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This paper looks at the relationship between enterprise size and pollution abatement costs using firm-level datasets in China, where size-dependent distortions may exist in the distribution of enterprises of different sizes. We find that the pollution abatement costs decrease with enterprise size. Compared to the smallest size, the magnitudes of marginal decreases in pollution abatement costs per 1000 CNY of industrial output in the largest size are 3.29 CNY, 0.54 CNY and 2.60 CNY for gas, SO 2 and dust, respectively. Moreover, we further explore the potential mechanisms for the decrease of pollution abatement costs from three perspectives: environmental protection-related investment, regulation intensity, and technological capability. Our findings suggest that the decrease in the intensity of environmental protection-related investment for large enterprises may be directly reduce the intensity of pollution abatement costs. Although large enterprises have low intensity of pollution abatement costs, this does not mean that they are subject to weaker environmental regulation. More importantly, we find that large enterprises may have more adoption of abatement technologies. For SO 2 and dust, the pollutant removal efficiency of the largest enterprises is 4.92% and 1.82% higher than that of the smallest enterprises, respectively. Our results are robust to different specifications. These findings help us enact appropriate policies for enterprises based on their size, thereby improving the efficacy of pollution abatement and achieving the optimal level of pollution abatement for the entire society., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Extreme PM 2.5 pollution has become a significant environmental problem in China in recent years, which is hazardous to human health and daily life. Noticing the importance of investigating the causes of extreme PM 2.5 pollution, this paper classifies cities across China into eight categories (four groups plus two scenarios) based on the generalized extreme value (GEV) distribution using hourly station-level PM 2.5 concentration data, and a series of multi-choice models are employed to assess the probabilities that cities fall into different categories. Various factors such as precursor pollutants and socio-economic factors are considered after controlling for meteorological conditions in each model. It turns out that SO 2 concentration, NO 2 concentration, and population density are the top three factors contributing most to the log ratios. Moreover, in both left- and right-skewed cases, the influence of a one-unit increase of SO 2 concentration on the relative probability of cities falling into different groups shows an increasing trend, while those of NO 2 concentration show a decreasing trend. At the same time, the higher the extreme pollution level, the bigger the effect of SO 2 and NO 2 concentrations on the probability of cities falling into normalized scenarios. The multivariate logit model is used for prediction and policy simulations. In summary, by analyzing the influences of various factors and the heterogeneity of their influence patterns, this paper provides valuable insights in formulating effective emission reduction policies., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

In China, despite the fact that the atmospheric environment quality has continued to improve in recent years, the PM 2.5 pollution still had not been controlled fundamentally and its driving mechanism was complex which remained to be explored. Based on the 1-km ground-level PM 2.5 datasets of China from 2000 to 2020, this study combined spatial autocorrelation, trend analysis, geographical detector, and multi-scale geographically weighted regression (MGWR) model to explore the spatial-temporal evolution of PM 2.5 in Shanxi Province and revealed its complex driving mechanism behind this process. The results reflected that (1) there was a pronounced spatial clustering of PM 2.5 concentration within Shanxi Province, with PM 2.5 concentrations decreasing from southwest to northeast. From 2000 to 2020, the levels of PM 2.5 pollution demonstrated a decline over time, with its concentrations decreasing by 9.15 µg/m 3 overall. The Hurst exponent indicated a projected decrease in PM 2.5 concentrations in the central and northern areas of Shanxi Province, contrasting with an anticipated increase in other regions. (2) The geographical detector indicated that all drivers had significant influences on PM 2.5 concentrations, with meteorological factors exerting the greatest effects then followed by human activity and vegetation cover showing the least effects. (3) Both gross domestic product and population density exhibited positive correlations with PM 2.5 concentration, while vegetation fractional cover, wind speed, precipitation, and elevation exerted negative influences on PM 2.5 concentration all over the space. This study enriched the research content and ideas on the driving mechanism of PM 2.5 and provided a reference for similar studies., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Abstract: Papermaking enterprises are major polluters that cause water pollution and air pollution. This paper aims to provide analysis of the energy saving and emission reduction in papermaking enterprises.It is the inevitable choice of establishment industrial ecology park as the leading of the papermaking industry by the implementation of cleaner production and circulation economy. Shandong Haiyun efficient ecological park is taken for example.Researches of the developing strategies and policy support of energy saving and emission reduction is discussed. The paper is divided into five parts, the first part is the preface, the second part introduces the general of Haiyun efficient industrial park, the third part puts forward the scheme of clean production of Haiyun efficient ecological industrial park, the fourth part puts forward ecological industry pattern of Haiyun efficient ecological industrial park, the fifth part puts forward policy guarantee measures, and the sixth part makes the summary. [Copyright &y& Elsevier]

The goal of the current study is to evaluate the heavy metal rainfall contamination in the vicinity brought on by the Erbil Steel Factory in Iraq during the study period. The study's findings revealed the concentration of all studied heavy metals in the precipitation near and around the factory is significantly higher than that of the rural area of Barzan village which is used as a control site. The average concentration of the metals is in descending order manganese (Mn) > lead (Pb) > iron (Fe) > arsenic (As) > cobalt (Co) > selenium (Se) > mercury (Hg) > and cadmium (Cd) for the polluted site. The geo-accumulation index (I-geo) of the heavy metal Mn in the rainfall around the steel factory site is 6.28 > 5 which indicates extreme contamination. While the Igeo values of Cd, As, and Fe are 4.87, 4.54, and 4.04 > 4 that indicate heavy to extreme contamination, for Pb, 3.80 > 3 indicates moderate to heavy contamination, Cd 1.68 > 1 indicates moderate contamination, Hg 0.46 > 0 indicates uncontaminated to moderate contamination, and Se - 0.36 < 0 indicates uncontaminated. The pollution load index (PLI) of the rainwater around the steel factory site is 13.46 > 1, demonstrating that the area is highly metal-contaminated., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Based on data analysis and documentary research, this paper indicates that the main causes which negatively affect the preservation of paper documents in China are acidification and damaging pests such as moths and insects. The priorities for the preservation of Chinese documents are protecting the documents against air pollution and controlling pests and insects. Taking these problems and the Chinese national condition into account, the paper proposes to create a favorable microenvironment for decreasing the acidification of papers and to adopt various measures for controlling pests and insects. [ABSTRACT FROM AUTHOR]

To deal with severe air pollution arising from rapid development, a series of air pollution control policies have been implemented in China. Previous literature has explored the short-term economic impacts of air pollution control, but the long-term economic impacts, which can better reflect the effectiveness of air pollution control, have received less attention. This paper has constructed an integrated assessment framework combining a multi-sectoral computable general equilibrium (CGE) model, air quality estimation module, and health impact module, to explore the short-term (2016) and long-term (2030) impacts of air pollution control on China's economy, by setting 2015 as the base year. The possible future air pollution reduction scenarios were set based on the Thirteenth Five-Year Plan (FYP13) proposed by the Chinese government. Our results have shown that air pollution control would harm China's economy and such adverse effects would be increased by stricter pollution reduction targets. Taking the health benefits of air pollution control into account can effectively alleviate the GDP losses, and even reverse them into economic benefits in the long term. Compared with business-as-usual (BAU) scenario (no policy constraints on SO 2 and NO x emissions), the impact of air pollution control on GDP in the TAC&#95;VIII scenario (emission reduction targets of 20% for SO 2 and NO x are set every five years after 2015) would change from a loss of 1.20% in 2016 to an increase of 0.28% in 2030. Our results can provide policy implications for the optimization of China's air emissions control in the future., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Taking detailed rules for implementing of the plan on atmospheric pollution prevention and control in Beijing-Tianjin-Hebei region and surrounding areas promulgated and implemented in 2013 as the starting point, taking air pollution as the research object, using the propensity score matching method and difference-in-differences method to answer whether coordinated development of Beijing-Tianjin-Hebei is conducive to resolving the problem of air pollution. Using panel data from 251 cities in China 2003-2016, this paper discusses the environmental effects and mechanisms of coordinated urban cluster development. The results showed that the coordinated development of Beijing-Tianjin-Hebei had a significant and continuous effect on the convergence of PM2.5 to low levels. The results of further mechanism identification show that the policy of atmospheric joint defense rules in the process of Beijing-Tianjin-Hebei coordinated development mainly promotes the convergence of PM2.5 through population and economic scale effect, structural effect and technical effect, and promotes the convergence of PM2.5 in the low direction by reducing the economic growth scale of the city itself. It is suggested to further promote the formation and development of Beijing-Tianjin-Hebei integration, strengthen cooperation among local governments in environmental governance e across regions, and promote the green growth and coordinated development of urban clusters., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The market-oriented reform of China's power market has gradually transformed power prices from government pricing to market regulation, which not only promotes the production efficiency of industrial enterprises, but also inhibits the excessive consumption and waste of power by residential power users. This paper uses the data from 2006–2018 combined with the precious industrial power price data and macroeconomic data of 100 cities in China, takes the marketization reform of the power market in 2015 as a quasi-natural experiment, and uses the difference-in-differences model to empirically study the causal relationship between power market reform and air pollution for the first time. The study found that power market reform can reduce air pollution, and this conclusion is also supported by a number of robustness tests. Mechanism analysis shows that power market reform can reduce air pollution by improving power market efficiency, promoting technological progress, and reducing power consumption. Heterogeneity analysis shows that power market reform can suppress air pollution more significantly in eastern regions, regions with severe air pollution, and regions with larger populations. This paper not only provides new research perspectives and research ideas for air pollution prevention and control, but also provides empirical evidence for the positive externalities of power market reform. [ABSTRACT FROM AUTHOR]

The reduction of greenhouse gas emissions is a shared challenge encountered by nations worldwide. As China is on its way toward a green economy, it is worth studying whether producer services agglomeration, a key driver of economic transition, can promote low-carbon urban development. Using panel data of 257 cities across China from 2006 to 2019, this paper examines the influence of producer services agglomeration on urban carbon emissions with spatial econometric models. The findings reveal a positive spatial correlation in regional carbon emissions. The agglomeration of producer services notably decreases the intensity of local carbon emissions, yet it appears to have minimal influence on the emissions from adjacent regions. Enhancing energy efficiency and adjusting the industrial structure are two critical mechanisms by which producer services agglomeration reduces urban carbon emissions. This beneficial effect varies with city type, the abatement effect of producer services agglomeration is more pronounced in non-resource-based cities. When considering city size, the carbon reduction potential of producer services agglomeration is not apparent in smaller cities. As city size increases, the emission reduction effect becomes more apparent. However, in mega-cities, this impact is somewhat diminished. Accordingly, this paper proposes exploring methods of coordinated air pollution management across cities, promoting producer services agglomeration in line with market mechanisms, and driving low-carbon urban development in a manner tailored to local conditions. [ABSTRACT FROM AUTHOR]

Air pollution poses a great danger to human health and economic development, and understanding people's willingness to pay for improved air quality (WTPA) impacts environmental protection. This paper investigates WTPA based on the perspective of subjective well-being (SWB) and analyzes the mediating role of social interaction on the relationship between the two. This paper distinguishes social interactions into online and offline interactions and analyzes whether the mediating effect of the two different interactions on SWB and WTPA exists separately. Using data from the 2018 China General Social Survey (CGSS), we find that SWB has a significant positive effect on WTPA, individuals with higher well-being have higher pro-environmental willingness; there is no age, education level, sex of the person, or regional heterogeneity in the effect of SWB on WTPA; offline social interactions play a partially mediating role between SWB and WTPA, while online social interactions failed to mediate between the two. This paper's policy implication is that improving residents' subjective well-being is both an important development goal and an essential way to resolve the conflict between economic development and environmental protection. [ABSTRACT FROM AUTHOR]

Imported air pollution has a significant impact on urban air quality. Relevant studies have shown that many urban air pollution events are not resourced by local emissions but are imported by air pollution from surrounding areas transported across regions. The prevention and control of air pollution is very necessary. However, the existing supervision of urban air quality mostly relies on ground monitoring stations, which are extremely limited in time and space, and cannot satisfy continuous time-space air pollution research. Therefore, aiming at the problem of urban air pollution control, this paper used MERRA-2 reanalysis data and ground monitoring data to establish a "Time-Longitude-Latitude" three-dimensional pollution curve, and then a genetic algorithm was used to optimize its fitting. This study finally reconstructed the imported air pollution transmission route. This paper takes an air pollution event that occurred in Xuzhou City, China, on 12 January 2020, as an example. Through the analysis of aerosol optical depth (AOD), particulate matter (PM), wind speed, and other factors, we found the source, transmission route, and impact time of this pollution. We have verified the correctness and accuracy of the reconstructed contamination transport paths. It is proved that the method is universal and it can quickly and accurately restore the air pollution transmission route and identify the urban imported air pollution transmission entrance. This method will also provide strong data support for the division of responsibilities of environmental protection departments in various regions for severe air pollution transmission events and provide effective governance ideas for the prevention and control of imported air pollution in recipient cities. [ABSTRACT FROM AUTHOR]

The rapid pace of economic and social development has led to a surge of particulate matter (PM) pollution incidents, which is detrimental to human health. Air quality in China has improved in recent years, but differences in PM exposure remain due to spatial heterogeneity in pollution and population. The identification of socioeconomic factors associated with PM pollution has attracted widespread attention. Current research predominantly targets cities, overlooking regional and county scale disparities. An analytical framework is established to evaluate the spatial heterogeneity in PM exposure and to identify the socioeconomic factors contributing to its pollution. The results revealed that: (1) the Theil index increased from 0.06 to 0.12, indicating a gradual increase in the inequality of PM exposure in Shaanxi Province. The differences within the Guanzhong Plain are obvious and the interregional differences substantially outweigh the intraregional variations. (2) Energy transition is pivotal in curbing air pollution in a region and economic growth consistently fuels particulate emissions. (3) For effective pollution mitigation, further emphasis on vegetation preservation and environmental inputs is imperative. The findings of this research provide decision support for optimizing air pollution control measures at the county scale., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Aiming to optimize straw resource utilization and to reduce air pollutant and greenhouse gas emissions in China, the environmental benefits of biochar production using straw and utilization need to be assessed. Two biochar production scenarios were studied: centralized production scenario (CPS) utilizing an industrialized production method with advanced air pollution control devices, and decentralized production scenario (DPS) based on a distributed production method of biochar at the village level, compared to an open-burning scenario (OBS). The potential GHG emission reductions under DPS, including the benefit of soil biochar application, were evaluated as 680 Tg CO 2 -eq yr -1 , which was lower than the reductions under CPS (780 Tg CO 2 -eq yr -1 ). Although the attentional centralized production method of biochar has more potential environmental benefits, at present the distributed production method is more practical in China due to its convenience, management ease, and lower costs. Modeling on air quality in China showed that in comparison with OBS, DPS could reduce the average monthly PM 2.5 concentration of key regions by 19%, and about 190 Tg yr -1 biochar could be produced with 150 Tg C yr -1 sequestered in biochar. Our findings support widespread biochar use in China for improved air quality and climate neutrality., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Valley topography is recognized for its role in constraining pollutant dispersion, which frequently results in elevated pollutant concentrations within valley regions. However, the specific mechanisms by which valley topography influences daytime ozone (O 3 ) production, nighttime O 3 depletion, and diurnal variations in O 3 concentrations remain inadequately understood. This study employs the online WRF-Chem air quality model to conduct sensitivity analyses, examining the effects of valley topography on summer O 3 concentrations in Lanzhou, a valley city in western China. The results indicate that valley topography generally reduces surface temperature and wind speed while also lowering the planetary boundary layer height (PBLH), which ultimately leads to increased concentrations of primary pollutants. Nevertheless, the impact of valley terrain on O 3 is time-dependent, with concentrations decreasing during the day and increasing at night. In Lanzhou, valley topography contributes to a 4.4% reduction in daytime O 3 concentrations. This reduction is largely due to the blocking of solar radiation by surrounding mountains, which results in a 7% decrease in shortwave radiation (SR) and a 17% decline in PBLH, subsequently limiting O 3 chemical production. Although valley topography restricts pollutant dispersion, the overall effect during the day is a net decrease in O 3 levels. In contrast, nighttime valley topography leads to a notable 19.8% increase in O 3 concentrations. This increase is driven by mountain breezes transporting O 3 -rich air from the slopes into urban areas, along with a 67.4% reduction in nitrogen oxide (NO) levels. Despite intensified NO titration within the valley, the combined effect of these local wind patterns contributes to elevated O 3 concentrations at night. These findings offer valuable insights into the unique factors contributing to urban O 3 pollution in areas with complex valley topography., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Despite recent enhancements in China's anthropogenic emission controls, ozone (O 3 ) concentrations have continuously increased owing to its nature as a secondary pollutant and the complexities of its production and consumption processes. This study quantified the contributions of urban and sectoral cross-emission sources to O 3 levels and identified the anthropogenic emission sources requiring targeted control. Moreover, O 3 sensitivity tests were conducted to determine optimal reduction ratios for nitrogen oxides (NOx) and volatile organic compounds (VOCs) emissions. The results were used to recommend effective measures for controlling O 3 pollution in the Central Plains urban agglomeration (CPUA). The top 35 cities and sectoral cross-emission sources accounted for 80% of the O 3 concentrations in the region, indicating the need for prioritized management of these sources. To achieve reductions in O 3 concentrations across all cities, it was found that a 10% reduction in total NOx emissions would require a minimum of 18% reduction in VOCs emissions. Our results indicated that the appropriate coordination of reductions in VOCs and NOx emissions reduced the maximum daily 8-h average O 3 (MDA8) concentrations in CPUA by 0.14%-4.78%. Enhancing control measures for prioritized emission sources reduced MDA8 concentrations by 0.78%-7.09%. Furthermore, adjusting the production and emission hours of the industrial sector resulted in a decrease in MDA8 concentrations by 1.10%-12.62%. Overall, our findings indicate that appropriately coordinated reduction of precursor emissions can reduce O 3 levels. Further efforts to mitigate O 3 pollution should include optimizing the timing of emissions from the industrial sector and other major sources of VOCs emissions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Air quality models are increasingly important in air pollution forecasting and control. Sectoral emissions significantly impact the accuracy of air quality models and source apportionment. This paper studied the 3DVar (three-dimensional variational) emission inversion method, which is based on machine learning, and then expanded it to sectoral emission inversion combined with source apportionment. Two machine learning conversion matrices were established to implement this method: a matrix that converts the total pollutant concentration to sectoral source apportionment results and a matrix that converts the sectoral source apportionment results to corresponding emissions. Combined with the O 3 (ozone) concentration contributed by VOCs (volatile organic compounds) and NO x (nitrogen oxides) precursors in source apportionment, the inversion ability for O 3 -NO x -VOCs nonlinear processes was improved. Taking the BTH (Beijing‒Tianjin-Hebei) region from January 15 to 30, 2019, as an example, the results revealed that the regional errors of PM 2.5 and O 3 in the inversion experiment were reduced by 47% and 45%, respectively, and the temporal errors were reduced by 44% and 16%, respectively., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Northeast Asia suffers from high concentrations of particulate matter (PM), prompting nations to actively implement emission reduction policies. This study evaluated the recent inter-annual changes in the chemical transformation and transboundary transport of PM over Northeast Asia, based on both ground and aircraft measurements, as well as WRF-Chem simulations, during two comprehensive campaigns: the Korea-United States Air Quality (KORUS-AQ) campaign in 2016 and the Satellite Integrated Joint Monitoring of Air Quality (SIJAQ) campaign in 2022, both conducted around the Korean Peninsula. Ground measurements in 2022 revealed significant reductions in air pollutants compared to 2016 levels. In the Beijing-Tianjin-Hebei region, PM 2.5 and SO 2 concentrations decreased by 47.2% and 73.9%, respectively, attributable to successful SO x and NO x emission reduction strategies. Similar trends were observed in downwind areas, including Seoul, where PM 2.5 and SO 2 levels declined by 30.0% and 41.4%, respectively. WRF-Chem model results indicated substantial decreases in sulfates, nitrates, and their precursors in both surface and upper atmosphere in 2022 compared to 2016. Moreover, model-calculated gas-to-particle conversion ratios, which peaked in the Yellow Sea in 2016, decreased by 15% in 2022 and shifted slightly eastward to the western Korean Peninsula. This shift suggests a potential decline in secondary PM formation processed in the Yellow Sea, coinciding with reduced long-range transport of gaseous pollutants. A comparison of model sensitivity experiments, accounting for both bottom-up emission changes and meteorological variations, revealed that while weather and climate factors such as precipitation and pressure patterns between 2016 and 2022 contributed to the overall decrease in PM concentrations, the primary driver was the reduction in emissions during this period. This study highlighted that the main driver of the substantial improvement in air quality over East Asia was the implementation of emission reduction policies targeting PM and its precursors in the main source regions in China., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Atmospheric ozone (O 3 ) has been placed on the priority control pollutant list in China's 14th Five-Year Plan. Due to their unique meteorological conditions, plateau regions contain high concentrations of atmospheric O 3 . However, traditional experimental methods for determining O 3 concentrations using automatic monitoring stations cannot predict O 3 trends. In this study, two machine learning models (a nonlinear auto-regressive model with external inputs (NARX) and a temporal convolution network (TCN)) were developed to predict O 3 concentrations in a plateau area in the Kunming region by considering the effects of meteorological parameters, air quality parameters, and volatile organic compounds (VOCs). The plateau O 3 prediction accuracy of the machine learning models was found to be much higher than those of numerical models that served as a comparison. The O 3 values predicted by the machine learning models closely matched the actual monitoring data. The temporal distribution of plateau O 3 displayed a high all-day peak from February to May. A correlation analysis between O 3 concentrations and feature parameters demonstrated that humidity is the feature with the highest absolute correlation (-0.72), and was negatively correlated with O 3 concentrations during all test periods. VOCs and temperatures were also found to have high positive correlation coefficients with O 3 during periods of significant O 3 pollution. After negating the effects of meteorological parameters, the predicted O 3 concentrations decreased significantly, whereas they increased in the absence of NO x . Although individual VOCs were found to greatly affect the O 3 concentration, the total VOC (TVOC) concentration had a relatively small effect. The proposed machine learning model was demonstrated to predict plateau O 3 concentrations and distinguish how different features affect O 3 variations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Maintaining normal levels of 25-hydroxyvitamin D [25(OH)D] and parathyroid hormone (PTH) is crucial for preserving skeletal health. However, evidence regarding the associations of exposure to air pollution with serum 25(OH)D and PTH were limited and ambiguous. Hence, the objective of this cross-sectional study was to systematically evaluate the association between air pollution [particulate matter ≤ 2.5 μm (PM 2.5 ) and ozone (O 3 )] exposure and serum 25(OH)D and PTH levels in males aged 50 and above and postmenopausal female., Materials and Methods: This study is multicenter, cross-sectional study within the framework of the ongoing China Community-based Cohort of Osteoporosis. The 1-year-average PM 2.5 and O 3 exposures prior to the baseline survey were estimated using random forest models with relatively high accuracy. Multiple linear regression models were employed to assess the associations between PM 2.5 and O 3 concentrations with the serum levels of 25(OH)D and PTH. Furthermore, mediation analysis was performed to scrutinize the potential mediating role of PTH in the interplay between PM 2.5 , O 3 , and serum 25(OH)D., Results: A total of 13194 participants were included. Our analysis showed that every 10 μg/m 3 increase in the 1-year average PM 2.5 , were associated with -0.32 units (95% CI: 0.48, -0.17) of change in the 25(OH)D and 0.15 units (95% CI: 0.11, 0.19) of change in the PTH, respectively. Every 10 μg/m 3 increase in the 1-year average O 3 , were associated with -0.78 units (95% CI: 1.05, -0.51) of change in the 25(OH)D and 0.50 units (95% CI: 0.43, 0.57) of change in the PTH, respectively. Estimates of the mediation ratio indicated that increased PTH mediated a 50.48% negative correlation between PM 2.5 exposure and circulating 25(OH)D level. Increased PTH mediated 69.61% of the negative effects of O 3 exposure on circulating 25(OH)D level., Conclusions: Exposure to PM 2.5 and O 3 significantly diminished 25(OH)D while elevating PTH levels. Notably, the elevated PTH concentration partially mediates the associations between PM 2.5 and O 3 exposure and 25(OH)D level., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Background: Air pollution and outdoor light at night (LAN) have been reported to be related to type 2 diabetes (T2D). However, their interaction with risk of T2D remains uncertain. Therefore, our study aimed to explore the relationship between outdoor LAN, air pollution and incident T2D., Methods: Our study included a cohort of 24,147 subjects recruited from 2015 to 2018 in Ningbo, China. Land use regression models were used to evaluate particulate matter with a diameter ≤2.5 μm (PM 2.5 ), ≤10 μm (PM 10 ) and nitrogen dioxide (NO 2 ). Satellite images data with a spatial resolution of 500m was used to estimate outdoor LAN levels. T2D new cases were identified by medical records based on health information system. Cox proportional hazards models were used to estimate Hazard ratios (HRs) and 95% confidence intervals (CIs). Moreover, we investigated the multiplicative and additive interactions between air pollution and outdoor LAN., Results: During 108,908 person-years of follow-up period, 1016 T2D incident cases were identified. The HRs (95% CIs) were 1.22 (1.15, 1.30) for outdoor LAN, 1.20 (1.00, 1.45) for PM 2.5 , 1.23 (1.11, 1.35) for PM 10 and 1.19 (1.04, 1.37) for NO 2 in every interquartile range increase, respectively. Furthermore, significant interactions were observed between outdoor LAN and NO 2 ., Conclusions: Our findings indicated that air pollution and outdoor LAN were positively associated with T2D. Moreover, we observed an interaction between outdoor LAN and NO 2 suggesting that stronger associations for outdoor LAN and T2D in areas with higher levels of NO 2 , and for NO 2 and T2D in areas with higher levels of outdoor LAN., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

In environmental protection, government and market forces must work together. However, existing literature often separates the two and discusses their roles independently. Therefore, this study incorporates both the market and government into a unified analytical framework. It redefines government force as environmental regulation and market force as allocation efficiency in environmental protection. The study empirically examines the synergistic emission reduction effects of active government and efficient market on air pollution, using data from 272 prefectural-level cities in China from 2003 to 2019. The results show: (1) Both government and market interventions can significantly reduce air pollution levels, and their combination achieves a synergistic emission reduction effect.(2) Within this synergy, government intervention remains dominant, while the market plays a supportive role. (3) Analyzing the heterogeneous effects under different scenarios reveals that collaboration and mutual monitoring between the government and the market can effectively counteract the adverse effects of economic policy uncertainty and corruption on the environment. Furthermore, when a single entity undertakes environmental protection, it may encounter issues of diminishing effectiveness over time. However, the synergistic effects of government and market cooperation can mitigate these challenges. (4) The synergy exhibits a threshold effect based on the varying strengths of government and market forces. (5) Public participation enhances the synergistic emission reduction effect of government and market, acting as a vital complement to their environmental protection efforts. This study provides new empirical evidence on the synergistic effect of current environmental protection measures in improving air pollution control., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Our study presents fresh insight into the impacts of air-related information accessibility and policy awareness on citizen's life satisfaction, through the lens of perceived air pollution sources. It is widely accepted that disclosing information about air pollution adversely affects an individual's life satisfaction. However, the impact of information accessibility and public policy awareness on life satisfaction remains poorly understood in real-life contexts and their interrelationship warrants exploration. Earlier studies suggested that public scrutiny via information disclosure is a means to lower air pollution levels, potentially enhancing life satisfaction through improved air quality perception and reduced health risks. However, much of that research was based on the flawed presumption that all individuals can access and understand this officially disclosed information. They overlooked the actual availability of information and public reflections on relevant policies that were influenced by their perception of air pollution. This research gap highlights the need for in-depth evidence of the impacts of information accessibility and policy awareness on life satisfaction. Employing a covariance-based Structural Equation Modelling, our study analyses the views of 1867 Beijing residents in 2022. We assessed information accessibility, policy awareness, perceived air pollution sources, life satisfaction, and socio-demographic characteristics covering two time periods: a) before the COVID-19 pandemic and b) during its normalisation phase. Our findings reveal that both information accessibility and policy awareness significantly and positively affect life satisfaction in both periods. Moreover, the indirect parameter analysis underscores the presence of significant heterogeneity when considering the mediating role of impacts of perceived air pollutants. The results of this study offer a novel contribution regarding the relationship between air pollution information accessibility, policy awareness, and life satisfaction., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In recent years, the precision of exposure assessment methods has been rapidly improved and more widely adopted in epidemiological studies. However, such methodological advancement has introduced additional heterogeneity among studies. The precision of exposure assessment has become a potential confounding factors in meta-analyses, whose impacts on effect calculation remain unclear. To explore, we conducted a meta-analysis to integrate the long- and short-term exposure effects of PM 2.5 , NO 2 , and O 3 on all-cause, cardiovascular, and respiratory mortality in the Chinese population. Literature was identified through Web of Science, PubMed, Scopus, and China National Knowledge Infrastructure before August 28, 2023. Sub-group analyses were performed to quantify the impact of exposure assessment precisions and pollution levels on the estimated risk. Studies achieving merely city-level resolution and population exposure are classified as using traditional assessment methods, while those achieving sub-kilometer simulations and individual exposure are considered finer assessment methods. Using finer assessment methods, the RR (under 10 μg/m 3 increment, with 95% confidence intervals) for long-term NO 2 exposure to all-cause mortality was 1.13 (1.05-1.23), significantly higher (p-value = 0.01) than the traditional assessment result of 1.02 (1.00-1.03). Similar trends were observed for long-term PM 2.5 and short-term NO 2 exposure. A decrease in short-term PM 2.5 levels led to an increase in the RR for all-cause and cardiovascular mortality, from 1.0035 (1.0016-1.0053) and 1.0051 (1.0021-1.0081) to 1.0055 (1.0035-1.0075) and 1.0086 (1.0061-1.0111), with weak between-group significance (p-value = 0.13 and 0.09), respectively. Based on the quantitative analysis and literature information, we summarized four key factors influencing exposure assessment precision under a conceptualized framework: pollution simulation resolution, subject granularity, micro-environment classification, and pollution levels. Our meta-analysis highlighted the urgency to improve pollution simulation resolution, and we provide insights for researchers, policy-makers and the public. By integrating the most up-to-date epidemiological research, our study has the potential to provide systematic evidence and motivation for environmental management., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Huan Liu reports financial support was provided by National Natural Science Foundation of China (grant nos. 42325505 and U2233203). Huan Liu reports financial support was provided by National Key Research and Development Program of China (No. 2022YFC3704200). This work is also supported by the Pathways to Equitable Healthy Cities grant from the Wellcome Trust (No. 209376/Z/17/Z). For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Existing studies have analyzed the spatio-temporal patterns of air pollutants by combining ground and satellite measurements, primarily for cross-validation purposes. However, the unique characteristics and discrepancies between satellite and ground measurements have rarely been leveraged to understand pollution patterns and identify air pollution sources. To our best knowledge, this study is the first to utilize these discrepancies to holistically analyze the spatial and temporal patterns and investigate local biomass-burning effects on the five typical air pollutants: particulate matter (PM 2.5 )/aerosol optical depth (AOD), carbon monoxide (CO), nitrogen dioxide (NO 2 ), sulphur dioxide (SO 2 ), and ozone (O 3 ). Guangdong (GD) province was selected as a case study due to its complex air pollution sources and patterns. Ground-based analysis from 2015 to 2023 shows significant decreases in PM 2.5 , CO, NO 2 , and SO 2 , and a significant increase in O 3 in urban areas, indicating the efficacy of stringent air pollution control policies. However, satellite analysis shows significant downtrend only in AOD, while the trends of other pollutants are almost negligible, which are likely to be evidence of industrial migration. Both measurements exhibit regular seasonal patterns for all air pollutants. In-depth time-series comparisons between ground and satellite data reveal seasonal consistency for NO 2 but noticeable discrepancies for both AOD and CO, which could be attributed to urban-rural differences and local versus transported pollution sources. Spatially, AOD and NO 2 exhibits the most significant regional discrepancies, followed by SO 2 and CO, with higher values observed over Pearl River Delta (PRD) compared to non-PRD regions. O 3 is more evenly distributed, showing more pronounced seasonal variations than regional differences. The synergetic use of satellite and ground measurements collectively verifies the significant local biomass-burning effects on the five pollutants. These findings can aid in developing more targeted air pollution control policies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Atmospheric boundary layer (ABL) structure was a crucial factor in altering the vertical aerosol distribution and modulating the impact of regional aerosol transport on the atmospheric environment in the receptor region. The long-term characteristics of ABL structures for different vertical aerosol distributions and the distinct influencing mechanisms between daytime and nighttime aerosol transport interacting with the diurnal ABL transition have rarely been studied in the receptor regions. Based on 9-year (2013-2021) satellite-retrieved profiles of aerosol extinction coefficients and meteorological sounding data, we targeted Wuhan, an urban city with noteworthy transport contribution in central China, to reveal the general wintertime transport height of ∼500 m and the corresponding unstable ABL structure during regional transport. By comparing typical daytime and nighttime aerosol transport with high-resolution Lidar observations, the aerosol transport near the ABL top coupled with intense mechanical mixing provided sufficient meteorological conditions for heavy aerosol pollution formation in the receptor regions, which was more favorable during nighttime transport followed by the adequate ABL development after sunrise. These findings enhance our comprehension of the ABL impact on air pollution in the receptor regions, which have implications for the precise prevention and control of the regional atmospheric environment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Accurately assessing the key factors influencing air pollution is crucial for effective air pollution control. To address this need, we propose a novel Hybrid Features Grey Incidence Model (HFGIM), which integrates geometric feature differences from both proximity and similarity perspectives. Firstly, we extract geometric feature difference vectors of proximity and similarity from time series data and measure the overall feature difference degree by calculating vector norms. Secondly, we calculate the relative feature differences and information contribution rates of proximity and similarity to derive the hybrid feature differences coefficient between sequences, thereby obtaining the hybrid features incidence degree. After detailing the model's properties and modelling steps, we introduce the Cross-sectional Data Hybrid Features Grey Incidence Model (C-HFGIM) and the Panel Data Hybrid Features Grey Incidence Model (P-HFGIM) for handling cross-sectional and panel data, respectively. Applying HFGIM, we identified the key pollutants and primary pollution source indicators of air pollution in Jiangsu Province. We also compared HFGIM with other classical grey incidence models to verify the proposed model's effectiveness. Based on the analysis results, we propose policy recommendations for air pollution control in Jiangsu Province., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Effectively capturing the risk of air pollution and informing residents is vital to public health. The widely used Air Quality Index (AQI) has been criticized for failing to accurately represent the non-threshold linear relationship between air pollution and health outcomes. Although the Air Quality Health Index (AQHI) was developed to address these limitations, it lacks comprehensive construction criteria. This work proposed a novel construction and prediction strategy of AQHI using machine learning methods. Our RF-Alasso-QGC method integrated Random Forest (RF), Adaptive Lasso (Alasso), and Quantile-based G-Computation (QGC) for effective pollutant selection and AQHI construction. The RF-Alasso method excluded CO, while identified PM 10 , PM 2.5 , NO 2 , SO 2 , and O 3 as major contributors to mortality. The QGC method controlled the additive and synergistic effects among these air pollutants. Compared to the Standard-AQHI, the new RF-Alasso-QGC-AQHI demonstrated a stronger correlation with health outcomes, with an interquartile (IQR) increase associated with a 1.80 % (1.44 %, 2.17 %) increase in total mortality, and the best goodness of fit. Additionally, the hybrid Auto Regressive Moving Average-Long Short Term Memory (ARIMA-LSTM) successfully forecast the new AQHI, achieving a coefficient of determination (R²) of 0.961. The work demonstrated that the improved AQHI construction and prediction strategy more efficiently communicate and provide early warnings of the health risks of multiple air pollutants., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Qiaoyuan Yang reports financial support was provided by Guangdong Provincial Department of Science and Technology. Qiaoyuan Yang reports financial support was provided by Guangdong Provincial Department of Education. Yuanyuan Chen reports was provided by Guangzhou Municipal Bureau of Science and Information Technology. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Interstitial lung diseases (ILDs) lead to increased morbidity and premature deaths, imposing a significant burden on public health worldwide. Recently, several studies have linked ambient air pollution with the acute exacerbation of certain ILDs, but the evidence remains limited and inconclusive. With a multicenter hospital-based case-crossover design, we investigated 9128 patients who resided in Jiangsu province, China, and were admitted for ILDs between 2019 and 2022. Residential exposure to particulate matter with an aerodynamic diameter ≤ 2.5 µm (PM 2.5 ), PM 10 , sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), carbon monoxide (CO), and ozone (O 3 ) was assessed using our validated grid datasets. We fitted conditional logistic regression models to examine associations of exposure to air pollutants with ILD admission. A 10 µg/m 3 increment of exposure to SO 2 and NO 2 was positively associated with a 16.18 % (95 % confidence interval [CI]: 3.79 %, 30.03 %) and 4.06 % (0.75 %, 7.49 %) increase in odds of ILD admission, respectively. All these associations appeared to be linear and the association of SO 2 exposure was significantly stronger among older adults. We estimated that over 10 % of ILD admissions could be attributable to exposure to SO 2 and NO 2 . This study provides compelling evidence on the association of exposure to ambient air pollutants (including SO 2 and NO 2 ) with an increased odds of ILD hospitalizations. Our findings indicate that SO 2 and NO 2 exposures can lead to the exacerbation of ILDs, especially in elderly, and that the disease burden is considerable., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Hong Sun and Yuewei Liu were supported by Jiangsu Provincial Health Commission. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Setting: off fireworks during the Spring Festival (SF) is a traditional practice in China. However, because of its environmental impact, the Chinese government has banned this practice completely. Existing evaluations of the effectiveness of firework prohibition policies (FPPs) lack spatiotemporal perspectives, making it difficult to comprehensively assess their effects on air quality. Consequently, this study used remote sensing technology based on aerosol optical depth and multiple variables, compared nine statistical learning methods, and selected the optimal model, transformer, to estimate daily spatiotemporal continuous PM 2.5 concentration datasets for Tianjin from 2016 to 2020. The overall model accuracy reached a root mean square error of 15.30 μg/m³, a mean absolute error of 9.55 μg/m³, a mean absolute percentage error of 21.07%, and an R 2 of 0.88. Subsequently, we analysed the variations in PM 2.5 concentrations from three time dimensions-the entire year, winter, and SF periods-to exclude the impact of interannual variations on the experimental results. Additionally, we quantitatively estimated firework-specific PM 2.5 concentrations based on time-series forecasting. The results showed that during the three years following the implementation of the FPPs, firework-specific PM 2.5 concentrations decreased by 52.70%, 49.76%, and 86.90%, respectively, compared to the year before the implementation of the FPPs. Spatially, the central urban area and industrial zones are more affected by FPPs than the suburbs. However, daily variations of PM 2.5 concentrations during the SF showed that high concentrations of PM 2.5 produced in a short period will return to normal rapidly and will not cause lasting effects. Therefore, the management of fireworks needs to consider both environmental protection and the public's emotional attachment to traditional customs, rather than simply imposing a blanket ban on fireworks. We advocate improving firework policies in four aspects-production, sales, supervision, and control-to promote sustainable development of the ecological environment and human society., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Assessing the emissions of heavy-duty diesel trucks (HDDTs) is crucial for managing air quality in megacities, especially concerning nitrogen oxides (NO X ) and black carbon (BC). This study employed mobile plume chasing to monitor the real-world emissions of over 7778 HDDTs in Shenzhen. The findings indicate that the real-world NO X emission factors (EF) of China IV trucks did not differ significantly from those of China III, whereas China V and VI vehicles demonstrated fleet-averaged reductions of 27% and 85%, respectively. For China V, a significant decrease in the NO X EF for HDDTs registered after 2017 was attributed to the installation of advanced aftertreatment systems, including diesel oxidation catalysts (DOC) and Diesel Particle Filters (DPF), along with selective catalytic reduction (SCR). These technologies led to an average reduction of 42% in NO X and 61% in BC emissions. Seasonal variations were pronounced, with winter (∼20 °C) NO X EF 40% higher than summer (∼35 °C) levels. Conversely, BC EF decreased by 26% in winter, indicating significant impacts of ambient temperature on emissions. Spatial analysis revealed that the average NO X EF of HDDTs on east freeways was 1.4 times higher than that on urban expressways, influenced by variations in the proportion of vehicle types segmented by usage. These findings offer a comprehensive perspective on HDDTs emissions, highlighting the importance of large-scale emission monitoring through plume chasing for precise and effective control of real-world HDDTs emissions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Cities, contributing over 70% of global emissions, are key areas for climate change mitigation. Heterogeneity within cities determines the need for spatialized urban emissions reduction policies. However, few studies have attempted to characterize the spatial distribution of carbon emissions at the urban scale. To address this issue, a novel mapping method was proposed, using Xi'an as an example to explore the spatial distribution of carbon emissions at the city scale. Firstly, multiple geospatial open-source data, such as point of interest (POI), road networks, and land use, were utilized to identify the locations of emission sources. High-resolution carbon emission distributions were then mapped by allocating emissions based on the Intergovernmental Panel on Climate Change (IPCC) methodology. The study employed Global Moran's I to analyze the changes in spatial heterogeneity at different resolutions. Additionally, the Local Indicators of Spatial Association index (LISA) and Standard Deviation Ellipses (SDE) were adopted to examine the spatiotemporal characteristics of carbon emissions in Xi'an. The results show that carbon emissions at Xi'an City rises from 45.112 million tons to 72.701 million tons between 2010 and 2021. The construction of multi-scale carbon emissions spatial distributions, with a resolution of up to 30 m, allowed for a more detailed characterization of carbon emissions, especially in urban fringe areas. In addition, the results indicate that urban carbon emissions exhibit the strongest spatial autocorrelation at a resolution of 350 m. The study can provide a reference for the development of regional carbon emission reduction policies and spatial planning. In addition, the proposed spatialized method of city carbon emissions depends on open-source data, which allows it to have the potential for application in other cities., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ling Han reports financial support was provided by Shaanxi University of Technology. Ziyan Liu reports financial support was provided by Chang'an University. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Understanding the factors that drive PM 2.5 concentrations in cities with varying population and land areas is crucial for promoting sustainable urban population health. This knowledge is particularly important for countries where air pollution is a significant challenge. Most existing studies have investigated either anthropogenic or environmental factors in isolation, often in limited geographic contexts; however, this study fills this knowledge gap. We employed a multimethodological approach, using both multiple linear regression models and geographically weighted regression (GWR), to assess the combined and individual effects of these factors across different cities in China. The variables considered were urban built-up area, land consumption rate (LCR), population size, population growth rate (PGR), longitude, and latitude. Compared with other studies, this study provides a more comprehensive understanding of PM 2.5 drivers. The findings of this study showed that PGR and population size are key factors affecting PM 2.5 concentrations in smaller cities. In addition, the extent of urban built-up areas exerts significant influence in medium and large cities. Latitude was found to be a positive predictor for PM 2.5 concentrations across all city sizes. Interestingly, the northeast, south, and southwest regions demonstrated lower PM 2.5 levels than the central, east, north, and northwest regions. The GWR model underscored the importance of considering spatial heterogeneity in policy interventions. However, this research is not without limitations. For instance, international pollution transfers were not considered. Despite the limitation, this study advances the existing literature by providing an understanding of how both anthropogenic and environmental factors, in conjunction with city scale, shape PM 2.5 concentrations. This integrated approach offers invaluable insights for tailoring more effective air pollution management strategies across cities of different sizes and characteristics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Understanding and quantifying the influences and contributions of air pollution emissions on water quality variations is critically important for surface water quality protection and management. To address this, we created a five-year daily data matrix of six water quality indicators-permanganate index (COD Mn ), NH 3 -N, pH, turbidity, conductivity, and dissolved organic matter (DOM)-and six air pollution indicators-O 3 , CO, NO 2 , SO 2 , 2.5 μm particulate matter (PM 2.5 ), and inhalable particles (PM 10 )-using data from seven national monitoring stations along the world's longest water-diversion project, the Middle Route of the South-to-North Water Diversion Project in China (MR-SNWD). Multivariate techniques (Mann-Kendall, Spearman's correlation, lag correlation, and Generalized Additive Models [GAMs]) were applied to examine the nonlinear relationships and lag effects of air pollution on water quality. Air pollution and water quality exhibited marked spatial heterogeneity along the MR-SNWD, with all water quality parameters meeting Class I or II national standards and the air pollution indicators exceeding those thresholds. Except for COD Mn and DOM, the other water quality and air pollution indicators exhibited significant seasonal differences. Air pollution exhibited significant lag effects on water quality at the northern stations, with NO 2 , SO 2 , PM 2.5 , and PM 10 being highly correlated with changes in pH, with an average lag of 17 d. Based on the GAMs, lag effects enhanced the significant nonlinear relationships between air pollution and water quality, increasing the average deviance explained for COD Mn , NH 3 -N, and pH by 93%, 24%, and 41%, respectively. These findings provide a scientific basis for protecting water quality along the long-distance inter-basin water-diversion project under anthropogenic air pollution., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Pollution control and carbon emission reduction pose significant challenges for developing countries, and achieving synergistic reductions in pollution and carbon emissions (SRPCs) has emerged as the optimal choice. Digitization, a prevailing trend in the current era, presents new opportunities for realizing the SRPC. We utilize data from Chinese A-share listed companies from 2007 to 2022 to investigate the impact of corporate digital transformation on the SRPC. The research reveals the following. (1) Enterprise digital transformation significantly promotes the SRPC, a conclusion validated through a series of robustness tests. However, the SRPC resulting from digital transformation is characterized by weak synergy, which is primarily observed in the coordinated reduction in air pollution and carbon emissions. (2) This effect is notably stronger for companies near environmental regulatory authorities, those receiving government environmental subsidies, and state-owned enterprises (SOEs). (3) Enhancing managerial collaborative management capabilities, promoting corporate technological innovation capabilities, and alleviating financing pressure are the primary mechanisms at play. This research provides important policy and practical insights for promoting the SRPC in the context of digitization, fostering sustainable development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The elevated concentration of suspended particulate matter in the atmosphere is closely associated with the morbidity and mortality of human diseases. In northwest China, fine particulate matter pollution has persistently affected local health, production, and daily life due to climatic factors and economic development. This paper first examines the distribution and variation of PM 2.5 concentrations in northwest China from 2013 to 2022 to understand changes in PM 2.5 exposure levels, as well as their associated health and economic benefits since the implementation of the "Air Pollution Prevention and Control Action Plan." Second, we estimated the number of premature deaths in the research region under various control scenarios based on PM 2.5 concentrations recorded in 2013 and 2021, utilizing existing epidemiological studies for reference. Finally, we calculated the financial benefits resulting from variations in PM 2.5 concentrations within this region. Results indicated that (1) from 2013 to 2022, the Tarim Basin exhibited the highest PM 2.5 concentration among all studied areas, while northern Xinjiang and Qinghai Province had notably lower levels. Additionally, wintertime concentrations were consistently higher than those observed during summer months; however, there has been a general trend toward reduced PM 2.5 content over the past decade. (2) A significant inverse relationship was found between premature mortality rates and PM 2.5 concentrations; specifically, fluctuations in PM 2.5 levels exerted a more pronounced impact on premature deaths attributable to cardiovascular diseases compared to respiratory conditions within this northwest region. (3) Males were disproportionately affected by increases in PM 2.5 concentration relative to females-experiencing two to three times more early fatalities than their female counterparts did. (4) Over a 9-year period within this research area, fiscal benefits derived from interventions targeting PM 2.5 treatment increased by approximately 891 million yuan., Competing Interests: Declarations Ethics approval Unethical actions were not made. Consent to participate All authors gave consent to participate in this publication. Consent for publication All authors gave consent for publication in the International Journal of Environmental Research. Conflict of interest The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Limited evidence was available on ambient air pollution and pediatric atopic dermatitis (AD). The study aimed to evaluate the associations between short-term exposure to air pollutants and outpatient visits for pediatric AD. From 2016-2018, we collected data on six criteria air pollutants (PM 2.5 , PM 10 , NO 2 , SO 2 , CO and O 3 ) and daily outpatient visits for pediatric AD in 66 hospitals, covering all districts in Shanghai, China. The over-dispersed Poisson generalized additive model (GAM) was applied to fit the associations of criteria air pollutants with hospital visits. Two-pollutant models were fitted and stratified analyses by sex, age and season were conducted. We identified 477,833 outpatient visits for pediatric AD. Each interquartile range (IQR) increase in PM 2.5 (IQR: 30.9 μg/m 3 ), PM 10 (8.9 μg/m 3 ), NO 2 (25.5 μg/m 3 ), SO 2 (5.8 μg/m 3 ) and CO (0.283 mg/m 3 ) on the concurrent day was significantly associated with increments of 2.08 % (95 % CI: 0.53 %, 3.65 %), 2.53 % (95 % CI: 0.87 %, 4.22 %), 8.14 % (95 % CI: 6.24 %, 10.08 %), 5.67 % (95 % CI: 3.58 %, 7.80 %), and 2.27 % (95 % CI: 0.70 %, 3.87 %) in pediatric AD outpatient visits, respectively. The effects of NO 2 remained robust after adjustment for other air pollutants. The exposure-response curves for PM 2.5 and PM 10 were steeper for moderate-lower concentrations, with a flatten curves at high concentration; nearly linear relationships were found for NO 2 . Higher associations of NO 2 exposure on AD were detected in children under 6 years old (p=0.01); and we observed larger effect of air pollutants in cool seasons (p<0.001 for PM 2.5 , PM 10 , NO 2 and CO; p=0.043 for SO 2 ). This study indicated that short-term exposure to air pollution could increase risk of outpatient visits for pediatric AD., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Aligning public demand with government supply of clean air aids in efficient air pollution control and enhancement of public happiness. However, comparative empirical analyses of public and government attention to air quality changes are still sparse due to data and methodological constraints. Here, we adopt multi-source data fusion approaches to assess the impacts of air pollution exposure on public and government attention. Specifically, remote and social sensing data, alongside keywords extracted from textual data, are utilized to quantify air pollution exposure and corresponding public and government attention levels in 273 Chinese cities from 2011 to 2019, and a two-stage least squares regression model is employed to tackle reverse causality issues underlying the exposure-response relationship. Our findings reveal that, on average, a unit increase in PM 2.5 levels would result in a 17.7% growth in public attention and a 12.7% rise in government attention, respectively, suggesting that demand-driven public attention tends to be more sensitive to air quality changes than policy-driven government attention. Results for the spatial-temporal heterogeneity further demonstrate that public attention varies across time and space, whereas government attention remains relatively consistent. Additionally, we have identified 116 cities exhibiting disparities between the public and government responses to air quality changes, calling for environmental policy refinements to better serve the needs of residents. This study emphasizes the necessity of public engagement in environmental governance and offers rich policy implications for air pollution control in China., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This study aims to balance the dual objectives of improving environmental quality and achieving carbon peaking and carbon neutrality goals. Utilizing panel data from 274 Chinese cities between 2006 and 2020, this paper employs the difference-in-differences method to examine the impact of the Air Pollution Prevention and Control Action Plan (APPCAP) on urban carbon emissions. Empirical results indicate that the impact of the APPCAP on regional carbon emissions follows an inverted U-shaped curve, exhibiting a dual impact path. Although the implementation of the APPCAP increased the annual carbon emissions of the city by an average of 1.637 million tons, it showed a trend of inhibiting the growth of carbon emissions, although it was not significant. Specifically, the APPCAP is most effective in reducing carbon emissions during the "source prevention" stage, primarily by increasing industrial electricity consumption and reducing coal production and consumption. However, a rebound effect occurs at the policy period's end, with compensatory increases in crude oil and electricity consumption, thereby elevating regional carbon emission levels. The APPCAP's effect on carbon emissions is more pronounced in areas with lower population density and higher environmental regulation intensity. It notably raises carbon emission levels in non-industrial base and non-resource-based cities compared to other cities. Furthermore, the APPCAP positively impacts regional carbon emission intensity and economic growth, significantly driving the secondary industry while minimally affecting the primary industry., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. There are no financial arrangements or affiliations, nor any other potential conflicts of interest, including patent or stock ownership, consultancy, expert testimony, grants or other funding received, or honoraria, that could be perceived as affecting the objectivity of the research presented herein., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The Chinese government implemented air pollution control policies in its Five-Year Plans (FYPs), setting specific emission reduction targets for different regions. This study employed the generalized difference-in-differences (GDD) method to assess the impacts of air pollution control policies on pollutants and carbon emissions from 2003 to 2020. This study had three main findings. (1) Air pollution control policies promoted the reduction of total SO 2 and carbon emissions, indicating that these policies had synergic impacts on pollutants and carbon emissions. (2) The reduction in emissions due to air pollution control policies primarily originated from industrial emissions, particularly in reducing industrial SO 2 emissions from process of production. (3) The emission reduction targets of different pollutants showed varying effects; SO 2 reduction targets significantly lowered overall SO 2 emissions, whereas NO x reduction targets effectively reduced industrial NO x and carbon emissions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

To meet the goal of the Paris Agreement, China pledges to realize the "Dual Carbon" targets by 2060. As the capital of China, Beijing plays a leading role in becoming zero-emission or carbon neutral in the future. We project the pollutants emissions of building sector based on current strict clean air policies (PO scenario) and China's carbon neutrality target by 2060 (CN scenario) from 2019 to 2050. Results show that PM 2.5 concentration will increase by 2.62 μg/m 3 under PO scenario; under the CN scenario, ozone concentration will increase by 2.53 μg/m 3 but PM 2.5 concentration will reduce by 9.04 μg/m 3 . It is projected that China carbon neutrality goals could avoid 11.12% of PM 2.5 -related health burden; With strict clean air policies, health burdens of ozone (3.9%) and PM 2.5 (4.1%) could be avoided, respectively. This study highlights the importance of achieving co-benefits of air quality and public health., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This paper leverages a data-driven two-step approach to effectively evaluate the effects of COVID-19 lockdown on air pollution in both the short and long-term in China. Using air pollution, meteorological conditions, and air mass clusters from 34 air quality monitoring stations in Beijing from 2015 to 2022, this study first employs a deweathering machine learning technique to decouple the confounding effects of meteorological on the air pollution. Furthermore, a detrending percentage change indictor is applied to remove the influence of seasonal variations on air pollution. The findings reveal that: (1) Human interventions are the primary drivers of changes in air pollution concentrations, whereas meteorological factors have a relatively minor impact. (2) During the COVID-19 lockdown, significant variations in air pollution levels are observed, with the effects of city lockdown ranging from a decrease of 40.11% ± 14.81% to an increase of 20.28% ± 14.36%. Notably, there is a decline in concentrations of NO 2 , PM 2.5 , CO, and PM 10 , while the levels of O 3 and SO 2 increase even during the strictest lockdown period. (3) In the year following the COVID-19 lockdown, there is a rebound in overall air pollution levels. However, by the second year, a general decline in air pollution is observed, except for O 3 . Therefore, it is imperative to integrate the confounding effects of meteorological factors into air quality management policies under various future scenarios: adopt high-intensity control measures for sudden air quality deteriorations, advance green recovery initiatives for long-term emission reductions, and coordinate efforts to reduce composite atmospheric pollution., Competing Interests: Declaration of competing interest No potential conflicts of interest were reported by the authors., (Copyright © 2024. Published by Elsevier Ltd.)

Policy synergy plays a crucial role in managing regional air pollution. However, the current process of formulating collaborative policies often focuses too heavily on achieving optimal emission reduction outcomes, while neglecting the execution costs at the local level. This not only leads to a lack of coordination between existing governance measures and new policies but may also result in inefficient policy implementation and the wastage of administrative resources. To address this issue, we have conducted a thorough analysis of the latent interconnections in local governance and explored the possibility of formulating collaborative policies based on latent policy synergies. Utilizing a dataset of 12,764 policy documents from China spanning from 2000 to 2023, we employed unsupervised learning techniques and panel regression models to investigate the impacts of latent policy synergies among local governments. Our findings reveal that local governments initially relied on rigid command-and-control strategies to address air pollution, which were later enhanced with economic incentives like emissions trading and subsidies, thus broadening the scope of policy tools and improving compliance flexibility. Subsequently, as comprehensive monitoring networks were established, enabling real-time data acquisition and more accurate pollution assessments, the focus of policy efforts shifted towards a more integrated approach to regional air pollution management. This similar evolution facilitated latent policy synergy among local governments, leading to significant reductions in air pollution emissions. Notably, regions such as the Central Plains and coastal cities, characterized by their dense industrial activity and high energy consumption, have shown remarkable potential for collaborative efforts, achieving greater pollution control efficiencies. This study provides a valuable reference for the coordinated governance of air pollution policies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Promoting good health and ensuring responsible production and consumption are essential components of the Sustainable Development Goals (SDGs) established by of the United Nations, as well as the goals of beautiful China. While the health impacts of air pollution have garnered significant attention, there remains a paucity of studies comparing the disparities in responsibility arising from production versus consumption. This paper integrates the Weather Research and Forecasting - Comprehensive Air Quality Model with Extensions (WRF-CAMx) model, the multiregional input‒output (MRIO) model, and the global exposure mortality model (GEMM) to assess the extent of PM 2.5 -related premature deaths caused by production and consumption activities in 30 Chinese provinces. The findings reveal a spatial mismatch in health burdens between production and consumption. Considering pollutant emissions and their transfer only through the supply chain leads to the finding that the net outflow of emissions from producers is mainly located in most of the northern provinces of China. However, when atmospheric transport and health impacts are included, the producing provinces are mainly located in central China, while the consuming provinces are located in the southeastern coastal and remote western and northern regions. Additionally, the long-range impact of consumption provinces with respect to the health burden is more than twice as large as that of production provinces, and its potential impact on the health burden cannot be ignored. From a sectoral perspective, production emissions from the non-electricity industry and services sectors contribute to 60% of the health burden, while their consumption emissions contribute to over 80% of the health burden. Furthermore, consumption activities in the non-electricity industry and services sectors significantly influence production emissions in the transport, agriculture, and electricity sectors. The geographical separation of consumption and production regions facilitated by trade is a critical yet often overlooked aspect in current regional air quality planning in China. A more comprehensive analysis of life-cycle emissions driven by final consumption could yield greater reductions compared to direct production reductions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

To mitigate the substantial losses incurred by air pollution, individuals undertake defensive behaviors in the form of health insurance expenses. Leveraging data from the 2011-2017 China Household Finance Survey (CHFS) encompassing 3033 residents, we estimate the causal impact of air pollution on defensive expenditures. Our findings are as follows: (1) Air pollution exhibits a significantly favorable effect on individual commercial health insurance expenses, with a 1% increase in PM 2.5 concentration correlating to an 11.02% rise in personal commercial health insurance expenditure. (2) Demographics such as younger individuals, married populations, lower educational attainment cohorts, and urban residents, displaying higher sensitivity to air pollution, tend to purchase more insurance coverage. (3) Risk perception emerges as a pivotal channel through which air pollution affects commercial health insurance expenditure. Our conclusions underscore the significance of risk perception in defensive expenditures, thereby optimizing individual risk mitigation strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

The infant gut microbiome matures greatly in the first year of life. Ambient air pollution (AAP) exposure is associated with the infant gut microbiome. However, whether time-varying AAP influences infant gut microbiome variation is rarely investigated. This study aimed to investigate the effects of PM 2.5 , PM 10 , and O 3 on infant gut microbiome variation longitudinally. Demographic information, stool samples, and AAP exposure concentrations were collected at 6, 12, 24 months from infants. Gut microbiome was processed and analyzed using 16S rRNA V3-V4 gene regions. AAP exposure concentrations were calculated using the China High Air Pollutants (CHAP) database. Multiple pollutant models were used to assess the mixed effects of PM 2.5 , PM 10 , and O 3 on infant gut microbiome variation. Infants' gut microbiomes at 6, 12, 24 months old had significant differences in alpha diversity, beta diversity, and community composition. PM 2.5 and O 3 respectively explained 6.3% and 5.3% of the differences in community composition for 24-month-old infants. Single pollutant exposure and multiple pollutant exposure in different periods were both associated with alpha diversity indices and specific gut microbial phyla and genera. AAP was more associated with infant gut microbial alpha diversity indices, phyla variations, and genera variations at 12-24 months than 6-12 months. Multiple pollutant exposure in 0-2 lag months showed negative correlations with 12-24 months variation in Escherichia-Shigella (β = -0.854, 95%CI: 1.398 to -0.310) and Enterococcus (β = -0.979, 95%CI: 1.429 to -0.530). This study highlighted that time-varying PM 2.5 , PM 10 , and O 3 synergistically influenced the variation of alpha diversity and abundance of gut microbial taxa in infants. Further research is needed to explore the effects and mechanisms of other environmental exposures on infant gut microbiome variation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Evidence linking greenness to all-site and site-specific cancers remains limited, and the complex role of air pollution in this pathway is unclear. We aimed to fill these gaps by using a large cohort in southern China. A total of 654,115 individuals were recruited from 2009 to 2015 and followed-up until December 2020. We calculated the normalized difference vegetation index (NDVI) in a 500-m buffer around the participants' residences to represent the greenness exposure. Cox proportional-hazards models were used to evaluate the impact of greenness on the risk of all-site and site-specific cancer mortality. Additionally, we assessed both the mediation and interaction roles of air pollution (i.e., PM 2.5 , PM 10 , and NO 2 ) in the greenness-cancer association through a causal mediation analysis using a four-way decomposition method. Among the 577,643 participants, 10,088 cancer deaths were recorded. We found a 10% (95% CI: 5-16%) reduction in all-site cancer mortality when the NDVI increased from the lowest to the highest quartile. When stratified by cancer type, our estimates suggested 18% (95% CI: 8-27%) and 51% (95% CI: 16-71%) reductions in mortality due to respiratory system cancer and brain and nervous system cancer, respectively. For the above protective effect, a large proportion could be explained by the mediation (all-site cancer: 1.0-27.7%; respiratory system cancer: 1.2-32.3%; brain and nervous system cancer: 3.6-109.1%) and negative interaction (all-site cancer: 2.1-25.7%; respiratory system cancer: 2.0-25.7%; brain and nervous system cancer: not significant) effects of air pollution. We found that particulate matter (i.e., PM 2.5 and PM 10 ) had a stronger causal mediation effect (25.0-109.1%) than NO 2 (1.0-3.6%), while NO 2 had a stronger interaction effect (25.7%) than particulate matter (2.0-2.8%). In summary, greenness was significantly beneficial in reducing the mortality of all-site, respiratory system, and brain and nervous system cancer in southern China, with the impact being modulated and mediated by air pollution., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Atmospheric fine particulate matter (PM 2.5 ) can trigger the production of cytotoxic reactive oxygen species (ROS), which can trigger or exacerbate oxidative stress and pulmonary inflammation. We collected 111 daily (∼24 h) ambient PM 2.5 samples within an urban region of North China during four seasons of 2019-2020. PM 2.5 samples were examined for carbonaceous components, water-soluble ions, and elements, together with their oxidative potential (represent ROS-producing ability) by DTT assay. The seasonal peak DTTv was recorded in winter (2.86 ± 1.26 nmol min -1 m -3 ), whereas the DTTm was the highest in summer (40.6 ± 8.7 pmol min -1 μg -1 ). WSOC displayed the highest correlation with DTT activity (r = 0.84, p < 0.0001), but the influence of WSOC on the elevation of DTTv was extremely negligible. Combustion source exhibited the most significant and robust correlation with the elevation of DTTv according to the linear mixed-effects model result. Source identification investigation using positive matrix factorization displayed that combustion source (36.2%), traffic source (30.7%), secondary aerosol (15.7%), and dust (14.1%) were driving the DTTv, which were similar to the results from the multiple linear regression (MLR) analysis. Backward trajectory analysis revealed that the major air masses originate from local and regional transportation, but PM 2.5 OP was more susceptible to the influence of short-distance transport clusters. Discerning the influence of chemicals on health-pertinent attributes of PM 2.5 , such as OP, could facilitate a deep understanding of the cause-and-effect relationship between PM 2.5 and impacts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The establishment of the Clean Development Mechanism (CDM) has greatly improved China's carbon emission trading system. However, due to the unbalanced development of CDM in China, the effects and mechanism of CDM on reducing pollution and carbon are still unclear. In order to explore the effects and mechanism of CDM on the synergistic effects of pollution mitigation and carbon reduction, we first set up a theoretical analysis framework. Utilizing panel data from 254 prefecture-level cities across China spanning from 2004 to 2021, we employ a synergy degree model of composite system to evaluate the synergistic effects of pollution mitigation and carbon reduction. By treating CDM as a quasi-natural experimental research subject, we construct a multi-period difference-in-difference model to assess the CDM projects' effects. Our findings indicate a positive association between CDM projects and the synergistic effects of pollution mitigation and carbon reduction. Heterogeneity analysis reveals that CDM projects located in the western region, areas with lower levels of economic development, non-resource cities, non-old industrial bases, and projects with Certified Emission Reductions issued exhibit the most pronounced synergistic effects. Specially, dynamic policy effect analysis shows that only non-resource cities and non-old industrial bases exhibit enhanced policy implementation regarding CDM. Mechanism analysis demonstrates that CDM primarily enhances synergistic effects through improved energy efficiency, technological innovation and energy transition. These findings enrich empirical investigations concerning market-driven emission reduction policy in China, shedding light on pivotal pathways for synergistic control of pollution mitigation and carbon reduction and offering valuable policy insights for comprehensive economic and social green transformation in China., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Air pollution poses significant health risks to urban areas, with limited focus on the chronic association of PM 2.5 and its constituents on cerebrovascular diseases (CERs), especially regarding the joint associations. This study explores the individual and joint associations between PM 2.5 constituents and CER hospitalization risks through a cohort analysis of 36,271 adults in the Pearl River Delta, South China, from 2015 to 2020. Cox proportional hazards regression and quantile-based g-computation models were used to quantify the individual and joint associations of annual mean concentrations of PM 2.5 constituents with hospitalization for CERs. 1151 participants were hospitalized due to CERs during the five-year follow-up period. Joint associations analyses identified that one quartile increase in co-exposure may result in hazard ratios of 1.530 (1.441-1.623), 1.840 (1.710-1.980), and 1.609 (1.491-1.737) for CERs, total, and ischemic stroke hospitalization, respectively. The adverse effect was primarily driven by organic matter and chlorine. Men, those with a history of tobacco or alcohol use or with low residential greenness, were more susceptible to CERs hospitalization following PM 2.5 constituents co-exposure. Upcoming strategies should focus on monitoring and regulating PM 2.5 constituents, encouraging healthy lifestyles, and enhancing urban greenery., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)