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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]

This paper comprehensively evaluates the dynamic effects on China's environment and economy during the COVID-19 pandemic. Results show that the COVID-19 lockdown resulted in a temporary improvement in air quality. Furthermore, nitrogen dioxide (NO2) levels in the atmosphere in China were 36% lower than in the week after last year's Lunar New Year holiday, but this also led to an economic downturn. Moreover, the aerosol optical depth (AOD) decreased significantly. During the back-to-work period, the economy recovered and there was an increase in energy consumption, and CO2, NO2 emissions sharply increased to pre-lockdown levels. In the post-lockdown period, the AOD was lower than that of the same period last year. This study can provide reference for environmental policy making, as it demonstrates to what extent the control of pollution sources can improve air quality. Precise emission reduction and regional joint prevention and control are important and effective means for the prevention and control of O3 pollution. The health and economic benefits of COVID-19 pandemic control measures are incalculable. And this can provide an effective scientific basis and theoretical support for the prevention and control of air pollution. [ABSTRACT FROM AUTHOR]

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As air pollution worsens, the fast prediction of air pollutant concentration becomes increasingly important for public health. This paper proposes a new cross-domain prediction model of air pollutant concentration based on federated learning (FL), differential privacy laplace mechanism (DPLA) and long and short-term memory network optimized by sparrow search algorithm (SSA-LSTM), named FL-DPLA-SSA-LSTM. Firstly, with FL, SSA-LSTM is used as local training model for each city and predicts air pollutant concentration. Secondly, DPLA is used to add noise to the local model parameters, which can protect local data security. Then, the global model is updated by using the federated averaging algorithm (FedAvg). Lastly, FL is used to share global model for all cities, which can safely and quickly cross-domain predict air pollutant concentration. For data set, it is taken from hourly air pollutants and meteorological data from 12 cities in the Fenhe River and Weihe River Plains in China in 2020. The experimental results show that the prediction performance of the proposed model is significantly better than all comparison models. FedAvg updating with local model parameters with DPLA noise has little effect on the performance of the global model and even exceeds that of the global model. The calculation time of FL-DPLA-SSA-LSTM model is reduced by 99.95% compared with that of not using FL-DPLA machine learning model. It is proved that the model is high sharing and high safety, which greatly improves the training efficiency and has better generalization ability. It is significant for joint air pollution prevention and control and environmental protection. [ABSTRACT FROM AUTHOR]

Air pollution has become an urgent issue affecting sustainable urban development. The Chinese government has implemented a series of air pollution control policies since 2012. Exploring the effectiveness of pollution control policies is important for future policy-making and improvements in air quality. Mean and variance tests were used for evaluation on the effectiveness of pollution control policies implemented in major cities and estimates of the heterogeneity among cities based on the distribution fitting and testing of daily PM2.5 data from January 2015 to January 2020. The nonparametric kernel density estimation adopted in this paper can effectively describe the data characteristics, and this is very important for air quality monitoring and control. Our findings demonstrate that air pollution prevention and control policies have significantly improved the levels and distribution of urban air quality in China. [ABSTRACT FROM AUTHOR]

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

China's cross-border pollution problem has attracted a growing level of attention from the domestic and international community. The elimination of environmental pollution greatly depends on professional environmental protection companies. China's environmental protection industry has sustained a rapid growth with 26.9% annual growing rate of output value since 2011. To effectively discover the potential investment fields and regions, this study examines the spatial distribution of 53 A-Share Listed Environmental Companies (ASLEC) in China and their 927 subsidiaries. Methods of hot spot analysis, Pearson's correlation analysis and coarsened exact matching were employed in our paper to reveal the spatial distribution characteristics of environmental protection industry and their main influencing indicators. Results show that ASLEC invested over US$ 13 billion distributed in 210 cities in China in 2017. Treatment of wastewater and municipal solid waste related to traditional water supply, drainage and sanitation are the main businesses of the environmental protection industry in China. This is because these businesses belong to conventional urban municipal works with low technological requirement and high economic return. Therefore, the government should support those environmental protection businesses with fine technology, such as air pollution prevention and industrial waste control. Our study also reveals that there is a strong and positive correlation between municipal indicators and environmental protection investment. This indicates that the municipal works attract much more investment of environmental protection companies than heavy industries. The eastern region of China remains a hot spot for investment whereas the investment in the western region increased significantly in 2017. The potential of future development will be located in the central and western regions. For serious air pollution and large-scale industrial transfer from eastern regions to the central and western regions in China, there is lack of industrialization environmental protection capacity to fulfill the ambitious national pollution reduction target. This opportunity implies to attract more investments from international environmental protection companies. [ABSTRACT FROM AUTHOR]

This paper analyzes the theoretical mechanism and transmission channel for the impact of air pollution on firms' domestic value-added ratio (DVAR) in exports. Based on the matched Chinese Industrial Enterprises Database and China Customs Enterprise Database, the DVAR in exports is measured, and this mechanism is empirically tested with standard measurement methods. The study concludes that air pollution is not conducive to raising the DVAR during the sample period. This conclusion remains robust to many issues, such as endogeneity, measurement error, extension of the sample interval, substitution of the air pollution index, and policy changes. The impact of air pollution on the DVAR varies by the type of firm ownership, size, and age, as well as the size and location of the city. In addition, the negative impact of air pollution on the DVAR grows with increases in the PM2.5 concentration. The growing factor prices of domestic intermediate goods and the falling productivity due to air pollution are important transmission channels for the negative impact of air pollution on the DVAR. In order for firms to open up on all fronts through quality trade development, we recommend the prevention and control of air pollution, fueling internal driving forces for firms' independent innovation, and encouraging firms to target the medium- and high-end markets. [ABSTRACT FROM AUTHOR]

PM2.5 is the key reason for the frequent occurrence of smog; therefore, identifying its key driving factors has far-reaching significance for the prevention and control of air pollution. Based on long-term remote sensing inversion of PM2.5 data, 21 driving factors in the fields of nature and humanities were selected, and the random forest model was applied to study the influencing factors of PM2.5 concentration in the Beijing–Tianjin–Hebei urban agglomeration (BTH) from 2000 to 2016. The results indicate: (1) The main factors affecting PM2.5 concentration not only include natural factors such as sunshine hours (SSH), relative humidity (RHU), elevation (ELE), normalized difference vegetation index (NDVI), wind speed (WIN), average temperature (TEM), daily temperature range (TEMR), and precipitation (PRE), but also human factors such as urbanization rate (URB), total investment in fixed assets (INV), and the number of employees in the secondary industry (INDU); (2) The concentration of PM2.5 changed into an inverted S-shape with the increase in SSH and WIN, and into an S-shape with the increase in RHU, NDVI, TEM, PRS, URB and INV. As for ELE and TEMR, it fluctuated and decreased with the increase in ELE, while it increased and then decreased with the increase in TEMR. However, its change was less pronounced with the increase in PRE and INDU; (3) The influence of natural factors is higher than that of human factors, but the role of human factors has been continuously strengthened in recent years. The adjustment and control of PM2.5 pollution sources from the perspective of human factors will become an effective way to reduce PM2.5 concentrations in the BTH. [ABSTRACT FROM AUTHOR]

The mobility of air pollution and the free-riding phenomenon in the process of pollution control determine that joint prevention and control policies should be implemented in a larger area. As China’ s economic development shifts from high-speed growth to high-quality development, the spatial structure of economic development is undergoing profound changes. Regional integration with urban agglomerations and megalopolis as units plays an increasingly important role in promoting economic development and environmental governance. Based on the regression method of geographical regression discontinuity, this study used the panel data of prefectures and counties in China from 2005 to 2019 to study whether the regional integration of urban agglomeration was conducive to better haze reduction. The empirical results showed that: ① On the whole, the regional integration of urban agglomeration played a role in haze reduction, and the annual average PM2.5 concentration of prefectures and counties within the urban agglomeration was 11.204 μg/m³ lower than that of those outside the urban agglomeration. ② Mechanism analysis showed that structural haze reduction and technical haze reduction were important ways for regional integration to exert haze reduction effects. The former was mainly realized through industrial division and transfer between the central area and the peripheral area, and the latter was mainly realized through technology transfer and knowledge spillover under the center-periphery structure. ③ The haze reduction effects produced by regional integration had large regional differences, and the three major urban agglomerations with relatively mature development had the most significant haze reduction effects. Furthermore, the joint prevention and control policy of air pollution with urban agglomeration as the unit exerted a better effect on haze reduction. ④ The implication of this paper showed that: regional integration has produced a significant positive environmental impact and it is of great practical significance to continuously build an inter-regional environmental collaborative governance mechanism within the scope of urban agglomeration. It is necessary to actively promote the overall control of air pollution on a larger geographical scale and pay attention to the division of labor and cooperation between industries in different regions in the process of regional integration, and at the same time, increase investment in technological innovation within the region and encourage local government and enterprises to carry out research and development of pollution control and emission reduction technologies. [ABSTRACT FROM AUTHOR]

Accurate prediction of the Particulate Matter 2.5 (PM 2.5) plays a crucial role in the accurate management of air pollution and prevention of respiratory diseases. However, PM 2.5 as a time series is extremely difficult to accurately predict. In this paper, a Hybrid Integration (HIG) algorithm that combines data pre-processing, time-series decomposition, signal decomposition, a prediction module, a matching strategy, and a hybrid integrated optimization algorithm is proposed. First, the optimal parameters for the four individual models were selected by integrating multiple evaluation perspectives. Additivity was then determined by Seasonal and Trend decomposition using LOWESS (STL), followed by refinement decomposition using signal decomposition. The four new sequences were reconstructed using Range Entropy (RangeEn) and mapped to the models. Additionally, Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) methods were optimized using the HIG algorithm. The results show that the HIG-RNN and HIG-LSTM are more advantageous than the ordinary method in terms of reasonable weight assignment. Finally, an innovative confusion test method was developed to test the stability of the prediction direction. To ensure generalizability, validation was performed using PM 2.5 data from two regions of China. The results show that the method significantly improves the prediction performance and provides a powerful tool for policy formulation and management. [Display omitted] • A hybrid integration (HIG) algorithm is proposed. • Prediction results were evaluated using the original COI and improved ICOI. • Creative time-frequency analysis bypasses traditional time series constraints. • RangeEn can assess entropy series for regularity to avoid excessive decomposition. • A pioneer confusion test for model direction consistency check was developed. [ABSTRACT FROM AUTHOR]

In the construction of resilient cities, regional air pollution prevention plays a pivotal role. Building on the previous research experience, the relationship between air pollution concentration and urban size exhibits a sublinear allometric growth pattern. To identify effective strategies for mitigating particulate matter air pollution, this study quantitatively explored 6 variables influencing urbanization in China’s cities and established an allometry model. Empirical analysis was conducted using data from 293 prefecturelevel cities and 1,827 county-level cities to examine the relationship between annual concentrations of fine particulate matter PM2.5 and PM10 in the atmosphere. ① The findings of this study provided partial validation for the Kuznets curve and demonstrated a reverse ‘U’-shaped association between urbanization and levels of PM2.5 and PM10 pollution. ② By partitioning the Hu Huanyong line, this study identified the spatial distribution pattern of PM2.5 and PM10. In central and western regions, as urban size expands, inhalable particle concentrations tended to increase further; whereas in the southeast region, inhalable particle concentrations gradually decreased and stabilized after a certain threshold of urban scale expansion was reached. Among the factors influencing urban size, green coverage within built-up areas exerted the most significant impact on both PM2.5 and PM10 concentrations, followed by the extent of built-up areas and the scale of secondary industries. This study presented an effective strategy for reconciling conflicts between urban expansion and air pollution management, while concurrently promoting resilient cities characterized by high levels of modernization and superior quality [ABSTRACT FROM AUTHOR]

Background With the rapid development of the global economy and the acceleration of urbanization, air pollution has become a major environmental problem threatening human health. There is limited evidence on the acute effects of air pollution on chronic obstructive pulmonary disease (COPD). Methods From 2014 to 2019, we collected data on daily admissions for COPD patients from a city in China. We used the generalized additive model together with distributed lag models to fit the associations of air pollutants with hospital admissions. Results We observed significant increments in the number of daily admissions (0.086–0.109%) for COPD for a unit range increase in air quality index, PM 2.5 and PM 10 over four lag days. The impact of air pollution on the number of daily admissions was mainly reflected in the COPD patients who were hospitalized through outpatient departments and tertiary hospitals. Discussion Short-term exposure to outdoor air pollution may induce the occurrence or exacerbation of COPD patients; therefore, government departments should strengthen the management of air pollution, improve supervision and control mechanisms, pay attention to the quality of medical services, and reduce the adverse effects of air pollution on patients' health. [ABSTRACT FROM AUTHOR]

The Bohai Sea (BS) region, an area of China that is severely polluted with atmospheric polycyclic aromatic hydrocarbons (PAHs), has received wide attention in recent decades. To characterize the variations of the concentrations and sources of PAHs from June 2014 to May 2019, 15 PAH congeners (Σ15PAHs) were measured in atmospheric samples (N = 228) collected at 12 sites around the BS, and the health risk and direct medical costs associated with lung cancer caused by exposure to PAHs were also estimated. The annual daily average concentration of Σ15PAHs was 56.78 ± 4.75 ngm-3 , which was dominated by low molecular weight PAHs (LMW-PAHs) containing three rings (58.7 ± 7.8 %). During the 5-year sampling period, the atmospheric Σ15PAHs concentration reduced by 17.5 % for the whole BS region, with an especially large drop of 51.7 % observed in the tightly controlled area of Tianjin (TJ), which was mainly due to a decrease in the concentration of high molecular weight PAHs (HMW-PAHs) containing five or six rings. Generally, the concentration of Σ15PAHs was highest in winter and lowest in summer, which can mainly be attributed to the change in LMW-PAH concentration. Based on the positive matrix factorization (PMF) model, PAHs in the BS region were mainly ascribed to coal combustion and biomass burning. The PAH contributions of coal combustion and motor vehicles showed different trends for the BS region (PAHs from coal combustion rose by 7.2 % and PAHs from motor vehicles fell by 22.4 %) and for TJ (PAHs from coal combustion fell by 12.6 % and PAHs from motor vehicles rose by 6.9 %). The incidence of lung cancer (ILCR) caused by exposure to atmospheric PAHs decreased by 74.1 % and 91.6 % from 2014 to 2018 in the BS region and in TJ, respectively. This was mainly due to the decrease in the concentration of highly toxic HMW-PAHs and was reflected in USD 10.7 million of savings in direct medical costs of lung cancer caused by exposure to PAHs, which represent a decrease of 46.1 % compared to the corresponding costs before air pollution prevention and control was implemented around the BS. There was an even higher reduction in medical costs of 54.5 % in TJ. Hence, this study proves that implementing pollution prevention and control not only effectively reduced the concentration of pollutants and the risks caused by them, but it also significantly reduced the medical costs of diseases caused by the corresponding exposure. [ABSTRACT FROM AUTHOR]

In order to investigate the causes of complex air pollution in coastal cities in the Bohai Rim of China, a one-month intensive field observation combined with model simulation was carried out in a typical city named Dongying in September 2022. The results showed that two PM2.5 and O3 complex pollution episodes occurred in Dongying in the observation period, with the primary pollutant being O3. Atmospheric photochemical reactions occurring under unfavorable meteorological conditions led to the production of O3 while at the same time facilitating the generation of nitrate, sulfate, and other secondary components of PM2.5 by enhancing the atmospheric oxidizing capacity, which promoted the formation of complex air pollution. It was worth noting that in the context of high pollutants emission, the occurrence of complex air pollution was more sensitive to the variation in meteorological conditions than to the change in pollutants emission. To continuously improve air quality and protect human health in Dongying, it is recommended that an effective regional joint air pollution prevention and control mechanism with neighboring cities should be established in the premise of effective local pollutants reduction, and special attention should be paid to the adverse effect of the air mass transportation from Bohai Bay. [ABSTRACT FROM AUTHOR]

The "Air Pollution Prevention and Control Action Plan" is regarded as China's war against air pollution, which is also noted as China's Clean Air Action (CAA policy). Although abundant studies have focused on the impact of CAA policy on social activities, such as air quality, pollutants emission, and economic output, limited studies explore its impact on enterprise competitiveness from a more micro perspective. Improving total factor productivity (TFP) is essential to achieve economic efficiency and sustainable development. Identifying the impact and influence mechanism of the CAA policy on TFP is important for China to realize the coordinated development of environment and economy and promote high-quality economic development. Based on the difference-in-differences model, this paper evaluates the impact of the CAA policy on enterprise productivity. The empirical analysis shows that there is a significantly negative effect from the CAA policy to the TFP of polluting enterprises, indicating that the CAA policy has decreased enterprise productivity. Our finding is robust, after conducting several robustness tests, such as replacing different dependent indicators, adopting different policy measurement variables, considering other policy interventions and using propensity score matching. Besides, this paper also discusses the possible heterogeneities. This paper adds additional empirical evidence to understand the relationship between air pollution control and enterprise competitiveness. The conclusions of this paper have important practical significance for China to further promote air pollution control and perfect the environmental control system. • We analyze the impact of China's Clean Air Action on enterprise competitiveness. • There is a negative effect from the Clean Air Action policy to enterprise total factor productivity. • Possible heterogeneities have been discussed from several aspects. • This paper adds additional empirical evidence to understand China's Clean Air Action. [ABSTRACT FROM AUTHOR]

Clarifying the spatiotemporal distribution and impact mechanism of pollution is the prerequisite for megacities to formulate relevant air pollution prevention and control measures and achieve carbon neutrality goals. Chongqing is one of the dual-core key megacities in Cheng-Yu region and as a typical mountain-city in China, environmental problems are complex and sensitive. This research aims to investigate the exceeding standard levels and spatio-temporal evolution of criteria pollutants between 2014 and 2020. The results indicated that PM10, PM2.5, CO and SO2 were decreased significantly by 45.91%, 52.86%, 38.89% and 66.67%, respectively. Conversely, the concentration of pollutant O3 present a fluctuating growth and found a "seesaw" phenomenon between it and PM. Furthermore, PM and O3 are highest in winter and summer, respectively. SO2, NO2, CO, and PM showed a "U-shaped", and O3 showed an inverted "U-shaped" seasonal variation. PM and O3 concentrations are still far behind the WHO, 2021AQGs standards. Significant spatial heterogeneity was observed in air pollution distribution. These results are of great significance for Chongqing to achieve "double control and double reduction" of PM2.5 and O3 pollution, and formulate a regional carbon peaking roadmap under climate coordination. Besides, it can provide an important platform for exploring air pollution in typical terrain around the world and provide references for related epidemiological research. Implications: Chongqing is one of the dual-core key megacities in Cheng-Yu region and as a typical mountain city, environmental problems are complex and sensitive. Under the background of the "14th Five-Year Plan", the construction of the "Cheng-Yu Dual-City Economic Circle" and the "Dual-Carbon" goal, this article comprehensively discussed the annual and seasonal excess levels and spatiotemporal evolution of pollutants under the multiple policy and the newest international standards (WHO,2021AQG) backgrounds from 2014 to 2020 in Chongqing. Furthermore, suggestions and measures related to the collaborative management of pollutants were discussed. Finally, limitations and recommendations were also put forward. Clarifying the spatiotemporal distribution and impact mechanism of pollution is the prerequisite for cities to formulate relevant air pollution control measures and achieve carbon neutrality goals. This study is of great significance for Chongqing to achieve "double control and double reduction" of PM2.5 and O3 pollution, study and formulate a regional carbon peaking roadmap under climate coordination and an action plan for sustained improvement of air quality. In addition, this research can advanced our understanding of air pollution in complex terrain. Furthermore, it also promote the construction of the China national strategic Cheng-Yu economic circle and build a beautiful west. Moreover, it provides scientific insights for local policymakers to guide smart urban planning, industrial layout, energy structure, and transportation planning to improve air quality throughout the Cheng-Yu region. Finally, this is also conducive to future scientific research in other regions of China, and even megacities with complex terrain in the world. [ABSTRACT FROM AUTHOR]

To abate the severe air pollution, the Chinese government released the Action Plan of Air Pollution Prevention and Control (APAPPC) in 2013. This paper regards the APAPPC as a quasi-experiment and uses the DID method to investigate the impact of environmental regulation on the growth of green total-factor productivity of China's industries. This article employs the non-radial directional distance function (NDDF) and the global Malmquist index to measure the total-factor carbon productivity of China's industries. Further regressions suggest that the implementation of the APAPPC has significantly promoted the growth of the total-factor carbon productivity in the air pollution-intensive industries, and its marginal effect has steadily increased with time. This result is still valid after using a series of counterfactual tests and robustness tests. The further mechanism study shows that the APAPPC has significantly promoted R&D investment, especially in instruments and equipment, which has effectively promoted technical efficiency and technological advancement. It indicates that stringent and well-designed environmental regulations should lead to a "win-win" situation of environmental improvement and economic development by encouraging enterprises to upgrade their technology and equipment. • This paper uses a typical case in China to demonstrate the porter's hypothesis. • This paper uses the DEA method to measure the total-factor carbon productivity of China's industries. • The Action Plan of Air Pollution Prevention and Control in China has significantly promoted the growth of total-factor carbon productivity. • The regulation promotes productivity by propelling enterprises to increase R&D investment and update their technical equipment. [ABSTRACT FROM AUTHOR]

Due to the spillover nature of air pollution, the territorial separate governance mode is ineffective in combating pollution, making Joint Prevention and Control of Air Pollution (JPCAP) among multiple regions the only viable option. However, determining the appropriate scopes and priorities for JPCAP is known to be a challenging and significant issue. To address this, we propose a new two-stage hybrid model. In the first stage, making use of long-term, wide area monitoring data provided by the air pollution monitoring network, we propose a new method for subdividing large regions into sub-regions by using data mining techniques. In the second stage, we propose a comprehensive decision-making framework to evaluate the priorities of JPCAP sub-regions from three different perspectives, namely, the impact of a sub-region on the pollution level of the entire target region, as well as the urgency and elasticity of sub-regional air pollution control. A case study is conducted on 27 cities of the Yangtze River Delta region of China. The case study demonstrates the validity and practicality of the proposed two-stage hybrid model. This work provides a viable tool for the effective implementation of air pollution control in China and other regions of the world. [ABSTRACT FROM AUTHOR]

China has been conducting a series of actions on air quality improvement for the past decades, and air pollutant emissions have been changing swiftly across the country. Provinces are an important administrative unit for air quality management in China; thus a reliable provincial-level emission inventory for multiple years is essential for detecting the varying sources of pollution and evaluating the effectiveness of emission controls. In this study, we selected Jiangsu, one of the most developed provinces in China, and developed a high-resolution emission inventory of nine species for 2015–2019, with improved methodologies for different emission sectors, best available facility-level information on individual sources, and real-world emission measurements. Resulting from implementation of strict emission control measures, the anthropogenic emissions were estimated to have declined 53 %, 20 %, 7 %, 2 %, 10 %, 21 %, 16 %, 6 %, and 18 % for sulfur dioxide (SO 2), nitrogen oxides (NO x), carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs), ammonia (NH 3), inhalable particulate matter (PM 10), fine particulate matter (PM 2.5), black carbon (BC), and organic carbon (OC) from 2015 to 2019, respectively. Larger abatement of SO 2 , NO x , and PM 2.5 emissions was detected for the more developed region of southern Jiangsu. During the period from 2016 to 2019, the ratio of biogenic volatile organic compounds (BVOCs) to anthropogenic volatile organic compounds (AVOCs) exceeded 50 % in the month of July, indicating the importance of biogenic sources for summer O 3 formation. Our estimates in annual emissions of NO x , NMVOCs, and NH 3 were generally smaller than the national emission inventory, MEIC (the Multi-resolution Emission Inventory for China), but larger for primary particles. The discrepancies between studies resulted mainly from different methods of emission estimation (e.g., the procedure-based approach for AVOC emissions from key industries used in this work) and inconsistent information of emission source operation (e.g., the penetration and removal efficiencies of air pollution control devices). Regarding the different periods, more reduction of SO 2 emissions was found between 2015 and 2017 and of NO x , AVOCs, and PM 2.5 between 2017 and 2019. Among the selected 13 major measures, the ultra-low-emission retrofit in the power sector was the most important contributor to the reduced SO 2 and NO x emissions (accounting for 38 % and 43 % of the emission abatement, respectively) for 2015–2017, but its effect became very limited afterwards as the retrofit had been commonly completed by 2017. Instead, extensive management of coal-fired boilers and the upgrade and renovation of non-electrical industry were the most important measures for 2017–2019, accounting collectively for 61 %, 49 %, and 57 % reduction of SO 2 , NO x , and PM 2.5 , respectively. Controls on key industrial sectors were the most effective for AVOC reduction in the two periods, while measures relating to other sources (transportation and solvent replacement) have become more important in recent years. Our provincial emission inventory was demonstrated to support high-resolution air quality modeling for multiple years. Through scenario setting and modeling, worsened meteorological conditions were found from 2015 to 2019 for PM 2.5 and O 3 pollution alleviation. However, the efforts on emission controls were identified to largely overcome the negative influence of meteorological variation. The changed anthropogenic emissions were estimated to contribute 4.3 and 5.5 µ g m -3 of PM 2.5 concentration reduction for 2015–2017 and 2017–2019, respectively. While O 3 was elevated by 4.9 µ g m -3 for 2015–2017, the changing emissions led to 3.1 µ g m -3 of reduction for 2017–2019, partly (not fully though) offsetting the meteorology-driven growth. The analysis justified the validity of local emission control efforts on air quality improvement and provided a scientific basis to formulate air pollution prevention and control policies for other developed regions in China and worldwide. [ABSTRACT FROM AUTHOR]

With the rapid development of economy, air pollution occurs frequently, which has a huge negative impact on human health and urban ecosystem. Air quality index (AQI) can directly reflect the degree of air pollution. Accurate AQI trend prediction can provide reliable information for the prevention and control of air pollution, but traditional forecasting methods have limited performance. To this end, a dual-scale ensemble learning framework is proposed for the complex AQI time series prediction. First, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and sample entropy (SE) are used to decompose and reconstruct AQI series to reduce the difficulty of direct modeling. Then, according to the characteristics of high and low frequencies, the high-frequency components are predicted by the long short-term memory neural network (LSTM), and the low-frequency items are predicted by the regularized extreme learning machine (RELM). At the same time, the improved whale optimization algorithm (WOA) is used to optimize the hyper-parameters of RELM and LSTM models. Finally, the hybrid prediction model proposed in this paper predicts the AQI of four cities in China. This work effectively improves the prediction accuracy of AQI, which is of great significance to the sustainable development of the cities. • A dual-scale ensemble framework is proposed for air quality index prediction. • CEEMDAN technology is adopted to obtain multiple intrinsic mode functions. • An improved WOA algorithm based on dimension learning-based hunting is proposed. • Long short-term memory network is used to predict high-frequency components. • Regularized extremum learning machine is used to predict low-frequency components. [ABSTRACT FROM AUTHOR]

To estimate the short term effect of environmental regulation on firm exports, this study considers China's 2003 City Air Pollution Prevention and Control Program (CAPPCP) in a quasi-natural experiment with microdata from 2000–2007. Using the difference-in-differences (DID) and matching DID methods, our findings indicate that CAPPCP reduced firm exports by 6.2% and 12.3%, respectively. A mechanism analysis examines the three channels through which CAPPCP affects firm exports: total factor productivity, production costs, and factor endowments. Furthermore, a heterogeneity analysis reveals that non-state-owned firms, small-scale firms, and firms in highly polluting industries have stronger negative responses to environmental regulations. [ABSTRACT FROM AUTHOR]

China is expected to have a total installed photovoltaic capacity of 1300 GW in 2050, accounting for 39% of the national electricity consumption. However, air pollutants consisting of gases and particulates have attenuation effects on the solar radiation reaching the photovoltaic panels. This work purports to assess the influence of air pollutants on the photovoltaic power potential. We calculated the hourly point-of-array irradiance (POAI) in China for 2010–2020 with a spatial resolution of 0.1° × 0.1° using the PV_LIB model and assessed the effect of air pollutants on POAI. The results indicated that the annual average POAI in China for 2010–2020 ranged from 118 to 286 Wm−2. The Air Pollution Control Action Plan (APPCAP) has played a certain role in photovoltaic power potential, and POAI has increased in areas where surface concentrations of air pollutants have declined. Especially in North China, the surface concentrations of CO, NO2, PM10, PM2.5, and SO2 decreased throughout the APPCAP period with −0.446, −4.985, −35.610, −30.700, and −26.251 μgm−3, respectively, corresponding to an increase in POAI of up to 4.917 Wm−2. The surface concentrations of CO, NO2, PM10, PM2.5, and SO2 were negatively correlated with POAI, with correlation coefficients of −0.764, −0.854, −0.204, −0.110, and −0.664, respectively. Surface concentrations of air pollutants (CO, NO2, PM10, PM2.5, and SO2) and clear-sky POAI in 2018 showed a High-Low clustering in Northeast China and North China. This study demonstrates the role of China's air pollution control policy in enhancing photovoltaic power potential. [ABSTRACT FROM AUTHOR]

Aerosol acidity plays a key role in regulating the chemistry and toxicity of atmospheric aerosol particles. The trend of aerosol pH and its drivers is crucial in understanding the multiphase formation pathways of aerosols. Here, we reported the first trend analysis of aerosol pH from 2011 to 2019 in eastern China, calculated with the ISORROPIA model based on observed gas and aerosol compositions. The implementation of the Air Pollution Prevention and Control Action Plan led to -35.8 %, -37.6 %, -9.6 %, -81.0 % and 1.2 % changes of PM 2.5 , SO42- , NHx , non-volatile cations (NVCs) and NO3- in the Yangtze River Delta (YRD) region during this period. Different from the drastic changes of aerosol compositions due to the implementation of the Air Pollution Prevention and Control Action Plan, aerosol pH showed a minor change of -0.24 over the 9 years. Besides the multiphase buffer effect, the opposite effects from the changes of SO42- and non-volatile cations played key roles in determining this minor pH trend, contributing to a change of +0.3 8 and -0.35 , respectively. Seasonal variations in aerosol pH were mainly driven by the temperature, while the diurnal variations were driven by both temperature and relative humidity. In the future, SO2 , NOx and NH3 emissions are expected to be further reduced by 86.9 %, 74.9 % and 41.7 % in 2050 according to the best health effect pollution control scenario (SSP1-26-BHE). The corresponding aerosol pH in eastern China is estimated to increase by ∼0.19 , resulting in 0.04 less NO3- and 0.12 less NH4+ partitioning ratios, which suggests that NH3 and NOx emission controls are effective in mitigating haze pollution in eastern China. [ABSTRACT FROM AUTHOR]

The benefit allocation of fair and justice is an inevitable guarantee for the long-term operation of the joint prevention and control of air pollution (JPCAP). The ignorance of interest demands of various governance subjects in the existing benefit allocation mechanism results in the widespread "free rider" behavior in the joint control and unsatisfactory effects of JPCAP. Given this, it is imperative to build a reasonable benefit allocation model. The innovation of this paper is proposing a benefit allocation model of JPCAP to achieve the symmetry between control costs and benefits based on environmental justice. The control objectives and total benefits of JPCAP are calculated through the adjustment of optimal removal rates. The interest demands of various control subjects and benefit compensation scheme are clarified by adopting an improved Shapley method, which comprehensively considers factors affecting environmental justice. An empirical analysis is conducted on SO 2 governance in Beijing-Tianjin-Hebei (BTH) and its surrounding areas. The results show that the benefit allocation model based on environmental justice can not only accurately evaluate the benefits of joint control, but also effectively achieve the symmetry between control costs and benefits. This study provides a scientific and reasonable theoretical basis for the benefit allocation of SO 2 control and can be extended to the researches and practices of other air pollutants control. • An optimization model is developed by considering removal costs and health benefits. • A benefit allocation model is established based on environmental justice. • The superiority of the benefit allocation model is demonstrated by sensitivity analysis. [ABSTRACT FROM AUTHOR]

In order to control the spread of the COVID-19 pandemic, the prevention and control measures of public health emergencies were initiated in all provinces of China in early 2020, which had a certain impact on air quality. In this study, taking Jiangsu Province in China as an example, the air pollution levels in different regions under different levels of pandemic prevention and control (PPC) measures are evaluated. The implementation of the prevention and control policies of COVID-19 pandemic directly affected the concentration of air pollutants. No matter what level of PPC measures was implemented, the air quality index (AQI) and pollutant concentrations of NO2, CO, PM10 and PM2.5 were all reduced by varied degrees. The higher the level of PPC measures, the greater the reduction was in air pollutant concentrations. Specifically, NO2 was the most sensitive to PPC policies. The concentrations of CO and atmospheric particulate matter (PM10 and PM2.5) decreased most obviously under the first and second level of PPC. The response speed of air quality to different levels of PPC measures varied greatly among different cities. Southern Jiangsu, which has a higher level of economic development and is dominated by secondary and tertiary industries, had a faster response speed and a stronger responsiveness. The results of this study reflect the economic vitality of different cities in economically advanced regions (i.e., Jiangsu Province) in China. Furthermore, the results can provide references for the formulation of PPC policies and help the government make more scientific and reasonable strategies for air pollution prevention and control. [ABSTRACT FROM AUTHOR]

In order to analyze the primary sources of air pollutants in Chang-Zhu-Tan region, this article selected the environmental monitoring data and meteorological data in the winter of 2019 to calculate the backward airflow trajectories with the Chang-Zhu-Tan region as the starting point by using the backward trajectory model. Combined with the ground concentration monitoring data, cluster analysis, potential source contribution factor (PSCF) analysis, and concentration weighted trajectory (CWT) analysis were carried out to determine the pollutant transportation paths and sources of the potential source area. The results show that air mass transportation mainly comes from three directions: northwest, northeast, and southwest China. The airflow in northwest China moves faster and cleaner, while the airflow from the northeast and southwest moves slowly and carries a high concentration of pollutants. PSCF and CWT analyses show that the critical potential sources are mainly located in this area and some cities next to the study area. This study has important practical significance for the environmental research of Chang-Zhu-Tan region and can provide theoretical reference for regional joint prevention and control of air pollution. [ABSTRACT FROM AUTHOR]

We analyze the real effects of the environmental regulation on technological innovation using an air pollution reduction governance policy promulgated in China under the 12th Plan in 2012. We treat the Air Pollution Prevention Policy as a quasi-natural experiment that is plausibly exogenous to the firms' innovation policy and thus use the difference in difference (DID) as an identification strategy in our analysis. We provide evidence that environmental regulation substantially promotes innovation productivity. Our findings reveal that this impact is more pronounced for state-owned firms, pollution-intensive industries, and high-tech-intensive industries. We uncover three possible underlying economic mechanisms through which the air pollution reduction policy impacts innovation. We show that government financing, external governance from the capital market, and R&D intensity are three underlying economic channels through which environmental regulation promotes technological innovation. Collectively this study's policy implication is that industrial policies that promote greener environments can enhance economic performance. [ABSTRACT FROM AUTHOR]

Coordinated control of fine particulate matter (PM 2.5) and ozone (O 3) has become a new and urgent issue for China's air pollution control. Biogenic volatile organic compounds (BVOCs) are important precursors of O 3 and secondary organic aerosol (SOA) formation. China experienced a rapid increase in BVOC emissions as a result of increased vegetation biomass. We applied WRF-Chem3.8 coupling with MEGAN2.1 to conduct long-term simulations for impacts of BVOC emissions on O 3 and SOA during 1981–2018, using the emission factors extrapolated by localized emission rates and annual vegetation biomass. In summer 2018, BVOC emissions were 9.91 Tg (in June), which led to an average increase of 8.6 ppb (16.75 % of the total) in daily maximum 8 h (MDA8) O 3 concentration and 0.84 µgm -3 (73.15 % of the total) in SOA over China. The highest contribution to O 3 is concentrated in the Great Khingan Mountains, Qinling Mountains, and most southern regions while in southern areas for SOA. Isoprene has the greatest contribution to O 3 , while monoterpene has the largest SOA production. BVOC emissions have distinguished impacts in different regions. The Chengdu–Chongqing (CC) region has the highest O 3 and SOA generated by BVOCs, while the Beijing–Tianjin–Hebei (BTH) region has the lowest. From 1981 to 2018, the interannual variation of BVOC emissions caused by increasing leaf biomass resulted in O 3 concentration increasing by 7.38 % at an average rate of 0.11 ppb yr -1 and SOA increasing by 39.30 % at an average rate of 0.008 µgm -3 yr -1. Due to the different changing trends of leaf biomass by region and vegetation type, O 3 and SOA show different interannual variations. The Fenwei Plain (FWP), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions have the most rapid O 3 increment, while the increasing rate of SOA in CC is the highest. BTH has the smallest enhancement in O 3 and SOA concentration. This study will help to recognize the impact of historical BVOC emissions on O 3 and SOA and further provide a reliable scientific basis for the precise prevention and control of air pollution in China. [ABSTRACT FROM AUTHOR]

With rapid urbanization and industrialization in developing countries, cities have become the major sources of air pollution. Studying urban atmospheric environmental efficiency has an important reference value for the prevention and control of air pollution. This study used data from 267 cities in China between 2001 and 2016 to assess the urban atmospheric environmental efficiency using the super-efficiency slacks-based measure model, to test the spatial characteristics of urban atmospheric environmental efficiency using the spatial autocorrelation method, and to identify factors influencing it using the Geodetector. The results are as follows: (1) The atmospheric environmental efficiency of most cities in China is increasing. The average efficiency in the entire country exhibits an upward "wavy" trend. The average urban atmospheric environmental efficiency in Eastern China is the highest, and that in Western China is the lowest. (2) The urban atmospheric environmental efficiency exhibits the characteristic of global spatial autocorrelation, and high–high and low–low are the main types of efficiency in local spatial autocorrelation. (3) Population density, industrialization, and science and technology are the main factors influencing urban atmospheric environmental efficiency. [ABSTRACT FROM AUTHOR]

The role of precursors' concentrations and meteorological conditions on the emerging ozone pollution problem in China has received wide attention, especially after the releasing of the Air Pollution Prevention Control and Action Plan (APPC) since 2013. With the decreasing trend of PM 2.5 nationwide, the effect of the strict control measures on increasing ozone variation has less been studied due to the challenge of complexity of nonlinear relationship among a number of factors on ozone formation. This paper evaluated the influence of both ozone precursors and meteorology on maximum daily average 8 h (MDA8) ozone at two urban sites and one rural site in Hebei province, China, from 2013 to 2017, by using a combined application of Kolmogorov-Zurbenko (KZ) filter and artificial neural network (ANN) model. Results showed that R2 was 0.80 between the measured and the simulated MDA8 ozone concentration when using meteorological factors and precursors' concentrations as input variables for ANN model. However, ANN model has limitation in estimating O 3 concentration peaks. The values of threat score (TS), probability of detection (POD) and false alarm rate (FAR) for MDA8 ozone concentration were 39%, 44% and 21%, respectively, throughout three studied sites in Hebei province. The annual average percentage change of precursor-related ozone was 0.67% from 2014 to 2017. Temperature, atmospheric pressure and boundary layer height were shown to account for 64% of the variability in long-term ozone levels. Ozone variation in Hebei province was reproduced mainly by meteorological parameters, and the contribution from precursors' concentration was smaller during the years when the APPC was implemented. [Display omitted] • Ozone level was estimated based on precursors' level and meteorological factors. • The ambient ozone concentration estimated by ANN model was acceptable. • Meteorology dominates the ozone variation in 2013–2017 in Hebei province. [ABSTRACT FROM AUTHOR]

Collaborative governance plays an important role in air pollution prevention (APP). This paper aims to evaluate the policy effects of collaborative governance for APP in the Yangtze River Delta (YRD) region, analyze the collaborative mechanism from the perspective of institutional collective action (ICA) theory, and discuss its implications for improving the policy effects and regional collaboration. First, the causal relationship between collaborative environmental governance and air pollutant reduction is confirmed using the regression discontinuity design (RDD). However, it is also established that regional APP policies initiated by the Regional Cooperation Office of Yangtze River Delta (RCOYRD) did not have a long-term effect on reducing air pollutants. Second, initiative heterogeneity and administrative hierarchy heterogeneity of policy effects between cities are analyzed. It demonstrates that the mechanism formulated by RCOYRD has very limited impact on the reduction of transaction costs and collaborative risks of collaborative environmental governance. Third, clear division of responsibilities, balanced cost-benefit sharing, and coordinated enforcement between local governments are needed to improve the policy effects of collaborative governance for APP. • Institutional collective action (ICA) framework was adopted to analyze cross-regional issues. • The causal relationship between collaborative governance and air pollutant reduction was confirmed with RDD method. • The collaborative governance for air pollution prevention did not have long-term policy effect. • Collaboration risk and transaction cost are key factors affecting regional coordination. [ABSTRACT FROM AUTHOR]

Background: Rapid economic growth in China has resulted in a commensurate increase in energy consumption, which in turn has seen an increase in environmental pollution problems. Past research has tended to focus on energy and environmental efficiency analyses and has rarely examined the media influence on environmental protection efforts. Further, most studies have used static models and ignored the dynamic changes over time. Methods: To go some way to filling this research gap, this study developed a modified undesirable Dynamic DEA model that included air quality index (AQI) and CO2 indicators to explore the relationships between energy, the environment, and media report efficiencies in 31 Chinese cities from 2013 to 2016. Results: It was found that: (1) Chongqing, Guangzhou, Nanjing and Shanghai had efficiencies of 1, but Lanzhou, Shijiazhuang, Taiyuan, Xining and Yinchuan needed significant improvements; (2) Chongqing, Guangzhou, Kunming, Nanning and Shanghai had relatively high media efficiencies, but the other cities had low efficiencies and required improvements; (3) the CO2 emissions efficiencies in most cities were better than the air quality index efficiencies; and 4. the media reports in most cities were found to have a more positive impact on the CO2 emissions efficiency than on the AQI efficiency. Conclusions: Environmental awareness enhances civilian health and promotes economic growth. Therefore, as the news media should be responsible for promoting environmental protection, they need to increase their environmental pollution coverage. It was found that the environmental pollution media report quality and especially air pollution reports needed improvements, and greater media coverage on environmental pollution and awareness was needed. [ABSTRACT FROM AUTHOR]

PM2.5 has attracted widespread attention since the public has become aware of it, while attention to PM10 has started to wane. Considering the significance of PM10, this study takes PM10 as the research object and raises a significant question: when will the influence of PM10 on public health end? To answer the abovementioned question, two promising research areas, i.e., air pollution forecasting and health effects analysis, are employed, and a novel hybrid framework is developed in this study, which consists of one effective model and one evaluation model. More specifically, this study first introduces one advanced optimization algorithm and cycle prediction theory into the grey forecasting model to develop an effective model for multistep forecasting of PM10, which can achieve reasonable forecasting of PM10. Then, an evaluation model is designed to evaluate the health effects and economic losses caused by PM10. Considering the significance of providing the future impact of PM10 on public health, we extend our forecasting results to evaluate future changes in health effects and economic losses based on our proposed health economic losses evaluation model. Accordingly, policymakers can adjust current air pollution prevention plans and formulate new plans according to the results of forecasting, evaluation and early-warning. Empirical research shows that the developed framework is applicable in China and may become a promising technique to enrich the current research and meet the requirements of air quality management and haze governance. [ABSTRACT FROM AUTHOR]