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Governments have implemented many regulations to solve traffic congestion and pollutant emissions. Traffic restriction (TR) policy is the most common and widely used traffic regulation. However, it is difficult to alleviate traffic pollution through TR policies. Only a few studies focus on TR policies in multiple cities. In this paper, we classify traffic restrictions by geographical factors and evaluate the impact of different policies based on PSM-DID first. Then, we classify the mixed restrictions and test their effects on air pollution. The results show that TR policy improves air quality regardless of the season. Moreover, the effect of TR policy is not affected by the superposition of the type of traffic restriction. For cities implementing the same TR policy, geographic factors can lead to different outcomes. This finding provides a reference for governments in reducing air pollution. • Traffic restrictions significantly affected air quality in different regions of China. • Cities implemented traffic restrictions have improved air quality in adjacent cities. • Effect of traffic restrictions does not depend on the number of mixed restrictions. [ABSTRACT FROM AUTHOR]

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

Air pollution is an important factor affecting human health and daily life. The Chinese government is making vigorous efforts to control air pollution. The upgrading of the industrial structure is a problem-solving tool in the environment and economic growth cases. This paper aims to explore the relationships among environmental regulation, the upgrading of the industrial structure and air pollution. The PVAR (Panel Vector Auto Regression) model and moderating effect model are used to conduct empirical analysis based on panel data of 30 provinces in China from 2004 to 2020. The analysis of the results provides the following findings. Firstly, environmental regulations can significantly reduce emissions, but the deterioration of air quality does not have a significant impact on the improvement of environmental regulations. Secondly, industrial structure upgrading can reduce air pollution, but the worsening of the air quality will hinder the upgrading of industrial structures. Thirdly, environmental regulation can promote industrial structure upgrading. Lastly, industrial structure upgrading is a moderating variable and can positively moderate the impact of environmental regulations on air pollution. [ABSTRACT FROM AUTHOR]

Adapting to the situation of one principal versus several agents, the Relative Performance Evaluation (RPE) is an effective incentive tool to motivate subordinates. The Chinese central government has adopted RPE to combat intractable air pollution, which is the Urban Air Quality Ranking (UAQR) system. This study uses a series of econometric methods including regression discontinuity design to examine its effectiveness in environmental governance. The results show that (1) since the implementation of UAQR, the overall air quality of cities has improved; (2) air quality of the bottom 3 cities in the ranking will improve in the coming month compared with their counterparts; (3) while top 3 cities in the ranking do not experience such change in air quality. Therefore, this paper argues that the activating based RPE does not stimulate effective competition, whereas competency based RPE does. UAQR with credible incentive commitment can be a useful measure to motivate local authorities to strengthen air pollution reduction. In this study, the results of the empirical analysis enrich the theoretical framework of RPE, and an innovative theoretical framework is adopted to explain the UAQR system. However, as a top-down environmental monitoring system, such ranking institution could mainly eliminate centralized discharged pollutants. A sound judicial system is the fundamental guarantee for sustainable development. [ABSTRACT FROM AUTHOR]

Based on the panel data of various daily air pollution indicators of 288 prefecture-level cities in China from January 1, 2015 to December 31,2018, this paper takes the first round of central environmental protection inspection (CEPI) as a quasi-natural experiment, and empirically analyzes its impact on urban air pollution by using multi-period difference-in-difference (DID) method. The results show that: (1) the first round of five batches of environmental inspection and two batches of "looking back" have significantly reduced the concentration of seven pollutants; (2) The first five batches of environmental inspection have a stronger impact on reducing air pollution than "looking back"; Moreover, in the short tel·in after the inspection, it still has a significant reduction effect on air pollution; (3) The impact of central environmental protection inspection on air pollution shows strong heterogeneity in different batches of inspections, urban scale and economic development level, as well as the air quality level of cities before the inspection. The research results of this paper provide corresponding policy recommendations and decision-making basis for the Chinese central government to further deepen the reform of environmental protection supervision mechanism and promote the emissions of multiple pollutants mitigation. [ABSTRACT FROM AUTHOR]

The massive construction of high-speed rail has profoundly changed China, and it has also had a significant impact on air pollution in Chinese cities. Based on the panel data of 278 cities in China from 2003 to 2019, this paper makes an empirical study on the influence of high-speed rail on city-level air quality by using the opening of high-speed rail as a quasi-natural experiment and taking the method of progressive DID estimation model. The results show that: 1) The opening of high-speed rail significantly improves city-level air quality, and the conclusions are still valid after a series of robustness tests and dealing with endogenous problems. 2) The opening of high-speed rail has a heterogeneous effect on air quality in cities of different regions and different scales. 3) The opening of high-speed rail can promote technological progress and accelerate the upgrading of industrial structures, as well as reduce air pollution levels in neighboring cities through the spillover effect. 4) The opening of high-speed rail improves urban air quality through the substitution effect of air passenger transport. [ABSTRACT FROM AUTHOR]

Background: Air pollution is an important factor affecting residents' health. Methods: Based on the health data of 9959 residents from 28 provinces in China in the 2017 Chinese General Social Survey (CGSS), this paper uses a hierarchical linear model (HLM) to scientifically analyze the impact of air pollution on residents' health and to explore the moderating role of air pollution in various factors influencing residents' health. Results: Air pollution has significant negative effects on residents' health (coef. −0.103, p < 0.01), while frequency of exercise (coef. 0.070, p < 0.01), education level (coef. 0.012, p < 0.1) and income level (coef. 0.383, p < 0.01) have positive effects on residents' health. Air pollution has a negative moderating effect on the positive impact of exercise frequency on residents' health (coef. −0.015, p = 0.004). Conclusions: Air pollution causes serious damage to residents' health and it is necessary to control air pollution. Increasing residents' income, education level and exercise frequency can help improve residents' health level. Air pollution significantly reduce the positive impact of exercise frequency on residents' health. In order to effectively improve their own health, it is a better choice for residents to exercise when the air quality is good. [ABSTRACT FROM AUTHOR]

In China, blue sky defense is a crucial part of ecological environment governance. Objective environmental governance performance needs to be perceived by the public to more truly affect the public's evaluation of the government's environmental governance. This paper focuses on the public's subjective perception of air pollution and evaluation of the local government's environmental governance. Based on the Chinese General Social Survey data, the matched economic indicators, and air pollution data, we conduct a diachronic study on the public's evaluation of local governments' environmental protection work, and we analyze the relationship between the subjective perception of air pollution, the objective air pollution data, and the evaluation of local governments' environmental protection work. The results showed the following: (1) People's evaluation of local governments' environmental protection work significantly improved from 2013 to 2021. The objective indicator improved, while the subjective perception declined. (2) The subjective perception of air pollution has a significant negative impact on the evaluation of local governments' environmental protection work and needs to be better considered to improve air quality. At the same time, the effect of the objective indicator is insignificant. [ABSTRACT FROM AUTHOR]

Photochemical air quality models (AQMs) are a vital tool for atmospheric pollution research and have been widely used in various applications, such as air quality prediction and evaluation of pollution control strategies. Before using these models for further studies, it is essential to thoroughly evaluate their reliability and accuracy. While previous guidelines and benchmarks have primarily focused on fine particulate matter (PM 2.5) and ozone (O 3), there is still a lack of benchmarks for evaluating the model performance on primary criteria pollutants such as sulfur dioxide (SO 2), nitrogen dioxide (NO 2), carbon monoxide (CO), and particulate matter with aerodynamic diameter less than or equal to 10 μm (PM 10). The use of air quality models in China has increased significantly in the past decades. However, there is still a lack of standardized benchmarks for the performance evaluation of these models. Building upon our previous work on PM 2.5 and its chemical species, we propose a set of benchmarks for evaluating the performance of the aforementioned four air pollutants. Initially, we identified a total of 475 papers published during 2007–2019 that utilized at least one of the five commonly used AQMs in China. From these papers, we selected 164 articles that provided model performance evaluation (MPE) results of the four primary air pollutants. The three most frequently used Model Performance Evaluation (MPE) metrics were selected to analyse the impact of different model configurations on the reported statistics, including modelling region, season, and emission inventory. Lastly, three commonly used statistical indicators, including normalized mean bias (NMB), normalized mean error (NME), and correlation coefficient (R), were proposed for the validation of simulated NO 2 , SO 2 , CO, and PM 10. Two sets of benchmarks are given, including the "goal" and "criteria". The "goal" represents the best range of performance that a model can be expected to achieve, and the "criteria" represents performance that the majority of studies have achieved. We recommend R values above 0.50, 0.35, 0.45, and 0.40 for NO 2 , SO 2 , PM 10 , and CO, respectively, in order to meet the "criteria" benchmark. If the "goal" benchmark is to be achieved, the corresponding R values are 0.60, 0.55, 0.60, and 0.60. The "goal" benchmarks of NMB for NO 2 , SO 2 , PM 10 , and CO are within ±20%, ±25%, ±20%, and ±25%, respectively; while the "goal" benchmarks of NME for the four pollutants are less than 40%, 45%, 45%, and 60%, respectively. These benchmarks supplement our previous benchmarks for PM 2.5 and its components and provide a more comprehensive guideline for the air quality modelling community in China. • A set of benchmarks for evaluation of the NO 2 , SO 2 , CO and PM 10 simulation over China are proposed. • Three commonly used statistical indicators (NMB, NME, and R) were proposed for the validation of the four pollutants. • The goal benchmarks of NMB for NO 2 , SO 2 , PM 10 , and CO are <20%, <25%, <20% and <25%, respectively. [ABSTRACT FROM AUTHOR]

• Urban green spaces have direct and indirect effects on improving residents' health. • The health benefits of urban green spaces exhibit regional heterogeneity. • Economic development weakens the health benefits of urban green spaces. • Environmental regulations can enhance the health benefits of urban green spaces. Urban green spaces have long been cherished as the "lungs of the city", it can not only improve air quality and enhance residents' health. The impact of urban green spaces on air pollution and residents' health has attracted great attention. In this paper, we select 31 provinces in China as research samples and use ArcGIS software to draw spatial distribution maps of urban green spaces, air pollution, and residents' health in China, characterizing their spatiotemporal changes in order to explore whether there is a correlation among the three. On this basis, this paper utilizes panel data of Chinese provincial-level from 2010 to 2020 and conducts empirical tests through a two-way fixed effect model and a threshold regression model. The research findings are as follows: (1) Urban green spaces not only bring environmental benefits, but also health benefits, and the health benefits are greater than the environmental benefits. (2) There is a health risk of air pollution in northern China, and the health benefits of urban green spaces are not significant. Conversely, there is no health risk of air pollution in southern China, and the health benefits of urban green spaces are significant. (3) Although environmental regulations can enhance the health benefits of urban green spaces, the role of environmental regulations in enhancing the health benefits of urban green spaces may be further weakened as the economy develops. [ABSTRACT FROM AUTHOR]

Air pollution control policies in China have been experiencing profound changes, highlighting a strategic transformation from total pollutant emission control to air quality improvement, along with the shifting targets starting from acid rain and NO x emissions to PM 2.5 pollution, and then the emerging O 3 challenges. The marvelous achievements have been made with the dramatic decrease of SO 2 emission and fundamental improvement of PM 2.5 concentration. Despite these achievements, China has proposed Beautiful China target through 2035 and the goal of 2030 carbon peak and 2060 carbon neutrality, which impose stricter requirements on air quality and synergistic mitigation with Greenhouse Gas (GHG) emissions. Against this background, an integrated multi-objective and multi-benefit roadmap is required to provide decision support for China's long-term air quality improvement strategy. This paper systematically reviews the technical system for developing the air quality improvement roadmap, which was integrated from the research output of China's National Key R&D Program for Research on Atmospheric Pollution Factors and Control Technologies (hereafter Special NKP), covering mid- and long-term air quality target setting techniques, quantitative analysis techniques for emission reduction targets corresponding to air quality targets, and pathway optimization techniques for realizing reduction targets. The experience and lessons derived from the reviews have implications for the reformation of China's air quality improvement roadmap in facing challenges of synergistic mitigation of PM 2.5 and O 3 , and the coupling with climate change mitigation. [Display omitted] [ABSTRACT FROM AUTHOR]

• A novel spatial general equilibrium model was constructed and the impact of air pollution on urban residents' welfare in China was studied. • Empirical study shows air pollution significantly reduces urban residents' welfare and migration cost reduces the negative impact of air pollution on the welfare of urban residents. • Policy suggestions suggested that air pollution should be balanced against the welfare of residents, and should not be cleaned up by "one size fits all" in the early stages of economic development. Air pollution and its effects are important issues of human society. However, few literatures on the effect of air pollution on urban residents' welfare considered the cost of migration. This paper constructs a novel spatial general equilibrium model and studies the impact of air pollution on urban residents' welfare in 274 cities at and above the prefectural level in China from 2007 to 2017, and investigates the role of migration costs. The theoretical analysis and empirical research show that air pollution has a significant negative impact on the welfare of residents. Further, the simulation results indicate that the reduction of migration cost and the improvement of air quality accounts for roughly 24.65% improvement of resident's welfare from 2007 to 2017 in China. Another conclusion is that there is an inverted U-shaped relationship between resident's welfare and air pollution. On another side, migration costs play a moderating role in the impact path of air pollution on residents' welfare. When the migration costs fall, it will help residents to move out of cities with poor air quality and improve the resident's welfare. The policy suggestions are as follows: (1) In the process of sustainable development, local governments need to coordinate the phased characteristics between air pollution and residents' welfare, effectively improve the livable degree of city and urban economic sustainability, and prevent the brain drain caused by excessive migration of residents. (2) Local governments should lower the migration costs between cities by deepening the reform of the household registration system, so as to promote the free flow of labor and ultimately improve the welfare of residents. [ABSTRACT FROM AUTHOR]

With the development of industry, air pollution has become a serious problem. It is very important to create an air quality prediction model with high accuracy and good performance. Therefore, a new method of CT-LSTM is proposed in this paper, in which the prediction model is established by combining chi-square test (CT) and long short-term memory (LSTM) network model. CT is used to determine the influencing factors of air quality. The hourly air quality data and meteorological data from Jan. 1, 2017 to Dec. 31, 2018 are used to train the LSTM network model. The data from Jan. 1, 2019 to Dec. 31, 2019 are used to evaluate the LSTM network model. The AQI level of Shijiazhuang of Hebei Province of China from Jan. 1, 2019 to Dec. 31, 2019 is predicted with five methods (SVR, MLP, BP neural network, Simple RNN and this paper's new method). Then, a contrastive analysis of the five prediction results is made. The experimental results show that the accuracy of this new method reaches 93.7%, which is the highest in the five methods and the maximum error of this new method is 1. The correct number of days predicted by this new method is also the highest among the five methods, which is 342 days. The new method also shows good characteristics in MAE, MSE and RMSE, which makes it more accurate for people to predict the AQI level. [ABSTRACT FROM AUTHOR]

Accurate prediction of PM2.5 concentration is one of the key tasks of air pollution assessment, early warning, and treatment. In this paper, four monitoring sites were arranged in Jiangbei New District of Nanjing City, China. The environmental parameters such as PM2.5/PM10 concentration, temperature, and humidity were monitored from January to February 2020. A gated recurrent unit (GRU) network based on the PM2.5 concentration prediction model was established to predict PM2.5 concentration. The mean relative error (MRE), root mean square error (RMSE), and Pearson correlation coefficient were selected as the evaluation criteria for the accuracy of the GRU model. The data set was divided into a training set, a test set and a validation set at a ratio of 7:2:1, and the GRU model was used to predict the hourly value of PM2.5 concentration in the next week. The prediction results show that the Pearson correlation coefficients between the predicted values and the monitored values of the four monitoring sites have reached more than 0.9, reflecting a strong correlation. The relative average errors are around 10%. The GRU model prediction of NJAU (Nanjing Agricultural University)-Pukou Campus Site is the most accurate, and the correlation coefficient, MRE, and RMSE are 0.970, 7.85%, and 9.6049, respectively, reflecting the good prediction performance of the model. Therefore, this research supports the prediction of air quality in different cities and regions, so people can take protective measures in advance and reduce the damage caused by air pollution to human bodies. [ABSTRACT FROM AUTHOR]

This paper collects and analyzes the 1954–2017 monthly average reanalysis data from the U.S. National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), the 1954–2017 haze days observation data from the National Meteorological Information Center/China Meteorological Administration (NMIC/CMA) and the PM2.5 daily average mass concentration data in 2013–2017 from the China Air Quality Online Monitoring Platform. The atmospheric apparent heat source Q1 (negative Q1 means cold source in winter) over the Tibetan Plateau in December of 1954–2017 is estimated based on thermodynamic equations, and the East Asian winter monsoon index (EAWMI) is calculated. In addition, the discrepancies of the air quality among China's five typical regions (Beijing–Tianjin–Hebei, Fen-Wei Plain, Yangtze River Delta, Pearl River Delta and Sichuan-Chongqing regions) under the joint influence of the Q1 and EAWMI are studied. Results show that when it is a strong cold source year, abnormal downdrafts and "temperature inversion covers" occur in areas far from the high terrain, resulting in increased pollution, while the opposite is true in weak cold source years. In strong EAWMI years, there is an abnormal northerly sinking cold flow in the lower layers of mid-high latitudes, which increases the pollution in the area south of 30° N, and the opposite is true in weak EAWMI years. Affected by the combined activities of the Q1 and the EAWMI, the meteorological conditions of the five typical regions are different, and thus present different air pollution characteristics. [ABSTRACT FROM AUTHOR]

Plastic waste trade has grown considerably in the last decades and has led to severe environmental problems in recipient countries. Being the largest recipient, China has permanently banned the imports of plastic waste since 2018. This paper examines the causal effect of plastic waste imports on air pollution by exploiting China's experience of importing plastic waste and the recent import ban. By combining data on plastic waste imports with PM 2.5 data at the city level for the years 2000–2011, we find that plastic waste imports increased PM 2.5 density significantly in recipient cities. To evaluate the impact of the import ban on air quality, we employ daily data on air pollution between 2015 and 2020. Our difference-in-differences results show that affected cities, relative to other cities, experienced considerable improvement in air quality following the ban. Further analysis reveals that increased incineration of non-recycled waste is the main channel. These findings provide insights into the costs of importing plastic waste and the potential environmental gains from banning such imports in other countries. • We assess the environmental impact of plastic waste trade in the context of China. • Chinese cities that imported more plastic waste experienced an increase in PM2.5 density between 2000 and 2011. • The primary factor contributing to worsened air quality was the increased incineration of non-recycled waste. • China's import ban in 2018 effectively improved air quality. • The findings highlight the importance of designing effective policies to regulate international trade in waste and domestic waste management. [ABSTRACT FROM AUTHOR]

Emissions from ships stand as one of the principal contributors to air pollution in coastal areas. China has implemented the Domestic Ship Emission Control Area (DECA) policy progressively since 2016 to alleviate sulfur oxide and particulate matter pollution in coastal cities by limiting the sulfur content in marine fuel oil. This study utilizes Synthetic Difference-in-Differences model to construct a counterfactual framework for evaluating the DECA policy effect on air quality of cities in the Yangtze River Delta, the Pearl River Delta, and the Bohai Rim region. Results revealed that DECA policy significantly reduced the urban sulfur dioxide (SO 2) concentrations of YRD, BR, and PRD regions by 9.93 %, 8.64 %, and 7.09 %, respectively. However, the policy's efficacy in restraining fine particulate matter (PM 2.5) concentrations was found to be non-significant. This study further estimated the dynamic changes in SO 2 concentration in coastal cities of different regions in the DECA coverage. The results showed that the DECA policy generally began to significantly reduce SO 2 concentration levels in the second year after implementation, and with the tightening of the DECA policy, the reduction effect followed a growing-and-then-falling trend. Finally, the paper offers policy recommendations to further reduce ship emissions, aligned with recent emission reduction advancements. • Assessing the impact of China's DECA on coastal urban air pollution using a synthetic difference-in-differences model. • DECA effectively reduces SO 2 concentrations in coastal cities, yet its control over PM 2.5 levels is limited. • The impact of DECA varies across regions, with the most significant effects in the YRD, followed by BR and the PRD. • Several policy recommendations are proposed to optimize the effectiveness of DECA. [ABSTRACT FROM AUTHOR]

The concentration changes of aerosols have attracted wide-ranging attention during the COVID-19 lockdown (CLD) period, but the studies involving aerosol optical properties (AOPs) are relatively insufficient, mainly AOD (fine-mode AOD (AODf) and coarse-mode AOD (AODc)), aerosol absorption optical depth (AAOD), and aerosol extinction coefficient (AEC). Here, the remote-sensing observations, Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) products, backward-trajectory, and potential-source-contribution models are used to assess the impact of AOPs, vertical distribution, and possible sources on the atmosphere environment in North China Plain (NCP), Central China (CC), Yangtze River Delta (YRD), Pearl River Delta (PRD), and Sichuan Basin (SB) during the CLD period. The results demonstrate that both AOD (MODIS) and near-surface AEC (CALIPSO, <2 km) decreased in most areas of China. Compared with previous years (average 2017–2019), the AOD (AEC) of NCP, CC, YRD, PRD, and SB reduced by 3.33% (10.76%), 14.36% (32.48%), 10.80% (29.64%), 31.44% (22.68%), and 15.50% (8.44%), respectively. In addition, MODIS (AODc) and MERRA-2 (AODc) decreased in the five study areas compared with previous years, so the reduction in dust activities also contributed to improving regional air quality during the epidemic. Despite the reduction of anthropogenic emissions (AODf) in most areas of China during the CLD periods, severe haze events (AODf > 0.6) still occurred in some areas. Compared to previous years, there were increases in BC, OC (MERRA-2), and national raw coal consumption during CLD. Therefore, emissions from some key sectors (raw coal heating, thermal power generation, and residential coal) did not decrease, and this may have increased AODf during the CLD. Based on backward -rajectory and potential source contribution models, the study area was mainly influenced by local anthropogenic emissions, but some areas were also influenced by northwestern dust, Southeast Asian biomass burning, and marine aerosol transport. This paper underscores the importance of emissions from the residential sector and thermal power plants for atmospheric pollution in China and suggests that these sources must be taken into account in developing pollution-mitigation plans. [ABSTRACT FROM AUTHOR]

Urban air pollution is an important issue facing China in the midst of rapid urbanization and economic development. To investigate the regional air quality characteristics and its drivers in Northeast China, this paper compares the spatial and temporal characteristics of air quality between cities and analyzes the influence of socioeconomic variables by using statistical analysis methods and geographic model. The results show that the air quality index (AQI) showed a downward trend in time and decreased from southwest to northeast in space. The duration of heavy pollution condition was not only controlled by the distribution of pollutant concentrations, but also influenced by the topography. Based on the mean concentrations of the 6 pollutants, 37 cities were divided into 4 categories by cluster analysis, reflecting the levels and characteristics of pollution. The level of industrialization was the most important cause for air quality, followed by the size of the city and the degree of economic development. The AQI predicted by geographic weighted regression model (GWR) showed a lower goodness of fit in developed cities, indicating that the factors controlling air quality are more complex in these regions. The influence of different socioeconomic metrics on AQI showed large spatial differences. AQI was more sensitive to variations in socioeconomic metrics in less developed small and medium-sized cities. This study provides a theoretical basis for revealing the causes of urban air pollution and formulating pollution control measures in Northeast China. [ABSTRACT FROM AUTHOR]

This paper presents the first development and evaluation of a reduced-complexity air quality model for China. In this study, the reduced-complexity Intervention Model for Air Pollution over China (InMAP-China) is developed by linking a regional air quality model, a reduced-complexity air quality model, an emission inventory database for China, and a health impact assessment model to rapidly estimate the air quality and health impacts of emission sources in China. The modeling system is applied over mainland China for 2017 under various emission scenarios. A comprehensive model evaluation is conducted by comparison against conventional Community Multiscale Air Quality (CMAQ) modeling system simulations and ground-based observations. We found that InMAP-China satisfactorily predicted total PM 2.5 concentrations in terms of statistical performance. Compared with the observed PM 2.5 concentrations, the mean bias (MB), normalized mean bias (NMB) and correlations of the total PM 2.5 concentrations are - 8.1 µ g m -3 , - 18 % and 0.6, respectively. The statistical performance is considered to be satisfactory for a reduced-complexity air quality model and remains consistent with that evaluated in the USA. The underestimation of total PM 2.5 concentrations was mainly caused by its composition, primary PM 2.5. In terms of the ability to quantify source contributions of PM 2.5 concentrations, InMAP-China presents similar results to those based on the CMAQ model, with variation mainly caused by the different treatment of secondary inorganic aerosols in the two models. Focusing on the health impacts, the annual PM 2.5 -related premature mortality estimated using InMAP-China in 2017 was 1.92 million, which was 250 000 deaths lower than estimated based on CMAQ simulations as a result of the underestimation of PM 2.5 concentrations. This work presents a version of the reduced-complexity air quality model over China that provides a powerful tool to rapidly assess the air quality and health impacts associated with control policy and to quantify the source contribution attributable to many emission sources. [ABSTRACT FROM AUTHOR]

Air pollution is one of the most serious threats to human health and is an issue causing growing public concern. Air quality forecasts play a fundamental role in providing decision-making support for environmental governance and emergency management, and there is an imperative need for more accurate forecasts. In this paper, we propose a novel spatial–temporal deep multitask learning (ST-DMTL) framework for air quality forecasting based on dynamic spatial panels of multiple data sources. Specifically, we develop a prediction model by combining multitask learning techniques with recurrent neural network (RNN) models and perform empirical analyses to evaluate the utility of each facet of the proposed framework based on a real-world dataset that contains 451,509 air quality records that were generated on an hourly basis from January 2013 to September 2017 in China. An application check is also conducted to verify the practical value of our proposed ST-DMTL framework. Our empirical results indicate the efficacy of the framework as a viable approach for air quality forecasts. [ABSTRACT FROM AUTHOR]

With the development of quality of life, people pay more and more attention to the surrounding environmental factors, especially air pollution. The problem of air pollution in China is becoming more and more serious, which poses a great threat to people's health. Therefore, the prediction of air quality concentration is very important. PM2.5 is the primary indicator for evaluating the concentration of smog. Currently, studies have been proposed to predict the concentration of PM2.5. Before, most methods use traditional machine learning or real-time monitoring to predict pollution of PM2.5 value. However, the previous prediction methods cannot meet the requirement of the accuracy. For this end, this paper uses Convnet and Dense-based Bidirectional Gated Recurrent Unit to predict PM2.5 value which combined Convnet, Dense and Bi-GRU. The feature in air quality data was extracted from convnets without max-pooling instead another convolutional layer and Bi-GRU with additional Dense could provide a more accuracy result. Experiments show that the effectiveness of our method PM2.5 mass concentration prediction model provides a more superior method for PM2.5 mass concentration prediction. [ABSTRACT FROM AUTHOR]

Air pollution is now a serious problem in China and elsewhere, makes a significant impact on human life. Air quality prediction is one of the research fields in environmental protection, which helps people to plan their lives reasonably and avoid pollution exposure. Considering that the atmospheric pollutants and meteorological data are ultimate time-series data, this paper proposed a prediction model based on LSTM which has low implementation complexity and an open cost of production. We selected atmospheric pollutants (SO2, PM10, O3, etc.), as well as meteorological parameters (temperature, atmospheric pressure, rainfall, etc.) as input of training model, these factors from the first few hours are used to forecast PM2.5 concentration in the next few hours. We evaluated our model on a large dataset of air pollution records and grid meteorological data in Qingdao. The results show that the proposed Long Short-Term Memory (LSTM) network generally has the marginal error compared with baselines, which indicates that the proposed LSTM network approach for PM2.5 forecast is effective and robust. [ABSTRACT FROM AUTHOR]

This paper analyzes hourly PM2.5 measurements from government-controlled and U.S. embassy-controlled monitoring stations in five Chinese cities between January 2015 and June 2017. We compare the two datasets with an impulse indicator saturation technique that identifies hours when the relation between Chinese and U.S. reported data diverges in a statistically significant fashion. These temporary divergences, or impulses, are 1) More frequent than expected by random chance; 2) More positive than expected by random chance; and 3) More likely to occur during hours when air pollution concentrations are high. In other words, relative to U.S.-controlled monitoring stations, government-controlled stations systematically under-report pollution levels when local air quality is poor. These results contrast with the findings of other recent studies, which argue that Chinese air quality data misreporting ended after a series of policy reforms beginning in 2012. Our findings provide evidence that local government misreporting did not end after 2012, but instead continued in a different manner. These results suggest that Chinese air quality data, while still useful, should not be taken entirely at face value. [ABSTRACT FROM AUTHOR]

The current nationally determined contributions, pledged by the countries under the Paris Agreement, are far from limiting climate change to below 2 ∘C temperature increase by the end of the century. The necessary ratcheting up of climate policy is projected to come with a wide array of additional benefits, in particular a reduction of today's 4.5 million annual premature deaths due to poor air quality. This paper therefore addresses the question how climate policy and air pollution–related health impacts interplay until 2050 by developing a comprehensive global modeling framework along the cause and effect chain of air pollution–induced social costs. We find that ratcheting up climate policy to a 2 ∘ compliant pathway results in welfare benefits through reduced air pollution that are larger than mitigation costs, even with avoided climate change damages neglected. The regional analysis demonstrates that the 2 ∘C pathway is therefore, from a social cost perspective, a "no-regret option" in the global aggregate, but in particular for China and India due to high air quality benefits, and also for developed regions due to net negative mitigation costs. Energy and resource exporting regions, on the other hand, face higher mitigation cost than benefits. Our analysis further shows that the result of higher health benefits than mitigation costs is robust across various air pollution control scenarios. However, although climate mitigation results in substantial air pollution emission reductions overall, we find significant remaining emissions in the transport and industry sectors even in a 2 ∘C world. We therefore call for further research in how to optimally exploit climate policy and air pollution control, deriving climate change mitigation pathways that maximize co-benefits. [ABSTRACT FROM AUTHOR]

This paper primarily studies the air quality in China's cities during the urbanization stage. Using a cross-sectional data of 282 cities, we discuss the influencing factors and the existence of the Kuznets Curve for three air pollutants based on the EKC (Environmental Kuznets Curve) theory and the BMA (Bayesian Model Average) method. The results show that the concentration of SO 2 and PM 10 presents the characteristics of an inverse-U shape. The urbanization process has a significant and negative effect on air pollutant concentration, which means that cities with higher urbanization rate tends to have lower air pollutant concentration. Population density, possession of civil motor vehicles, the proportion of secondary industry, and annual average temperature are the main influencing factors of air pollution. Based on these results, this paper suggests that China should speed up the “new” type urbanization process, constantly optimize the industrial structure, and promote the harmonious development of the economy and environment. [ABSTRACT FROM AUTHOR]

Air pollution in China has changed substantially since 2013, and the effects such changes bring to the human health and environment has been an increasingly hot topic in many scientific fields. Such studies, however, are often hindered by a lack of long-term air quality dataset in China of high accuracy and spatiotemporal resolutions. In this study, a six-year long high-resolution Chinese air quality reanalysis datasets (CAQRA) has been developed by assimilating over 1000 surface air quality monitoring sites from China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and the Nested Air Quality Prediction Modeling System (NAQPMS). Surface fields of six conventional air pollutants in China, namely PM2.5, PM10, SO2, NO2, CO and O3 for period 2013-2018, are provided at high spatial (15 km ×15 km) and temporal (1 hour) resolutions. This paper aims to document this dataset by providing the detailed descriptions of the assimilation system and presenting the first validation results for the reanalysis dataset. A five-fold cross validation (CV) method was used to assess the quality of CAQRA. The CV results show that the CAQRA has excellent performances in reproducing the magnitude and variability of the surface air pollutants in China (CV R2 = 0.52-0.81, CV RMSE = 0.54 mg/m3 for CO and 16.4-39.3 μg/m3 for other pollutants at the hourly scale). The interannual changes of the air quality in China were also well represented by CAQRA. Through the comparisons with the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) produced by the European Centre for Medium-Range Weather Forecasts (ECWMF) based on assimilating satellite products, we show that the CAQRA has higher accuracy in representing the surface gaseous air pollutants in China due to the assimilation of surface observations. The finer horizontal resolution of CAQRA also makes it more suitable for the air quality studies in the regional scale. We further validate the PM2.5 reanalysis dataset against the independent datasets from the U.S. Department of State Air Quality Monitoring Program over China, and the accuracy of PM2.5 reanalysis was also compared to that of the satellite estimated PM2.5 concentrations. The results indicate that the PM2.5 reanalysis shows good agreement with the independent observations (R2 = 0.74-0.86, RMSE = 16.8-33.6 μg/m3 in different cities) and its accuracy is higher than most satellite estimates. This dataset would be the first high-resolution air quality reanalysis dataset in China that can simultaneously provide the surface concentrations of six conventional air pollutants in China, which should be of great value for many studies, such as the assessment of health impacts of air pollution, investigation of the changes of air quality in China and providing training data for the statistical or AI (Artificial Intelligence) based forecast. The whole datasets are freely available at: https://doi.org/10.11922/sciencedb.00053 (Tang et al., 2020a), and a teaser product which contains the monthly and annual mean of the CAQRA has also been released at https://doi.org/10.11922/sciencedb.00092 (Tang et al., 2020b) to facilitate the potential users to download and to evaluate the improvement of CAQRA. [ABSTRACT FROM AUTHOR]

Urban air pollution is the most serious environmental pollution problem in China. It not only causes serious losses to our economy, but also brings great hidden dangers to the physical and mental health of urban residents. Therefore, it is urgent to prevent and control air pollution. Air quality prediction and forecasting must be carried out to prevent air pollution. Timely and accurate air quality prediction can not only help urban managers to make scientific and effective preventive measures, but also provide more healthy and safe travel strategies for urban residents. China's air monitoring system is gradually improving, its scale is expanding, and a large amount of air quality data is accumulating. With the rapid expansion of data scale, the traditional method of air quality prediction technology has been unable to deal with these massive data. In this paper, 365 sets of air pollutant data from January 1, 2018 to December 31, 2018 in Shen Zhen were used as experimental objects. The improved SAPSO algorithm and PSO algorithm were used to optimize the parameters of SVM model and construct the air quality evaluation model. By analyzing the classification results of air quality grade, selecting relevant data and using partial least squares, the correlation coefficient matrix is established for the classification results, and the pollutant factors affecting air quality in Shenzhen are obtained. The results are ideal, which provides a scientific theoretical basis for the prevention and control of air pollution and urban management planning. [ABSTRACT FROM AUTHOR]

It is an urgent challenge to coordinate industrialization and air quality in China since the rapid industrialization over the past forty years caused serious air pollution. In this paper, we measured the industrialization levels of the 31 regions in China from 2003 to 2017 as a sum of per capita gross domestic product (GDP), urban population (% of total), tertiary vs. secondary industry output ratio, non-agricultural output value (% of GDP), non-agricultural employment proportion, and the proportion of added value of manufacturing in the total added value of commodity. We measured air quality as a sum of annual SO 2 , NO 2 and PM 10 concentrations. In general, the industrialization level continuously increased while air pollution was reduced in spite of some fluctuations in China. Our results show air pollution presented N-shape relations with the industrialization level in China in the periods from 2003 to 2008, 2009 to 2013, and 2014 to 2017. Air pollution increases as the industrialization occurs but later deceases as the industrialization level improves. However, air pollution rebounds as the industrialization moves further but this rebound became weaker in recent years. With the association rule mining, we identified ranges of the six indicators for industrialization associated with good and poor air quality, respectively. Using industrialization indicators associated with poor air quality as references, we pointed out differentiated strategies for regions to coordinate the industrialization and air quality protection. • We analyzed industrialization progress & air quality from 2003 to 2017 in China. • Air pollution in China exhibits N-shape relations with industrialization level. • We mined association rules between industrialization indicators and air quality. • Summary of industrialization indicator ranges associated with good/poor air quality. • Differential strategy by region to coordinate development & air quality protection. [ABSTRACT FROM AUTHOR]

Understanding the complex impact of air pollution is crucial to assessing exposure risk and defining public health policies in China. However, the evidence and hence knowledge of how urban activity responds to air pollution are limited. In this paper, we propose to use geotagged check-in records on Weibo, a Tweeter-like platform, to systematically investigate the effect of air pollution on urban activity. Based on panel models, we found clear evidence that such effect exists and varies between pollutants, visitors and residents, and different activity types. Typically, SO 2 has the largest impact, followed by PM 2.5 , NO 2 , and PM 10 ; local citizens' activities are more susceptible than visitors; leisure-related activity has a sensitivity at least twofold higher than work-related activities. Additionally, we tested hypotheses about the heterogeneous effect. We confirmed the role of Income and air quality, showing that people who live in richer and more polluted cities are more likely to experience the effects of air pollution. Specifically, people who live in a more polluted city with 100 unit increments in AvgAQI show on average the same sensitivity as those who live in a less polluted city and earn about 20.3 thousand yuan more in average Income. This reveals new insights about environmental injustice in China. By presenting a portrait of the spatial heterogeneity, we argued that environmental injustice in terms of air pollution is not just about the difference in exposure risk measured based on population distribution, rather the measurement should also consider the disparity derived from urban activity. Secondly, new injustice may arise in underdeveloped areas where manufacture industry is transferred to but people barely take avoidance behavior. Finally, the map also reveals the general neglect of the detrimental effect of light air pollution, which we speculate is partly due to China's comparatively low standard in governmental regulations. We believe our finding contributes significantly to exposure risk assessment and environmental justice debates. Hence it highlights the necessity and urgency of public healthy polices that spread the health consequence of air pollution, especially in the underdeveloped region. • Assessing exposure risk in China. • How urban activity responds to air pollution. • Using geo-tagged check-ins on Weibo, a Twitter-like platform, to investigate effects of air pollution on urban activity. [ABSTRACT FROM AUTHOR]

Developing scientific and effective air quality early warning system has important scientific and practical significance for protecting people's physical health and promoting social harmony and stability. Therefore, the isolation forest (iForest) algorithm is employed to analyze the outlier data in original air quality index (AQI) data, and then an effective air quality early warning system is developed in this paper. The system is consists of four modules:data preprocessing module, optimization module, forecasting module and correction module, which combines time varying filtering based empirical mode decomposition (TVF-EMD), modified butterfly optimization algorithm (MBOA), outlier robust extreme learning machine (ORELM), and nonlinear correction strategy, and successfully achieves effective early warning for air quality. In order to verify the effectiveness of the developed air quality early warning system, five cities with different air pollution level in China are employed for empirical research. The results show that:1) Compared with empirical mode decomposition (EMD), TVF-EMD can be more effective to reduce the nonlinear and non-stationary features of the original data; 2) The developed MBOA-based error nonlinear correction strategy is superior to other error correction strategies, which can significantly improve the performance of the early warning system; 3) The performance of the developed system is superior to other compared methods, which can provide effective early warning for cities with different air pollution level. [ABSTRACT FROM AUTHOR]

Severe air quality hurts human capital and threatens innovative outcomes. Using unique data containing 12.8 million patent applications in China, this paper examines the causal effect of particulate matter with a diameter of 2.5 μm or less (PM 2.5) on patent innovation. We estimate a two-stage least square model with thermal inversion as an instrumental variable. Our findings show that a one μg/m3 increase in the annual average PM 2.5 concentration leads to a 1.3% decrease in the number of patents. Annual fluctuations in PM 2.5 concentration levels across cities caused the total number of patents to decrease by 1.1% during the 2006–2010 period. From 2011 to 2015, the improvement in air quality increased the number by about 2.0%. It demonstrates another innovation co-benefit of improving air quality due to the tightened regulation. [ABSTRACT FROM AUTHOR]

In the past decades, continuous efforts have been made at a national level to reduce Nitrogen Dioxide (NO2) emissions in the atmosphere over China. However, public concern and related research mostly deal with tropospheric NO2 columns rather than ground-level NO2 concentrations, but actually ground-level NO2 concentrations are more closely related to anthropogenic emissions, and directly affect human health. This paper presents one method to derive the ground-level NO2 concentrations using the total column of NO2 observed from the Ozone Monitoring Instrument (OMI) and the simulations from the Community Multi-scale Air Quality (CMAQ) model in China. One year's worth of data from 2014 was processed and the results compared with ground-based NO2 measurements from a network of China's National Environmental Monitoring Centre (CNEMC). The standard deviation between ground-level NO2 concentrations over China, the CMAQ simulated measurements and in-situ measurements by CNEMC for January was 21.79 μg/m3, which was improved to a standard deviation of 18.90 μg/m3 between our method and CNEMC data. Correlation coefficients between the CMAQ simulation and in-situ measurements were 0.75 for January and July, and they were improved to 0.80 and 0.78, respectively. Our results revealed that the method presented in this paper can be used to better measure ground-level NO2 concentrations over China. [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]

An operational multimodel forecasting system for air quality has been developed to provide air quality services for urban areas of China. The initial forecasting system included seven state-of-the-art computational models developed and executed in Europe and China (CHIMERE, IFS, EMEP MSC-W, WRF-Chem-MPIM, WRF-Chem-SMS, LOTOS-EUROS, and SILAMtest). Several other models joined the prediction system recently, but are not considered in the present analysis. In addition to the individual models, a simple multimodel ensemble was constructed by deriving statistical quantities such as the median and the mean of the predicted concentrations. The prediction system provides daily forecasts and observational data of surface ozone, nitrogen dioxides, and particulate matter for the 37 largest urban agglomerations in China (population higher than 3 million in 2010). These individual forecasts as well as the multimodel ensemble predictions for the next 72 h are displayed as hourly outputs on a publicly accessible web site (http://www.marcopolo-panda.eu , last access: 27 March 2019). In this paper, the performance of the prediction system (individual models and the multimodel ensemble) for the first operational year (April 2016 until June 2017) has been analyzed through statistical indicators using the surface observational data reported at Chinese national monitoring stations. This evaluation aims to investigate (a) the seasonal behavior, (b) the geographical distribution, and (c) diurnal variations of the ensemble and model skills. Statistical indicators show that the ensemble product usually provides the best performance compared to the individual model forecasts. The ensemble product is robust even if occasionally some individual model results are missing. Overall, and in spite of some discrepancies, the air quality forecasting system is well suited for the prediction of air pollution events and has the ability to provide warning alerts (binary prediction) of air pollution events if bias corrections are applied to improve the ozone predictions. [ABSTRACT FROM AUTHOR]

Decades of gross domestic product-focused economic development has not only made China the world's second largest economy, but has also led to massive environmental problems, including air pollution, which has been shown to lead to multiple adverse health outcomes. This review paper aims to contribute a better understanding regarding the interactive relationships between ambient air pollution and public health in China, based on a literature review from international journals, public data from government agencies and relevant reports from international organisations. Various studies have been conducted regarding air pollution and public health in China and their outcomes, which are valuable for further investigations and policymaking. However, current knowledge regarding air pollution and public health is still inadequate and has become a key barrier to supporting recommendations for further policymaking. This is despite the fact that China has launched relevant laws, regulations and standards and built about 106 national monitoring sites. Finally, two recommendations in particular are proposed: to enable public participation and improving the executive power of the government for reducing air pollution and to improve public health in China. [ABSTRACT FROM AUTHOR]

In January 2012, Beijing began releasing air quality report of PM 2.5. The move has been credited as a major breakthrough in China’s environmental governance as it is argued that online environmental activists in China were the major driving force behind it. Since then, the issue of air pollution has reemerged as one of the hottest issues and a series of new policies addressing air pollution have been adopted. Facing a relatively new phenomenon (wumai) and heightened public concerns, to what extent is the state still capable of guiding the pubic perception and opinions about the issue? By analyzing the discourse of air pollution in People’s Daily and comparing it with public opinion survey data, the paper finds that the state’s ability to guide public opinion is rather limited, indicating that opportunities do exist for civil society to play a bigger role in China’s environmental governance. [ABSTRACT FROM AUTHOR]

Severe air pollution associated with the rapid urbanization is a pressing issue in China. Moreover, the public awareness of environmental protection in China is awakening, which poses enormous pressure on governments to enforce environmental regulations. The study of environmental problems from the public perspective plays a crucial role in effective environmental governance. The Baidu search engine is the China's largest search engine. The search index of haze based on Baidu search engine reflects the public concern on air quality in China. The aim of this study is to uncover important relationships between public concern and air quality monitoring data based on the case study of haze pollution crisis in China. The results indicate that: (1) the year 2013 is the turning point of the public concern on air quality in China; (2) according to daily data analysis, the search index of haze has increased progressively with increased PM 2.5 concentration with a time lag of 0–4 days and the lag time has a declining tendency from 2013 to 2017; (3) according to annual data analysis, the public concern showed a weak correlation with air quality and they showed an opposite temporal trend. However, when the long-term annual trend was removed, the strong positive correlation emerges between the fluctuation parts of the search index of haze and monitoring data of air quality. This indicates the public is more sensitive to the short-term fluctuation of air quality. The results of this paper provide statistical evidence on the evolution of public concern on air quality from 2013 to 2017. This study will help policy makers to better understand the patterns of the public's perception of environmental problems and consequently improve the government's capability to deal with these challenges. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the impact on air pollution of China's 2017 policy banning waste imports. We exploit a difference-in-differences strategy combining the time variation and cross-prefecture variation in waste imports before the policy change. We find that prefectures that imported waste in the prepolicy period experienced greater air quality improvements after the policy shock. This effect is stronger in regions with loose environmental regulations and poor waste utilization capacity. Further investigation shows that reductions in both the burning of unrecyclable waste and the level of industrial production are channels through which the waste import ban reduced air pollution. [ABSTRACT FROM AUTHOR]

The PM 2.5 is the culprit of air pollution, and it leads to respiratory system disease when the fine particles are inhaled. Therefore, it is increasingly significant to develop an effective model for PM 2.5 forecasting and warnings that informs people to foresee the air quality. People can reduce outdoor activities and take preventive measures if they know the air quality is bad ahead of time. In addition, reliable forecasting results can remind the relevant departments to control and reduce pollutants discharge. According to our knowledge, the current hybrid forecasting techniques of PM 2.5 do not take the meteorological factors into consideration. Actually, meteorological factors affect the concentrations of air pollution, but it is unclear whether meteorological factors are helpful for improving the PM 2.5 forecasting results or not. This paper proposes a hybrid model called CEEMD-PSOGSA-SVR-GRNN, based on complementary ensemble empirical mode decomposition (CEEMD), particle swarm optimization and gravitational search algorithm (PSOGSA), support vector regression (SVR), generalized regression neural network (GRNN) and grey correlation analysis (GCA), for the daily PM 2.5 concentrations forecasting. The main steps of proposed model are described as follows: the original PM 2.5 data decomposition with CEEMD, optimal SVR selection with PSOGCA, meteorological factors selection with GCA, residual revision by GRNN and forecasting results analysis. Three cities (Chongqing, Harbin and Jinan) in China with different characteristics of climate, terrain and pollution sources are selected to verify the effectiveness of proposed model, and CEEMD-PSOGSA-SVR*, EEMD-PSOGSA-SVR, PSOGSA-SVR, CEEMD-PSO-SVR, CEEMD-GSA-SVR, CEEMD-GWO-SVR are considered to be compared models. The experimental results show that the hybrid CEEMD-PSOGSA-SVR-GRNN model outperforms other six compared models. Therefore, the proposed CEEMD-PSOGSA-SVR-GRNN model can be used to develop air quality forecasting and warnings. [ABSTRACT FROM AUTHOR]

As one of the most serious meteorological disasters in modern society, air pollution has received extensive attention from both citizens and decision-makers. With the complexity of pollution components and the uncertainty of prediction, it is both critical and challenging to construct an effective and practical early-warning system. In this paper, a novel hybrid air quality early-warning system for pollution contaminant monitoring and analysis was proposed. To improve the efficiency of the system, an advanced attribute selection method based on fuzzy evaluation and rough set theory was developed to select the main pollution contaminants for cities. Moreover, a hybrid model composed of the theory of “decomposition and ensemble”, an extreme learning machine and an advanced heuristic algorithm was developed for pollution contaminant prediction; it provides deterministic and interval forecasting for tackling the uncertainty of future air quality. Daily pollution contaminants of six major cities in China were selected as a dataset to evaluate the practicality and effectiveness of the developed air quality early-warning system. The superior experimental performance determined by the values of several error indexes illustrated that the proposed early-warning system was of great effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

This paper presents the first attempt to investigate the emission source control of the Middle Reaches of Yangtze River Urban Agglomerations (MRYRUA), one of the national urban agglomerations in China. An emission inventory of the MRYRUA was developed as inputs to the CAMx model based on county-level activity data obtained by full-coverage investigation and source-based spatial surrogates. A classification technology method for priority control of atmospheric emission sources was introduced and applied in the MRYRUA for the evaluation of the emission sources control on the region-scale and city-scale, respectively. The results demonstrated that the emission sources in the Hefei-centered urban agglomerations contributed the biggest on the mean PM 2.5 concentrations of the MRYRUA and should be taken the priority to control. The emission sources in the Ma'anshan city, Xiangtan city, Hefei city and Wuhan city were the bigger contributors on the mean PM 2.5 concentrations of the MRYRUA among the cities and should be taken the priority to control. In generally, emission sources in cities along the Yangtze River and the tributary should be given the special attention for the regional air quality target attainments. This study can give an understanding of Chinese emissions and provide a valuable preference to policy makers for finding effective mitigation measures and control strategies for reducing national and regional air pollution in China. [ABSTRACT FROM AUTHOR]

With the economic growth and increasing urbanization in the last three decades, the air quality over China has continuously degraded, which poses a great threat to human health. The concentration of fine particulate matter (PM2.5) directly affects the mortality of people living in the polluted areas where air quality is poor. The Beijing-Tianjin-Hebei (BTH) region, one of the well organized urban regions in northern China, has suffered with poor air quality and atmospheric pollution due to recent growth of the industrial sector and vehicle emissions. In the present study, we used the back propagation neural network model approach to estimate the spatial distribution of PM2.5 concentration in the BTH region for the period January 2014-December 2016, combining the satellite-derived aerosol optical depth (S-DAOD) and meteorological data. The results were validated using the ground PM2.5 data. The general method including all PM2.5 training data and 10-fold cross-method have been used for validation for PM2.5 estimation (R2 = 0.68, RMSE = 20.99 for general validation; R2 = 0.54, RMSE = 24.13 for cross-method validation). The study provides a new approach to monitoring the distribution of PM2.5 concentration. The results discussed in the present paper will be of great help to government agencies in developing and implementing environmental conservation policy. [ABSTRACT FROM AUTHOR]

Urban form may play an important role in improving air quality, but studies on already-high-density cities are relatively limited. In this paper, we developed an integrated land use-transport-emission model with TRANUS and MOBILE 6.2, selected an already-high-density city, Xiamen, as the study area, and explored the effects of compact growth on traffic emissions and exposure to air pollution. The results show that compact growth may significantly reduce regional traffic emissions even in an already-high-density city, but high-density developments may result in higher population-weighed exposures to air pollution. The finding implies that compact growth strategies should be carried out and the metric of population-weighed exposures should be considered in already-high-density cities. Integrated land use-transport-emission models may serve as a useful tool for urban planning agencies to decide appropriate locations for urban growths. [ABSTRACT FROM AUTHOR]

The beginning of October is the high occurrence period of straw burning in North China and Jianghuai region. Heavy pollution weather caused by straw burning often takes place in recent two years, and thus human factor increases the unpredictability of the air quality forecast. In the early autumn of 2015, a continuous fog-haze weather and heavy air pollution event occurred in North China and Jianghuai region due to the straw burning. In this paper, the effect of straw burning on air quality under the same adverse diffusive meteorological conditions has been analyzed by using the ground and upper air meteorological observation data and air quality monitoring data of Environmental Protection Agency In recent two years. The comparison results show that the basic conditions of heavy pollution are weak wind and the unfavorable diffusion situation. In contrast, relative humidity is the key factor, only 40% of relative humidity during the straw burning can reach heavy pollution level during no straw burning. [ABSTRACT FROM AUTHOR]

The hallmark of development in the Yangtze River Delta (YRD) of East China has been sprawling urbanization. However, air pollution is a significant problem in these urban areas. In this paper, we investigated and analyzed the air pollution index (API) in four cities (Shanghai, Nanjing, Hangzhou and Ningbo) in the YRD from 2001 to 2012. We attempted to empirically examine the relationship between meteorological factors and air quality in the urban areas of the YRD. According to the monitoring data, the API in Shanghai, Nanjing, Hangzhou slightly declined and that in Ningbo increased over the study period. We analyzed the inter-annual, seasonal, and monthly variations of API, from which we found that the air quality had different temporal changes in the four cities. It was indicated that air quality was poor in winter and spring and best in summer. Furthermore, different weather conditions affected air quality level. The wind direction was considered as an important and influential factor to air pollution, which has an impact on the accumulating or cleaning processes of pollutants. The air quality was influenced by the different wind directions that varied with seasons and cities. [ABSTRACT FROM AUTHOR]

The official air-quality statistic reported that Beijing had a 9.9% decline in the annual concentration of PM2.5 in 2016. While this statistic offered some relief for the inhabitants of the capital, we present several analyses based on Beijing's PM2.5 data of the past 4 years at 36 monitoring sites along with meteorological data of the past 7 years. The analyses reveal the air pollution situation in 2016 was not as rosy as the 9.9% decline would convey, and improvement if any was rather uncertain. The paper also provides an assessment on the city's PM2.5 situation in the past 4 years. [ABSTRACT FROM AUTHOR]

Urban building disposition plays an important role in determining local microclimate including air quality. Ancient cities normally have some special building dispositions to reduce the penetration of cold wind in winter, which, however, may impact adversely on air pollutant dilution today. This paper investigated the pedestrian-level air quality at a common building disposition in Chinese ancient cities, namely a U-type street canyon. On-site measurements were conducted comparatively at a U-type street canyon and a nearby open space in Xi'an China during January 2015. Three primary air pollutants (PM 10 , PM 2.5 and NO 2 ) as well as wind speed and direction, air temperature and relative humidity were measured continuously from 8:00 a.m. to 8:00 p.m. for a six-day period that covered both clean and hazy days. Pedestrian-level wind condition at the U-type street canyon is mostly independent of that above the canyon, where adverse dilution condition is clearly evident for pollutants. PM 2.5 /PM 10 ratio at the street canyon reached up to 0.9, which is nearly twice that at the nearby observatory. Overall, air quality index (AQI) in the street canyon is, on average, higher by 20% than that at the open space. These findings suggest that this ancient design should be discouraged. [ABSTRACT FROM AUTHOR]

To improve ambient air quality and return citizens blue sky and white clouds, national and local administrative departments all issue corresponding countermeasures to strictly prevent and control air pollution, reduce total pollutant emission, and Improve environmental management level. In this paper, taking Tianjin as an example, change situation of regional air pollution and Its prevention and control management situation were summarized. [ABSTRACT FROM AUTHOR]

Air pollution shares the attributes of significant spatial spillover effects and environmental public goods, leading to the territorial governance model that easily falls into a state of failure. Despite a growing number of studies on the local spatial spillover effect of air pollution, scant evidence currently exists on its global spatial association effect and a good subgroup governance model. Based on a panel data set of the daily prefecture-level city data on air quality measured by the air quality index (AQI) in "2 + 26" cities of China in 2015 and 2018, this study first builds an air pollution transport network (APTN), i.e., the cities as the nodes and the association relationships between the nodes as the edges. Furthermore, this paper reveals the spatial association effect and the temporal lagged attribute of the APTN using the Social network analysis (SNA) and the Generalized impulse response function (GIRF). The results are summarized as follows. (1) Every city has significant spatial association effects of air pollution with at least another city in the APTN, and northern APTN affects most to the air pollution of other cities, while southern APTN is obviously always affected by air pollution in other cities. (2) Transport strength peaks on the second day of an air pollution transport process, and the transport process lasts for 7–12 days. (3) The APTN is divided into four subgroups: Sycophants, Primary, Bidirectional, and Brokers, with Baoding, Zhengzhou, Heze, and Hengshui as the central cities of each group, respectively. Overall, our study provides a networked, modular, and early-warning governance model for policymakers. [Display omitted] • An air pollution transport network (APTN) is constructed using granger causality test method in "2 + 26" cities, China. • Spatially, there is a significant association effect between any two cities. • Temporally, air pollution transport shock strengths peaks on the second day and the transport process lasts for 7–12 days. • The APTN is divided into four subgroups with every group has a governing body, respectively. • A networked, modular and early-warning governance model for policymakers is provided. [ABSTRACT FROM AUTHOR]