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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.)

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.)

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.)

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]

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.)

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 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]

Since 2010, the Chinese government has developed several batches of low-carbon pilot provinces and cities continuously. And in the backdrop of carbon peak and carbon neutralisation recently, the carbon emission reduction policies and achievements of China have attracted more and more worldwide attention. In the field of human health, this paper investigates whether the Low-carbon Province Pilot Policy of China has positive effects on residents' health based on the micro data of China Family Panel Studies (CFPS) from 2010 to 2016. Using the time-varying difference-in-difference model to solve the endogeneity problem commonly faced in the literature, we find that Low-carbon Province Pilot Policy has a positive impact on public health through air quality improvement and the unemployment level alleviation mainly. And in urban areas, the policy effect is more obvious than that in rural regions. This paper expands the content of national low-carbon governance effects to residents' health further and contribute to China's low-carbon development. Specifically, it reminds us to focus on the dynamic adjustment and effective continuation of the policy, and also the all-round support for 'the disadvantaged' in environmental governance process. [ABSTRACT FROM AUTHOR]

As the ownership of vehicles and frequency of utilization increase, vehicle emissions have become an important source of air pollution in Chinese cities. An accurate emission inventory for on-road vehicles is necessary for numerical air quality simulation and the assessment of implementation strategies. This paper presents a bottom-up methodology based on the local emission factors, complemented with the widely used emission factors of Computer Programme to Calculate Emissions from Road Transport (COPERT) model and near real time (NRT) traffic data on road segments to develop a high temporal-spatial resolution vehicle emission inventory (HTSVE) for the urban Beijing area. To simulate real-world vehicle emissions accurately, the road has been divided into segments according to the driving cycle (traffic speed) on this road segment. The results show that the vehicle emissions of NOx, CO, HC and PM were 10.54×104, 42.51×104 and 2.13×104 and 0.41×104 Mg, respectively. The vehicle emissions and fuel consumption estimated by the model were compared with the China Vehicle Emission Control Annual Report and fuel sales thereafter. The grid-based emissions were also compared with the vehicular emission inventory developed by the macro-scale approach. This method indicates that the bottom-up approach better estimates the levels and spatial distribution of vehicle emissions than the macro-scale method, which relies on more information. Additionally, the on-road vehicle emission inventory model and control effect assessment system in Beijing, a vehicle emission inventory model, was established based on this study in a companion paper (He et al., 2015). [ABSTRACT FROM AUTHOR]

Scant research has pinpointed the year of minimum PM 2.5 concentration through extensive, uninterrupted monitoring, nor has it thoroughly assessed carcinogenic risks associated with analyzing numerous components during this nadir in Beijing. This study endeavored to delineate the atmospheric PM 2.5 pollution in Beijing from 2015 to 2022 and to undertake comprehensive evaluation of carcinogenic risks associated with the composition of atmospheric PM 2.5 during the year exhibiting the lowest concentration. PM 2.5 concentrations were monitored gradually in 9 districts of Beijing for 7 consecutive days per month from 2015 to 2022, and 32 kinds of PM 2.5 components collected in the lowest PM 2.5 concentration year were analyzed. This comprehensive dataset served as the basis for carcinogenic risk assessment using Monte Carlo simulation. And we applied the Positive Matrix Factorization (PMF) method to identity the sources of atmospheric PM 2.5 . Furthermore, we integrated this source appointment model with risk assessment model to discern the origins of these risks. The findings revealed that the annual average PM 2.5 concentration in 2022 stood at 43.1 μg/m 3 , marking the lowest level recorded. The mean carcinogenic risks of atmospheric PM 2.5 exposure calculated at 6.30E-6 (empirical 95% CI 1.09E-6 to 2.25E-5) in 2022. The PMF model suggested that secondary sources (35.4%), coal combustion (25.6%), resuspended dust (15.1%), biomass combustion (14.1%), vehicle emissions (7.1%), industrial emissions (2.0%) and others (0.7%) were the main sources of atmospheric PM 2.5 in Beijing. The mixed model revealed that coal combustion (2.41E-6), vehicle emissions (1.90E-6) and industrial emissions (1.32E-6) were the main sources of carcinogenic risks with caution. Despite a continual decrease in atmospheric PM 2.5 concentration in recent years, the lowest concentration levels still pose non-negligible carcinogenic risks. Notably, the carcinogenic risks associated with metals and metalloids exceeded that of PAHs. And the distribution of risk sources did not align proportionally with the distribution of PM 2.5 mass concentration., 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 Chinese government has implemented environmental regulations to address the deterioration of air quality associated with rapid industrialization. However, there is no consensus on whether environmental regulations are beneficial to environmental performance. The technical challenges related to endogeneity and spatial correlation may bias the estimation of the emission reduction effect of regulations. In this study, we comprehensively evaluate the environmental performance of sulfur dioxide regulations in Chinese cities using a novel stochastic frontier model that introduces the single control function to correct estimation errors caused by spatial spillovers and endogeneity. Our analysis emphasizes that insufficient resolution of endogeneity or spatial spillovers may lead to underestimation or neglect of the environmental performance improvements achieved by these regulations. On the contrary, our revised research results indicate that regulations aimed at reducing sulfur dioxide emissions not only successfully control sulfur dioxide emissions, but also have a positive impact on reducing carbon emissions. In addition, we conduct in-depth research on the mechanisms by which environmental regulations improve performance by stimulating green technology innovation and promoting industrial structure upgrading. Based on our research findings, we propose policy recommendations to establish a city cooperation mechanism of technology exchange to achieve synergistic emission reduction and strengthen regional factor circulation., 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.)

Air pollution has a significant global impact on natural resources and public health. Accurate prediction of air pollution is crucial for effective prevention and control measures. However, due to regional variations, different cities may have varying primary pollutants, posing new challenges for accurate prediction. In this paper, we propose a novel method called FP-RF, which integrates clustering algorithms to categorize multiple cities according to their air quality index values. Subsequently, we apply functional principal component analysis to extract the primary components of air pollution within each cluster. Furthermore, an enhanced random forest algorithm is utilized to predict air quality grades for each city. We conduct experimental evaluations using authentic historical data from Anhui Province spanning from 2018 to 2023. The results unequivocally establish the effectiveness of our model, with an average accuracy rate of 98.6% in forecasting six pollution grades and 96.04% accuracy in predicting 16 prefecture-level cities, surpassing performance compared to other baseline models., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Ozone pollution is the focus of current environmental governance in China and high-quality prediction of ozone concentration is the prerequisite to effective policymaking. The studied ozone pollution time series exhibits distinct seasonality and secular trends and is associated with various factors. This study developed an interpretable hybrid model by combining STL decomposition and the Transformer (STL-Transformer) with the prior information of ozone time series and global multi-source information as prediction basis. The STL decomposition decomposes ozone time series into trend, seasonal, and remainder components. Then, the three components, along with other air quality and meteorological data, are integrated into the input sequence of the Transformer. The experiment results show that the STL-Transformer outperforms the other five state-of-the-art models, including the standard Transformer. Specially, the univariate forecasting for ozone relies on mimicking the patterns and trends that have occurred in the past. In contrast, multivariate forecasting can effectively capture complex relationships and dependencies involving multiple variables. The method successfully grasps the prior and global multi-source information and simultaneously improves the interpretability of ozone prediction with high precision. This study provides new insights for air pollution forecasting and has reliable theoretical value and practical significance for environmental governance., 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.)

In recent years, China has adopted numerous policies and regulations to control NO x emissions to further alleviate the adverse impacts of NO 3 - -N deposition. However, the variation in wet NO 3 -N deposition and its sources changes after such air pollution control through field observation and isotope tracing. Results showed that the annual mean concentrations of NO - -N deposition under such policies is not clear. In this study, the southeastern area, with highly developed industries and traditional agriculture, was selected to explore the variation in NO 3 - -N deposition and its sources changes after such air pollution control through field observation and isotope tracing. Results showed that the annual mean concentrations of NO 3 - -N in precipitation were 0.67 mg L -1 and 0.54 mg L -1 in 2014-2015 and 2021-2022, respectively. The average wet NO 3 - -N depositions in 2014-2015 and 2021-2022 was 7.76 kg N ha -1 yr -1 and 5.03 kg N ha -1 yr -1 , respectively, indicating a 35% decrease. The δ 15 -N deposition, followed by vehicle exhaust (22.9%), other sources (17.1%), and soil emissions (6.4%) during 2014-2015. However, the contribution of vehicle exhaust (33.3%) overpassed the coal combustion (32.3%) and followed by other sources (25.4%) and soil emissions (9.0%) in 2021-2022. Apart from the control of air pollution, meteorological factors such as temperature, precipitation, and solar radiation are closely related to the changes in atmospheric N transformation and deposition. The results suggest phased achievements in air pollution control and that more attention should be paid to the control of motor vehicle exhaust pollution in the future, at the same time maintaining current actions and supervision of coal-powered plants.3 - and δ 18 O-NO 3 - values were lower in warm seasons and higher in cold seasons, and both showed a lower trend in 2021-2022 compared with 2014-2015. The Bayesian model results showed that the NO x emitted from coal-powered plants contributed 53.6% to wet NO 3 - -N deposition, followed by vehicle exhaust (22.9%), other sources (17.1%), and soil emissions (6.4%) during 2014-2015. However, the contribution of vehicle exhaust (33.3%) overpassed the coal combustion (32.3%) and followed by other sources (25.4%) and soil emissions (9.0%) in 2021-2022. Apart from the control of air pollution, meteorological factors such as temperature, precipitation, and solar radiation are closely related to the changes in atmospheric N transformation and deposition. The results suggest phased achievements in air pollution control and that more attention should be paid to the control of motor vehicle exhaust pollution in the future, at the same time maintaining current actions and supervision of coal-powered plants., 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: As the global aging process accelerates, the health challenges posed by sarcopenia among middle-aged and older adults are becoming increasingly prominent. However, the available evidence on the adverse effects of air pollution on sarcopenia is limited, particularly in the Western Pacific region. This study aimed to explore relationships of multiple air pollutants with sarcopenia and related biomarkers using the nationally representative database., Methods: Totally, 6585 participants aged over 45 years were enrolled from the China Health and Retirement Longitudinal Study (CHARLS) in 2011 and 3443 of them were followed up until 2015. Air pollutants were estimated from high-resolution satellite-based spatial-temporal models. In the cross-sectional analysis, we used generalized linear regression, unconditional logistic regression analytical and restricted cubic spline (RCS) methods to assess the single-exposure and non-linear effects of multiple air pollutants on sarcopenia and related surrogate biomarkers (serum creatinine and cystatin C). Several popular mixture analysis techniques such as Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS) regression, and quantile-based g-computation (Qgcomp) were further used to examinate the combined effects of multiple air pollutants. Logistic regression was used to further analyze the longitudinal association between air pollution and sarcopenia., Results: Each interquartile range increase in PM 2.5 , PM 10 and NO 2 was significantly associated with an increased risk of sarcopenia, with adjusted odds ratios (aORs) of 1.09 [95 % confidence interval (CI): 1.01, 1.20], 1.24 (95 % CI: 1.14, 1.35) and 1.18 (95 % CI: 1.08, 1.28), respectively. Our findings also showed that five air pollutants were significantly associated with the sarcopenia index. In addition, employing a mixture analysis approach, we confirmed significant combined effects of air pollution mixtures on sarcopenia risk and associated biomarkers, with PM 10 and PM 2.5 identified as major contributors to the combined effect. The results of the exposure-response (E-R) relationships, subgroup analysis, longitudinal analysis and sensitivity analysis all showed the unfavorable impact of air pollution on sarcopenia risk and related vulnerable populations., Conclusions: Single-exposure and co-exposure to multiple air pollutants were positively associated with sarcopenia among middle-aged and older adults in China. Our study provided new evidence that air pollution mixture was significantly associated with sarcopenia related biomarkers., 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.)

Ammonia (NH 3 ) plays an important role in the formation of atmospheric particulate matter, but the contribution of traffic-related emissions remains unclear, particularly in megacities with a large number of vehicles. Taking the opportunity of the stringent COVID-19 lockdowns implemented in Beijing and Shanghai in 2022, this study aims to estimate the traffic-related NH 3 emissions in these two megacities based on satellite observations. Differences between urban and suburban areas during the lockdown and non-lockdown periods are compared. It was found that despite different dominating sources, the overall NH 3 concentrations in urban and suburban areas were at a similar level, and the lockdown resulted in a more prominent decrease in urban areas, where traffic activities were most heavily affected. The traffic-related contribution to the total emission was estimated to be ∼30% in megacities, and ∼40% in urban areas, which are about 2-10 times higher than that in previous studies. The findings indicate that the traffic-related NH 3 emissions have been significantly underestimated in previous studies and may play a more critical role in the formation of air pollution in megacities, especially in winter, when agricultural emissions are relatively low. This study highlights the importance of traffic-related NH 3 emissions in Chinese megacities and the need to reassess the emissions and their impacts on air quality., 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 global population influx during the COVID-19 pandemic poses significant challenges to public health, making the prevention and control of infectious diseases a pressing concern. This paper aims to examine the impact of population influx on the spread of infectious diseases, with a specific emphasis on the mediating role of air pollution in this process. A theoretical analysis is conducted to explore the relationship between population influx, air pollution, and infectious diseases. Additionally, we establish a series of econometric models and employ various empirical tests and analytical techniques, including mediation effect test, threshold effect test, and systematic GMM test, to evaluate our hypotheses. The results indicate that: (1) Population influx directly and indirectly impacts infectious diseases. Specifically, population influx not only directly elevates the risk of infectious diseases, but also indirectly increases the incidence rate of infectious diseases by intensifying air pollution. (2) The impact of population inflow on infectious diseases exhibits regional heterogeneity. Compared to central and western China, the eastern regions exhibit a significantly higher risk of infectious diseases, exceeding the national average. (3) External factors influence the relationship between population influx and infectious diseases differently. Personal income and medical resources both help mitigate the risk of infectious diseases due to population influx, with medical resources having a more substantial effect. Contrary to expectations, abundant educational resources have not reduced the risk, instead, they have exacerbated the risk associated with population influx. This paper provides a scientific basis for formulating effective strategies for the prevention and control of infectious diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Fu and Zhu.)

Air pollution shares the attributes of multi-factorial influence and spatiotemporal complexity, leading to air pollution control assistance models easily falling into a state of failure. To address this issue, we design a framework containing improved data fusion method, novel grey incidence models and air pollution spatiotemporal analysis to analyze the complex characteristics of air pollution under the fusion of multiple factors. Firstly, we improve the existing data fusion method for multi-factor fusion. Subsequently, we construct two grey spatiotemporal incidence models to examine the spatiotemporal characteristics of multi-factorial air pollution in network relationships and changing trends. Furthermore, we propose two new properties that can manifest the performance of grey incidence analysis, and we provide detailed proof of the properties of the new models. Finally, in the Jing-Jin-Ji region, the novel models are used to study the network relationships and trend changes of air pollution. The findings are as follows: (1) Two highly polluted belts in the region require attention. (2) Although the air pollution network under multi-factorial fusion obeys the first law of geography, the network density and node density exhibit significant variations. (3) From 2013 to 2021, all pollutants except O 3 show improvement. (4) Recommendations for responses are presented based on the above-mentioned results. (5) The parameter analyses, model comparisons, Monte Carlo experiments and model feature summaries illustrate that the proposed models are practical, interpretable and considerably outperform various prevailing competitors with remarkable universality., 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.)

Background: Diabetic retinopathy (DR), one of the most common microvascular complications of diabetes mellitus (DM), is a major contributor of vision impairment and blindness worldwide. Studies have shown that air pollution exposure is adversely associated with DM. However, evidence is scarce regarding how air pollution exposure affects DR. This study aimed to investigate the association between ambient air pollution exposure and DR risk., Methods: The study population was based on the Fujian Eye Study (FJES), an ophthalmologic, epidemiologic survey investigating the eye health condition of residents in Fujian Province from 2018 to 2019. Daily average concentrations of ambient air pollutants (PM 2.5 , PM 10 , SO 2 , NO 2 , and O 3 ) were acquired from a high-resolution air quality dataset in China from 2013 to 2018. We used a logistic regression model to examine the associations between DR risk and long-term air pollution at various exposure windows., Results: A total of 2405 out of the 8211 participants were diagnosed with diabetes, among whom 183 had DR. Ambient air pollution, especially particulate matter (i.e., PM 2.5 and PM 10 ) and NO 2 were positively associated with DR prevalence among all the study subjects. Ambient SO 2 and O 3 concentrations were not associated with DR prevalence. PM 2.5 and NO 2 seemed to be borderline significantly associated with increased prevalence of DR in subjects with DM, especially under the model adjusted for sex, age, BMI, SBP, and DBP., Conclusions: These findings showed that long-term exposure to ambient particulate matter and NO 2 was associated with a high DR risk in Fujian province, where ambient air pollution is relatively low., 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.)

Objective: To explore the association between green space, multiple ambient air pollutants and depressive/anxiety symptoms and the mediating role of physical activity (PA) in Chinese adolescents., Method: A school-based health survey was conducted in eight provinces in China in 2021. 22,868 students aged 14.64 (±1.77) years completed standard questionnaires to record details of depressive, anxiety symptoms and PA. We calculated the average normalized difference vegetation index (NDVI) in circular buffers of 200 m, 500 m and 1000 m and estimated the concentrations of PM 10 , PM 2.5 , CO, NO 2 , O 3 , SO 2 around the adolescents' school addresses., Results: The exposure-response curves showed that the lower the NDVI value, the higher the risk of depressive and anxiety symptoms. CO, PM 2.5 and SO 2 and air pollution score were associated with increased risk of depressive and anxiety symptoms. NDVI in all circular buffers decreased the risk of depressive and anxiety symptoms at low levels of PA, but the associations were not significant at high levels of PA. In the subgroup analysis, PM 10 , PM 2.5 , CO, NO 2 , SO 2 , AQI and air pollution score increased the risk of depressive and anxiety symptoms at low PA levels, but the associations were not significant at high levels of PA. Mediation analysis indicated that the mediating effect of PA on the association between NDVI, NDVI-200 m NDVI-500 m, CO, PM 10 , PM 2.5 , SO 2 , AQI and depressive/anxiety symptoms was statistically significant（p < 0.05）., Conclusion: Middle-high level PA could reduce the strength of association between air pollution and depressive and anxiety symptoms. Meanwhile, the association between green space/air pollution and depressive/anxiety symptoms was partly mediated by PA., 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.)

Despite concerted efforts in emission control, air pollution control remains challenging. Urban planning has emerged as a crucial strategy for mitigating PM 2.5 pollution. What remains unclear is the impact of urban form and their interactions with seasonal changes. In this study, base on the air quality monitoring stations in the Yangtze River Delta urban agglomeration, the relationship between urban spatial indicators (building morphology and land use) and PM 2.5 concentrations was investigated using full subset regression and variance partitioning analysis, and seasonal differences were further analysed. Our findings reveal that PM 2.5 pollution exhibits different sensitivities to spatial scales, with higher sensitivity to the local microclimate formed by the three-dimensional structure of buildings at the local scale, while land use exerts greater influence at larger scales. Specifically, land use indicators contributed sustantially more to the PM 2.5 prediction model as buffer zone expand (from an average of 2.41% at 100 m range to 47.30% at 5000 m range), whereas building morphology indicators display an inverse trend (from an average of 13.84% at 100 m range to 1.88% at 5000 m range). These results enderscore the importance of considering building morphology in local-scale urban planning, where the increasing building height can significantly enhance the disperion of PM 2.5 pollution. Conversely, large-scale urban planning should prioritize the mixed use of green spaces and construction lands to mitigate PM 2.5 pollution. Moreover, the significant seasonal differences in the ralationship between urban spatical indicatiors and PM 2.5 pollution were observed. Particularly moteworthy is the heightened association between forest, water indicators and PM 2.5 concentrations in summer, indicating the urban forests may facilitate the formation of volatile compunds, exacerbating the PM 2.5 pollution. Our study provides a theoretical basis for addressing scale-related challenges in urban spatial planning, thereby forstering the sustainable development of cities., 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.)

China's carbon emission trading policy plays a crucial role in achieving both its "3060" dual carbon objectives and the United Nations Sustainable Development Goal 13 (SDG 13) on climate action. The policy's effectiveness in reducing pollution and mitigating carbon emissions holds significant importance. This paper investigated whether China's carbon emission trading policy affects pollution reduction (PM 2.5 and SO 2 ) and carbon mitigation (CO 2 ) in pilot regions, using panel data from 30 provinces and municipalities in China from 2005 to 2019 and employing a multi-period difference-in-differences (DID) model. Furthermore, it analyzed the heterogeneity of carbon market mechanisms and regional variations. Finally, it examined the governance pathways for pollution reduction and carbon mitigation from a holistic perspective. The results indicate that: (1) China's carbon emission trading policy has reduced CO 2 emissions by 18% and SO 2 emissions by 36% in pilot areas, with an immediate impact on the "carbon mitigation" effect, while the "pollution reduction" effect exhibits a time lag. (2) Higher carbon trading prices lead to stronger "carbon mitigation" effect, and larger carbon market scales are associated with greater "pollution reduction" effects on PM 2.5 . Governance effects on pollution reduction and carbon mitigation vary among pilot regions: Carbon markets of Beijing, Chongqing, Shanghai, and Tianjin show significant governance effects in both "pollution reduction" and "carbon mitigation", whereas Guangdong's carbon market exhibits only a "pollution reduction" effect, and Hubei's carbon market demonstrates only a "carbon mitigation" effect. (3) Currently, China's carbon emission trading policy achieves pollution reduction and carbon mitigation through "process management" and "end-of-pipe treatment". This study could provide empirical insights and policy implications for pollution reduction and carbon mitigation, as well as for the development of China's carbon emission trading market., 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.)

Fine particulate matter (PM 2.5 ) harms human health and hinders normal human life. Considering the serious complexity and obvious regional characteristics of PM 2.5 pollution, it is urgent to fill in the comprehensive overview of regional characteristics and interannual evolution of PM 2.5 . This review studied the PM 2.5 pollution in six typical areas between 2014 and 2022 based on the data published by the Chinese government and nearly 120 relevant literature. We analyzed and compared the characteristics of interannual and quarterly changes of PM 2.5 concentration. The Beijing-Tianjin-Hebei region (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) made remarkable progress in improving PM 2.5 pollution, while Fenwei Plain (FWP), Sichuan Basin (SCB) and Northeast Plain (NEP) were slightly inferior mainly due to the relatively lower level of economic development. It was found that the annual average PM 2.5 concentration change versus year curves in the three areas with better pollution control conditions can be merged into a smooth curve. Importantly, this can be fitted for the accurate evaluation of each area and provide reliable prediction of its future evolution. In addition, we analyzed the factors affecting the PM 2.5 in each area and summarize the causes of air pollution in China. They included primary emission, secondary generation, regional transmission, as well as unfavorable air dispersion conditions. We also suggested that the PM 2.5 pollution control should target specific industries and periods, and further research need to be carried out on the process of secondary production. The results provided useful assistance such as effect prediction and strategy guidance for PM 2.5 pollution control in Chinese backward areas., 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.)

This study addresses the pressing need for cost-effective emission reduction strategies that maximize co-benefits in terms of air pollution and carbon emissions. Our research contributes to the literature by accurately measuring these co-benefits, thereby facilitating their prompt realization in different regions. We employ an input-output framework that integrates carbon emissions and air pollution, allowing us to calculate marginal abatement costs using the shadow price of undesired output. Through this approach, we quantify the co-benefits and analyze the factors influencing them at both spatiotemporal and factor levels using spatial kernel density and geographical detectors. Our findings reveal several key insights: (1) under joint emission reduction efforts, we observe average annual reduction rates of 6.46% for marginal pollution and 6.10% for carbon reduction costs. Importantly, we document an increase in co-benefits from 0.50 to 0.86, characterized by an initial fluctuation followed by a linear increase. (2) the marginal cost difference for carbon emission and pollution reduction in western China was 179.45 and 155.08 respectively, compared to 321.51 and 124.70 in the Northeast, highlighting the crucial role of regional differences in shaping co-benefit outcomes. (3) we identify a negative spatial spillover effect between provinces, which diminishes over time, leading to heterogeneous effects when local provincial co-benefits exceed a threshold of 0.9. (4) during the adjustment period, we find that the industrial structure exerts significant single and interactive effects on co-benefits. Additionally, we highlight the critical role of environmental governance investment and government intervention as drivers of co-benefits in the current era. By offering the quantification of co-benefits under the marginal abatement costs, our study provides valuable scientific insights for planning and implementing effective synergy 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 Elsevier Inc. All rights reserved.)

Background: The connections between fine particulate matter (PM 2.5 ) and coarse particulate matter (PM 2.5-10 ) and daily mortality of viral pneumonia and bacterial pneumonia were unclear., Objectives: To distinguish the connections between PM 2.5 and PM 2.5-10 and daily mortality due to viral pneumonia and bacterial pneumonia., Methods: Using a comprehensive national death registry encompassing all areas of mainland China, we conducted a case-crossover investigation from 2013 to 2019 at an individual level. Residential daily particle concentrations were evaluated using satellite-based models with a spatial resolution of 1 km. To analyze the data, we employed the conditional logistic regression model in conjunction with polynomial distributed lag models., Results: We included 221,507 pneumonia deaths in China. Every interquartile range (IQR) elevation in concentrations of PM 2.5 (lag 0-2 d, 37.6 μg/m 3 ) was associated with higher magnitude of mortality for viral pneumonia (3.03%) than bacterial pneumonia (2.14%), whereas the difference was not significant (p-value for difference = 0.38). An IQR increase in concentrations of PM 2.5-10 (lag 0-2 d, 28.4 μg/m 3 ) was also linked to higher magnitude of mortality from viral pneumonia (3.06%) compared to bacterial pneumonia (2.31%), whereas the difference was not significant (p-value for difference = 0.52). After controlling for gaseous pollutants, their effects were all stable; however, with mutual adjustment, the associations of PM 2.5 remained, and those of PM 2.5-10 were no longer statistically significant. Greater magnitude of associations was noted in individuals aged 75 years and above, as well as during the cold season., Conclusion: This nationwide study presents compelling evidence that both PM 2.5 and PM 2.5-10 exposures could increase pneumonia mortality of viral and bacterial causes, highlighting the more robust effects of PM 2.5 and somewhat higher sensitivity of viral pneumonia., 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.)

Exploring whether informal environmental regulations (INER) can achieve carbon reduction in the context of pollution reduction and carbon reduction, as well as how to achieve carbon reduction, can help solve the dual failures of the market and government in environmental protection. Based on the polycentric governance theory and considering the characteristics of social subject environmental participation, the Stackelberg game is used to demonstrate the impact mechanism of INER on CO 2 . In addition, using the panel data of China's 30 provinces from 2003 to 2018, this paper validates the effectiveness of INER by Pooled Ordinary Least Square (POLS) and threshold panel model. Then, the mediating effect model is used to test the mechanism of INER's effect on carbon reduction. The results show that corruption is not conducive to CO 2 reduction. The reduction effect of INER on CO 2 exhibits heterogeneity with changes in other non-greenhouse gas pollutants. While INER effectively reduces local corruption, its more substantial indirect impact on CO 2 reduction is prominent when levels of other pollutants are lower. Comparative analysis reveals that there are still biased governance behaviors to cope with INER's pressure in some regions nowadays. The findings show that for countries facing the dual task of pollution control and carbon reduction, the key to leveraging the supervisory role of INER should be focused on mitigating information asymmetry caused by the characteristics of CO 2 . Therefore, in the process of environmental protection, the public environmental participation system should be improved, and the process of disclosing polluters' carbon information should be accelerated., 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.)

Background: Previous research has shown that lack of leisure activities, either outdoor or social activities, impedes cognitive function. However, the interrelationship between poor cognition and deficient activities is understudied. In addition, whether exposure to air pollution, such as PM 2.5 , can accelerate the detrimental 'inactivity-poor cognition' cycle, is worthy of investigation., Methods: We used data from the 2008, 2011, 2014, and 2018 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). We assessed the frequency of outdoor or social activities at each wave. The cognitive function was examined using a China-Modified Mini-mental State Examination. We estimated the residential exposure to fine particular matter (PM 2.5 ) via a satellite-based model. We applied cross-lagged panel (CLP) model to examine the bi-directional relationship between outdoor or social activities and cognitive function. We then examined the effect of PM 2.5 exposure with sequent cognitive function and activities using generalized estimation equation (GEE) model., Findings: Overall, we observed significant bi-directional associations between outdoor or social activities and cognitive function. Participants with better cognitive function in the last wave were more likely to engage in outdoor or social activities in the following wave (outdoor activities: β = 0.37, 95% CI [0.27,0.48], P < 0.01; social activities: β = 0.05, 95% CI [0.02,0.09] P < 0.01). Meanwhile, higher engagement in outdoor or social activities in the last wave was associated with more favorable cognitive function in the following wave (outdoor activities: β = 0.06, 95% CI [0.03,0.09], P < 0.01; social activities: β = 0.10, 95% CI [0.03,0.18], P < 0.01). Notably, an increase in PM 2.5 exposure during the preceding year was significantly associated with a declining cognitive function (β = -0.05, 95% CI [-0.08,-0.03], P < 0.01), outdoor activities (β = -0.02, 95% CI [-0.04, -0.01], P < 0.01) and social activities (β = -0.02, 95% CI [-0.02, -0.01], P < 0.01) in the current year; the lagged effects of the PM 2.5 exposure in the past year of the last wave on activities and cognitive function of the following wave were also observed., Interpretation: Our findings not only indicate the bi-directional links between the frequency of outdoor or social activities and cognitive function, but also report that PM 2.5 exposure plays a role in catalyzing the detrimental inactivity-poor cognition cycle. Future research should investigate whether the policy-driven interventions, such as clean air policies, can break the unfavorable activity-cognition cycle, and thereby promoting health from the dual gains in leisure activities and cognition., 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.)

Introduction: Vehicle emissions have become an important source of urban air pollution, and the assessment of air pollution emission characteristics and health effects caused by specific pollution sources can provide scientific basis for air quality management., Methods: In this paper, vehicle PM 2.5 pollution in typical urban agglomerations of China (the Beijing-Tianjin-Hebei urban agglomeration (BTHUA), the triangle of the Central China urban agglomeration (TCCUA) and the Chengdu-Chongqing urban agglomeration (CCUA)) were used as research samples to evaluate the emission characteristics, health effects and economic losses of vehicle PM 2.5 pollution based on the emission inventory, air quality model and exposure-response function from 2010 to 2020., Results: The results indicated that PM 2.5 emissions from vehicles in the three urban agglomerations during 2010-2020 first showed an upward yearly trend and then showed a slow decrease in recent years. Heavy-duty trucks and buses are the main contribution vehicles of PM 2.5 , and the contribution rates of light-duty vehicles to PM 2.5 is increasing year by year. The contribution rate of PM 2.5 in Beijing decreased significantly. In addition to capital cities and municipalities directly under the central Government, the emission of pollutants in other cities cannot be ignored. The evaluation results of the impact of PM 2.5 pollution from vehicles on population health show that: the number of each health endpoint caused by PM 2.5 pollution from vehicles in the BTHUA and CCUA showed an overall upward trend, while the TCCUA showed a downward trend in recent years. Among them, PM 2.5 pollution from vehicles in the three major urban agglomerations cause about 78,200 (95% CI: 20,500-138,800) premature deaths, 122,800 (95% CI: 25,600-220,500) inpatients, and 628,400 (95% CI: 307,400-930,400) outpatients and 1,332,400 (95% CI: 482,700-2,075,600) illness in 2020. The total health economic losses caused by PM 2.5 pollution from vehicles in the three major urban agglomerations in 2010, 2015 and 2020 were 68.25 billion yuan (95% CI: 21.65-109.16), 206.33 billion yuan (95% CI: 66.20-326.20) and 300.73 billion yuan (95% CI: 96.79-473.16), accounting for 0.67% (95% CI: 0.21-1.07%), 1.19% (95% CI: 0.38%-1.88%) and 1.21% (95% CI: 0.39%-1.90%) of the total GDP of these cities., Discussion: Due to the differences in vehicle population, PM 2.5 concentration, population number and economic value of health terminal units, there are differences in health effects and economic losses among different cities in different regions. Among them, the problems of health risks and economic losses were relatively prominent in Beijing, Chengdu, Chongqing, Tianjin and Wuhan., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Song and Hao.)

Air pollution in China has becoming increasingly serious in recent years with frequent incidents of smog. Parts of southwest China still experience high incidents of smog, with PM 2.5 (particulate matter with diameter ≤2.5 μm) being the main contributor. Establishing the spatial distribution of PM 2.5 in Southwest China is important for safeguarding regional human health, environmental quality, and economic development. This study used remote sensing (RS) and geographical information system (GIS) technologies and aerosol optical depth (AOD), digital elevation model (DEM), normalized difference vegetation index (NDVI), population density, and meteorological data from January to December 2018 for southwest China. PM 2.5 concentrations were estimated using ordinary least squares regression (OLS), geographic weighted regression (GWR) and geographically and temporally weighted regression (GTWR). The results showed that: (1) Eight influencing factors showed different correlations to PM 2.5 concentrations. However, the R 2 values of the correlations all exceeded 0.3, indicating a moderate degree of correlation or more; (2) The correlation R 2 values between the measured and remote sensed estimated PM 2.5 data by OLS, GWR, and GTWR were 0.554, 0.713, and 0.801, respectively; (3) In general, the spatial distribution of PM 2.5 in southwest of China decreases from the Northeast to Northwest, with moderate concentrations in the Southeast and Southwest; (4) The seasonal average PM 2.5 concentration is high in winter, low in summer, and moderate in spring and autumn, whereas the monthly average shows a "V" -shaped oscillation change., 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.)

After the resumption of work and production following the COVID-19 pandemic, many cities entered a "transition phase", characterized by the gradual recovery of emission levels from various sources. Although the overall PM 2.5 emission trends have recovered, the specific changes in different sources of PM 2.5 remain unclear. Here, we investigated the changes in source contributions and the evolution pattern of pollution episodes (PE) in Wuhan during the "transition period" and compared them with the same period during the COVID-19 lockdown. We found that vehicle emissions, industrial processes, and road dust exhibited significant recoveries during the transition period, increasing by 5.4%, 4.8%, and 3.9%, respectively, during the PE. As primary emissions increased, secondary formation slightly declined, but it still played a predominant role (accounting for 39.1∼ 43.0% of secondary nitrate). The reduction in industrial activities was partially offset by residential burning. The evolution characteristics of PE exhibited significant differences between the two periods, with PM 2.5 concentration persisting at a high level during the transition period. The differences in the evolution patterns of the two periods were also reflected in their change rates at each stage, which mostly depend on the pre-PE concentration level. The transition period shows a significantly higher value (8.4 μg m -3 h -1 ) compared with the lockdown period, almost double the amount. In addition to local emissions, regional transport should be a key consideration in pollution mitigation strategies, especially in areas adjacent to Wuhan. Our study quantifies the variations in sources between the two periods, providing valuable insights for optimizing environmental planning to achieve established goals., 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.)

Objective: To investigate the relationship between ambient fine particulate matter (PM 2.5 ) exposure and fecundability., Methods: This study included 751,270 female residents from Henan Province who participated in the National Free Pre-conception Check-up Projects during 2015-2017. Ambient cycle-specific PM 2.5 exposure was assessed at the county level for each participant using satellite-based PM 2.5 concentration data at 1-km resolution. Cox proportional hazards models with time-varying exposure were used to estimate the association between fecundability and PM 2.5 exposure, adjusted for potential individual risk factors., Results: During the study period, 568,713 participants were pregnant, monthly mean PM 2.5 concentrations varied from 25.5 to 114.0 µg/m 3 across study areas. For each 10 µg/m 3 increase in cycle-specific PM 2.5 , the hazard ratio for fecundability was 0.951 (95 % confidence interval: 0.950-0.953). The association was more pronounced in women who were older, with urban household registration, history of pregnancy, higher body mass index (BMI), hypertension, without exposure to tobacco, or whose male partners were older, with higher BMI, or hypertension., Conclusion: In this population-based prospective cohort, ambient cycle-specific PM 2.5 exposure was associated with reduced fecundability. These findings may support the adverse implications of severe air pollution on reproductive 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. Published by Elsevier Ltd.)

To confront the challenges posed by air pollution and climate change, China has undertaken significant initiatives to develop strategies that address both issues concurrently. However, the health benefits of these initiatives have not been clearly articulated. In this study, the dynamic changes in health impacts under air pollution and carbon reduction actions in China are evaluated by employing the latest concentration-response models and projected PM 2.5 concentrations under future scenarios. From 2020 to 2060, the enforcement of clean air and climate mitigation policies is expected to increase the percentage of the population living with PM 2.5 concentrations meeting the 10 μg/m 3 standard by 79 %. Without the implementation of relevant mitigation measures, PM 2.5 -associated deaths are projected to double due to an aging population. In comparison to the 2060 reference scenario, the joint implementation of clean air and carbon neutrality measures is expected to reduce nationwide PM 2.5 -associated mortality by 62 %, equivalent to 2.15 (95 % CI: 1.80-2.48) million deaths. Stringent pollution controls are crucial for reducing PM 2.5 -associated deaths before 2030, after which carbon neutrality actions become increasingly significant from 2030 to 2060. The challenges of mitigating future PM 2.5 -associated deaths vary greatly across regions, showing a critical response to pollution control and carbon reduction. The research proves the effectiveness of China's future air pollution control and carbon reduction policies in mitigating PM 2.5 -associated deaths., 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 Author(s). Published by Elsevier Ltd.. All rights reserved.)

Air pollution has been one of the biggest environmental challenges for cities in their pursuit of sustainability. The removal of air pollutants usually entails a cost that can negatively impact economic activities. Assessing urban environmental performance can offer valuable insights for cities to balance their economic production and environmental protection. This paper develops a novel approach to measuring environmental performance by using the Euclidean distance function. An appealing feature of this approach is its ability in endogenously allocating the optimization pathway to each emitter, thereby avoiding arbitrary estimation results and distorted managerial implications. We apply this approach to study the environmental performance of Chinese key environmental protection cities. We find the heterogeneity in performance estimates and endogenous optimization pathways. Prioritizing the reduction of a specific type of emissions while simultaneously increasing industrial output value seems to be the most appropriate objective for the majority of cities. Our study can serve as a basis for urban governments to pinpoint the underlying factors contributing to low urban environmental performance and establish diverse objectives for enhancing environmental performance., 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 devastating effects of COVID-19 pandemic have widely affected human lives and economy across the globe. There were significant changes in the global environmental conditions in response to the lockdown (LD) restrictions made due to COVID-19. The direct impact of LD on environment is analysed widely across the latitudes, but its secondary effect remains largely unexplored. Therefore, we examine the changes in particulate matter (PM₂.₅) during LD, and its impact on the global croplands. Our analysis finds that there is a substantial decline in the global PM₂.₅ concentrations during LD (2020) compared to pre-lockdown (PreLD: 2017-2019) in India (10-20%), East China (EC, 10%), Western Europe (WE, 10%) and Nigeria (10%), which are also the cropland dominated regions. Partial correlation analysis reveals that the decline in PM₂.₅ positively affects the cropland greening when the influence of temperature, precipitation and soil moisture are limited. Croplands in India, EC, Nigeria and WE became more greener as a result of the improvement in air quality by the reduction in particulates such as PM₂.₅ during LD, with an increase in the Enhanced Vegetation Index (EVI) of about 0.05-0.1, 0.05, 0.05 and 0.05-0.1, respectively. As a result of cropland greening, increase in the total above ground biomass production (TAGP) and crop yield (TWSO) is also found in EC, India and Europe. In addition, the improvement in PM₂.₅ pollution and associated changes in meteorology also influenced the cropland phenology, where the crop development stage has prolonged in India for wet-rice (1-20%) and maize (1-10%). Therefore, this study sheds light on the response of global croplands to LD-induced improvements in PM₂.₅ pollution. These finding have implications for addressing issues of air pollution, global warming, climate change, environmental conservation and food security to achieve the Sustainable Development Goals (SDGs)., 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.)

Air pollution exhibits significant spatial spillover effects, complicating and challenging regional governance models. This study innovatively applied and optimized a statistics-based complex network method in atmospheric environmental field. The methodology was enhanced through improvements in edge weighting and threshold calculations, leading to the development of an advanced pollutant transport network model. This model integrates pollution, meteorological, and geographical data, thereby comprehensively revealing the dynamic characteristics of PM 2.5 and O 3 transport among various cities in China. Research findings indicated that, throughout the year, the O 3 transport network surpassed the PM 2.5 network in edge count, average degree, and average weighted degree, showcasing a higher network density, broader city connections, and greater transmission strength. Particularly during the warm period, these characteristics of the O 3 network were more pronounced, showcasing significant transport potential. Furthermore, the model successfully identified key influential cities in different periods; it also provided detailed descriptions of the interprovincial spillover flux and pathways of PM 2.5 and O 3 across various time scales. It pinpointed major pollution spillover and receiving provinces, with primary spillover pathways concentrated in crucial areas such as the Beijing-Tianjin-Hebei (BTH) region and its surrounding areas, the Yangtze River Delta, and the Fen-Wei Plain. Building on this, the model divided the O 3 , PM 2.5 , and synergistic pollution transmission regions in China into 6, 7, and 8 zones, respectively, based on network weights and the Girvan Newman (GN) algorithm. Such division offers novel perspectives and strategies for regional joint prevention and control. The validity of the model was further corroborated by source analysis results from the WRF-CAMx model in the BTH area. Overall, this research provides valuable insights for local and regional atmospheric pollution control strategies. Additionally, it offers a robust analytical tool for research in the field of atmospheric 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 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The Beijing-Tianjin-Hebei (BTH) is one of the key areas with PM 2.5 air pollution in China. Driven by the PM 2.5 target accessibility of the Interim Target-1 (IT-1) by World Health Organization (WHO) and China's carbon neutrality, this study explored and quantified the contribution of climate change and anthropogenic emission to future PM 2.5 in the region. The experiments considered future climate change scenarios RCP8.5, RCP4.5, and RCP2.6 with the baseline (Base) and reduced emission (EIT1) inventories in 2030, and RCP4.5 climate scenario with 3 emission inventories in 2050, the additional strong control emission scenario called Best-Health-Effect (BHE). Under various climate scenarios, the future air quality research modelling system projected annual PM 2.5 concentrations nearing 35 μg/m 3 in 2030. However, considering only the effect of emission reduction, the annual PM 2.5 concentrations under EIT1 emission scenario is about 35% less than under Base scenario in different key years. The future PM 2.5 concentrations are highly related to anthropogenic emission from human activities, while climate change by 2030 or 2050 has little impact on future air quality over the BTH region. The BHE emission reduction is significantly required for China to meet the new PM 2.5 guideline value of WHO in the future., 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.)

Carbon dioxide (CO2) emissions and haze pollution are often thought to have the same origin, the burning of fossil fuels. However, their relationship is not always synergistic and may even exhibit mutual constraints. Carbon-biased technological progress has emerged as a promising approach for simultaneously achieving three goals - to reduce CO2 emissions, alleviate the haze pressure, and keep economic growth. This study empirically investigates the impact and mechanisms of carbon-biased technological progress on carbon haze collaborative governance using data from 286 Chinese cities during 2006-2021. The results indicate that: (1) Carbon biased technological progress positively influences carbon haze collaborative governance. (2) This progress achieves coordination by enhancing element allocation efficiency, carbon efficiency, and responding to public environmental demands. (3) The facilitating role of carbon biased technological progress to carbon haze collaborative governance will work better if external conditions are met. Moreover, the effectiveness of carbon-biased technological progress in promoting coordination is contingent upon high levels of marketization, government intervention, environmental regulation, and technical advancements. Local and regional governments should foster conducive conditions for carbon dioxide and haze pollution coordination, optimize the allocation and flow of carbon resources, ensure harmonization between environmental regulation policies and other sectors, and bolster international cooperation and technical knowledge exchange to collectively address global environmental challenges., 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.)

Against the backdrop of global climate change and the "dual carbon" target, cities have a significant responsibility to achieve carbon reduction targets. As a crucial urban agglomeration in northern China, effectively balancing economic growth with CO 2 emission reduction to achieve high-quality economic development remains a significant challenge that the Beijing-Tianjin-Hebei region should address both presently and in the future. The objective of this study is to utilize nighttime lighting data and energy consumption information to quantify the CO 2 emissions of diverse cities within the Beijing-Tianjin-Hebei region spanning from 2006 to 2020. The research aims to analyze the spatial progression patterns of CO 2 emissions across these urban centers, identify key determinants and their interrelations, and delve into the underlying mechanisms pivotal for advancing carbon mitigation strategies within urban agglomerations. The results indicate that: with an exception in Beijing where CO 2 emissions slightly decreased compared to 2006, CO 2 emissions increased across cities in the Beijing-Tianjin-Hebei region by 2020. High-value CO 2 emission areas are primarily concentrated in central of the study area, exhibiting negative spatial correlation characteristics. Based on its urban development positioning, it is imperative for the Beijing-Tianjin-Hebei urban agglomeration to formulate and implement carbon reduction strategies on innovative development, industrial upgrading, and ecological protection among other aspects towards coordinated low-carbon 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 Ltd. All rights reserved.)

In response to the dual challenges of air pollution control and carbon mitigation, China has strategically shifted its focus towards the synergistic reduction of air pollutants and CO 2 emissions. This study identifies the potential areas and specific air pollutant species (including CO, NO x , and SO 2 ) for co-reduction with carbon mitigation. We also reveal the driving forces behind the emissions of each air pollutant at both the national and regional scales. Our findings are as follows: (1) The potential for synergistic reduction of CO and SO 2 with CO 2 emissions has diminished in economically developed areas. There is a significant opportunity for co-reduction of SO 2 and CO 2 in the western and northern regions of China, particularly within Heilongjiang Province. (2) NOx is the key species for synergistic reduction with CO 2 emissions across China, especially in the Chengyu Plain. (3) Cleaner production and the synergistic reduction effect are the primary contributors to national air pollutant reduction in China from 2008 to 2017. Conversely, efforts in economic development and energy efficiency have led to emission increases. Energy and industrial structures have only made limited contributions to emission reductions, and carbon mitigation shows an inhibition effect on emission reductions. These results offer valuable insights for developing targeted regional strategies for deeper air pollution control, considering the specific characteristics and needs of each region. Additionally, our findings highlight the importance of addressing policy misalignments and strengthening mutual-influence mechanisms between air pollution control and carbon mitigation, ensuring that policies for carbon reduction also effectively contribute to air quality improvements., 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.)

PM 2.5 is a main atmospheric pollutant with various sources and complex chemical compositions, which are influenced by various factors, such as anthropogenic emissions (AE) and meteorological conditions (MC). MC have a significant impacts on variations in atmospheric pollutant; therefore, emission reduction policies and ambient air quality are non-linearly correlated, which hinders the accurate assessment of the effectiveness of control measures. In this study, we conducted online observations of PM 2.5 and its chemical composition in Hohhot, China, from December 1, 2019, to February 29, 2020, to investigate how the chemical compositions of PM 2.5 respond to the variations in AE and MC. Moreover, the random forest (RF) model was used to quantify the contributions of AE and MC to PM 2.5 and its chemical composition during severe hazes and the COVID-19 pandemic lockdown period. During the clean period, MC reduced PM 2.5 concentrations by 124%, while MC incresed PM 2.5 concentrations by 49% during severe pollution episode. Inorganic aerosols (SO 4 2- , NO 3 - , and NH 4 + ) showed the strongest response to MC. MC significantly contributed to PM 2.5 (36%), SO 4 2- (32%), NO 3 - (29%), NH 4 + (28%), OC (22%), and SOC (17%) levels during pollution episodes. From the pre-lockdown to lockdown period, AE (MC) contributed 52% (48%), 81% (19%), 48% (52%), 68% (32%), 59% (41%), and 288% (-188%) to the PM 2.5 , SO 4 2- , NO 3 - , NH 4 + , OC, and SOC reductions, respectively. The variations in MC (especially the increase in relative humidity) rapidly generated meteorologically sensitive species (SO 4 2- , NO 3 - , and NH 4 + ), which led to severe winter pollution. This study provides a reference for assessing the net benefits of emission reduction measures for PM 2.5 and its chemical compositions., 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.)

Changes in aerodynamic and thermal conditions caused by urbanization can impact regional meteorological conditions, subsequently affecting air quality. Updated Moderate-resolution Imaging Spectroradiometer (MODIS) land use data and coupled with the urban canopy models (UCMs) in the Weather Research and Forecasting (WRF) model. This enabled the impact of urban land expansion on meteorological conditions and ozone (O 3 ) concentrations to be evaluated. Urban expansion increased the temperature at 2 m (T 2 ) and the probability of precipitation in urban expansion areas, and enhanced the surface urban heat island at night. As the expansion areas became progressively larger, the increase in T 2 became more pronounced. The proportions of urban surfaces in June 2016, 2018, and 2020 compared to 2001 increased by 0.69%, 0.83%, and 1.04%, respectively, while T 2 increased by 0.12, 0.19, and 0.20 °C in urban areas, respectively. With urban expansion, the O 3 concentration increased by 1.12, 1.37, and 0.76 μg/m 3 (three-year averages) in urban, suburban, and rural areas, respectively. After coupling a multi-layer urban canopy model (building effect parameterization, BEP), or a multi-layer urban canopy model with a building energy model including anthropogenic heat due to air conditioning (BEP + BEM, abbreviated as BEM simulation), the O 3 concentration changed significantly in the urban expansion area, compared to the results of a single-layer urban canopy model (UCM). O 3 concentrations decreased most in the BEP simulation (-0.77 μg/m 3 ), while O 3 concentrations increased most in the BEM simulation (+1.85 μg/m 3 ). The average observed O 3 concentration was 108.35 μg/m 3 (three-year average), while the simulated value was 75.65-83.72 μg/m 3 (R = 0.69-0.77). The validation results in the BEM and Global Optimal Scenario (GOS) simulations were relatively good, with the GOS simulation producing slightly better results than the BEM. The simulation of O 3 in urban agglomerations could be improved by integrating the results of the UCMs., 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, ozone (O 3 ) has emerged as the primary air pollutant in China, superseding PM 2.5 . Previous studies have concentrated on the spatiotemporal variation of ozone pollution, the analysis of its sources and drivers, as well as its environmental impacts and health benefits. Since ozone pollution can be dynamically transferred through industrial activities and meteorological factors, it is crucial to scientifically identify the spatial spillover and path-dependent effects of ozone pollution in China. However, existing studies have not yet addressed this issue. Therefore, we investigate the spatiotemporal evolution and the spatial spillover of ozone pollution by means of the exploratory spatial data analysis (ESDA) using panel data of 30 Chinese provinces from 2013 to 2020 in this study. The dynamic spatial Durbin model (SDM) was employed to reveal the key drivers of ozone pollution from the perspectives of spatial spillover and path-dependence effects. The direct and spillover effects of each driver on ozone pollution are systematically analyzed. The results show that from 2013 to 2020 ozone concentrations followed a fluctuating upward trend at national and provincial scales. Ozone pollution presented significant spatial spillover and path dependence effects. The direct effects indicated that economic growth, technological level, industrial structure, energy structure, ventilation coefficient, relative humidity and precipitation were the key drivers of local ozone pollution. The spillover effects indicated that population density, technology level, industrial structure, environmental regulations, ventilation coefficient, sunshine hours and relative humidity had significant spatial spillover effects on ozone pollution of surrounding regions. These findings will contribute to the understanding of the spatial spillover and path-dependent effects of O 3 pollution, and provide scientific guidance for regional synergy and long-term ozone control policies 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.)

Green space exposure has been inversely associated with blood pressure (BP) levels and hypertension risk. However, empirical evidence on the underlying mechanisms are lacking. This study examined the association of green space exposure with BP and hypertension, and assessed the mediating effects by air pollution, mental health, physical activity, and weight status. Survey data from 719 adults, who lived in Guangzhou (China) in 2016, were used. Three area-level green space indicators, including network distance to the nearest park, percentage of green space and Normalized Difference Vegetation Index within a 1 km Euclidean buffer around residence and workplace, were calculated and linked to individual-level BP measurements. Structural equation models were applied to estimate the direct and indirect associations of the various green space indicators on systolic BP (SBP), diastolic BP (DBP), and hypertension, respectively. After adjusting for multiple covariates, longer network distance to green space was directly associated with higher SBP. Compared to the reference group (0-500m), the differences were 0.11 mmHg (95% CI = 0.03 to 0.19, P = 0.006) for 500-1000m, 0.03 mmHg (95% CI = -0.05 to 0.12, P = 0.45) for 1000-1500m, and 0.16 mmHg (95% CI = 0.09 to 0.23, P < 0.001) for >1500m, respectively. The overall and direct associations were significant for all three indicators (distance or density) with or without considering workplace exposure. The association between network distance to green and SBP was partially (18.4%, 95% CI = 0-42.1%) mediated by mental health. There was no statistical evidence that air pollution, physical activity, or weight status mediate the association. Secondary analyses for other indicators and other outcomes showed similar results. Both distance to green space and more green space around residence and workplace were associated with lower BP and lower risk of hypertension in adults living in a Chinese metropolitan. Mental health partly mediated the association., 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 explore the relationship between air pollution and total factor productivity and new pathways, This paper examines the impact of air pollution on total factor productivity of A-share listed companies in Shanghai and Shenzhen between 2015 and 2019. It investigates this relationship by considering two pathways: investor sentiment and government attention. The findings indicate that air pollution suppresses total factor productivity of firms. However, air pollution stimulates investor sentiment, which in turn increases R&D investment and total factor productivity, reducing to some extent the dampening effect of air pollution on total factor productivity. There exists a notable positive correlation between air quality and government attention, acting as a mediating variable. This implies that air pollution has the potential to capture the attention of governmental entities, leading to the implementation of appropriate measures aimed at managing and mitigating the occurrence of air pollution caused by industrial enterprises.And the relevant governments should formulate a series of policies to meet the different needs of different enterprises. These two approaches have varying impacts depending on the type of enterprises, thus governments should develop laws to cater to the various demands of different types of enterprises., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Yang, Yin. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

With the acceleration of air cleaning activities in China, air pollution has entered a new stage characterized by seasonal interplay and predominance of fine particulate matter (PM 2.5 ) and ozone (O 3 ) pollutants. However, the differing peak seasons of these two pollution preclude the use of a unified indicator for air pollution complex. Given that peroxyacetyl nitrate (PAN) originates from secondary formation and persists under low-temperature conditions for extended periods, it is vital to determine whether its concentration can be used as an indicator to represent air pollution, not only in summer but also in winter. Here, PAN observational data from 2018 to 2022 for Beijing were analyzed. The results showed that during photochemical pollution events in summer, secondary formation of PAN was intense and highly correlated with O 3 (R = 0.8), while during PM 2.5 pollution events in winter, when the lifetime of PAN is extended due to the low temperature, the PAN concentration was highly consistent with the PM 2.5 concentration (R = 0.9). As a result, the PAN concentration essentially exhibited consistency with both the seasonal trends in the exceedance of air pollution (R = 0.6) and the air quality index (R = 0.8). When the daily average concentration exceeds 0.5 and 0.9 ppb, the PAN concentration can be used as a complementary indicator of the occurrence of primary and secondary standard pollution, respectively. This study demonstrated the unique role of PAN as an indicator of air pollution complex, highlighting the comprehensive ability for air quality characterization and reducing the burden of atmospheric environment management., 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.)

By analyzing environmental and meteorological monitoring data over recent years of 2015-2022, the Twain-Hu Basin (THB) in central China was identified as a regional O 3 pollution center over China with the highest increasing trend at 1.10 %⸱yr -1 in interannual variations of O 3 concentrations with deteriorating O 3 pollution over recent years. We explored the spatiotemporal variations in O 3 pollution in the THB with ozone suppression (OS) under high air temperature over metropolitan, small urban, and mountainous areas. The bipolarized interannual trends in interannual O 3 variations in urban and mountainous areas over central China were characterized with the increasing and decreasing 90th percentiles of the daily maximum 8-h (MDA8-90) O 3 concentrations respectively in polluted urban areas and clean mountainous areas over recent eight years. The changes of the near-surface O 3 concentrations with air temperature exhibited the inflection points of OS from increasing to decreasing O 3 at air temperature of 30.5 °C in mountainous areas, 32.5 °C in small urban areas, and 34.5 °C in metropolitan areas, and the intensity of OS was estimated in the ranking with mountainous areas (-2.30 μg⸱m -3 ⸱°C -1 ) > small urban areas (-1.96 μg⸱m -3 ⸱°C -1 ) > metropolitan areas (-1.54 μg⸱m -3 ⸱°C -1 ), indicating that the OS was more significant over the lower-O 3 mountainous areas. This study has implications for understanding O 3 pollution variations with the meteorological drivers., 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.)

Due to global climate change and intensifying anthropogenic pollution, China confronts the dual challenge of controlling particulate matter 2.5 μm (PM 2.5 ) pollution and reducing carbon emissions. Quantifying the characteristics of PM 2.5 concentrations and CO 2 emissions, as well as identifying the driving factors and synergistic effects of PM 2.5 reduction and CO 2 mitigation, are crucial steps in promoting sustainable urban development and achieving the Sustainable Development Goals (SDGs) in China. In this study, we selected 168 cities as our case-study, and quantified spatial characteristics of PM 2.5 concentrations and CO 2 emissions from 2015 to 2020 in China. Then we analyzed driving factors affecting the spatial heterogeneity of PM 2.5 reduction and CO 2 mitigation applying Multi-scale Geographically Weighted Regression (MGWR) model. By employing coupling coordination degree (CCD) model, we further detected the spatiotemporal evolution patterns of the synergistic effects between PM 2.5 reduction and CO 2 mitigation in key Chinese cities. The result showed that: (a) From 2015 to 2020, PM 2.5 concentrations experienced a significant reduction from 59.78 μg/m 3 to 49.83 μg/m 3 , while CO 2 emissions increased from 44.88 × 10 6  t in 2015 to 45.77 × 10 6  t in 2020; (b) Green economy efficiency (gee), government attention (gover), and environmental regulation (envir) demonstrate the most pronounced synergistic effect on pollution reduction and carbon mitigation, with the drivers exhibiting obvious spatial heterogeneity; (c) The overall coupling coordination level of PM 2.5 pollution and CO 2 emissions in China dropped from 0.49 in 2015 to 0.46 in 2020, and the coupling coordination grade in northern cities was notably higher than that in southern cities. The result enhances our understanding of spatiotemporal patterns of synergistic effects between PM 2.5 reduction and CO 2 mitigation, and provides the theoretical basis for policy decision-making to realize pollution decrease and carbon neutral and regional environment governance., 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.)