Search

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]

Background: Short-term exposure to air pollution has been demonstrated in previous studies to correlate with respiratory disease (RD) in children. Due to regional heterogeneity, our objective was to explore the correlation between short-term exposure to ambient air pollution and hospital admissions for respiratory ailments in children in Suzhou City from January 1, 2017, to December 31, 2022, alongside assessing the influence of the COVID-19 pandemic on this relationship. Methods: We collected data on air pollutant levels and hospital admissions for childhood respiratory disease (RD) in Suzhou, China, from 2017 to 2022. We utilized a time-stratified case-crossover design along with a conditional logistic regression model to assess the short-term impacts of air pollutants on RD in children through stratified analysis and sensitivity analysis. Results: A total of 13,408 children with respiratory diseases were included in the study. The findings revealed significant associations between hospitalization for respiratory diseases in children and exposure to PM2.5, PM10, SO2, NO2, and CO. The maximum effect values (95%CI, best lag days) for each 10 µg/m3 increase in the concentrations of PM2.5, PM10, SO2, and NO2 were as follows: 1.017 (1.003–1.031, lag0-2), 1.015 (1.004–1.026, lag0-2), 1.117 (1.001–1.247, lag0-1), and 1.036 (1.009–1.064, lag0-7). Additionally, the maximum effect value (95%CI, best lag days) for each 1 mg/m3 increase in CO concentration was found to be 1.267 1.017–1.579, lag0-7). Stratified analysis indicated that sex, season of admission, and stage of admission did not modify these correlations significantly; however, differential effects on various age groups and sexes were primarily observed among school-age and older children as well as boys. Conclusions: The short-term exposure to PM2.5, PM10, SO2, NO2, and CO in Suzhou, China, exhibited a positive correlation with RD hospitalization. Prior to the COVID-19 pandemic, the adverse impacts of air pollutants on hospitalizations for childhood respiratory disease were mitigated compared to the period following the pandemic. Local governments should continue promoting decisions and measures for air pollution prevention and control to reduce further pollutant concentration, which is crucial for public health in reducing the burden of childhood respiratory diseases. [ABSTRACT FROM AUTHOR]

Air Pollution Prevention and Control Action Plan (APPCAP) is one of the most influential command-and-control environmental regulations (CMC) in China. Whether it can promote the green innovation performance of enterprises remains unclear. Based on the 'Green Patent List' issued by the World Intellectual Property Organization (WIPO) and the Chinese listed companies' data, this paper applies the quasi-natural experiment methods of the difference-in-differences model and difference-in-difference-in-differences model to identify the impact of environmental regulation pressure on the green innovation of enterprises. The study finds that implementing the APPCAP promotes the enterprises' green innovation performance in quantity and quality. Heterogeneity analysis shows that the green innovation effect of APPCAP is more obvious for those samples in the eastern region, with features of capital-intensive, labour-intensive, and low market concentration. Furthermore, our findings further support the weak version of the Porter hypothesis (PH), Whose path is that the implementation of APPCAP induces enterprises' green innovation by promoting their innovation investment. These findings provide policy implications for the coordination of environmental regulation and green transformation in pursuit of the goal of carbon peaking and carbon neutrality goals. [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]

The accurate prediction of PM2.5 concentration across extensive temporal and spatial scales is essential for air pollution control and safeguarding public health. To address the challenges of the uneven coverage and limited number of traditional PM2.5 ground monitoring networks, the low inversion accuracy of PM2.5 concentration, and the incomplete understanding of its spatiotemporal dynamics, this study proposes a refined PM2.5 concentration estimation model, Bi-LSTM-SA, integrating multi-source remote sensing data. First, utilizing multi-source remote sensing data, such as MODIS aerosol optical depth (AOD) products, meteorological data, and PM2.5 monitoring sites, AERONET AOD was used to validate the accuracy of the MODIS AOD data. Variables including temperature (TEMP), relative humidity (RH), surface pressure (SP), wind speed (WS), and total precipitation (PRE) were selected, followed by the application of the variance inflation factor (VIF) and Pearson's correlation coefficient (R) for variable screening. Second, to effectively capture temporal dependencies and emphasize key features, an improved Long Short-Term Memory Network (LSTM) model, Bi-LSTM-SA, was constructed by combining a bidirectional LSTM (Bi-LSTM) model with a self-adaptive attention mechanism (SA). This model was evaluated through ablation and comparative experiments using three cross-validation methods: sample-based, temporal, and spatial. The effectiveness of this method was demonstrated on Beijing–Tianjin–Hebei urban agglomeration, achieving a coefficient of determination (R2) of 0.89, root mean squared error (RMSE) of 12.76 μg/m3, and mean absolute error (MAE) of 8.27 μg/m3. Finally, this model was applied to predict PM2.5 concentration on Beijing–Tianjin–Hebei urban agglomeration in 2023, revealing the characteristics of its spatiotemporal evolution. Additionally, the results indicated that this model performs exceptionally well in hourly PM2.5 concentration forecasting and can be used for PM2.5 concentration hourly prediction tasks. This study provides technical support for the large-scale, accurate remote sensing inversion of PM2.5 concentration and offers fundamental insights for regional atmospheric environmental protection. [ABSTRACT FROM AUTHOR]

Strategies for sensing toxic gases have garnered significant attention for environmental monitoring and air pollution control. In this study, we investigated the adsorption behavior of hazardous gases (H2S, SO2, SO3, N2O, and NO2) on an organic macrocyclic compound, hexaazaphenH2 (HA), using a quantum chemical approach. Density functional theory (DFT) was employed to study the interactions of HA with the target gases. Optimized geometries, electronic parameters, and natural bond orbital (NBO) charge transfer analyses confirmed stable interactions between the gases and HA. The charge-transfer spectra (CTS) analysis shows distinct absorption features in HA complexes, influenced by the attached analyte, highlighting their potential for selective gas sensing. Non-covalent interaction analysis revealed electrostatic interactions, steric repulsion, and van der Waals dispersion forces, indicating physisorption. The interaction energies followed the trend SO3@HA ≫; SO2@HA > H2S@HA > NO2@HA > N2O@HA, highlighting the significant adsorption of sulfur-containing analytes. Furthermore, the effect of an applied external electric field (EEF) ranging from −0.26 to 0.26 V Å−1 was studied, revealing that increasing EEF enhances adsorption strength and polarization, with SO3 showing the most significant changes. Additionally, electronic and charge transfer absorption spectroscopy indicated that the HA complexes exhibit distinct absorption peaks, which are influenced by the nature of the attached analyte. These findings suggest that HA is highly sensitive to harmful gases, making it a promising candidate for developing advanced environment-monitoring sensors. [ABSTRACT FROM AUTHOR]

Strategies for sensing toxic gases have garnered significant attention for environmental monitoring and air pollution control. In this study, we investigated the adsorption behavior of hazardous gases (H2S, SO2, SO3, N2O, and NO2) on an organic macrocyclic compound, hexaazaphenH2 (HA), using a quantum chemical approach. Density functional theory (DFT) was employed to study the interactions of HA with the target gases. Optimized geometries, electronic parameters, and natural bond orbital (NBO) charge transfer analyses confirmed stable interactions between the gases and HA. The charge-transfer spectra (CTS) analysis shows distinct absorption features in HA complexes, influenced by the attached analyte, highlighting their potential for selective gas sensing. Non-covalent interaction analysis revealed electrostatic interactions, steric repulsion, and van der Waals dispersion forces, indicating physisorption. The interaction energies followed the trend SO3@HA ≫; SO2@HA > H2S@HA > NO2@HA > N2O@HA, highlighting the significant adsorption of sulfur-containing analytes. Furthermore, the effect of an applied external electric field (EEF) ranging from −0.26 to 0.26 V Å−1 was studied, revealing that increasing EEF enhances adsorption strength and polarization, with SO3 showing the most significant changes. Additionally, electronic and charge transfer absorption spectroscopy indicated that the HA complexes exhibit distinct absorption peaks, which are influenced by the nature of the attached analyte. These findings suggest that HA is highly sensitive to harmful gases, making it a promising candidate for developing advanced environment-monitoring sensors. [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]

Fine particulate matter (PM2.5) is closely linked to human health, with its sources generally divided into local emissions and regional transport. This study combined concentration-weighted trajectory (CWT) analysis with the HYSPLIT trajectory ensemble to obtain hourly-resolution pollutant source results. The Extreme Gradient Boosting (XGBoost) model was then employed to simulate local emissions and ambient PM2.5 in Beijing from 2013 to 2020. The results revealed that clean air masses influencing the Beijing area mainly originated from the north and east regions, exhibiting a strong winter and weak summer pattern. Following the implementation of the Air Pollution Prevention and Control Action Plan (Action Plan) by the Chinese government in 2017, pollution in Beijing decreased significantly, with the most substantial reduction in regional transport pollution events occurring in the west region during summer. Regional transport pollution events were most frequent in spring, up to 1.8 times higher than in winter. Pollutants mainly originated from the west and south regions, while polluted air masses from the east showed the least reduction, and the proportion of pollution sources from this region is gradually increasing. From 2013 to 2020, local emissions were the main contributors of pollution events in Beijing. The Action Plan has more effectively reduced pollution caused by regional transport, particularly during autumn and winter. This finding underscores the importance of Beijing prioritizing local emission reduction while also considering potential contributions from the east region to effectively mitigate pollution events. [ABSTRACT FROM AUTHOR]

Inhomogeneities in the sunshine duration (SSD) observational series, caused by non-climatic factors like China's widespread transition from manual to automatic SSD recorders in 2019 or station relocations, have hindered accurate estimate of near-surface solar radiation for the analysis of global dimming and brightening as well as related applications, such as solar energy planning and agriculture management. This study compiled raw SSD observational data from 1961 to 2022 at more than 2,200 stations in China and clearly found that the improved precision from 0.1 hour to 1 minute following the instrument update in 2019 led to a sudden reduction in the frequency of zero SSD from 2019 onwards, referred to as the day0-type discontinuity. For the first time, we systematically corrected this known day0-type discontinuity at 378 stations (17 %) in China, resulting in an SSD series with comparable frequencies of zero value before and after 2019. On this base, we constructed a homogenization procedure to detect and adjust discontinuities in both the variance and mean of daily SSD from 1961 to 2022. Results show that a total of 1,363 (60 %) stations experienced breakpoints in SSD, of which ~65 % were confirmed by station relocations and instrument replacements. Compared to the raw SSD, the homogenized SSD is more continuous to the naked eye for various periods, and presents weakened dimming across China from 1961 to 1990 but a non-significant positive trend by a reduction of 60 % in the Tibetan Plateau, suggesting that the homogenized SSD tends to better capture the dimming phenomenon. The northern regions continue dimming from 1991 to 2022 but the southern regions of China brighten slightly. The implementation of the Action Plan for Air Pollution Prevention and Control since 2013 has contributed to a reversal of SSD trend thereafter, which is better reflected in the homogenized SSD with a trend shift from -0.02 to 0.07 hours·day-1/decade from 2013 to 2022 in China, especially in heavily polluted regions. Besides, the relationships of cloud cover fraction and aerosol optical depth with SSD are intensified in the homogenized dataset. These results highlight the importance of the homogenized SSD in accurately understanding the dimming and brightening phenomena. The homogenized SSD dataset is publicly available for community use at https://doi.org/10.11888/Atmos.tpdc.301478 (He et al., 2024). [ABSTRACT FROM AUTHOR]

In recent years, the application of machine learning methods has become increasingly common in atmospheric science, particularly in modeling and predicting processes that impact air quality. This study focuses on predicting hydrogen production from solid oxide electrolytic cells (SOECs), a technology with significant potential for reducing greenhouse gas emissions and improving air quality. We developed two models using artificial neural networks (ANNs) and support vector machine (SVM) to predict hydrogen production. The input variables are current, voltage, communication delay time, and real-time measured hydrogen production, while the output variable is hydrogen production at the next sampling time. Both models address the critical issue of production hysteresis. Using 50 h of SOEC system data, we evaluated the effectiveness of the ANN and SVM methods, incorporating hydrogen production time as an input variable. The results show that the ANN model is superior to the SVM model in terms of hydrogen production prediction performance. Specifically, the ANN model shows strong predictive performance at a communication delay time ε = 0.01–0.02 h, with RMSE = 2.59 × 10−2, MAPE = 33.34 × 10−2%, MAE = 1.70 × 10−2 Nm3/h, and R2 = 99.76 × 10−2. At delay time ε = 0.03 h, the ANN model yields RMSE = 2.74 × 10−2 Nm3/h, MAPE = 34.43 × 10−2%, MAE = 1.73 × 10−2 Nm3/h, and R2 = 99.73 × 10−2. Using the SVM model, the prediction error values at delay time ε = 0.01–0.02 h are RMSE = 2.70 × 10−2 Nm3/h, MAPE = 44.01 × 10−2%, MAE = 2.24 × 10−2 Nm3/h, and R2 = 99.74 × 10−2, while at delay time ε = 0.03 h they become RMSE = 2.67 × 10−2 Nm3/h, MAPE = 43.44 × 10−2%, MAE = 2.11 × 10−2 Nm3/h, and R2 = 99.75 × 10−2. With this precision, the ANN model for SOEC hydrogen production prediction has positive implications for air pollution control strategies and the development of cleaner energy technologies, contributing to overall improvements in air quality and the reduction of atmospheric pollutants. [ABSTRACT FROM AUTHOR]

The release of mercury (Hg) from coal-fired power plants (CPPs) into local ecosystems poses substantial environmental and health hazards. This study was conducted in Chungcheong-nam-do, South Korea, a region featuring over half of the country's coal power facilities, to estimate the impacts of CPPs on Hg distribution in forest ecosystems. By analyzing Hg concentrations in pine tree rings and soil at 21 locations around CPPs and comparing them to control sites and industrial zones, we present a nuanced understanding of the effects of CPPs on Hg concentration. The analysis of Hg concentrations in tree rings showed a significant decrease in Hg levels as the distance from the power plants increased, suggesting that CPPs primarily influence Hg distribution in trees within a 25 km radius. In contrast, soil Hg concentrations did not exhibit a clear trend. This may reflect the limitations of this study in accounting for the physicochemical properties of the soil at each sampling site. Nevertheless, the Potential Ecological Risk Index for soil Hg contamination indicated a higher risk rating within a 1 km radius of the CPPs compared to other locations. Hg concentrations in tree rings have shown a steady decline since the 1970s, suggesting the positive effects of air pollution regulations. This also highlights the value of tree core samples as effective tools for monitoring historical Hg pollution. Furthermore, the higher historical concentrations of Hg in tree rings imply that trees may have acted as sinks for atmospheric Hg in the past. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of meteorological, emission and chemical influences on severe haze is essential for air pollution mitigation. However, the nonlinearity of the atmospheric system greatly hinders this understanding. In this study, we developed the quantitative decoupling analysis (QDA) method by applying the Factor Separation (FS) method into the model processes to quantify the effects of emissions (E), meteorology (M), chemical reactions (C), and their nonlinear interactions and impact on fine particulate matter (PM2.5) pollution. Taking a heavy‐haze episode in Beijing as an example, we show that different from the integrated process rate (IPR) and the scenario analysis approach (SAA) in previous studies, the QDA method explicitly demonstrate the nonlinear effects by decomposing the variation of PM2.5 concentration into individual contributions of E, M and C terms as well as the contributions from interactions among these processes. Results showed that M dominated the hourly fluctuation of the PM2.5 concentration. The C terms increase with increasing the level of haze, reaching maximum (0.37 μg · $\mathit{\cdot }$ m−3· $\mathit{\cdot }$ h−1) at the maintenance stage. Moreover, our method reveals that there are non‐negligible non‐linear effects of meteorological, emission, and chemical processes during pollution stage, with the mean accounting for 50% of the increase in PM2.5 concentrations, which is often ignored in the current air pollution control strategies. This study highlights that the QDA approach can be used to gain insight into the formation of heavy pollution, and to identify uncertainty in numerical models. Plain Language Summary: In this study, we developed the quantitative decoupling analysis (QDA) method by applying the factor separation method into the model processes for the first time to quantify the effects of emissions (E), meteorology (M), chemical reactions (C), and their nonlinear interactions on fine particulate matter (PM2.5) pollution. Different from the IPR and the SAA that used in previous studies, the QDA method explicitly considers the nonlinear effects by decomposing the hourly changes in the PM2.5 concentration into three pure contributions of E, M and C and the four multi‐contributions from interactions among these processes (i.e., ME, MC, CE, and MCE). Not only does this technique provide new reference ideas for the treatment of air pollution, but it is also an important tool for further studying the formation processes of heavy pollution and the influence of different physicochemical mechanisms. Key Points: The quantitative decoupling analysis (QDA) method applies the factor separation method into the model processes for the first timeThe QDA method quantify the effects of emissions, meteorology, chemical reactions, and their nonlinear interactions on PM2.5 pollutionThe QDA method provide a new technique for studying the formation processes of heavy pollution [ABSTRACT FROM AUTHOR]

Microgrid can realize energy saving and emission reduction and multi‐energy complementation, but the fluctuation of renewable energy output and the error of day‐ahead scheduling will threaten the stability of microgrid operation. For this reason, this article proposes a microgrid multi‐timescale optimal scheduling method based on new energy output scenario generation. First, the microgrid framework of this article is introduced, and an energy cycle emission reduction model taking into account electricity‐to‐gas conversion is designed; second, a new energy prediction error model is established based on the prediction box and Gaussian hybrid model, and the scenario of wind and photovoltaic power generation is generated by correcting the sampling intervals for error sampling; and then, based on the day‐ahead scheduling plan, an intraday cooling, heating and electricity two‐layer rolling optimization model is established to correct the day‐ahead scenario. Finally, the example analysis shows that the cyclic emission reduction model can realize the recycling of resources, reduce the fuel cost and carbon emission, and the generation of scenarios for wind power can smooth out the fluctuation of new energy power, which, together with the intra‐day two‐layer rolling optimization, can reduce the day‐ahead scheduling error and improve the stability of microgrid operation. [ABSTRACT FROM AUTHOR]

Although long-term exposures to air pollutants have been linked to mental disorders, existing studies remain limited and inconsistent. We investigated the relationship between exposure to particulate matter (PM) and depressive symptoms, as well as the potential role of sleep duration and physical activity. Using the surveillance data (2019 to 2022) of common diseases and risk factors among 312,390 students aged 10–25 years, logistic regression, generalized liner model (GLM) and restricted cubic spline (RCS) were employed to investigate the relationship between long-term exposure to PM and depressive symptoms. Significant associations were found between PM 1 (OR = 1.21, 95 % CI: 1.12–1.32), PM 2.5 (OR = 1.24, 95 % CI: 1.19–1.38), and PM 10 (OR = 1.87, 95 % CI: 1.69–2.07) and increased risks of depressive symptoms. Sleep duration and physical activity relieved these associations. The odds ratios (ORs) of PM 1 , PM 2.5 , and PM 10 on depressive symptoms were lower in group with sufficient sleep (1.02 vs. 1.49, 1.20 vs. 1.80, 2.15 vs. 2.23), lower in group with high level MVPA (1.13 vs. 1.48, 1.14 vs. 1.58, 1.85 vs. 2.38), and lower in group with high level outdoor activity (1.19 vs. 1.55, 1.23 vs. 1.63, 1.83 vs. 2.72). Conclusions about causality remain speculative because of the cross-sectional design. Sufficient sleep duration and outdoor activity may mitigate the decline in mental health among adults in developing countries caused by long-term exposure to PM. This contribution enhanced our understanding of the mechanisms linking air pollution to mental health. • Three-year average levels of PM before the survey year were used as long-term exposure based on educational institutions address. • Particulate matters were associated with an increased risk of depressive symptoms. • Sleep duration and physical activity relieved these associations. • This study provided evidence supporting the urgent need for air pollution control and providing effective ways for mental health. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions and controlling air pollution is a dual challenge for China in addressing climate change. Analyzing the synergistic relationship between PM2.5 and CO2 in urban green spaces has become an important part of promoting pollution control. The study investigated the influence and synergistic relationship between the spatial pattern of community parks on PM2.5 and CO2 in Xi'an City, Shaanxi Province, through practical measurement and ENVI-met/Open Studio simulation calculations. The results showed that: (1) Within the sphere of influence, community parks exhibit a positive synergy varying with distance, peaking at 400 m and declining as 300 m > 500 m > 200 m > 100 m. (2) The green space rate, total edge (TE), and mean patch shape index (SHAPE_MN) positively influence the synergistic mitigation of PM2.5 and CO2, with a defined maximum impact boundary. The strongest synergistic reduction of PM2.5 and CO2 occurs at a green space rate of 85%, TE1200, and SHAPE1.2, with optimal influence boundaries of 300 m, 200 m, and 100 m, respectively. This conclusion demonstrates the key role of green space in community parks in the synergistic abatement and provides a scientific basis and practical guidance for the planning and design of urban green space under the goal of "dual-carbon". [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]

Urban forest policies have garnered increasing global attention for their critical role in providing key ecosystem services such as carbon sequestration, air pollution control, microclimate regulation, and biodiversity enhancement, as well as their potential to drive green innovation and sustainable urban development. This study utilized panel data from 273 Chinese cities between 2000 and 2022, employing a quasi-natural experiment and a difference-in-differences (DID) model to systematically evaluate the impact of the National Forest City Policy (NFCP) on urban green innovation. The results indicate that NFCP significantly enhances urban green innovation, with these findings remaining robust across a series of validation tests. Mechanism analysis revealed that the policy fosters green innovation by increasing environmental attention, facilitating talent aggregation, and reducing carbon emissions. Furthermore, heterogeneity analysis showed that the policy's effects are more pronounced in small- and medium-sized cities, non-transportation hub cities, and economically developed regions. Based on these findings, this paper offers recommendations for optimizing policy implementation across different city types to further promote sustainable urban green economic development. This study broadens the research perspective on the relationship between urban policies and green innovation, providing more precise decision-making guidance for policymakers while also highlighting the important role urban forests play in enhancing ecosystem services and driving sustainable urban growth. [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]

The emission of CO toxic gases seriously endangers environmental safety and human health. At present, the use of catalysts for catalytic oxidation of CO has become the mainstream research direction. In this study, CuO@TiO2 catalysts with different ratios were prepared by solvent hydrothermal method, and the effects of reaction pretreatment temperature and other factors on the catalytic oxidation performance of CO were investigated. The experimental results show that the catalyst can achieve 100% conversion of CO at 115°C under the condition of pretreament at 350°C for 2 h. This excellent performance is due to the uniform distribution of the active component on the surface of the TiO2 support, and the large pore structure constructed by the solvent hydrothermal method enhances the adsorption and activation of CO. This work provides an idea for the preparation of CO oxidation catalysts. [ABSTRACT FROM AUTHOR]

The endless adverse effects of air pollution incidents have raised significant public concerns in the past few decades. The measure of air pollution, that is, the air quality index (AQI), is highly volatile and associated with different kinds of uncertainties. Following this, the study and development of accurate fuzzy time series forecasting (TSF) methods for predicting the AQI have a significant role in air pollution control and management. Motivated by this, in this paper, a systematic study is made to evaluate the true potential of fuzzy TSF methods employing traditional fuzzy set (TFS), intuitionistic fuzzy set (IFS), hesitant fuzzy set (HFS), and neutrosophic fuzzy set (NFS) in forecasting the AQI. Two novel high‐order fuzzy TSF methods, TFS‐multilayer perceptron (MLP) and HFS‐MLP, are proposed employing TFS and HFS in which ratio trend variation of AQI data is used instead of original AQI, MLP is used to model the fuzzy logical relationships (FLRs), and none/mean of aggregated membership values are used while modeling the FLRs using MLP. The results from the proposed fuzzy TSF methods are compared with recently proposed fuzzy TSF methods employing TFS, IFS, and NFS and six popular machine learning models, including MLP, support vector regression (SVR), Bagging Regressors, XGBoost, long‐short term memory (LSTM), and convolutional neural network (CNN). The "Wilcoxon Signed‐Rank test" and "Friedman and Nemenyi hypothesis test" are applied to the results obtained by employing different ratios in the train‐validation‐test to draw decisive conclusions reliably. The simulation results show the statistical dominance of the proposed TFS‐MLP method over all other crisp and fuzzy TSF methods employed in this paper. [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]

[Display omitted] Exposure to polycyclic aromatic hydrocarbons (PAHs) in the Chinese population was among the highest globally and associated with various adverse effects. This study examines the impact of China's two-phase clean air initiatives, namely the Air Pollution Prevention and Control Action Plan (APPCAP) in 2013–2017 and the Blue-Sky Defense War (BSDW) in 2018–2020, on PAH levels and human exposures in Beijing. To evaluate the effects of APPCAP, we measured 16 PAHs in 287 PM 2.5 samples collected in Beijing and 9 PAH metabolites in 358 urine samples obtained from 54 individuals who traveled from Los Angeles to Beijing between 2014 and 2018. The concentration of PM 2.5 -bound benzo[a]pyrene equivalents (BaPeq) decreased by 88.5% in 2014–2018 due to reduced traffic, coal, and biomass emissions. PAH metabolite concentrations in travelers' urine decreased by 52.3% in Beijing, correlated with changes in PM 2.5 and NO 2 levels. In contrast, no significant changes were observed in Los Angeles. To evaluate BSDW's effects, we collected 123 additional PM 2.5 samples for PAH measurements in 2019–2021. We observed sustained reductions in BaPeq concentrations attributable to reductions in coal and biomass emissions during the BSDW phase, but those from traffic sources remained unchanged. After accounting for meteorological factors, China's two-phase clean air initiatives jointly reduced Beijing's PM 2.5 -bound BaPeq concentrations by 96.6% from 2014 to 2021. These findings provide compelling evidence for the effectiveness of China's clean air actions in mitigating population exposure to PAHs in Beijing. [ABSTRACT FROM AUTHOR]

Metal‐organic frameworks (MOFs), an emerging class of porous crystalline material, are identified as a promising candidate for tackling the most formidable global challenges due to their unique and intriguing properties including well‐defined porous crystal structure, large specific surface area, and vast tunable chemical and physical property. Air pollution resulting from various toxic gases, volatile organic compounds, and fine particulate matters has posed a big threat to human health and ecological sustainability. In recent years, various MOFs are studied and shown great potential as active materials for air pollution control in both pollutant detection and air quality remediation. In this review, the most recent research progress for MOFs‐based materials for air pollution control is summarized. The discussion is categorized based on the different approaches for air quality control with a focus on the performance correlations to the structure and functionality of MOFs. In addition, the technical merits and downsides of each approach are discussed based on their application scenarios. Finally, the remaining challenges and future research directions in this field are proposed. [ABSTRACT FROM AUTHOR]

In megacities, road traffic is a major source of particulate matter (PM), requiring a critical understanding of effective air pollution control. Despite existing methods to determine PM emission factors (EFs) of vehicles, accurate estimation of PM emissions under real driving conditions remains challenging. We aimed to assess the EFs of organic aerosol (OA) and equivalent black carbon (eBC) from vehicles through on-road measurements in Seoul, Korea, to understand real-world PM emissions. We used a mobile laboratory equipped with an aerosol mass spectrometer and an aethalometer to measure the composition of PM. On-road measurements were conducted in vehicle tunnels, urban roadways, and residential areas, and the characteristics of measurement points were compared and analyzed. Our results showed that concentrations of OA increased proportionally with the influence of vehicle exhaust, while oxidation states of the OA decreased. Mobile measurements revealed spatial heterogeneities in aerosols, highlighting distinct characteristics of fresh OA on vehicle roads and elevated oxidation state values in residential areas. Active nitrate formation near vehicles led to elevated NO3 concentrations on roads compared to residential areas. Our study shows that mobile PM measurements, including OA and eBC, are valuable for the direct evaluation of emission inventories. However, given that the calculated EFs may not be applicable to other cities due to differences in vehicle composition and traffic conditions, the development of city-specific EFs will be necessary in the future. Furthermore, it is recommended to integrate this methodology with conventional emission inventories to identify vehicle-type-specific emissions. [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 decreases and increases in nitrogen dioxide (NO2) concentrations in Mexico during the periods are mainly due to changes in human activities and not only due to factors such as traffic and weather conditions. The confinement imposed by the pandemic has produced positive effects, such as the reduction of polluting emissions, including nitrogen dioxide (NO2). The pandemic offered a glimpse into how human activities impact air quality. To investigate the changes in the spatial concentration of NO2 in Mexico during the COVID-19 confinement and the subsequent period, comparing the months of April 2019 and 2020, as well as April 2023. The Sentinel-5P TROPOMI sensor was used to obtain images of NO2. They were processed with SNAP software for Geometric Re-projections and ArcGIS 10.2 for change detection. During the confinement in Mexico in April 2020, NO2 concentrations decreased by 21.45% compared to April 2019. However, in April 2023, concentrations increased by 14.48% compared to 2020. The findings support that the Confinement measures temporarily reduced NO2 levels in Mexico. Similar patterns were observed globally. However, once normal activities resumed, NO2 emissions increased. Lockdown restrictions produced a temporary decrease in NO2 pollution in Mexico, but when the measures were lifted, emissions increased again. More rigorous policies are needed to maintain air quality. Continuous use of Sentinel-5P can help monitor and control air pollution in the country. In addition, the implementation of sustainable practices is suggested to reduce polluting emissions and promote a more resilient and sustainable society. [ABSTRACT FROM AUTHOR]

Introduction: Alzheimer’s disease (AD) and ambient particulate matter 10 (PM10) have been associated in epidemiological studies. However, the relationship between PM10 and risk of AD has not been proven to be causal. Thus we used two-sample multivariable Mendelian randomization (MR) to examine this relationship. Material and methods: Genome-wide association studies (GWAS) for PM10 from UK Biobank, AD from EBI GWAS and IEU OpenGWAS were used for discovery and replication, respectively. Pooled meta-analysis of the inverse variance weighted (IVW) method was the main method. Sensitivity analyses included MR-Egger regression, weighted median, weighted mode and leave-one-out methods. The multivariable MR model adjusted for education. The MR estimates of causality association were expressed as odds ratios (OR) and corresponding 95% confidence intervals (CI). Results: There were in total 17 and 19 genetic variants associated with PM10 in the discovery and replication steps, respectively. In the univariate MR, pooled meta-analysis of genetically predicted PM10 was associated with a 99% increased risk of AD (95% CI: 1.25, 3.15, p = 0.004) per 1 standard deviation (SD) increment of PM10 by IVW, and in the multivariable MR with pooled meta-analysis, we found that each SD increase in PM10 was associated with a 127% increase in the risk of AD (95% CI: 1.33, 3.86, p = 0.002) after accounting for education levels. Conclusions: Increased PM10 levels were found to be significantly related to an increased risk of AD. This study provided evidence of genetic prediction of a causal relationship between PM10 and the risk of AD, suggesting that air pollution control may have significant implications for the prevention of AD. [ABSTRACT FROM AUTHOR]

Electrostatic precipitators (ESPs) have shown promise in reducing particulate matter (PM) emissions, but their potential for simultaneous NOx reduction in small-scale combustion systems remains underexplored. This study focuses on using non-thermal plasma generated in a corona discharge to reduce PM and NOx emissions from small-scale combustion. ESP was specifically designed for a commercially available 15 kW boiler with wood pellet combustion and used with both positive and negative discharge polarity to control emissions without any chemical additives. ESP performance was evaluated across a range of specific input energies (SIE) in terms of particle mass and number concentrations and NOx concentrations obtained by continuous gas analysis. ESP ensured the reduction in PM concentrations from 48 mg/m3 to the magnitude of PM content in the ambient air. The highest precipitation efficiency was observed for particles in the 20–200 nm range. Concurrently, NOx emissions were reduced by up to 78%, from 178 mg/m3 to 39 mg/m3. These results were achieved at specific input energies of 36 for positive and 48 J/L for negative corona, which is significantly lower than those reported for many existing separate PM and NOx control systems. This study demonstrates the potential of ESPs as a compact, energy-efficient solution for simultaneous PM and NOx removal in small-scale combustion systems, offering promising implications for improving air pollution control technologies for small-scale combustion systems. [ABSTRACT FROM AUTHOR]

In the rapid development of air pollution over the past two decades in Shandong Province, it has played a detrimental role, causing severe damage to regional ecological security and public health. There has been little research at the county scale to explore the spatiotemporal causes and heterogeneity of PM2.5 pollution. This study utilizes a Geographically and Temporally Weighted Regression Model (GTWR) to environmentally model meteorological elements and socioeconomic conditions in Shandong Province from 2000 to 2020, aiming to identify the key driving factors of PM2.5 concentration changes across 136 counties. The results show that PM2.5 pollution in Shandong Province peaked in 2013, followed by a rapid decline in pollution levels. Geographically, counties in the western plains of Shandong generally exhibit higher pollution levels, while most counties in the central hills of Shandong and the Jiaodong Peninsula are in low pollution areas. Strong winds positively influence air quality in the southeast of Shandong; high temperatures can ameliorate air pollution in areas outside the southeast, whereas air pressure exhibits the opposite effect. Precipitation shows a significant negative correlation in the Laizhou Bay and central Shandong regions, while relative humidity primarily exerts a negative effect in coastal areas. The impact of fractional vegetation cover is relatively mild, with positive effects observed in southern Shandong and negative effects in other regions. Population density shows a significant positive correlation in the western plains of Shandong. Economic factors exhibit predominantly positive relationships, particularly in the northwest and the Jiaodong Peninsula. Electricity consumption in southern Shandong correlates positively, while industrial factors show positive effects province-wide. PM2.5 pollution in Shandong Province demonstrates significant spatiotemporal heterogeneity, aligning with governmental expectations for the effectiveness of air pollution control measures. The conclusions of this study can be utilized to assess the efficiency of air pollution abatement at the county level and provide quantitative data support for the revision of regional emission reduction policies. [ABSTRACT FROM AUTHOR]

Air Pollution has been an eclectic ecological problem exposed and exaggerated by constant urbanization, massive industrialization, population explosion, and unregulated exploitation of resources, which has been affecting both the flora and fauna for far too long. So, a need has arisen in the past few decades to monitor, predict, and finally provide scientific control measures for Air Pollution. The primary focus of this review is on the changing trends in Air Quality research over time and assess where the research stands now in the giant scheme of things regarding Air Pollution. Many modern techniques have been employed in its study, both at the academic and research level, such as the usage of satellite-based atmospheric imagery and datasets, high-resolution sensing systems, and deep learning analysis techniques, which have amplified the research in this field. From manual monitoring to ground-based local sensors, to now advanced high-resolution space-based satellite monitoring and the usage of different kinds of computational intelligence/soft-computing techniques for analysis, forecasting, and modelling, giant leaps have been made in this research field. Recent research is focused on cumulating data availability, including geospatial datasets, deep learning, advanced statistical modelling, and cloud computing platforms in air pollution and environmental studies. In this review, a comprehensive analysis of current and previous studies of air pollution is conducted to give a basic idea about the problem, and the sciences of its monitoring and modelling and different techniques available at its disposal, such as computer simulations, data analytics and computing techniques. Finally, a brief critical analysis of past research, methodologies, present trends, emerging challenges and future research directions are discussed. [ABSTRACT FROM AUTHOR]

Biomass plays a crucial role in the low‐carbon energy transition, with a projected contribution of 18.7% to the global energy supply by 2050. However, biofuel combustion has been a notable source of toxic mercury emissions, yet the future trends and distribution of the emissions remain inadequately understood. Here, we projected biofuel combustion under various Shared Socioeconomic Pathways (SSPs) using the Global Change Assessment Model and assessed associated mercury emissions in cooking, heating, and power generation over 2020–2050, aligning with the carbon neutrality target. Our analysis reveals that global biofuel mercury emissions are projected to be 9.90–18.40 tons by 2050, compared to the annual emissions of 13.89 tons in 2020. Notably, a substantial increase in emissions from power generation is expected, escalating from 0.57 tons in 2020 to 4.69–8.27 tons by 2050, with China and Southeast Asia emerging as primary contributors. Conversely, mercury emissions from cooking and heating are expected to decrease from 13.32 tons in 2020 to 4.40–11.53 tons by 2050, except in Africa under SSP2, where the emissions may increase from 5.91 to 6.69 tons. Our findings provide a scientific basis for policies aimed at achieving carbon neutrality targets while adhering to the Minamata Convention on Mercury. Plain Language Summary: Biomass plays a crucial role in the low‐carbon energy transition, but its combustion releases toxic mercury. Our study projected mercury emissions from biofuel combustion from 2020‐2050 under different Shared Socioeconomic Pathways scenarios and global net‐zero emissions constraints. We find that while mercury emissions from cooking and heating will decrease, emissions from power generation will significantly increase, especially in China and Southeast Asia. To achieve carbon neutrality and reduce mercury pollution, there is an urgent need for the deployment of air pollution control devices in biomass direct‐fired power plants and the transformation of biomass into cleaner bioenergy should be promoted. Key Points: Spatial and temporal patterns of mercury emissions from biofuel combustion across 32 country/regions under carbon neutrality are delineatedCooking and heating constituted 95.9% of mercury emissions from biofuel combustion in 2020, dropping to one‐third of that level by 2050Mercury emissions of biomass power generation rise by 7.2–13.4 times in 2020–2050, dominated by carbon capture and storage technology in 2050 [ABSTRACT FROM AUTHOR]

Global warming and urban heat island effects negatively impact the development of urban thermal environments, making them very uncomfortable to live in. Green space plays an essential role in controlling and improving air pollution, regulating the microclimate, and enforcing compliance with public health requirements. Therefore, this study explored the relationship between green space and thermal comfort in the historical neighborhood of Dazhalan in Beijing through questionnaires, observational interviews, and numerical simulations. The current situation of the microclimate environment in the green space of the block was observed first. Then, the microclimate environment was simulated by the ENVI-met 5.6 software. The thermal comfort of the three types of space, such as enclosed space, strip space, and corner space, was also evaluated to explore the coupling relationship between different green space elements and microclimate evaluation factors. It was found that the thermal comfort PET had a positive correlation with the sky openness SVF. The green space morphology was quantitatively measured, and it was found that the thermal comfort PET had a negative correlation with the three-dimensional green quantity of green space. The paper developed managing strategies for optimizing the layout and construction mode of the green space. The ultimate goal was to rationally match the greening planting, improve the pavement of the underlying surface of the block, and optimize the design of the internal space topography. [ABSTRACT FROM AUTHOR]

PM2.5 poses a serious threat to human life and health, so the accurate prediction of PM2.5 concentration is essential for controlling air pollution. However, previous studies lacked the generalization ability to predict high-dimensional PM2.5 concentration time series. Therefore, a new model for predicting PM2.5 concentration was proposed to address this in this paper. Firstly, the linear rectification function with leakage (LeakyRelu) was used to replace the activation function in the Temporal Convolutional Network (TCN) to better capture the dependence of feature data over long distances. Next, the residual structure, dilated rate, and feature-matching convolution position of the TCN were adjusted to improve the performance of the improved TCN (LR-TCN) and reduce the amount of computation. Finally, a new prediction model (GRU-LR-TCN) was established, which adaptively integrated the prediction of the fused Gated Recurrent Unit (GRU) and LR-TCN based on the inverse ratio of root mean square error (RMSE) weighting. The experimental results show that, for monitoring station #1001, LR-TCN increased the RMSE, mean absolute error (MAE), and determination coefficient (R2) by 12.9%, 11.3%, and 3.8%, respectively, compared with baselines. Compared with LR-TCN, GRU-LR-TCN improved the index symmetric mean absolute percentage error (SMAPE) by 7.1%. In addition, by comparing the estimation results with other models on other air quality datasets, all the indicators have advantages, and it is further demonstrated that the GRU-LR-TCN model exhibits superior generalization across various datasets, proving to be more efficient and applicable in predicting urban PM2.5 concentration. This can contribute to enhancing air quality and safeguarding public health. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

To study the level of atmospheric PM2.5 and its heavy metal pollution near the coal gangue mountain, this study analyzed the content of seven heavy metals (Zn, Pb, Cu, Cd, Hg, Ni, Cr) and As through the PM2.5 samples from the vicinity of a large-scale coal gangue filed of Tongting coal mine in Huaibei, and evaluated the level of pollution, sources, and health effects. The results showed that during the sampling period, the average concentration of PM2.5 near the coal gangue field was 169.83 µg·m-3, which was 2.26 times that of the national air quality level II daily standard. The coal gangue field may be an important source of air pollution, with the degree of heavy metal and arsenic pollution in the order of Cd, Pb, Zn, Hg (extremely heavy pollution) > As, Cu (medium pollution) > Ni, Cr (light pollution) and coal gangue dust and mining dust contributed more. This study provides data for atmospheric particulate matter and heavy metal and arsenic pollution levels near coal gangue fields and provides a theoretical basis for air pollution prevention and control near coal gangue hills. [ABSTRACT FROM AUTHOR]

Abstract Microgrid can realize energy saving and emission reduction and multi‐energy complementation, but the fluctuation of renewable energy output and the error of day‐ahead scheduling will threaten the stability of microgrid operation. For this reason, this article proposes a microgrid multi‐timescale optimal scheduling method based on new energy output scenario generation. First, the microgrid framework of this article is introduced, and an energy cycle emission reduction model taking into account electricity‐to‐gas conversion is designed; second, a new energy prediction error model is established based on the prediction box and Gaussian hybrid model, and the scenario of wind and photovoltaic power generation is generated by correcting the sampling intervals for error sampling; and then, based on the day‐ahead scheduling plan, an intraday cooling, heating and electricity two‐layer rolling optimization model is established to correct the day‐ahead scenario. Finally, the example analysis shows that the cyclic emission reduction model can realize the recycling of resources, reduce the fuel cost and carbon emission, and the generation of scenarios for wind power can smooth out the fluctuation of new energy power, which, together with the intra‐day two‐layer rolling optimization, can reduce the day‐ahead scheduling error and improve the stability of microgrid operation.

• A high-resolution refined speciated VOCs emission inventory of Central China was established. • 168 subsectors speciation of VOCs emission were established, alkanes (30.5 %) and aromatic hydrocarbons (28.8 %) were the main groups while toluene (6.6 %) was the highest specie. • Based on VOCs online data, tracer ratio method by trace gas CO and PMF receptor model were used to evaluate the species-specific emission and source structure. • Remote sensing satellite inversion of HCHO emission was used to indirectly verify the VOCs spatial distribution. Improving the accuracy of anthropogenic volatile organic compounds (VOCs) emission inventory is crucial for reducing atmospheric pollution and formulating control policy of air pollution. In this study, an anthropogenic speciated VOCs emission inventory was established for Central China represented by Henan Province at a 3 km × 3 km spatial resolution based on the emission factor method. The 2019 VOCs emission in Henan Province was 1003.5 Gg, while industrial process source (33.7%) was the highest emission source, Zhengzhou (17.9%) was the city with highest emission and April and August were the months with the more emissions. High VOCs emission regions were concentrated in downtown areas and industrial parks. Alkanes and aromatic hydrocarbons were the main VOCs contribution groups. The species composition, source contribution and spatial distribution were verified and evaluated through tracer ratio method (TR), Positive Matrix Factorization Model (PMF) and remote sensing inversion (RSI). Results show that both the emission results by emission inventory (EI) (15.7 Gg) and by TR method (13.6 Gg) and source contribution by EI and PMF are familiar. The spatial distribution of HCHO primary emission based on RSI is basically consistent with that of HCHO emission based on EI with a R -value of 0.73. The verification results show that the VOCs emission inventory and speciated emission inventory established in this study are relatively reliable. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract Background Air pollution poses a significant threat to global public health. While broad mitigation policies exist, an understanding of the economic consequences, both in terms of health benefits and mitigation costs, remains lacking. This study systematically reviewed the existing economic implications of air pollution control strategies worldwide. Methods A predefined search strategy, without limitations on region or study design, was employed to search the PubMed, Scopus, Cochrane Library, Embase, Web of Science, and CEA registry databases for studies from their inception to November 2023 using keywords such as “cost–benefit analyses”, “air pollution”, and “particulate matter”. Focus was placed on studies that specifically considered the health benefits of air pollution control strategies. The evidence was summarized by pollution control strategy and reported using principle economic evaluation measurements such as net benefits and benefit–cost ratios. Results The search yielded 104 studies that met the inclusion criteria. A total of 75, 21, and 8 studies assessed the costs and benefits of outdoor, indoor, and mixed control strategies, respectively, of which 54, 15, and 3 reported that the benefits of the control strategy exceeded the mitigation costs. Source reduction (n = 42) and end-of-pipe treatments (n = 15) were the most commonly employed pollution control methodologies. The association between particulate matter (PM) and mortality was the most widely assessed exposure-effect relationship and had the largest health gains (n = 42). A total of 32 studies employed a broader benefits framework, examining the impacts of air pollution control strategies on the environment, ecology, and society. Of these, 31 studies reported partially or entirely positive economic evidence. However, despite overwhelming evidence in support of these strategies, the studies also highlighted some policy flaws concerning equity, optimization, and uncertainty characterization. Conclusions Nearly 70% of the reviewed studies reported that the economic benefits of implementing air pollution control strategies outweighed the relative costs. This was primarily due to the improved mortality and morbidity rates associated with lowering PM levels. In addition to health benefits, air pollution control strategies were also associated with other environmental and social benefits, strengthening the economic case for implementation. However, future air pollution control strategy designs will need to address some of the existing policy limitations.

Satellite remote sensing predominantly employs optical properties for aerosol classification, often neglecting aerosol mixing and lacking validation accuracy. This study defines five aerosol types: marine, continental, dust, urban‐industrial, and biomass‐burning. Proposing the external optical mixing optimization solver (EOMOS) model based on the external mixing assumption, the model's accuracy is enhanced by approximately 95.0% through constraints and optimization. Perturbation experiments on particle size distribution and complex refractive index validate the model's robustness. The EOMOS model analyzes aerosol mixing states, and quantifies contributions to aerosol optical depth (AOD) for each aerosol type, surpassing traditional methods by at least 139.7%. Additionally, the EOMOS model examines trends in aerosol type AOD, revealing a noticeable post‐2013 reduction in AOD of urban‐industrial aerosols in Beijing, suggesting pollution mitigation. In Brazil, urban‐industrial and biomass‐burning aerosol AODs were 328.1% and 107.7% higher in 2005, 2007, and 2010, primarily due to fire impact. Plain Language Summary: Aerosols are tiny particles suspended in the atmosphere, and have complex impacts on climate, human health, and the environment. This study analyzes the external mixing state of aerosols based on optical properties and provides a practical and feasible method for characterizing them. Five primary aerosol types were predefined and their mixing states were determined by relating them to optical observations. The results revealed that urban‐industrial aerosols in Beijing have gradually decreased since 2013, partly due to the implementation of the "Action Plan for the Prevention and Control of Air Pollution (2013–2017)" by the Chinese government. However, they remain the predominant pollutants, with peak concentrations occurring in July. In contrast, the Brazilian region experienced a sudden increase in biomass‐burning aerosols in 2005, 2007, and 2010 due to wildfires. Additionally, seasonal variations in aerosol types were identified in various regions, aligning well with actual conditions. Sensitivity experiments, site analyses, and verification collectively demonstrated the model's robustness. Key Points: A novel optimal model was developed to infer the mixing states of five aerosol types by using AERONET observations as constraintsThe effectiveness of the model was validated by the consistency of the retrieved optical parameters in eight observation bandsThe model was applied to analyze the dominant aerosol type and the tendency of global sites in the long term [ABSTRACT FROM AUTHOR]

Biogenic volatile organic compounds (BVOCs) are regarded as important precursors for ozone and secondary organic aerosol, mainly from vegetation emissions. In the context of the expanding trend of vegetation greening, the development of high‐precision vegetation data and accurate BVOC emission estimates are essential to develop effective air pollution control measures. In this study, by integrating the multi‐source vegetation cover data, we established a high‐resolution vegetation distribution (HRVD) data set to develop a high spatio‐temporal resolution emission inventory and investigated the impact of different land cover data sets on emission simulation and impact of land cover change on BVOC emissions during 2001–2020. The annual total BVOC emissions in China for 2020 was 15.66 Tg, which were mainly from trees. The emissions simulated by CNLUCC and MODIS data sets were 1.53% and 1.72% higher than those simulated by HRVD data sets, respectively. The spatial distribution of emission differences was consistent with that of land cover differences. The simulated BVOC emissions by the HRVD data set had the best accuracy as they improved the bias between modeling and observation from 69.06% to 65.35% and decreased the underprediction of observations by a factor of 2.13 compared with simulation by MEGAN default vegetation data. The annual BVOC emissions caused by changing vegetation distribution and LAIv (LAI of vegetation covered surfaces) enhanced at a rate of 72.06 Gg yr−1 during 2001–2020. LAIv was the main driver of emission variations. The total OH reactivity of the resulted BVOC emissions increased at a rate of 1.59 s−1 yr−1, with isoprene contributed the most. Plain Language Summary: Biogenic Volatile organic compounds (BVOCs) are the key precursors of fine particulate matter and ozone, that mainly from vegetation emissions. To help to develop effective air pollution control measures in the context of expanding vegetation coverage for realizing carbon neutralization in China, it is urgent to develop highly precise vegetation data and accurately estimate BVOC emission. A high‐resolution vegetation distribution data set was established through integrating multi‐source vegetation cover data. Using it, the simulated annual BVOC emission in China was 15.66 Tg and mainly emitted from trees. Emissions from varied growth forms had different compound compositions. The BVOC emission simulated using the high‐resolution vegetation distribution data set we developed had better accuracy than that using the single vegetation databases. The annual BVOC emissions caused by changing vegetation cover and leaf area index (LAI) enhanced at a rate of 72.06 Gg yr−1 during 2001–2020. LAI was the main driver of BVOC emission variations. The interannual variation and its spatial pattern of the OH loss rates of BVOCs during 2001–2020 were consistent with that of BVOC emissions, especially isoprene. Key Points: Annual total BVOC emissions in China for 2020 was 15.66 Tg and emissions from varied growth forms had different compositionsBVOC emission inventory simulated by the developed high‐resolution vegetation distribution (HRVD) data set had better accuracyBVOC emission enhanced at a rate of 72.06 Gg yr−1 during 2001–2020 caused by land cover change, mainly driven by changing leaf area index [ABSTRACT FROM AUTHOR]

Background: Both interpregnancy intervals (IPI) and environmental factors might contribute to low birth weight (LBW). However, the extent to which air pollution influences the effect of IPIs on LBW remains unclear. We aimed to investigate whether IPI and air pollution jointly affect LBW. Methods: A retrospective cohort study was designed in this study. The data of birth records was collected from the Jiangsu Maternal Child Information System, covering January 2020 to June 2021 in Nantong city, China. IPI was defined as the duration between the delivery date for last live birth and date of LMP for the subsequent birth. The maternal exposure to ambient air pollutants during pregnancy—including particulate matter (PM) with an aerodynamic diameter of ≤ 2.5 μm (PM2.5), PM10, ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2) and carbon monoxide (CO)—was estimated using a hybrid kriging-LUR-RF model. A novel air pollution score was proposed, assessing combined exposure to five pollutants (excluding CO) by summing their concentrations, weighted by LBW regression coefficients. Multivariate logistic regression models were used to estimate the effects of IPI, air pollution and their interactions on LBW. Relative excess risk due to interaction (RERI), attributable proportion of interaction (AP) and synergy index (S) were utilized to assess the additive interaction. Results: Among 10, 512 singleton live births, the LBW rate was 3.7%. The IPI-LBW risk curve exhibited an L-shaped pattern. The odds ratios (ORs) for LBW for each interquartile range increase in PM2.5, PM10, O3 and the air pollution score were 1.16 (95% CI: 1.01–1.32), 1.30 (1.06–1.59), 1.22 (1.06–1.41), and 1.32 (1.10–1.60) during the entire pregnancy, respectively. An additive interaction between IPI and PM2.5 was noted during the first trimester. Compared to records with normal IPI and low PM2.5 exposure, those with short IPI and high PM2.5 exposure had the highest risk of LBW (relative risk = 3.53, 95% CI: 1.85–6.49, first trimester). Conclusion: The study demonstrates a synergistic effect of interpregnancy interval and air pollution on LBW, indicating that rational birth spacing and air pollution control can jointly improve LBW outcomes. [ABSTRACT FROM AUTHOR]

Ozone‐depleting substances (ODSs) are well known as primary emission from the production and consumption of traditional industrial sectors. Here, we reported the unintentional emission of ODSs from iron and steel plants as a new source, basing on real‐world measurements of flue gases emitted from their major processes. The sintering was found to be the major emission process of ODSs, including chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), halons, methyl halide (CH3Cl), methyl chloroform, carbon tetrachloride, methyl bromide and halogenated very short‐lived substances. The median emission factors of CFC‐113, CFC‐115, HCFC‐22, and CH3Cl for typical sintering processes are 1.7, 0.7, 44.5 and 237.0 mg/t, respectively. Quantum chemical calculation figures out that the ODS species are mainly formed in the low efficiency combustion process of halogenated materials. Annual amounts of ODS and CFC‐11‐equivalent emissions were estimated to be 1,785 tons and 78 tons in 2019 over mainland China, respectively. Given these findings, this study provides a new prospective on searching for ODS emission sources, especially unintentional sources such as iron and steel industry and other combustion related activities. Plain Language Summary: Ozone‐depletion substances (ODSs) are halocarbons that can damage the ozone layer. Although the tropospheric chlorine has been continuously declined, some halogenated organic gaseous species are still found to be increasingly emitted. This study firstly reports the unintentional emission of ODSs from iron and steel plants via real‐world measurements. ODS emission from the sintering process shows larger ozone depleting effect than that from ironmaking and steelmaking processes. The halogen‐containing iron ore, coke and fluxing agent provide mineral halogens for the generation of halogenated compounds, while the high temperature of combustion atmosphere in the sinter machine benefits for the chemical combination of halogens and hydrocarbons. Current air pollution control devices cannot effectively remove ODSs. This study aims at attracting attention to the identification and control of unintentional ODS emission sources in the future. Key Points: CFCs, HCFCs and halons were observed to be unintendedly emitted from iron and steel industryThe total CFC‐11‐eq emission from iron and steel industry was 78 tons over mainland China in 2019Homogenous gas‐phase formation routes of ODS species in flue gas were proposed and calculated [ABSTRACT FROM AUTHOR]