum increase of 1200), and respiratory outpatient visits (with a maximum increase of 250). The GAMs also revealed a dose effect in the relationship between outpatient visits and PM2.5 exposure. In moderately polluted air (based on air quality standards of China, GB3095-2012), when the average concentration of PM2.5 increased by 1 mg/m3, the cardiovascular outpatient visits increased the most (by 1200 people), followed by cerebrovascular outpatient visits (by 200 people) and respiratory outpatient visits (by 20 people). We concluded that outpatient visits in cardiovascular, cerebrovascular, and respiratory disease are closely correlated with the concentration and exposure duration of air pollution. There is a linear relationship between short-term air pollution exposure (exposure within less than 15 days) and outpatient visits. As PM2.5 concentration increases, cardiovascular outpatient visits increase gradually, with its growth trend exceeding that of cerebrovascular and respiratory disease. There is a nonlinear relationship between medium and long-term air pollution exposure (exposure with more than 30 days) and outpatient visits, with cardiovascular and cerebrovascular outpatient visits showed a nonlinear but overall upward trend when the atmosphere is moderately polluted. [ABSTRACT FROM AUTHOR]

Background: The insatiable demand for rock supplies has enticed numerous building and construction enterprises to participate in stone quarrying. However, this has had an environmental impact on air quality. This paper examines the environmental impact of quarrying on air quality in Ebonyi State, Nigeria. To achieve the main aim of the study, an objective was set to detect air pollutants at the quarry sites. A total of 220 air samples were measured from six points around the quarry locations and recorded in situ for analysis. The samples were measured three times a day (morning, afternoon, and evening) for three days. Gas monitors were used to monitor air pollutants. The generated data were subjected to completely random design (CRD) sampling techniques. The separation of means and tests was performed using Fisher's Least Significant Difference (FLSD) at a significance probability level of 5%. Results: Based on statistical analysis, the findings detected significantly higher concentration levels of particulate matter, nitrogen dioxide, hydrogen sulphide, carbon monoxide, sulphur dioxide, chlorine, volatile organic compounds, ammonia, and hydrogen cyanide in the quarry areas than the value detected in the control area. The findings also confirmed higher noise levels in the locations. It was also observed that the concentration levels of the parameters differed from point to point and at different times of the day. This really means the occurrence of a high rate of air pollution in the study locations. Conclusions: Based on the above findings, it is highly recommended that, (i) if situation is not timely addressed, it will lead to a severe environmental disaster or hazard, as nobody selects the air he or she breathes; (ii) air pollution control equipment be installed in-situ at quarry sites where free air flow is available in order to reduce gaseous (pollutant) emissions, and (iii) the seasonal effects, meteorological parameters and time that were influenced by the activities of quarry should be put in check. [ABSTRACT FROM AUTHOR]

Solketal is produced using glycerin and acetone with the green catalytic membrane as a heterogeneous catalyst. Solketal is a bio-additive that improves combustion efficiency and fuel properties to control air pollution. The catalytic membrane was created using polyvinyl alcohol (PVA) green polymer, poly (2-acrylamido-2-methyl-1-propanesulfonic acid) (PAMPS) homopolymer, and sulfosuccinic acid (SSA) as a crosslinking agent. By catalyzing the reaction and absorbing water by-product, the catalytic membrane increased the yield. The experiments were conducted in a batch reactor, and the impact of various factors, such as the amount of catalyst, reaction temperature, and the initial molar ratio of reactants, on the reaction yield was evaluated. The optimal solketal yield of 95% was obtained after 3 hours of reaction time, with a reaction temperature of 60°C, an acetone/glycerin molar ratio of 6/1, and a PVA/PAMPS catalytic membrane ratio of 70/30. The catalytic membrane's capacity to absorb water had a favorable impact on the reaction yield, which could be used to reduce air pollution. Sorption enhanced eco-friendly production of solketal by the catalytic membrane is a promising alternative method to the methods that use other catalysts. [ABSTRACT FROM AUTHOR]