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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]

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]

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]

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]

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]

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]

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]

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]

Aerosol particles are the major contributor to the deterioration of air quality in China's capital, Beijing. Using ground-based sun photometer observations from 2005 to 2014, the long-term variations in optical properties and microphysical properties of aerosol in and around Beijing were investigated in this study. The results indicated little inter-annual variations in aerosol optic depth (AOD) but an increase in the fine mode AODs both in and outside Beijing. Furthermore, the single scattering albedo in urban Beijing is larger, while observations at the site that is southeast of Beijing suggested that the aerosol there has become more absorbing. The intra-annual aspects were as follow: The largest AOD and high amount of fine mode aerosols are observed in the summer. However, the result of air pollution index (API) that mainly affected by the dry density of near-surface aerosol indicated that the air quality has been improving since 2006. Winter and spring were the most polluted seasons considering only the API values. The inconsistency between AOD and API suggested that fine aerosol particles may have a more important role in the deterioration of air quality and that neglecting particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) in the calculation of API might not be appropriate in air quality evaluation. Through analysis of the aerosol properties in high API days, the results suggested that the fine mode aerosol, especially PM2.5 has become a major contributor to the aerosol pollution in Beijing. [ABSTRACT FROM AUTHOR]

The 3 km Dark Target (DT) aerosol optical depth (AOD) products, 10 km DT and Deep Blue (DB) AOD products from the Collection 6 (C6) product data of Moderate Resolution Imaging Spectroradiometer (MODIS) are compared with Sun-sky Radiometer Network (SONET) measurements at Song Mountain in central China, where ground-based remote sensing measurements of aerosol properties are still very limited. The seasonal variations of AODs are significant in the Song Mountain region, with higher AODs in spring and summer and lower AODs in autumn and winter. Annual mean AODs (0.55 μm) vary in the range of 0.5-0.7, which indicates particle matter (PM) pollutions in this mountain region. Validation against one-year ground-based measurements shows that AOD retrievals from the MODIS onboard Aqua satellite are better than those from the Terra satellite in Song Mountain. The 3 km and 10 km AODs from DT algorithms are comparable over this region, while the AOD accuracy of DB algorithm is relatively lower. However, the spatial coverage of DB products is higher than that of 10 km DT products. Moreover, the optical and microphysical characteristics of aerosols at Song Mountain are analyzed on the basis of SONET observations. It suggests that coarse-mode aerosol particles dominate in spring, and fine-mode particles dominate in summer. The aerosol property models are also established and compared to aerosol types used by MODIS algorithm. [ABSTRACT FROM AUTHOR]

Ground-level ozone (O3) pollution has become a serious environmental issue in major urban agglomerations in China. To investigate the spatiotemporal patterns and regional transports of O3 in Beijing–Tianjin–Hebei (BTH-UA), the Yangtze River Delta (YRD-UA), the Triangle of Central China (TC-UA), Chengdu–Chongqing (CY-UA), and the Pearl River Delta urban agglomeration (PRD-UA), multiple transdisciplinary methods were employed to analyze the O3-concentration data that were collected from national air quality monitoring networks operated by the China National Environmental Monitoring Center (CNEMC). It was found that although ozone concentrations have decreased in recent years, ozone pollution is still a serious issue in China. O3 exhibited different spatiotemporal patterns in the five urban agglomerations. In terms of monthly variations, O3 had a unimodal structure in BTH-UA but a bimodal structure in the other urban agglomerations. The maximum O3 concentration was in autumn in PRD-UA, but in summer in the other urban agglomerations. In spatial distribution, the main distribution of O3 concentration was aligned in northeast–southwest direction for BTH-UA and CY-UA, but in northwest–southeast direction for YRD-UA, TC-UA, and PRD-UA. O3 concentrations exhibited positive spatial autocorrelations in BTH-UA, YRD-UA, and TC-UA, but negative spatial autocorrelations in CY-UA and PRD-UA. Variations in O3 concentration were more affected by weather fluctuations in coastal cities while the variations were more affected by seasonal changes in inland cities. O3 transport in the center cities of the five urban agglomerations was examined by backward trajectory and potential source analyses. Local areas mainly contributed to the O3 concentrations in the five cities, but regional transport also played a significant role. Our findings suggest joint efforts across cities and regions will be necessary to reduce O3 pollution in major urban agglomerations in China. [ABSTRACT FROM AUTHOR]

Smog pollution in China has drawn worldwide attention. Using companies' data from Chinese Securities Markets and Accounting Research database (CSMAR) and air quality monitoring data from China National Environmental Monitoring Centre(CNEMC), we employ the PM2.5 concentration as a proxy for smog pollution and examine the effect of smog pollution on company environmental uncertainty and operating investment in 74 key cities in China. The empirical results show that smog pollution causes an increase in company environmental uncertainty and a decrease in operating investment for Chinese listed companies, with environmental uncertainty as a mediating variable. Smog pollution can positively influence companies' environmental uncertainty through their employees and high pressure from the public and government. According to the real-options-based investment approach, companies choose to "wait and see" and, correspondingly, reduce operating investment under high environmental uncertainty such as that caused by smog pollution. Additionally, we find that state-owned enterprises are more significantly influenced by smog pollution in terms of environmental uncertainty and operating investment because of their close relationships with the government and their responsibility to set an example among Chinese companies in the fight against smog pollution. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) pollution has been a major concerning issue in China, and many cities have developed emergency plans for heavy air pollution. The aim of this study is to delimitate PM2.5 pollution regions of Xianyang, which is very important to the regional environmental prevention and control. The result showed that PM2.5 air pollution had significant cross-administrative characteristics in Xianyang. Using spatial clustering algorithm under adjacent matrix constrain, this study classified the air quality monitoring sites into two clusters. For each monitoring site, we generated Voronoi polygons and ultimately Xianyang was delimitated into two regions, south and north. The air pollution of the southern region was more serious with 64 days of heavy and severe pollution since 2018, while the northern region had only 10 days. The southern region consisted of four complete administrative districts and parts of three administrative districts. While the northern region consisted of six complete administrative districts and parts of three administrative districts. Visualization of the spatiotemporal characteristics of the PM2.5 air pollution in the two regions further illustrated the significant difference. We suggest when heavy pollution happens, the two regions should be considered separately. If the southern region is heavily polluted while the northern region not, only the southern region needs to implement the emergency plan to minimize the damage to society and economy. Seventy-five percent of the city area, 2.3 million people, 59% of schools, and 43% of GDP would not be impacted if air pollution was controlled by region separately. The sensitive analysis shows that clustering result is robust against different pollution degree and missing values. [ABSTRACT FROM AUTHOR]

Strict social distancing rules are being implemented to stop the spread of COVID-19 pandemic in many cities globally, causing a sudden and extreme change in the transport activities. This offers a unique opportunity to assess the effect of anthropogenic activities on air quality and provides a valuable reference to the policymakers in developing air quality control measures and projecting their effectiveness. In this study, we evaluated the effect of the COVID-19 lockdown on the roadside and ambient air quality in Hong Kong, China, by comparing the air quality monitoring data collected in January–April 2020 with those in 2017–2019. The results showed that the roadside and ambient NO2, PM10, PM2.5, CO and SO2 were generally reduced in 2020 when comparing with the historical data in 2017–2019, while O3 was increased. However, the reductions during COVID-19 period (i.e., February–April) were not always higher than that during pre-COVID-19 period (i.e., January). In addition, there were large seasonal variations in the monthly mean pollutant concentrations in every year. This study implies that one air pollution control measure may not generate obvious immediate improvements in the air quality monitoring data and its effectiveness should be evaluated carefully to eliminate the effect of seasonal variations. [ABSTRACT FROM AUTHOR]