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With the rapid development of the real estate industry in China, urban air quality is inevitably affected. By using data of Chinese cities located in the Yangtze River Delta from 2009 to 2018, this paper is aim at examining the casual impact of house prices on air quality. By considering the endogenous problems, in this study, first, the birth rate of the previous six years and the area of state-owned land supply per capita in the previous year are taken as instrumental variables of house price. Second, the two-stage least squares method is used to assess the causal impact of house price on air quality. The findings of this study show that there is no significant relationship between urban house prices and air quality, and these results are obtained without considering the endogenous problem. While, after considering the endogenous problem, urban house prices showed a significant negative effect on overall air quality, especially for low administrative level cities. The increase in house prices has hindered the improvement of air quality mainly through the blind-scale expansion of real estate development investments and the inhibitory effect of innovation. [ABSTRACT FROM AUTHOR]

The air quality is generally graded into five levels in a simple way or released by the Air Pollution Index (API) which does not take the fine particulate matter (PM2.5) emission into account. The fuzzy algorithm in this paper solves the mutability aroused by the grading approach by counting in the six items of pollutants pertaining to Short-lived climate forcers (SLCFs). Firstly, six items of air pollutants as well as driving factors of human activities are introduced. Secondly, based on the relevant observation data from the China Air Quality Online Monitoring Platform (CAQOMP), 18 experimental cities covering North China, East China, Central China, and South China are selected in this paper for air quality comparison and analysis in the application of the fuzzy algorithm. Thirdly, this paper looks into the air quality ranking of experimental 18 cities from a regional perspective. North China's air quality is not optimistic owing to being in a location near the coal producer and sand-sourced Outer Mongolia. Central China's air quality is more impacted by high-speed economic development and urbanization during the last couple of years. O3 emission is not well controlled across China. It turns out that the ranking by using the fuzzy algorithmic method is more scientific and holistic than API. [ABSTRACT FROM AUTHOR]

This paper uses the panel data of 245 cities in China from 2005 to 2020 to build an ambient air pollution index to measure ambient air quality. Taking China's national smart city pilot as a quasiexperiment, the impact of smart city construction on urban ambient air quality and its impact path is tested by using the multi-period double difference method. The research results show that the construction of smart cities has significantly improved the urban ambient air quality, and with the improvement of the level of smart city construction, the degree of improvement in urban ambient air quality shows a positive fluctuation trend. The mechanism test results show that the construction of smart cities improves urban ambient air quality by enhancing the level of technological innovation and marketization in the city. Heterogeneity testing shows that the ambient air quality improvement effects of smart city construction vary depending on the size of the city's population and the geographical location of the city. Furthermore, the moderating effect test shows that financial autonomy and industrial structure upgrading have a positive moderating effect on the improvement of ambient air quality in smart cities, while local government environmental regulation intensity has a reverse moderating effect. [ABSTRACT FROM AUTHOR]

Social lockdowns improved air quality during the COVID-19 pandemic. Governments had previously spent a lot of money addressing air pollution without success. This bibliometric study measured the influence of COVID-19 social lockdowns on air pollution, identified emerging issues, and discussed future perspectives. The researchers examined the contributions of countries, authors, and most productive journals to COVID-19 and air pollution research from January 1, 2020, to September 12, 2022, from the Web of Sciences Core Collection (WoS). The results showed that (a) publications on the COVID-19 pandemic and air pollution were 504 (research articles) with 7495 citations, (b) China ranked first in the number of publications (n = 151; 29.96% of the global output) and was the main country in international cooperation network, followed by India (n = 101; 20.04% of the total articles) and the USA (n = 41; 8.13% of the global output). Air pollution plagues China, India, and the USA, calling for many studies. After a high spike in 2020, research published in 2021 declined in 2022. The author's keywords have focused on "COVID-19," "air pollution," "lockdown," and "PM25." These keywords suggest that research in this area is focused on understanding the health impacts of air pollution, developing policies to address air pollution, and improving air quality monitoring. The COVID-19 social lockdown served as a specified procedure to reduce air pollution in these countries. However, this paper provides practical recommendations for future research and a model for environmental and health scientists to examine the likely impact of COVID-19 social lockdowns on urban air pollution. [ABSTRACT FROM AUTHOR]

Urbanization, industrialization, and regional economic integration have developed rapidly in China in recent years. Air pollution has attracted more and more attention. However, PM2.5 is the main particulate matter in air pollution. Therefore, how to predict PM2.5 accurately and effectively has become a concern of experts and scholars. For the problem, atmosphere PM2.5 concentration prediction algorithm is proposed based on time series and interactive multiple model in this paper. PM2.5 concentration is collected by using the monitor at different air quality levels. The time series models are established by historical PM2.5 concentration data, which were given by the autoregressive model (AR). In the paper, three PM2.5 time series models are established for three different air quality levels. Then, the three models are converted to state equation, respectively, by autoregressive integrated with Kalman filter (AR-Kalman) approaches. Besides, the proposed interactive multiple model (IMM) algorithm is, respectively, compared with autoregressive (AR) model algorithm and AR-Kalman prediction algorithm. It is turned out the proposed IMM algorithm is more accurate than the other two approaches for PM2.5 prediction, and it is effective. [ABSTRACT FROM AUTHOR]

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

This paper 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]

In different regions of China that can range from 5 to 100 km2, various production plants and facilities such as petrochemicals, pesticides, pharmaceuticals, paper mills, thermal power plants, hazardous waste disposal sites makes chemical industrial parks with Chinese characteristics. At the same time, these chemical industry parks in various places are in urgent need of advanced monitoring, prewarning, and tracing technologies, in order to strengthen monitoring of atmospheric emissions in chemical industrial parks, and timely prevention of the leakage of exhaust gas and explosion accidents in the production process of these enterprises. Most of the Chinese chemical industry parks still use traditional air monitoring technologies that requires setting up passive air monitoring collection points at the boundary of enterprises and industrial parks and use the "point" sensor and pollution factors collected by air stations. However, the operation of traditional air monitoring equipment is affected by the prevailing wind conditions, thus the traditional air monitoring equipment and technology is of limited use to conduct monitoring. One of the authors of this manuscript, Weimin Zhang with Jiangsu China‐U.S. Environmental Monitoring Co. Ltd. (hereinafter referred to as EMCUS), with 10 years of has been testing and observing air diffusion law in chemical industrial parks for 10 years and has achieved fruitful success. The best way to truly achieve tracing and prewarning about the air pollution is to conduct timely three‐dimensional dynamic collection of chemical factors and then perform qualitative and quantitative analysis. Based on this theory, EMCUS has abandoned the traditional "point" passive monitoring theories. After a comprehensive analysis and comparison of air quality monitoring technology and equipment around the world, especially several investigations of advanced Fourier transform infrared spectroscopy (FTIR) equipment and software in the United States, EMCUS initiated the three‐dimensional monitoring technology for exhaust gas emissions which fully combined with the current situations of chemical industry parks. During the EcoPartnership project, the stepwise vertical and radial air monitoring technology was fully promoted in Chinese chemical industry parks to generate key emission data. [ABSTRACT FROM AUTHOR]

A 6-year-long high-resolution Chinese air quality reanalysis (CAQRA) dataset is presented in this study obtained from the assimilation of surface observations from the China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and Nested Air Quality Prediction Modeling System (NAQPMS).This dataset contains surface fields of six conventional air pollutants in China (i.e. PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3) for the period 2013–2018 at high spatial (15km×15km) and temporal (1 h) resolutions. This paper aims to document this dataset by providing detailed descriptions of the assimilation system and the first validation results for the above reanalysis dataset. The 5-fold cross-validation (CV) method is adopted to demonstrate the quality of the reanalysis. The CV results show that the CAQRA yields an excellent performance in reproducing the magnitude and variability of surface air pollutants in China from 2013 to 2018 (CV R2=0.52 –0.81, CV root mean square error (RMSE) =0.54 mg/m3 for CO, and CV RMSE =16.4 –39.3 µg/m3 for the other pollutants on an hourly scale). Through comparison to the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) dataset produced by the European Centre for Medium-Range Weather Forecasts (ECWMF), we show that CAQRA attains a high accuracy in representing surface gaseous air pollutants in China due to the assimilation of surface observations. The fine horizontal resolution of CAQRA also makes it more suitable for air quality studies on a regional scale. The PM 2.5 reanalysis dataset is further validated against the independent datasets from the US Department of State Air Quality Monitoring Program over China, which exhibits a good agreement with the independent observations (R2=0.74 –0.86 and RMSE =16.8 –33.6 µg/m3 in different cities). Furthermore, through the comparison to satellite-estimated PM 2.5 concentrations, we show that the accuracy of the PM 2.5 reanalysis is higher than that of most satellite estimates. The CAQRA is the first high-resolution air quality reanalysis dataset in China that simultaneously provides the surface concentrations of six conventional air pollutants, which is of great value for many studies, such as health impact assessment of air pollution, investigation of air quality changes in China, model evaluation and satellite calibration, optimization of monitoring sites, and provision of training data for statistical or artificial intelligence (AI)-based forecasting. All datasets are freely available at 10.11922/sciencedb.00053 (Tang et al., 2020a), and a prototype product containing the monthly and annual means of the CAQRA dataset has also been released at 10.11922/sciencedb.00092 (Tang et al., 2020b) to facilitate the evaluation of the CAQRA dataset by potential users. [ABSTRACT FROM AUTHOR]

The randomness, non-stationarity and irregularity of air quality index series bring the difficulty of air quality index forecasting. To enhance forecast accuracy, a novel model combining grey accumulated generating technique and Holt-Winters method is developed for air quality index forecasting in this paper. The grey accumulated generating technique is utilized to handle non-stationarity of random and irregular data series and Holt-Winters method is employed to deal with the seasonal effects. To verify and validate the proposed model, two monthly air quality index series from January in 2014 to December in 2016 collected from Shijiazhuang and Handan in China are taken as the test cases. The experimental results show that the proposed model is remarkably superior to conventional Holt-Winters method for its higher forecast accuracy. [ABSTRACT FROM AUTHOR]

With the establishment of China's national air quality monitoring network, large amounts of monitoring data are available for different kinds of users. How to process and use this big data is a tough problem for users: most users have limited computing power, and new data are collected at every moment. Cloud computing may be an efficient and low-cost way to solve this problem. This paper investigates a problem of a complex system: the impact of PM2.5 on hospitalization for respiratory diseases. A change-point detection method based on grey relation analysis was used to solve this problem. Daily air pollution monitoring data and patient data were used in this study. Our results showed that (1) PM2.5 pollution showed a positive correlation on hospital admission for respiratory disease; (2) most patients went to hospital 2 days after PM2.5 pollution events; and (3) male, children, and old people were significantly affected by PM2.5 pollution. Our study is of great significance to help the government formulate suitable policies to reduce the damage caused by PM2.5 pollution and help hospitals allocate medical resources efficiently. [ABSTRACT FROM AUTHOR]

In September 2018, China's air quality monitoring protocol was amended from the standard conditions to actual conditions for particulate matter and to reference conditions for gaseous pollutants. Due to the amendment, the reported concentrations of the gaseous pollutants decreased by a constant rate of 8.4%, and the averages of PM2.5 (particulate matter that has an aerodynamic diameter of 2.5 microns or smaller) reported during September 2017 and August 2018 decreased by 7.9 ± 6.1% at 99% of the monitoring stations. Comparing the periods before and after the amendment, the 12-month PM2.5 concentrations at 17.2% of the stations actually increased despite appearing to decrease if the amendments were not considered. We reviewed 370 papers published in 2020 that utilized this air quality dataset, and 21% of these papers used the data before and after the amendment without explicitly stating whether or how conversions were conducted. It is urgent to widely broadcast the protocol amendment to ensure proper use of this extensively cited dataset. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization and industrialization, air pollution has become increasingly serious, and the pollution control situation is not optimistic. Climate change has become a major global challenge faced by mankind. To actively respond to climate change, China has proposed carbon peak and carbon neutral goals. However, atmospheric pollutants and meteorological factors that affect air quality are complex and changeable, and the complex relationship and correlation between them must be further clarified. This paper uses China's 2013-2018 high-resolution air pollution reanalysis open data set, as well as statistical methods of the Pearson Correlation Coefficient (PCC) to calculate and visualize the design and analysis of environmental monitoring big data, which is intuitive and it quickly demonstrated the correlation between pollutants and meteorological factors in the temporal and spatial sequence, and provided convenience for environmental management departments to use air quality routine monitoring data to enable dynamic decision-making, and promote global climate governance. The experimental results show that, apart from ozone, which is negatively correlated, the other pollutants are positively correlated; meteorological factors have a greater impact on pollutants, temperature and pollutants are negatively correlated, air pressure is positively correlated, and the correlation between humidity is insignificant. The wind speed has a significant negative correlation with the six pollutants, which has a greater impact on the diffusion of pollutants. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy, people's demand for a healthy living environment is increasing, and air quality has gradually been widely concerned by all sectors of society. Using the big data of air quality monitoring from 1998 to 2016, based on the exploratory spatio-temporal analysis method, this paper explored the spatio-temporal evolution of PM2.5 at the national scale, and drew the following conclusions; (T) PM2.5 heavy pollution is mainly in central and eastern China, north and south China, and the pollution degree is relatively light in northwest and northeast China. Meanwhile, PM2.5 concentration in heavily polluted areas increased significantly over time, while PM2 s concentration in low-polluted areas showed a long-term stable trend. (2) The number and area of cities with moderate and high PM2.5 pollution levels showed an inverted U-shaped curve from 1998 to 2016, and 2007 was the inflection point. (3) The spatial autocorrelation coefficient of PM2.5 is high over the years, and the spatial neighbor effect of PM2.5 is significant. The high-pollution clusters are mainly concentrated in the Beijing - Tianjin - Hebei region, the Yangtze River Delta and the Pearl River Delta, and the pollution concentration in these three regions has increased rapidly in recent years. It is necessary to focus on joint prevention and control. [ABSTRACT FROM AUTHOR]

Skyrise greenery, including green roofs and vertical gardens, has emerged as an indispensable tool for sustainable urban planning with multiple ecological and economic benefits. A bibliometric analysis was used to provide a systematic review of the functions associated with skyrise greenery in urban landscapes. Key research tools, including the "Bibliometrix" R package and "CiteSpace" 6.2 R4, highlight the depth and breadth of the literature covering skyrise greenery. In 2000–2022, a total of 1474 original journal articles were retrieved. Over this period, there was an exponential increase in the number of publications, reflecting both enhanced knowledge and increasing concerns regarding climate change, the urban heat island, and urbanization. Of the total, ~58% of the articles originated from China, followed by the USA, Italy, Australia, and Canada. The research themes, such as urban heat islands, carbon sequestration, hydrology, and air quality, have been identified as the frontier in this fields. Furthermore, researchers from developed countries contributed the most publications to this domain, while developing countries, such as China, play an increasing role in the design and performance evaluation of vertical greenery. Key benefits identified in vertical green systems (e.g., green roofs and walls) include thermal regulation, sustainable water management, air-quality improvement, noise reduction, and biodiversity enhancement. In addition, several potential future research prospectives are highlighted. This review provides a comprehensive insight into exploring the pivotal role of skyrise greenery in shaping sustainable, resilient urban futures, coupled with sustainable urban planning. [ABSTRACT FROM AUTHOR]

Field monitoring is critical in the examination of indoor air quality (IAQ) which offers an integrated assessment of human exposure. A good measurement protocol includes appropriate methodologies and compliance of quality control and quality assurance procedures. In China, there has been a huge economic growth in the past 20 years and living standard for Chinese citizens has been improving with a rapid development in urban cities and infrastructures. With these developments, there have been a growth of increasing pollution sources, and IAQ has been a major concern to the government and many Chinese citizens with widespread reports of the coherent health impacts. However, the indoor monitoring in China is full of technical challenges. In this paper, we review the difficulties in conducting IAQ assessment in China. Techniques and practical consideration have been proposed accordingly. [ABSTRACT FROM AUTHOR]

The absence of nationwide distribution data regarding heavy metal emissions into the atmosphere poses a significant constraint in environmental research and public health assessment. In response to the critical data deficiency, we have established a dataset covering Cr, Cd, As, and Pb emissions into the atmosphere (HMEAs, unit: ton) across 367 municipalities in China. Initially, we collected HMEAs data and covariates such as industrial emissions, vehicle emissions, meteorological variables, among other ten indicators. Following this, nine machine learning models, including Linear Regression (LR), Ridge, Bayesian Ridge (Bayesian), K-Neighbors Regressor (KNN), MLP Regressor (MLP), Random Forest Regressor (RF), LGBM Regressor (LGBM), Lasso, and ElasticNet, were assessed using coefficient of determination (R2), root-mean-square error (RMSE) and Mean Absolute Error (MAE) on the testing dataset. RF and LGBM models were chosen, due to their favorable predictive performance (R2: 0.58–0.84, lower RMSE/MAE), confirming their robustness in modelling. This dataset serves as a valuable resource for informing environmental policies, monitoring air quality, conducting environmental assessments, and facilitating academic research. [ABSTRACT FROM AUTHOR]

Monitoring air pollution at a city scale is essential for controlling urban air pollution in cities, especially megacities, in China. In this paper, data for 1 city center site and 5 paired (stands and non-forested) PM monitoring sites across urban-rural areas in Beijing were collected, and air PM and influential factors were analyzed. The results showed that the mean value of PM (February to December 2013) was lower in the stands (67.00 μg m) than in the non-forested sites (78.02 μg m). The diurnal variation was a bimodal double-dip type, and the average daily PM was lower in the vegetation areas than in the non-vegetation areas in both Changping Dingling (vegetation) and Changping town (non-vegetation) and showed day > night. In the typical pollution process, accompanied by an increase or decrease in PM and air quality index values, the higher the PM, the lower the wind speed. PM in the vegetation areas lagged behind PM in the non-vegetation areas on typical sunny days but not on typical rainy and windy days. On typical sunny days, the variation of PM in the vegetation areas was less than that in the non-vegetation areas. PM did not change significantly under rainy conditions and before a rain, but the variation was smaller after a rain under the typical rainfall process; under typical windy conditions, the variation was not significant. Therefore, in the typical pollution process and under typical weather conditions, the annual and diurnal variations of PM were smaller in the vegetation areas than in the non-vegetation areas. The above results confirm that the forest has a strong ability to purify air and absorb PM particulate matter and that the air quality in vegetation areas is better than the air quality in non-forested areas. [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]

Transparent environmental information is fundamental for the government to better regulate pollution and for the public to be aware of pollution. However, how the improvement of environmental information transparency affects firms' environmental behavior has been underexplored. This paper fills this gap by exploring the impact of China's air quality monitoring and disclosure program on the green innovation of firms; via this program, real-time air quality monitoring stations have been constructed nationwide, and the information is promptly released to the public. The results of a difference-in-differences estimation show that the information program has significantly increased the green innovation of firms, especially the number of green invention patent applications. Moreover, this increase is realized via the regulatory effect of both the local government and public attention. Additionally, the green innovation effect performs stronger in state-owned enterprises (SOEs) and firms in heavily polluting industries. This paper contributes to the related literature from the perspective of central government-led real-time transparent environmental information disclosure. The findings of this study highlight policy implications from the perspective of the importance of the application of information technology in environmental regulation, as well as the vital role of information-based instruments in exerting a public regulatory effect on environmental governance. • Effect of pollution information disclosure on firm's green innovation is tested. • DID is used exploiting China's Air Quality Monitoring-and-Disclosure Program. • The pollution information disclosure significantly boosts firm' s green innovation. • The mechanism is the regulatory effect from local governments and public attention. • Porter's Hypothesis is supported from the new view of information disclosure. [ABSTRACT FROM AUTHOR]

To improve the performance of the calibration model for the air quality monitoring, a low-cost multi-parameter air quality monitoring system (LCS) based on different machine learning algorithms is proposed. The LCS can measure particulate matter (PM 2.5 and PM 10) and gas pollutants (SO 2 , NO 2 , CO and O 3) simultaneously. The multi-input multi-output (MIMO) prediction model is developed based on the original signals of the sensors, ambient temperature (T) and relative humidity (RH), and the measurements of the reference instrumentations. The performance of the different algorithms (RF, MLR, KNN, BP, GA–BP) with parameters such as determination coefficient R2 , root mean square error (RMSE), and mean absolute error (MAE) are compared and discussed. Using these methods, the R2 of the algorithms (RF, MLR, KNN, BP, GA–BP) for the PM is in the range 0.68–0.99; the RMSE values of PM 2.5 and PM 10 are within 2.36–18.68 and 4.55–45.05 µ g m -3 , respectively; the MAE values of PM 2.5 and PM 10 are within 1.44–12.80 and 3.21–23.20 µ g m -3 , respectively. The R2 of the algorithms (RF, MLR, KNN, BP, GA–BP) for the gas pollutants (O 3 , CO and NO 2) is within 0.70–0.99; the RMSE values for these pollutants are 4.05–17.79 µ g m -3 , 0.02–0.18 mg m -3 , 2.88–14.54 µ g m -3 , respectively; the MAE values for these pollutants are 2.76–13.46 µ g m -3 , 0.02–0.19 mg m -3 , 1.84–11.08 µ g m -3 , respectively. The R2 of the algorithms (RF, KNN, BP, GA–BP, except for MLR) for SO 2 is within 0.27–0.97, the RMSE value is in the range 0.64–5.37 µ g m -3 , and the MAE value is in the range 0.39–4.24 µ g m -3. These measurements are consistent with the national environmental protection standard requirement of China, and the LCS based on the machine learning algorithms can be used to predict the concentrations of PM and gas pollution. [ABSTRACT FROM AUTHOR]

In order to improve air quality, China has implemented a series of the most stringent control measures ever in recent years. Quantitatively analyzing the contribution of emissions to the trend change in air pollutants is an essential scientific basis for verifying the effectiveness of air pollution control. We based our study on the air quality online monitoring data and meteorological element data of 11 cities in the Fenwei Plain from 2018 to 2022. We quantitatively investigated the changing patterns of NO2, O3, PM10, and PM2.5 and their influencing factors in the major cities of the Fenwei Plain by using the KZ filtering and MLR modeling analysis methods. The results show the following: (1) The long-term fractions of NO2, PM10, and PM2.5 in the Fenwei Plain decreased by 10.5, 33.1, and 17.1 μg·m−3, with decreases of 25.8%, 29%, and 28.8%, respectively, from 2018 to 2022. The long-term fractions of O3 showed the characteristics of decreasing and then increasing, with 2020 as the dividing line. (2) The short-term components of NO2, PM10, and PM2.5 contributed the most to the total variance, with the proportion of short-term components ranging from 34.7% to 69.8%, 53% to 73%, and 43% to 58%, respectively. The seasonal components of O3 contributed the most to the total variance, with the proportion of short-term components ranging from 54% to 70.7%. (3) Most cities in the Fenwei Plain had unfavorable meteorological conditions with regard to NO2, PM10, and PM2.5 in 2018–2020 and favorable meteorological conditions in terms of NO2, PM10, and PM2.5 in 2020–2022. O3 showed different characteristics from the other three pollutants. Most cities in the Fenwei Plain had meteorological conditions in 2018–2019 that were unfavorable for improving O3 levels. In 2019–2021, meteorological conditions were favorable for improving O3 levels, while in 2021–2022, meteorological conditions were unfavorable for improving O3 levels. [ABSTRACT FROM AUTHOR]

Risk-related information regarding air pollution can help people understand the risk involved and take preventive measures to reduce health loss. However, the health benefits through these protective behaviors and the health threat of information inequality have not been systematically measured. This article reports the health gains and losses caused by the interaction of "air pollution—air pollution information—human", and studies the heterogeneity and impact of this interaction. Based on field investigations and transfer learning algorism, this study compiled the first nationwide city-level risk-related information (ERI) response parameter set in China. Then, we developed a Information-Behavioral Equivalent PM2.5 Exposure Model (I-BEPEM) model to project the health benefits caused by the impact of environmental risk-related information on residents' protective behaviors under different scenarios. The protective behavior led by air pollution risk information reduces 5.7% PM2.5-related premature deaths per year. With a 1% increase in regional ERI reception, PM2.5-related premature mortality decreases by 0.1% on average; If the level of information perception and behavioral protection in all cities is the same as that in Beijing, PM2.5-related premature deaths will decrease by 6.9% annually in China. Further, changing the air quality standard issued by China to the American standard can reduce the overall PM2.5-related premature deaths by 9.9%. Meanwhile, compared with men, other age groups and rural residents, women, older persons, and urban residents are more likely to conceive risk information and adopt protective behaviors to reduce the risk of premature death from air pollution. Air pollution risk information can significantly reduce people's health loss. Changing the real-time air quality monitoring information indicator standard to a more stringent level can quickly and effectively enhance this effect. However, the uneven distribution of this information in regions and populations has resulted in the inequality of health gains and losses. [ABSTRACT FROM AUTHOR]

With rapid urbanization and industrialization, the number of light-duty gasoline vehicles (LDGVs) in China has continued to grow rapidly, leading to a significant increase in traffic pollution. Therefore, it is essential to accurately calculate the emission of LDGVs for air quality monitoring and management. Fortunately, Motor Vehicle Emission Simulator (MOVES) is a sophisticated model for estimating mobile source emissions with good prediction accuracy. However, the parameters of MOVES are based on the field tests in the US, which is worth exploring whether MOVES can be applied to other countries. Hence, in this paper, we used the portable emission measurement system (PEMS) to conduct real driving emission (RDE) tests of LDGVs, aiming to explore the possibility of the MOVES application in China. Based on the field tests, we modified basic parameters in the MOVES model, but unsatisfactory prediction performance was obtained. Existing research on improving MOVES performance mainly involved new binning of operating modes, but these methods had limited improvements. Though studies have also used machine learning methods for predicting LDGV emissions, they lacked comparison and integration with the MOVES model. To further improve the prediction accuracy, we proposed a novel road vehicle emission model that integrated the machine learning method and the MOVES to predict the road-level emission rates of NO and CO 2 emissions of LDGVs. In addition, we employed the Boruta algorithm to capture the key influencing factors and promote prediction performance. The enhanced model outperformed MOVES and achieved higher R2 values. On average, the improvement for CO 2 was 0.132, and for NO, it was 0.261. This work will provide references for MOVES improvements in practical scenarios and better predict pollutant emissions for LDGVs using limited resources of field tests in cities outside the US. • Integrating engine characteristics with the MOVES model can improve model performance. • Integrating machine learning with MOVES performs well in prediction of vehicle CO 2 and NO emission. • The proposed emission model provides a possibility for calculation rather than costly an experiment in countries outside the USA. [ABSTRACT FROM AUTHOR]

Environmental regulations may lead to changes in individual behaviors. This paper investigates the effect of environmental regulations on population migration. Using the new ambient air quality standard (NAAQS) as an event study, the difference-in-differences (DID) strategy is constructed for estimation. We find that, on average, over 18.76 thousand in the net inflow of the permanent population decreases after implementing the NAAQS. The reduction primarily occurs in cities that implement the new standard in the first stage. The mechanism analysis shows the NAAQS both increases the population inflow by improving air quality and reduces it through polluting industry transfer and industrial upgrading. The heterogeneous analyses show that the NAAQS has significantly negative effects on population migration in cities that monitor air quality more strictly, cities located southeast of the Hu Huanyong Line, and cities with higher economic development levels. Conversely, the NAAQS has no significant impact on cities in the northwest of the Hu Huanyong Line and cities with low economic development. [ABSTRACT FROM AUTHOR]

The Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) Aerosol Optical Thickness (AOT) dataset is a consistent and comprehensive dataset combining observations from various satellite instruments and other sources with a numerical model, supporting climate studies, atmospheric modeling, air quality monitoring, and environmental research. Due to the uneven and sparse distribution of the Aerosol Robotic Network (AERONET) in China, the validation for the MERRA-2 AOT dataset over China is inadequate. The construction of the National Civil Space Infrastructure Satellite Aerosol Product Validation Network (SIAVNET) is helpful to compensate for MERRA-2 AOT dataset validation over China. The validation results show that the accuracy of the MERRA-2 AOT goes down along with the aerosol loading in the atmosphere increase. In general, when the AOT is less than 1.0, the slope can reach 0.712 with R2 = 0.584. The percentage of data pairs that fall within the GCOS minimum requirement is less than 60%. Research also shows that MERRA-2 has a lower simulation quality of AOT at high altitudes than at low altitudes in China. Additionally, MERRA-2's AOT simulation quality varies by season. Simulated quality is worst in spring, improving in subsequent seasons. During the winter season, simulations are of the highest quality. [ABSTRACT FROM AUTHOR]

Air pollution is a hotspot of wide concern in Chinese cities. With the worsening of air pollution, urban agglomerations face an increasingly complex environment for air quality monitoring, hindering sustainable and high-quality development in China. More effective methods for predicting air quality are urgently needed. In this study, we employed seven single models and ensemble learning algorithms and constructed a hybrid learning algorithm, the LSTM-SVR model, totaling eight machine learning algorithms, to predict the Air Quality Index in six major urban agglomerations in China. We comprehensively compared the predictive performance of the eight algorithmic models in different urban agglomerations. The results reveal that, in areas with higher levels of air pollution, the situation for model prediction is more complicated, leading to a decline in predictive accuracy. The constructed hybrid model LSTM-SVR demonstrated the best predictive performance, followed by the ensemble model RF, both of which effectively enhanced the predictive accuracy in heavily polluted areas. Overall, the predictive performance of the hybrid and ensemble models is superior to that of the single-model prediction methods. This study provides AI technological support for air quality prediction in various regions and offers a more comprehensive discussion of the performance differences between different types of algorithms, contributing to the practical application of air pollution control. [ABSTRACT FROM AUTHOR]

With the insidiously growing impact of urban development on the environment, the issue of air quality has attracted extensive attention nationally and globally. It is of great significance to study the influence of urbanization on air quality for the rational development of cities. MODIS-MAIAC (Moderate Resolution Imaging Spectroradiometer-Multi-Angle Implementation of Atmospheric Correction) Aerosol optical depth (AOD) product, DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) and NPP/VIIRS (Suomi National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite) night-light were used to explore the spatiotemporal variation and correlation between AOD and urbanization development before and after the promulgation of environmental governance policies in Jinan City from 2009 to 2018. Results show that (1) the spatial distribution of AOD in Jinan had the characteristics of high in the north and low in the south, high in the west and low in the east, and low in some parts of the central region; there was a significant seasonal variation in time, with the highest AOD in summer and the lowest in winter. During 2009–2013, the annual average variation of AOD increased by 20.6%, while during 2014–2018, it decreased by 35.3%; (2) The distribution of night-light in Jinan City has progressively expanded, mirroring the city's ongoing development. The spatial distribution of aerosols in urban areas was relatively low compared to the surrounding areas of the city. (3) From 2009 to 2013, there existed a significant positive correlation between the spatial and temporal distribution of AOD and night-light. However, from 2014 to 2018, with the implementation of environmental governance policies, this relationship shifted to a significant negative correlation between the spatial and temporal distribution of AOD and night-light. Through an analysis of the correlation between urban development and aerosol depth in Jinan City over the past decade, it can be concluded that urban development does not inevitably result in elevated AOD levels. Notably, the Jinan government has achieved remarkable results in controlling the atmospheric environment, as evidenced by recent years' improvements. [ABSTRACT FROM AUTHOR]

The resource allocation of air pollution treatment in China is a complex problem, since many alternatives are available and many criteria influence mutually. A number of stakeholders participate in this issue holding different opinions because of the benefits they value. So a method is needed, based on the analytic network process (ANP) and large-group decision-making (LGDM), to rank the alternatives considering interdependent criteria and stakeholders’ opinions. In this method, the criteria related to air pollution treatment are examined by experts. Then, the network structure of the problem is constructed based on the relationships between the criteria. Further, every participant in each group provide comparison matrices by judging the importance between criteria according to dominance, regarding a certain criteria (or goal), and the geometric average comparison matrix of each group is obtained. The decision weight of each group is derived by combining the subjective weight and the objective weight, in which the subjective weight is provided by organizers, while the objective weight is determined by considering the consensus levels of groups. The final comparison matrices are obtained by the geometric average of comparison matrices and the decision weights. Next, the resource allocation is made according to the priorities of the alternatives using the super decision software. Finally, an example is given to illustrate the use of the proposed method. [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]

Comprehensive analyses of urban traffic carbon emissions are critical in achieving low-carbon transportation. This paper started from the architecture design of a carbon emission mobile monitoring system using multiple sets of equipment and collected the corresponding data about traffic flow, meteorological conditions, vehicular carbon emissions and driving characteristics on typical roads in Shanghai and Wuxi, Jiangsu province. Based on these data, the emission model MOVES was calibrated and used with various sensitivity and correlation evaluation indices to analyze the traffic carbon emissions at microscopic, mesoscopic and macroscopic levels, respectively. The major factors that influence urban traffic carbon emissions were investigated, so that emission factors of CO, CO 2 and HC were calculated by taking representative passenger cars as a case study. As a result, the urban traffic carbon emissions were assessed quantitatively, and the total amounts of CO, CO 2 and HC emission from passenger cars in Shanghai were estimated as 76.95 kt, 8271.91 kt, and 2.13 kt, respectively. Arterial roads were found as the primary line source, accounting for 50.49% carbon emissions. In additional to the overall major factors identified, the mobile monitoring system and carbon emission quantification method proposed in this study are of rather guiding significance for the further urban low-carbon transportation development. [ABSTRACT FROM AUTHOR]

At present, the prevalence of fog and haze seriously affect indoor air quality. More and more attention has been paid to indoor particulate pollution from the society. This paper studies the correlation and influence factors of indoor and outdoor particle concentration of cold area in the summer, which four kinds of building types(office, classroom, rural residential and urban residential) that a total of 110 households in Daqing were measured and analyzed by applying the particle on-line monitors. The results shows that the overall air quality in Daqing is better in summer. There is a linear relationship between the indoor and outdoor particles concentration, and the infiltration factor is applied to represent the degree of correlation. For office and residential buildings, the infiltration factor is 0.7214 and 0.7499. As for the classroom and rural housing, the infiltration factor is 0.9217 and 0.9019 due to the higher air exchange rate. In summer, there are about 80% of the indoor particles coming from outside with natural ventilation buildings. Moreover, there is a significant positive correlation among indoor particles concentration and outdoor particles concentration, temperature and humidity (p<0.05), but different building types have obvious difference. Temperature and humidity as an important factor affecting the concentration of indoor particulate matter, the influence of indoor and outdoor temperature is greater for offices (O1) and classrooms with the glass exterior wall, and the relative humidity is the main factor for the rest of the building with concrete wall structure. [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]

Since 2013, frequent haze pollution events in China have been attracting public attention, generating a demand to identify the haze areas using satellite observations. Many studies of haze recognition algorithms are based on observations from space-borne imagers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Advanced Himawari Imager (AHI). Since the haze pixels are frequently misidentified as clouds in the official cloud detection products, these algorithms mainly focus on recovering them from clouds. There are just a few studies that provide a more precise distinction between haze and clear pixels. The Medium Resolution Imaging Spectrometer II (MERSI-II), the imager aboard the FY-3D satellite, has similar bands to those of MODIS, hence, it appears to have equivalent application potential. This study proposes a novel MERSI haze mask (MHAM) algorithm to directly categorize haze pixels in addition to cloudy and clear ones. This algorithm is based on the fact that cloudy and clear pixels exhibit opposing visible channel reflectance and infrared channel brightness temperature characteristics, and clear pixels are relative brighter, and as well as this, there is a positive difference between their apparent reflectance values, at 0.865 μm and 1.64 μm, respectively, over bright surfaces. Compared with the Aqua/MODIS and MERSI-II official cloud detection products, these two datasets treat the dense aerosol loadings as certain clouds, possible clouds and possible clear pixels, and they treat distinguished light or moderate haze as possible clouds, possible clear pixels and certainly clear pixels, while the novel algorithm is capable of demonstrating the haze region's boundary in a manner that is more substantially consistent with the true color image. Using the PM2.5 (particle matter with a diameter that is less than 2.5 μm) data monitored by the national air quality monitoring stations as the test source, the results indicated that when the ground-based PM2.5 ≥ 35 μg/cm3 is considered to be haze days, the samples with the recognition rate that is higher than 85% accounted for 72.22% of the total samples. When PM2.5 ≥ 50 μg/cm3 is considered as haze days, 83.33% of the samples had an identification rate that was higher than 85%. A cross-comparison with similar research methods showed that the method proposed in this study had better sensitivity to bright surface clear and haze areas. This study will provide a haze mask for subsequent quantitative inversion of aerosol characteristics, and it will further exert the application benefits of MERSI-II instrument aboard on FY3D satellite. [ABSTRACT FROM AUTHOR]

Relevant studies have demonstrated that urban green spaces composed of various types of plants are able to alleviate the morbidity and mortality of respiratory diseases, by reducing air pollution levels. In order to explore the relationship between the spatial pattern of urban green spaces and air pollutant concentrations, this study takes 37 garden cities with subtropical monsoon climate in China as the research object and selects the urban air quality monitoring data and land use type data in 2019 to analyze the relationship between the spatial pattern and the air pollutant concentration through the landscape metrics model and spatial regression model. Moreover, the threshold effect of the impact of green space on air pollutant concentrations is estimated, as well. The results showed that the spatial pattern of urban green space was significantly correlated with the concentrations of PM2.5 (PM with aerodynamic diameters of 2.5 mmor less), NO2 (Nitrogen Dioxide), and SO2 (Sulfur dioxide) pollutants in the air, while the concentrations of PM10 (PM with aerodynamic diameters of 10 mmor less) pollutants were not significantly affected by the green space pattern. Among them, the patch shape index (LSI), patch density (PD) and patch proportion in landscape area (PLAND) of forest land can affect the concentration of PM2.5, NO2, and SO2, respectively. The PLAND, PD, and LSI of grassland and farmland can also have an additional impact on the concentration of SO2 pollutants. The study also found that there was a significant threshold effect within the impact mechanism of urban green space landscape pattern indicators (LSI, PD, PLAND) on the concentrations of PM2.5, NO2, and SO2 air pollutants. The results of this study not only clarified the impact mechanism of the spatial pattern of urban green space on air pollutant concentrations but also provided quantitative reference and scientific basis for the optimization and updating of urban green space to promote public health. [ABSTRACT FROM AUTHOR]

Dense air quality monitoring network (AQMN) is one of main ways to surveil industrial air pollution. This paper is concerned with the design of a dense AQMN for H2S for a chemical industrial park in Shanghai, China. An indicator (Surveillance Efficiency, SE) for the long-term performance of AQMN was constructed by averaging pollution detection efficiency (rd) and source identification efficiency (rb). A ranking method was developed by combing Gaussian puff model and Source area analysis for improving calculation efficiency. Candidate combinations with highest score were given priority in the selection of next site. Two existing monitors were suggested to relocate to the west and southwest of this park. SE of optimized AQMN increased quickly with monitor number, and then the growth trend started to flatten when the number reached about 60. The highest SE occurred when the number reached 110. Optimal schemes of AQMNs were suggested which can achieve about 98% of the highest SE, while using only about 60 monitors. Finally, the reason why the highest SE is less than 1 and the variation characteristics of rd and rb were discussed. Overall, the proposed method is an effective tool for designing AQMN with optimal SE in industrial parks. [ABSTRACT FROM AUTHOR]

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Numerous studies and monitoring data indicate that fine particle ( PM 2.5 ) pollution in China is still comparatively severe. Given the sparse and uneven distribution of air quality monitoring base stations established in China and the limitation of geographical conditions, inversion of aerosol optical depth by satellite remote sensing can achieve low-cost air quality monitoring in global areas. In this study, we use the machine learning algorithm XGBoost to build a prediction model to achieve nationwide average PM 2.5 concentration prediction. Meanwhile, we used aerosol data from Moderate Resolution Imaging Spectroradiometer (MODIS) in a specific band, combined with a land use regression (LUR) model as predictors of surface PM 2.5 concentrations in China, for the period Dec. 2019-Nov. 2021. In order to provide more accurate PM 2.5 concentration prediction, the correspondence between PM 2.5 and aerosol optical depth (AOD) under different seasons was studied. The coefficients of determination (R2) for different seasons are 0.86 (spring), 0.80 (summer), 0.90 (autumn), and 0.88 (winter), indicating that the fit is best for autumn and worse for summer. The study shows the potential usefulness of using the LUR model with the XGBoost algorithm for predictive assessment of PM 2.5 spatial distribution. [ABSTRACT FROM AUTHOR]

Ozone (O 3) pollution poses health risks and premature mortality, and gas stations are one of the largest sources of urban volatile organic compounds (VOCs, the main precursor to O 3). This paper investigates whether the government's call for night refueling, which can be regarded as a green nudge, can guide changes in consumer behavior and consequently improve environmental quality. Using a difference-in-differences (DID) estimation and weekly monitoring site air quality panel data, we analyze the effect of the Night Refueling Preferential Policy on O 3 concentrations. We find that the policy can reduce O 3 concentrations by 10% by encouraging consumers to refuel at night. The reduction in O 3 has brought great benefits to human health, leading to a 4–5‰ reduction in non-accidental mortality and a 6–8‰ reduction in cardiovascular mortality in Jiangsu province. The economic benefits of this policy would be approximately 62–189 billion Chinese Yuan (CNY) if it were implemented nationwide. The findings of this study suggest that the government can influence consumer behavior to promote environmental quality. [Display omitted] • The government's call for night refueling can reduce O3 concentration by 9% by drawing consumers to refuel at night. • The Night Refueling Preferential Policy can reduce non-accidental and cardiovascular death rates by 3–4‰ and 5–7‰. • The Night Refueling Preferential Policy can bring 57–174 billion CNY in economic benefits if it is implemented nationwide. [ABSTRACT FROM AUTHOR]

Due to the advantage of geostationary satellites, Himawari-8/AHI can provide near-real-time air quality monitoring over China with a high temporal resolution. Satellite-based aerosol optical depth (AOD) retrieval over land is a challenge because of the large surface contribution to the top of atmosphere (TOA) signal and the uncertainty of aerosol modes. Here, by combining satellite TOA reflectance, sun-sensor geometries, meteorological factors and vegetation information, we propose a data-driven AOD detection algorithm based on a deep neural network (DNN) model for Himawari-8/AHI. It is trained by sample data of 2018 and 2019 and is applied to derive hourly AODs over China in 2020. By comparison with ground-based AERONET measurements, R2 for DNN-estimated AOD is up to 0.8702, which is much higher than that for the AHI AOD product with R2 = 0.4869. The hourly AOD results indicate that the DNN model has a good potential in improving the performance of AOD retrieval in the early morning and in the late afternoon, and the spatial distribution is reliable for capturing the variation of aerosol pollution on the regional scale. By analyzing different DNN modeling strategies, it is found that seasonal modeling can hardly increase the accuracy of AOD retrieval to a certain extent, and R2 increases from 0.7394 to 0.8168 when meteorological features, especially air pressure, are involved in the model training. [ABSTRACT FROM AUTHOR]

Due to the COVID-19 pandemic outbreak, the home quarantine policy was implemented to control the spread of the pandemic, which may have a positive impact on the improvement of air quality in China. In this study, Google Earth Engine (GEE) cloud computing platform was used to obtain CO, NO2, SO2 and aerosol optical depth (AOD) data from December 2018-March 2019, December 2019-March 2020, and December 2020-March 2021 in Shandong Province. These data were used to study the spatial and temporal distribution of air quality changes in Shandong Province before and after the pandemic and to analyze the reasons for the changes. The results show that: (1) Compared with the same period, CO and NO2 showed a decreasing trend from December 2019 to March 2020, with an average total change of 4082.36 mol/m2 and 167.25 mol/m2, and an average total change rate of 4.80% and 38.11%, respectively. SO2 did not have a significant decrease. This is inextricably linked to the reduction of human travel production activities with the implementation of the home quarantine policy. (2) The spatial and temporal variation of AOD was similar to that of pollutants, but showed a significant increase in January 2020, with an average total amount increase of 1.69 × 107 up about 2.54% from December 2019 to March 2020. This is attributed to urban heating and the reduction of pollutants such as NOx. (3) Pollutants and AOD were significantly correlated with meteorological data (e.g., average temperature, average humidity, average wind speed, average precipitation, etc.). This study provides data support for atmospheric protection and air quality monitoring in Shandong Province, as well as theoretical basis and technical guidance for policy formulation and urban planning. [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]

Coal-to-gas/electricity conversion (hereafter referred to as CTGC/CTEC) as the core project of a clean heating campaign has been widely adopted to replace and reduce the combustion of residential coal in Northern China since 2017. In this study, simulations based on the WRF-Chem model were carried out to quantitatively assess the impacts of the CTGC/CTEC project on air quality in the Beijing–Tianjin–Hebei (BTH) region. It was found that the CTGC/CTEC projects exert a remarkable effect on improving the air quality in the BTH region, especially in the plain area. The maximum decrease in the concentrations of PM2.5 and PM10 averaged during January can reach 30 and 40 μg/m3, respectively. In addition, the spillover effects due to CTGC/CTEC projects are rather small; that is, the local reduced emissions tend to provide more benefit to the local air quality but less for its surrounding regions. It is also noteworthy that the effects due to meteorological condition changes are comparable with, or even larger, than those due to CTGC/CTEC projects, which are not spatially uniform for the BTH region among various cities. Overall, these results not only demonstrate the effectiveness of CTGC/CTEC projects on air-quality improvement in the BTH region, but also indicate the importance of meteorological conditions in modulating the local air quality. To sustain better air quality in the future, residential coal replacement, all over China, can be further promoted. In addition, continued policy refinement can be essential for the nationwide implementation of clean heating projects. [ABSTRACT FROM AUTHOR]

In recent years, haze pollution is frequent, which seriously affects daily life and production process. The main factors to measure the degree of smoke pollution are the concentrations of PM2.5 and PM10. Therefore, it is of great significance to study the prediction of PM2.5/PM10 concentration. Since PM2.5 and PM10 concentration data are time series, their time characteristics should be considered in their prediction. However, the traditional neural network is limited by its own structure and has some weakness in processing time related data. Recurrent neural network is a kind of network specially used for sequence data modeling, that is, the current output of the sequence is correlated with the historical output. In this paper, a haze prediction model is established based on a deep recurrent neural network. We obtained air pollution data in Chengdu from the China Air Quality Online Monitoring and Analysis Platform, and conducted experiments based on these data. The results show that the new method can predict smog more effectively and accurately, and can be used for social and economic purposes. [ABSTRACT FROM AUTHOR]

We propose a dust‐event detection and tracking procedure based on air quality data from the ground monitoring network by detecting temporal and spatial change‐points in PM10 concentration. It supplements the existing remote sensing based approach with high temporal resolution and better weather adaptivity. Applications of the procedure on the labeled data showed its having high discriminating power for dust events, pollution events, and clean periods. Our study finds changing correlation patterns between PM10 and other air pollutants at the start of the dust events, which are utilized to enhance the discriminating power of the dust‐event detection procedure. The detection and tracking procedure allows the construction of transport networks of the dust‐events as well as the identification of the source regions and the transportation pattern, and assess the intensity and severity of the dust‐events in North China. Our analysis find the dust‐events contributed to 23.3%–34.6% for PM10 and 18.2–33.2% for PM2.5 in the source regions and 2.0%–7.3% and 0.8%–4.0%, respectively, in the down‐stream provinces in the spring season from 2015 to 2020. Plain Language Summary: Dust storm can be tracked via the air quality monitoring network via a statistical semi‐supervised learning algorithm. Key Points: Develop a dust event detection procedure based on air quality data with finer spatial‐temporal resolution and better weather adaptationConstruct dust transport networks and find altering dependence between PM10 and CO, SO2 and NO2 at the start of dust eventsDust‐events contributed to 23.3%–34.6% for PM10 and 18.2–33.2% for PM2.5 in the source regions and 2.0%–7.3% and 0.8%–4.0%, respectively, in the down‐stream provinces in spring in North China [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM 2.5) has become a major pressing challenge for China and remains a concern of its central government. This paper draws on a natural experiment generated by the National Ambient Air Quality Monitoring Network (NAAQMN) program in China to explore whether national air quality monitoring reduces local air pollution. In this study, we use a city-level dataset for 4200 Chinese cities covering 2001–2015 and a difference-in-differences (DID) assessment design to assess the impact of the NAAQMN program on local PM 2.5 emissions in China. The results suggest that the NAAQMN program significantly reduces the local PM 2.5 concentrations by 1.325 mg/m3, and each additional NAAQMN program will cause a decrease of 0.154 mg/m3 in the local PM 2.5 concentrations. Furthermore, we determine the heterogeneous impacts of the NAAQMN program on local PM 2.5 emission levels through the local government leaders' characteristics, PM 2.5 emission levels, and economic development levels. In addition, a mediation effect is found between the NAAQMN program and local PM 2.5 emissions through the efficiency of environmental governance. The Chinese government should continue to promote the implementation of the NAAQMN program by promoting the NAAQMN program to the county and rural areas as well as adding the sites of the NAAQMN program in the existing cities. Also, during the process of promoting the NAAQMN program, sufficient differentiation in policies should be developed for different cities. • We explore the linkage between national air quality monitoring and local air pollution in China. • Impact of the National Ambient Air Quality Monitoring Network (NAAQMN) program on local PM 2.5 emissions is evaluated. • We determine that the NAAQMN program significantly reduces the local PM 2.5 concentrations by 1.325 mg/m3. • The mediation effect between the NAAQMN program and local PM 2.5 emissions is found through efficiency of environmental governance. [ABSTRACT FROM AUTHOR]

Indoor air quality (IAQ) is a leading apprehension currently especially in the perilous atmosphere, like hospitals. Clean and fresh air is very crucial for the patients and healthcare professionals in the hospitals. Therefore, we examined IAQ indicators (PM1.0, PM2.5, PM10, NO2, CO2, and TVOC) at sixteen locations of three hospitals with an emphasis on seasonal variations, indoor/outdoor correlation, and concomitant toxicity potential (TP) of human exposure between October 2019 and January 2020. For the measurement of trace gases (NO2, CO2, and TVOC), Aeroqual 500 series (New Zealand) sampler was used; particulate matter (PM1.0, PM2.5, and PM10) concentrations and relative humidity (RH) were measured using the IGERESS air quality monitoring device (WP6930S, China). The total average concentration of IAQ indicators were 104.1 ± 67.6 (PM1.0), 137.4 ± 89.2 (PM2.5), and 159.0 ± 103.3 (PM10) μgm−3; 0.11 ± 0.02 (NO2), 1047.1 ± 234.2 (CO2), and 176.5 ± 117.7 (TVOC) ppm. Significant variations of IAQ indicators were observed between different locations of the hospitals. Winter IAQ indicators were much higher than post-monsoon season. Indoor particulate matter (PM) levels were lower than outdoor, but gaseous pollutants were higher in indoor than outdoor except NO2. Indoor TVOC was about two times higher than outdoor and also higher in post-monsoon than winter. A good positive correlation was observed between indoor and outdoor particulate matter during winter. A strong positive correlation was obtained between NO2 and RH with PM in winter. Very high (> 10) indoor toxicity potential (TP) values of PM2.5 and PM10 were determined during winter. Extremely high TP values indicated potential severe health consequences of the healthcare professionals and patients in indoor hospitals' environment. [ABSTRACT FROM AUTHOR]

Maternal exposure to air pollution during pregnancy is associated with adverse outcomes in the offspring, but limited studies focused on the impacts of gaseous air pollution on newborn congenital hypothyroidism (CH). Therefore, a national data-based analysis was conducted to explore the association between maternal exposure to gaseous air pollution and the incidence of CH in China. Annual average exposure levels of SO2, NO2, CO, and O3 from January 1, 2014, to December 30, 2014, were acquired from the Chinese Air Quality Online Monitoring and Analysis Platform. The annual incidence of newborn CH from October 1, 2014, to September 30, 2015, was collected from the Chinese Maternal and Child Health Surveillance Network. Temperature and toxic metal in wastewater in 2014 were also collected as covariates. Maternal exposure to O3 and NO2 in 1 μg/m3 level increment was positively associated with newborn CH, with an OR of 1.055 (95% CI 1.011, 1.102) and 1.097 (95% CI 1.019, 1.182) after adjusting for covariates completely. Compared with the lowest level of O3, maternal exposure to the 4th quartile of O3 was positively associated with newborn CH (OR 1.393, 95% CI 1.081, 1.794) after adjusting for covariates completely. And the 3rd and 4th quartiles of NO2 were associated positively with CH (OR 1.576, 95% CI 1.025, 2.424, and OR 1.553, 95% CI 0.999, 2.414, respectively) compared with the lowest level of NO2. By fitting the ROC curve, 93.688 μg/m3 in O3 might be used as cutoff to predict the incidence of newborn CH in China. [ABSTRACT FROM AUTHOR]

In situ PM 2.5 concentration observations have long been used as critical data sources in haze-related studies. Due to the frequently occurring haze pollution events, China started to regularly monitor PM 2.5 concentration nationwide from the newly established air quality monitoring network in 2013. Nevertheless, the acquisition of these invaluable air quality samples is challenging given the absence of a publicly available data download interface. In this study, we provided a homogenized in situ PM 2.5 concentration dataset that was created on the basis of hourly PM 2.5 data retrieved from the China National Environmental Monitoring Center (CNEMC) via a web crawler between 2015 and 2019. Methods involving missing value imputation, change point detection, and bias adjustment were applied sequentially to deal with data gaps and inhomogeneities in raw PM 2.5 observations. After excluding records with limited samples, a homogenized PM 2.5 concentration dataset comprising of 1309 5-year long PM 2.5 data series at a daily resolution was eventually compiled. This is the first attempt to homogenize in situ PM 2.5 observations in China. The trend estimations derived from the homogenized dataset indicate a spatially homogeneous decreasing tendency of PM 2.5 across China at a mean rate of about - 7.6 % per year from 2015 to 2019. In contrast to raw PM 2.5 observations, the homogenized data record not only has complete data integrity but is more consistent over space and time. This homogenized daily in situ PM 2.5 concentration dataset is publicly accessible at 10.1594/PANGAEA.917557 (Bai et al., 2020a) and can be applied as a promising dataset for PM 2.5 -related studies such as satellite-based PM 2.5 mapping, human exposure risk assessment, and air quality management. [ABSTRACT FROM AUTHOR]

To test the impact of the COVID-19 pandemic on air quality, this article matches the city-level real-time air quality monitoring data with the big data on population migration provided by Baidu. The article uses urban samples from the same data sample of the Chinese lunar calendar in 2019 to construct the counterfactual status of the COVID-19 pandemic. Then, the difference-in-differences (DID) model is employed to estimate the impact of the COVID-19 pandemic on air quality. It is found that the COVID-19 pandemic caused PM2.5 and AQI to decrease by about 7 μg/m3 and 5-points, respectively. [ABSTRACT FROM AUTHOR]