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• This empirical study investigated WFH's impact on air quality and mechanisms. • WFH reduced the intra-city travel intensity (ITI) by 65.0 % and the AQI by 26.3 %. • ITI completely mediated WFH's impact on air pollutants NO 2 and PM 10 emissions. • WFH has the potential to become a sustainable urban development strategy. Working from home (WFH) arrangements are already acknowledged as a sustainable measure for alleviating urban congestion and air quality issues, but fewer large-scale empirical studies investigating the benefits and mechanisms of WFH on air pollution (AP). The exogenous shock of the epidemic provides unique conditions that enable us to conduct empirical studies on the positive impacts of working from home. Building upon this background, this paper designs a series of empirical models to investigate the benefits of WFH on AP and the mediating effect of intra-city travel intensity (ITI) concerning the WFH-AP relationship to depict the mechanism of the impact of WFH on AP. This paper collected panel data on ITI and AP from January 1 to March 15, 2020, in 19 cities in China for regression analyses. The findings indicate that WFH reduces ITI by 65.0 %, while an increase in ITI leads to higher AP emissions, particularly of NO 2. WFH positively impacts urban air quality, resulting in a 26.3 % decrease in the Air Quality Index (AQI) and reductions of 17.2 %, 4.1 %, 32.7 %, 18.8 %, and 13.6 % for the five air pollution categories (CO, NO 2 , PM 2.5 , PM 10 , SO 2) respectively. Intermediate analyses demonstrated that ITI partially mediated the decrease in AQI, PM 2.5 , and CO, while PM 10 and NO 2 showed complete mediation. However, ITI had a weak mediating effect on SO 2 emissions. The contribution of this study is that it provides supporting evidence that working from home positively impacts urban air quality and commuting. Additionally, it offers urban administrators a fact-based approach to traffic management and environmental protection, pointing to important directions for future urban sustainability strategy development. [ABSTRACT FROM AUTHOR]

This study examined the relationship between the air quality index (AQI), COVID-19, and the oil market using China's provincial data for the first four months of the pandemic (1 January–22 April 2020). The results show that air quality improved significantly during this period. Our income and regional group analyses further clarified that the middle-income group and Eastern and Central provinces, which were most affected by COVID-19, showed a more robust relationship between AQI and COVID-19 cases than other Chinese provinces. The national oil market, which saw a 57% decline in the oil price over the period of 13 January to 22 April, was also found to be insignificantly related to AQI. All in all, our paper implies that an improvement in air quality due to a drastic decline in economic activity. We provide some future research directions in the paper. [ABSTRACT FROM AUTHOR]

The emergency response to the COVID-19 pandemic had an extreme exogenous impact on society and the economy. This paper aims to explore the impacts of the national emergency response and the subsequent emergency response termination on air quality and its policy implications through regression discontinuity design (RDD) estimation by employing panel data on daily air quality from January 1, 2019, to July 31, 2020, for 290 cities in China. The empirical results showed that the emergency response resulted in a significant decrease in most of the major pollutant concentrations within a short time frame, and the average air quality index (AQI) decreased by approximately 11.0%. The concentrations of PM 2.5 , PM 10 , SO 2 , NO 2 , and CO decreased by approximately 18.8%, 13.1%, 13.5%, 11.1% and 6.7%, respectively, while the O 3 concentration did not change significantly. Further causal analysis found that mandatory traffic restrictions and the shutdown of industries were two important factors that contributed greatly to air quality improvement. Moreover, since the process of returning to normal daily activities and promoting the economy were gradual, the results showed that air pollution did not rebound immediately after the government called for the "resumption of production and work" and announced the "termination of the emergency response". Our findings suggest that to achieve a substantial and sustainable improvement in air quality, it is necessary to continuously implement strict emission control routines and take co-control measures for various VOCs precursors of ozone. [Display omitted] • Emergency response to COVID-19 (ER) had spill-over effect on air quality. • Empirical analysis was made with RDD to evaluate the impact of ER on air quality. • AQI and PM 2.5 , PM 10 , SO 2 and NO 2 concentrations decreased by approximately 10%. • Traffic restrictions and shutdown of industries during ER were important factors. • Socioeconomic resumption and ER termination did not affect air quality. [ABSTRACT FROM AUTHOR]

Regional haze pollution has frequently occurred in China over the past several years, and this haze has hindered the development of the economy and harmed the health of people in China. Currently, several studies have analyzed the impact of different influencing factors on haze. However, few studies have comprehensively analyzed the influential factors of haze from different perspectives. In this paper, we utilized global and local regression models to explore the main influential factors on air quality index (AQI) in China from global and local perspectives. The results are as follows: (1) the AQIs of Chinese cities have significant positive spatial correlation, and higher values of AQI were typically found in Beijing-Tianjin-Hebei, Shandong, Henan, Shanxi and Shaanxi Province; (2) from a global perspective, as there is one unit of increase in the average AQI of one city's neighbors, the city's AQI will increase by 0.827 unit. An increase in the industrial structures and the number of civilian vehicles will also lead to an increase in the AQI, but the impact of precipitation is reversed; and (3) from a local perspective, there are spatial differences in the effects of different factors on the AQI. In northern China, an appropriate temperature reduction and an appropriate increase in atmospheric pressure is helpful for reducing haze pollution; however, opposing conditions are found in southern China. Compared with China's coastal cities, the increase in precipitation is more effective at reducing the AQI in inland cities. Compared with other cities, reducing the industrial structure and the number of civilian vehicles was more effective for haze management in Beijing, Tianjin, Shandong, Henan, Shanxi, and Shaanxi provinces. These results of this paper are helpful for government departments to formulate regionally differentiated governance policies regarding haze. Image 1 • Both natural and anthropogenic factors are used to detect their impacts on haze. • Identification of influential factors on haze pollution from global and local perspectives. • AQI of most cities are High-High (38.98%) and Low-Low (38.14%) clustering types. • A city's AQI increases 0.827 unit for every 1 unit addition in the average AQI of neighbors. • GWR result shows that there are obvious spatial differences of influential factors on AQI. This paper identifies the main influential factors and spatial differences of air quality in China. [ABSTRACT FROM AUTHOR]

Under the background of climate change, the haze days in China have increased significantly, which seriously hinders the sustainable development of society and arouses wide attention from the public. However, the researches on the effect of air pollution on the stock yield of heavy pollution enterprise in key control cities are quite limited, This paper collects the Air Quality Index (AQI) from key control cities above prefecture level in China as well as the stock yield data of these cities’ heavy pollution enterprises in December from 2014 to 2016 and through multi-discontinuities regression model, testes the effect of air pollution on the stock yield of heavy pollution enterprise. The results show that: (1) Heavy air pollution (AQI = 300) has negative influence on the stock yield. (2) There exists time effect of air pollution on stock yield. Since the year 2014, heavy air pollution has exerted significantly negative influence on the stock yield in that 2013 may be the “first year” when the Chinese society began to take air pollution seriously. This paper gives a brief discussion on the cause of it and suggests that severe air pollution should be strictly controlled. Only by facing air pollution seriously, can we eliminate air pollution with collective wisdom and concerted efforts and achieve the sustainable development of city. Being the first study to look into the effect of air pollution on stock yield in key control cities in China, this paper provides empirical reference for government supervision departments, stock investors as well as enterprises. [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]

Addressing the urgent need for accurate carbon emissions forecasting to support global emissions reduction goals, this paper introduces a novel approach for carbon emissions prediction that dynamically considers the spatial–temporal correlation of carbon emissions across forecasting targets. Traditional statistical and machine learning models have limitations, such as oversimplification and inefficiency in capturing evolving dynamics among multiple forecasting targets, alongside a focus on longer forecasting horizons like monthly or yearly. To tackle these, we propose an innovative Dynamic Spatial–Temporal Graph Convolutional Recurrent Network (DSTGCRN), a blend of graph convolutional and recurrent neural network structures customized for up-to-date multi-regional spatial–temporal predictions at a daily level. Comprehensive empirical analyses on datasets from China, the US, and the EU confirm DSTGCRN's superior performance and robustness across diverse geographical contexts. It outperforms the second-best among 10 baseline models, achieving 40.6%, 24.6%, and 38.5% improvements in MAE, RMSE, and MAPE, respectively, across various horizons. The incorporation of environmental data, including temperature and Air Quality Index, as supplementary predictors in the DSTGCRN model, has demonstrated its efficacy in the context of the China dataset. The DSTGCRN model's improved accuracy and daily multi-regional forecasting deepen emission dynamics understanding, supporting the development of informed and region-specific environmental policies that can respond promptly to changes. [ABSTRACT FROM AUTHOR]

Beijing, the capital of China, has experienced rapid industrialization, urbanization and motorization in recent decades. Consequently, air pollution in Beijing, especially fine particulate matter (PM 2.5 ) pollution, has gradually become a severe environmental issue, due to the continuing growth in energy consumption and the resulting multiple pollutant emissions. In response to the increasingly serious PM 2.5 pollution, Beijing's government implemented a series of policies, measures and regulations on air pollution prevention and control and took some concrete actions to improve air quality. In this paper, firstly, we summarize China's ambient air quality standards, China's policies and regulations on air pollution prevention and control. Secondly, we illustrate historical evolution and current status of air pollution in Beijing. Finally, we introduce control measures and actions in Beijing and its surrounding areas. The paper aims to help environmental scientists and policy makers around the world understand the past and current air pollution in Beijing and control strategies and actions taken by Beijing's government. [ABSTRACT FROM AUTHOR]

As an economically intensive city within the greater bay area (GBA), Shenzhen has suffered from severe ozone pollution in recent years. Numerical modeling is an important tool in daily air quality prediction. However, a combination of factors, such as outdated emission inventories and the uncertainty of weather and numeric models, pose challenges for accurately forecasting pollution periods and levels. Therefore, systematic evaluation of the numerical model performance is crucial for better manual revised prediction of the daily MAD8h and air quality index (AQI). In this paper, a multimodel air quality forecast system, which was built in our public-owned monitoring center station, was evaluated to understand the possibilities of improvements. Based on the 2021 observed data of the national air monitoring station, we utilized a series of metrics to access the predicted air quality index (AQI) and pollution concentration levels from models (including WRF-CMAQ, NAQPMS, and WRF-CHEM) and compared them to the manual prediction. The results indicated that WRF-CMAQ performs outstandingly in comprehensive analysis, with a yearly 24 h (hour) forecast accuracy (A) exceeding 91%. The NAQPMS 24 h forecast presented the lowest accuracy (A = 71%) with a high overprediction rate. The forecast accuracy of WRF-CHEM and NAQPMS in the winter period was 100%. However, frequently changing weather patterns interfered with spring forecasting. Forecasters can accurately predict mild pollution cases (A = 81.8% for 24 h, A = 72.7% for 48 h), whereas numerical models (NAQPMS and WRF-CHEM) showed better sensitivity to moderate pollution cases. For fine particulate evaluation, CMAQ's low biases occurred in winter, and the manually revised air quality forecast (MRAQR) normally gave higher prediction values except in pollution cases. Model underestimation could be explained by missing the long-range dust transport contribution from the northern part of China. The ozone 24 h forecast part revealed that WRF-CMAQ exhibited the highest correlation coefficient (R = 0.70, 19.74% < RMSE < 42.7%), whereas NAQPMS showed relatively poor results with the highest ME (43 μg/m3) and MFE (41.92%). Close examination of synoptic information revealed that ozone pollution cases were subjected to the western Pacific subtropical high (WPSH) system, tropical cycle, and regional strong pollution transport. The NAQPMS ozone overprediction could be explained by the overestimation of troposphere transmission from the surroundings during nonconductive weather patterns. The study investigates a theoretical method for model optimization, which provides solid support for precise emission reduction and targeted pollution control. • Established a method for evaluating operational air quality forecasting. • The performance in predicting mild pollution cases in the GBA is slightly weak. • The transportation process is the main source of current prediction bias. [ABSTRACT FROM AUTHOR]

Abstract: China is currently facing the threat of serious air pollution and has adopted a series of governance policies. However, the fact that PM 2.5 indicator in some regions often reached far higher than the official threshold since 2015 has reflected that China's current Air Quality Index system for measuring and evaluating air quality is still not perfect. Therefore, this paper extended the current air quality indicator system officially used by China. Using improved Entropy-weighted Factor Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) Method, we calculated the air quality scores of 26 cities in China's most economically developed region - the Yangtze River Delta region based on their daily average concentration numbers of main air pollutants from February 2015 to January 2018. Based on the air quality calculation result, we further studied the effectiveness of air pollution control policies of this region by using the Box-Jenkins Model with fuzzy strategy parameter adjustment. Our findings are: the pollution control policy is more effective to Shanghai, Jiangsu and Zhejiang. The air quality score of Shanghai has improved by 51.07% compared with its level before the policy. Meanwhile, the air quality improvement in Zhejiang province has shown the characteristics of "Campaign-style Governance" right before the G20 Summit, especially that in Hangzhou, the host city of the Summit. On the other hand, maybe due to late start of air pollution control programs, the cities of Anhui province haven't shown obvious improvements in air quality, and some cities have even seen deterioration in air quality scores during our study period. Graphical abstract Image 1 Highlights • We extended the current air quality indicator system officially used by China. • We used the Entropy-weighted Factor Method and TOPSIS Method for assessing. • We studied the control policies by Box-Jenkins Model with fuzzy strategy parameter. • Results and corresponding reasons were analyzed according to provinces and cities. • Policy recommendations for air pollution control in YRD were proposed. [ABSTRACT FROM AUTHOR]

Highlights • We investigate the weather's impact on school commute mode choices in Beijing, China. • A large survey of middle school commutes is adopted, containing 97,427 observations. • Multinomial probit and multinomial logit models are estimated and compared. • The air quality index has a significant impact on school commute mode choices. Abstract Weather conditions have been recognized as important factors affecting school commute mode choices. This paper aims to explore the modal shift of middle school commutes with respect to the variation in weather-related variables, with empirical emphases on the situation in Beijing, China. Data from the latest Beijing School Commute Survey (2014–2015) were adopted, and multinomial probit (MNP) and multinomial logit (MNL) models were developed. The modeling results are in favor of the MNP model because it has better statistical performance. Weather-related variables, including sky condition, wind speed, highest temperature, humidity, air quality index (AQI), and some interaction terms, were found to have a significant impact on students' commute mode choices. Based on these models, an empirical sensitivity measure was defined as the expected percentage change in the probability of choosing each mode with respect to an order of magnitude change in the influential factors. Most of the results are in line with those of previous studies, and some unique results reflect features of Beijing. For example, on days with extremely poor air quality, students are more likely to turn to public transport rather than use a car from active transportation modes. This is probably due to the special urban traffic regulations that restrict household car ownership and car travel in Beijing. These findings could have implications for promoting active transportation for students and serve as references for policymakers and planners. [ABSTRACT FROM AUTHOR]

Highlights • The overall performance of rail TOD is better than that of BRTOD in China. • The rail-based TOD index correlates negatively with air quality measured by AQI. • The regular bus coverage and the BRT-based TOD index show no significant impact. Abstract In this paper, we aim to see whether transit-oriented development (TOD) in Chinese cities is associated with better air quality. We first identify 37 Chinese cities with existing urban rail transit and/or bus rapid transit (BRT) systems. For each of these cities we generate performance-based TOD indicators – including measures such as urban area coverage, population coverage, street network density and land use mix within station catchment areas – and construct composite TOD indices for each city using those indicators. We also collect daily air quality index (AQI) data from the Ministry of Environmental Protection of China over the entire year 2014 for 152 cities and calculate annual and seasonal average AQIs for each city. Regression models provide some evidence that rail-based TOD is associated with better air quality, after controlling for meteorological, demographic and economic characteristics. BRT-based TOD shows no significant relationship. [ABSTRACT FROM AUTHOR]

Abstract This paper adopts multifractal methods to analyze the multifractal characteristics of polluted time series in Beijing, Zhengzhou, and Jinan. The results of the multifractal detrended fluctuation analysis (MF-DFA) show that each of three air quality index (AQI) time series has multifractal characteristics, which provides evidence that the multifractal method but the traditional linear or monofractal method is feasible. The sources of multifractality in three AQI time series are all from long-range correlations and fat tail distributions, although the highlighted sources are different in three cities. The results of the coupling detrended fluctuation analysis (CDFA) show that SO 2 has a vital impact on air quality in each of three cities. O 3 has an important impact on air quality in Beijing and Jinan city and NO 2 has an important impact on air quality in Zhengzhou city. It is noted that PM 2.5 has not as large an impact as we imagine in three cities. It has a relatively large impact on air quality of Zhengzhou city, while it has a small impact on air quality of Beijing and Jinan city. The chi square χ 2 test further confirms the above results. Highlights • Each AQI time series has multifractal rather than monofractal characteristics. • There are two sources of multifractality. • The coupled correlations of six air polluted series have multifractal features. • SO 2 , O 3 and NO 2 have different impacts on air quality in each of three cities. [ABSTRACT FROM AUTHOR]

A growing literature on the pollution haven hypothesis has surged for decades. In this paper, we employ a city-level data set of 150 Chinese cities in 2014, and take spatial spillovers into account by using spatial econometric models, to investigate whether foreign capital inflows drive environmental degradation in China. The results suggest that foreign direct investment is negatively related to air pollution in China, indicating evidence of pollution halo hypothesis. Moreover, foreign direct investment has significant spatial technological spillovers, improving air quality in China. This study also finds that there is no evidence of an inverted U-shaped curve between income and air pollution. As income levels increase, air quality continues to worsen. The development of the tertiary industry is found to have a positive effect on air pollution. Densely populated cities tend to demand for better environmental quality. From the above analysis, it follows that policies handles are urgently needed to improve air quality in China. [ABSTRACT FROM AUTHOR]

The heat source types in China were not limited to the coal, other types of energy were introduced into the heating energy consumption in recent years, and therefore the specific regional impact of the heating emissions could not be obtained accurately according to the current achievement. Based on this, this study aimed to quantify the impact of the heating emissions on the air quality in an efficient and accurate way. A new method was proposed for investigating the quantitative impact of the heating emissions on the air quality index in this paper and its assumptions and accuracy were verified as well. The data employed in this method is extracted from Ministry of Environmental Protection Data Centre, Qingyue open source and Shanghai Central Meteorological Observatory. The key prerequisites of the proposed method were discussed in this study. By calculating, the averaged absolute difference of the meteorological parameters, namely Clo, WD, WS and P, is 3.58%, 2.40%, 3.21% and 3.94%, respectively. The difference ratio of the industrial emissions relative to the total emissions is as low as 4.3%. The optimal principal component number is six for the proposed method, which can satisfy the accuracy requirement with minimum workload. Compared with the results in previous similar studies, the proposed method showed excellent performance in analyzing the regional impact of the heating emissions on ambient air quality in China. Moreover, a total of 64 North cities in China were analyzed based on the proposed method to promote the knowledge on the influence of heating emissions on the environmental quality and the related parameters were provided in this study. [ABSTRACT FROM AUTHOR]

Utilizing the Comprehensive Environmental Administrative Enforcement Reform (CEAER) in China as a quasi-natural experiment, this paper has adopted 179 prefecture-level cities from July 2018 to December 2020 as research sample to assess whether the limited decentralization of environmental enforcement power can improve air quality through a time-varying difference-in-difference (DID) approach. Results show that CEAER can achieve better air quality improving effects, which can not only contribute to the decrease of daily mean values of Air Quality Index (AQI), but also contribute to the decrease of daily mean values of PM 2.5 , PM 10 , SO 2 , and CO. And this promotion is mainly achieved through the micro mechanism of raising environmental penalties strength. Further examination of the heterogeneity in terms of economic factors, city rank, pollution abatement pressure, and political constraints reveals that CEAER's policy effects are mostly insignificant in cities with higher levels of economic development and administrative rank, and positively significant in poor historical air quality cities and key environmental protection cities. These results underscore the positive effects of the limited decentralization of environmental enforcement power and provide ideas for other regions to draw on for environmental management system reform. [ABSTRACT FROM AUTHOR]

Previous studies analysing the effects of air pollution on work efficiency did not pay much attention to indoor impacts. This paper attempts to fill this gap by using technical data from Chinese Basketball Association (CBA) players. For the first time, we present personal work efficiency using a comprehensive evaluation index, the player efficiency rating (PER). The information is then combined with daily air quality data. We find that air pollution reduces work productivity: for every 10% increase in the air quality index (AQI), the average PER decreases by 0.4221, and the effects vary by air pollutant. Heterogeneity analysis shows that at the level of competition venues, the negative impact of air pollution on work efficiency can be weakened at home, while the rising temperature in the competition city can amplify the negative impact of air pollution on work efficiency. At the level of individual players, older players and foreign players are less affected by air pollution. Moreover, we find that air pollution affects work efficiency by reducing total work output and work accuracy. Our research constructs a comprehensive evaluation index for work efficiency, enriches research on the influence of air pollution on work efficiency, and provides a policy basis for identifying groups that are sensitive to air pollution, which might help with air pollution control policy making. [ABSTRACT FROM AUTHOR]

• We investigate the transmission of AQI fluctuation by complex network theory. • AQI networks of various time periods display small-world effect. • It is observed that some extreme short-term AQI fluctuation can be controlled. • Complex network analysis contributes to the prediction of AQI fluctuation. • Policy recommendations for environment management are proposed. Although many works have focused on the issues related to Air Quality Index (AQI) from the perspective of statistics or economics, the dynamic and temporal characteristics of AQI time series remain unclear. The main reason is that AQI time series has both non-linearity and complexity, which cannot be characterized by conventional ways. The inherent transmission laws of AQI volatility are of great importance for environment monitoring and management system. Thus, this paper has endeavored to address the issue from the interdisciplinary perspective of complex network theory. First, we collect the annual and the total AQI data of three cities (Shanghai, Wuhan and Guangzhou) in China from January 1, 2015 to December 31, 2017. Then the coarse graining method is employed in combination with the theory of phase space reconstruction to convert AQI time series into complex network. The coarse graining method is used to transform yearly and total AQI time series into the corresponding symbol sequences. In accordance with the theory of phase space reconstruction, we adopt C C method and false nearest neighbor algorithm to estimate the optimal time delay and embedding dimension of AQI time series, respectively. The optimal time delay is integrated with the embedding dimension to map symbol sequences into AQI complex network. Based on complex network theory, we investigate the transmission of AQI fluctuation by the temporal characteristics and dynamics of yearly AQI networks (YAN) and total AQI networks (TAN), which include node strength, betweenness centrality and community structure. The empirical results show: (1) the AQI networks of various periods display small-world effect; (2) network evolution tends to be more complex and the modality in YAN has less alternative transformation paths than that in TAN; (3) the node strength and betweenness centrality in measuring the importance of nodes achieve a relative consistency; (4) the three cities have the same dominant fluctuation modalities both in YAN and in TAN; (5) the uneven distribution of transmission ability between clusters implies a preferential transmission whether in YAN or TAN. These findings not only help us to interpret the frequent volatility of AQI and control some extreme short-term AQI fluctuation, but also contribute to the reliability assessment of AQI and the prediction of AQI fluctuation. In summary, this paper not only reveals the temporal dynamic properties of AQI time series, but also provides some policy recommendations for environmental monitoring and management. [ABSTRACT FROM AUTHOR]

To reduce air pollution, China gradually upgrades the fuel for vehicles while building the subway system in some major cities. Using panel data covering 16 major cities in China during the period between 2014 and 2017, this paper investigates the impacts of China Fuel Standard V for petrol and diesel and subway operation on Air Quality Index (AQI) and four pollutants, namely PM2.5, SO 2, NO 2 and CO. Empirical results find the positive impacts of Fuel Standard V on the reduction in SO 2 , PM2.5 and CO. The impact is strongest in the context of SO 2 , with Fuel Standard V Diesel having a larger impact compared to Fuel Standard V Petrol. In addition, the operation of subway systems leads to a decrease in CO. Furthermore, this paper explores not only separate but also the joint effects of fuel standards and subway. Interestingly, the results indicate that while the operation of the subway system itself cannot affect PM2.5 concentrations, it contributes to the reduction in PM2.5 when Fuel Standard V is implemented. These results emphasise the importance of policy coordination to reduce air pollution in China. Image 1 • China Fuel Standard V has positive impacts on the reduction in SO 2 , PM2.5 and CO. • China Fuel Standard V has the strongest impact in the context of SO 2. • Fuel Standard V Diesel has a larger impact compared to Fuel Standard V Petrol. • The subway system leads to a decrease in CO, but not on other pollutants. • Introducing the Fuel Standard V and subway jointly can reduce PM2.5 pollution. [ABSTRACT FROM AUTHOR]

In this paper, a novel hybrid learning method is carried out to forecast urban air quality index (AQI). Wavelet packet decomposition (WPD) is firstly performed to decompose the original AQI data into lower-frequency subseries. Then, we improve the pigeon-inspired optimization through using the particle swarm optimization algorithm. The improved pigeon-inspired optimization (IPIO) approach is applied to optimize the initial weights and thresholds of extreme learning machine (ELM) and then the modified ELM (MELM) is employed to forecast the subseries respectively. Moreover, multidimensional scaling and K-means (MSK) clustering methods are utilized to cluster the forecasting outcomes into high frequency, medium–high frequency, medium–low frequency and low frequency subseries. Finally, MELM, as an ensemble approach, is applied to ensemble the subseries together and achieve the final results. To test the predictive precision of the proposed hybrid WPD-MELM-MSK-MELM learning method, AQI of Harbin in China is adopted to make short-term, middle-term and long-term predictions separately. Different decomposition approaches are utilized to compare with WPD, and the non-clustering hybrid model is also compared with the proposed method. The forecasting outcomes indicate that WPD is more suitable for predicting AQI and the proposed WPD-MELM-MSK-MELM learning method has better predictive performance on horizontal precision, directional precision and robustness than some existing methods and benchmark models in this paper. • A novel hybrid learning approach is presented. • Pigeon-inspired optimization is improved through using particle swarm optimization. • Extreme learning machine is modified by pigeon-inspired optimization. • Based on the multidimensional scaling and K-means clustering, the ensemble approach can enhance the predictable precision of AQI. [ABSTRACT FROM AUTHOR]

Pollution emissions have become a matter of public concern in recent years. However, the vast majority of existing researches on PM 2.5 pollution are from the natural science perspective, and few studies have been conducted from an economic point of view. This paper adopts provincial panel data from 2001 to 2012 and panel data models to analyze the key driving forces of PM 2.5 emissions at the regional level in China. The results show that economic growth is a decisive factor of PM 2.5 emissions. The impacts of urbanization on PM 2.5 emissions vary across regions and decrease continuously from the central region to the western and eastern regions. The effects of private cars and coal consumption on PM 2.5 emissions in the eastern region are greater than that in the central and western regions because of significant differences in R&D investment, private car ownership and total coal consumption. Energy efficiency improvement has greater potential to mitigate PM 2.5 emissions in the central and western regions than that in the eastern region due to its low level of technology. Hence, in order to effectively reduce emissions, the Chinese government should consider all these factors as well as regional heterogeneity in developing appropriate mitigation policies. [ABSTRACT FROM AUTHOR]

Central environmental inspection (CEI), an innovative incentive mechanism to position the central authority to complement local regulators of pollution mitigation in China, may play a vital role in air pollution control. This paper describes an investigation that drew upon daily air quality data from 282 prefecture-level and above cities and employed an event-study design to scrutinize the impact of policy shock on air pollution mitigation. Our main finding is that CEI significantly reduced the Air Quality Index (AQI) and concentrations of PM 2.5 , PM 10 , SO 2 , NO 2 , and CO. Only O 3 increased during the on-site inspection probably due to the opposite effect with high NO X concentrations relative to VOCs. When NO X is reduced, ozone therefore increases. However, according to dynamic evidence, CEI failed to bring about long-term enduring sustained reductions. A probable explanation is that local officials has informed the pollution-contributing firms in advance, which consequently decreased their emissions by a small amount in the month prior to the inspection and then allowed emissions to return to historic levels after the inspectors departed. Notably, while PM 2.5 , NO 2 , and CO increased above their normal levels, this pattern did not apply to SO 2 , suggesting that some firms, with the exception of the power sector, recovered from previously lost production by subsequently increasing production. We also found evidence from the analysis of the VIIRS Night-Time Light data that the unsustainable improvement in environmental performance was indeed achieved by the firms' restrictions on production. Moreover, the data also provide strong evidence that more heavily polluted cities and inland areas of China were more responsive to the enforcement of CEI, perhaps as the result of an emphasis on heavy industry in those locations. This report concludes by proposing further policy implementations for the future optimization of China's environmental governance and sustained environmental performance. • China's central authority is deepening the inspection reform for environmental performance. • The Central Environmental Inspection mitigates China's air pollution effect. • There is a pollution rebound in the aftermath of the on-sited inspection. • Performance of the policy is largely due to limitations on production activities. [ABSTRACT FROM AUTHOR]

• The annual averages of the AQI in the Yangtze River Delta (YRD) region from 2016 to 2018 are 78.6, 74.2, and 69.7, respectively, exhibiting a pattern of year-to-year decrease. • Among the 41 metropolitan areas of the YRD, the correlation coefficients of PM 2.5 across metropolitan areas are between 0.75 and 0.95. PM 2.5 is the primary pollutant in 36 metropolitan areas, accounting for 88% of the pollution in the studied areas. • The study results demonstrate that the six impact factors have positive or negative correlations with AQI with significant spatial differences. The mean values of the regression coefficients indicate that relative humidity has the greatest effect on AQI values, followed by rainfall, temperature, wind speed, air pressure, and normalized difference vegetation index (NDVI); of which relative humidity has a positive correlation with AQI while air pressure has negative correction with AQI in the whole study area. Air pollution is a great concern due to its adverse health effects, and reducing air pollution is a major challenge worldwide. Utilizing the air quality monitoring data of the Yangtze River Delta (YRD) from 2016 to 2018, this paper first investigates the spatiotemporal patterns of the Air Quality Index (AQI), identifies the primary pollutants for each metropolitan area using gray relational analysis (GRA) method, and analyzes the spatial heterogeneity of the effects of six influencing factors on AQI in different metropolitan areas using geographically weighted regression (GWR). The study results show that: (1) The annual averages of the AQI in the YRD region from 2016 to 2018 are 78.6, 74.2, and 69.7, respectively, exhibiting a pattern of year-to-year decrease. (2) Among the 41 metropolitan areas of the YRD, the correlation coefficients of PM 2.5 across metropolitan areas are between 0.75 and 0.95. PM 2.5 is the primary pollutant in 36 metropolitan areas, accounting for 88% of the pollution in the studied areas. (3) The study results demonstrate that the six impact factors have positive or negative correlations with AQI with significant spatial differences. The mean values of the regression coefficients indicate that relative humidity has the greatest effect on AQI values, followed by precipitation, temperature, wind speed, air pressure, and Normalized Difference Vegetation Index (NDVI); of which relative humidity has a negative correlation with AQI while air pressure has positive correction with AQI in the whole study area. Since there is spatial heterogeneity on the degree of influence of each influencing factor on AQI, it is necessary to consider the spatial variability on the degree of influence different from indicators when formulating control countermeasures of air quality, so that the management countermeasures can be more targeted. [ABSTRACT FROM AUTHOR]

The air pollution index in northern hina rises sharply every autumn and winter. However, few relevant studies have focused on what causes this situation. In this paper, we used the daily meteorological and major air pollutants data of 21 cities in North China plain to study whether heating will aggravate air pollution. The results show that more than half of the major air pollutants in northern China come from coal-fired heating in autumn and winter. Heating will cause AQI, PM2.5, PM10 and SO 2 to increase 1.4 times, 1.9 times, 1.5 times and 0.9 times respectively compared to the seasonal average. Due to the government's control over heating prices, direct changes in coal prices are not transmitted to consumers, so direct changes in coal prices will not affect the level of air pollution. However, it is very interesting that the change in the price gap between high-quality coal and low-quality coal can effectively affect the concentration of air pollutants. This may be due to changes in the behavior of non-central heating users. During the heating season, when the price gap between high-calorie coal and low-calorie coal becomes larger, the concentration of air pollutants will increase. Therefore, it is necessary to carry out a market-oriented reform of heating prices while improving energy efficiency in the future. [ABSTRACT FROM AUTHOR]

To address the problems of environmental pollution, from 2015, the Chinese government launched its most stringent environmental supervision program in history, Central Inspections of Environmental Protection, and completed the first round nationwide inspections by the end of 2017. This paper focuses on the impact of this policy shock on air quality based on daily data from 286 cities in China and uses a regression discontinuity design to test the effectiveness of the policy on improving air quality. Cross-city analyses suggest that, the overall reduction effects on Air Quality Index (AQI) were not significant. Of the examined pollutants, more substantial reductions in SO 2 and PM 10 concentrations, by 6.3%–7.7% and 3.3%–5.4%, respectively, were observed immediately after the policy implementation. However, the reduction effects on PM 2.5 , CO, and NO 2 were not significant, and no reduction effect was observed for O 3. City-specific analyses show that most of the cities that experienced statistically significant policy effects are located in North China and feature high baseline pollutant concentration levels. The early warning effect and persistence of the effect of the policy were also discussed. On the one hand, significant reductions in NO 2 , CO, and SO 2 occurred 6 weeks, 4 weeks, and 2 weeks, respectively, prior to the entry dates of the inspection team. On the other hand, the policy effects decayed significantly immediately after the "on-site" inspection ended. This study implies that a sustainable environmental governance system is necessary and that applying a regression discontinuity design based on high-density time series data is valid for environmental policy evaluation. Image 1 • Policy effects of Chinese CIEP on air quality were assessed with RD method. • CIEP was found to have statistically significant reductions effects on SO 2 and PM 10. • CIEP had insignificant reduction effects on PM 2.5 , CO, and NO 2 but no effect on O 3. • More substantial policy effects were found in cities with serious air pollution. • Early warning exists for various pollutants but no effects persistence was found. [ABSTRACT FROM AUTHOR]

This paper investigates the relation between subway development and air pollution. Using yearly data from 160 Chinese cities from 2014 to 2017 and the spatial difference-in-differences (SDID) method, we obtain four findings. First, air pollution has a positive spatial spillover effect, and the impact of subway development will be overestimated if this spillover effect is not considered. Second, we find that increasing the mileage of subways will reduce the level of air pollution. Third, the development of subways has different impacts on various air pollutants. Subway system development can reduce the concentrations of PM 2.5 and PM 10 but has no obvious impacts on SO 2 , NO 2 , CO and O 3. Fourth, different development modes for the subway system have different impacts on reducing air pollution. Increasing the mileage, lines and stations of subways could decrease the levels of air quality index (AQI) and PM 2.5 , but increasing the mileage and stations of subways have negative but nonsignificant effects on PM 10. Thus, policymakers should focus on actively advocating for green transportation and the rational planning of subway system networks. [ABSTRACT FROM AUTHOR]

• County-level air quality has significant spatial clustering and spillover effect. • Spatial econometric is used to study the impact of HSR on air quality. • Air quality is improved in counties with HSR stations. • HSR does not produce a significant "corridor effect" on air quality along the route. HSR can improve air quality at the city-level, but it is still a lack of evidence from the county-level. Due to the spatial interaction effect of air quality, the spatial econometric method is selected. Based on Air Quality Index (AQI) daily panel data of 47 counties in southern Beijing-Tianjin-Hebei (BTH) from 2014 to 2017, Spatial Lag Model (SLM) and Spatial Durbin Model (SDM) are used to study the impact of HSR on county-level air quality. We found that: (1) air quality has a significant spatial spillover effect. (2) During the observation period, an HSR station can improve the air quality of the local county, but it will not affect the neighboring counties in the short run. (3) HSR does not produce a significant "corridor effect" on the air quality along the route. This paper provides some policy implications for the improvement of county-level air quality and planning of HSR. [ABSTRACT FROM AUTHOR]

Air pollution are known to have negative impacts on human health and the ecosystem, and it also contributes to climate change. Hence, prevention and control of air pollution is an urgent need in China, and air pollution prediction can provide reliable information for this process. Therefore, it is essential to establish effective air pollution prediction with an early warning model. Currently, one widely used air pollution prediction technology is the error correction model. However, this traditional method does not use data preprocessing technology. Therefoere, this paper presents an improved hybrid model named CEEMD-SLM-ECM (Complementary Set Empirical Mode Decomposition-Statistical Learning Model-Error Correction Model), which used the CEEMD data preprocessing technology together with statistical learning models. Furthermore, selected AQI (air quality index) data of 17 port cities in the 21st Century Maritime Silk Road Economic Belt were selected to test the forecasting ability of the proposed model. Data analysis shows that the CEEMD-SLM-ECM model has much higher accuracy compared with the traditional error correction model. So, the CEEMD-SLM-ECM is a very effective predictive model that can provide accurate prediction for air quality early warning. • A new improved error correction model CEEMD-SLM-ECM is established. • Data preprocessing is helpful for improving the prediction accuracy. • CEEMD-SLM-ECM is effective for AQI forecasting. [ABSTRACT FROM AUTHOR]

Most of the previous researches estimate influencing factors impact on air quality average without considering the heterogeneity of influential factors on different levels of air quality. In order to detect the different effects of influencing factors on air quality index (AQI) between lower-AQI and higher-AQI cities, this study applies a spatial quantile regression model (SQRM) to investigate heterogeneity of influential factors on AQI, while accounting for spatial autocorrelation of AQI. The results show that heterogeneity effects of windspeed, terrain slope, urbanization sprawl and spatial autocorrelation on AQI are large across the entire AQI spectrum, while heterogeneity effects of precipitation, temperature, relative humidity, terrain fluctuation and urbanization intensity on AQI are not obvious. The spatial positive autocorrelation of AQI in higher-AQI cities is greater than that in lower-AQI cities. Compared with higher-AQI cities, the negative impact of terrain slope on AQI is lager in lower-AQI cities. One unit increase in wind speed contributes AQI to decrease 9.31 to 5.64 then to 5.39 for lower, medium and higher-AQI cities. One unit increase in urbanization sprawl would lead AQI increase 25.6 to 15.6 then to 10.5 for lower, medium and higher-AQI cities. The heterogeneity analysis of meteorological, topographic and socioeconomic factors effects on air quality are of guiding significance for realizing the differentiation of policy measures for air pollution prevention and control. Image 1 • Nighttime light data is used to construct urbanization indicators. • SQRM detects heterogeneity of influential factors on lower and higher AQI cities. • Spatial positive autocorrelation of AQI is not balanced across entire AQI spectrum. • Wind speed contributes to air quality to different extent in cities of China. • Effects of urbanization breadth and intensity on AQI are positive and negative. This paper detects the heterogeneity of influencing factors on AQI between lower-AQI and higher-AQI cities in China. [ABSTRACT FROM AUTHOR]