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The rapid pace of economic growth and urbanization in China affects both large and small cities of the country, causing an increase of pollutant concentrations in the air. The South Gobi is one of the main deserts and semidesert regions of the country; therefore, the study of air pollution near the potential source of natural aerosols is of great importance. Data obtained in the period from 1 January 2016 to 31 December 2019 was used to analyze spatial-temporal characteristics of atmospheric pollutants (PM2.5, PM10, SO2, NO2, and CO) in eight cities. Total mean concentrations of PM2.5 and PM10 were 36.1 ± 21.1 μg/m3 and 98.6 ± 108.7 μg/m3. The occurrence rates of concentrations exceeding the Chinese National Ambient Air Quality Standard (CNAAQS) grade 1 and grade 2 were 40.1% and 5.4% for PM2.5 and 82.9% and 11.64% for PM10 in the region. Total concentrations of SO2, NO2, and CO did not exceed the CNAAQS standard and were 20.8 ± 23.6 μg/m3, 22.6 ± 11.9 μg/m3, and 0.72 ± 0.39 mg/m3, respectively. The PM2.5 to PM10 ratio increased from 0.35 in spring to 0.46 in winter suggesting the predominance of coarse aerosol fractions in the atmosphere. Based on data on aerosol optical depth (AOD) and Ångström exponent (AE) ratio obtained from Moderate Resolution Imaging Spectroradiometer (MODIS), the predominant aerosol types in the region are Clean Сontinental and Mixed. Maximum concentrations of pollutants and the highest AOD values in the region air are observed in spring and winter. Results set forth in this article will be an important basis for further regional studies on air quality and distribution of sources. [ABSTRACT FROM AUTHOR]

This study reviews spatial and temporal variability of aerosol optical depth (AOD) at 550 nm, obtained with Moderate Resolution Imaging Spectroradiometer (MODIS) (Terra) Collection 6.1 aerosol products with use of combined Dark Target and Deep Blue algorithm. Data was analyzed for 18-year period from 2000 to 2017 over the territory of the whole continental China, covering the largest cities as well as various ecological and geographical regions. Spatial distribution of AOD has distinct geographical differences with gradual decrease from the east to the west of the country. The lowest values (up to 0.25) of annual mean AOD at 550 nm occur in sparsely populated areas on the Tibetan Plateau and in the north forest ecosystems in the north-eastern part of China. Areas of desert and semidesert landscapes of Northwest China are characterized by high concentrations of naturally occurring aerosols with moderate values of AOD (0.4–0.7). The most populous regions (Pearl River Delta, Yangtze River Delta, North China Plain, and Sichuan Basin) with the highest density of agricultural and industrial activity are characterized with maximum values of AOD (over 0.7). Seasonal variation of aerosols in the most regions of China has maximum AODs in spring or summer and minimum in autumn or winter. Ångström exponent (AE), being 0.31–1.7 for the most part of China, was used to detect the size of aerosol particles, with the lowest values (0.31–0.84) in desert north and north-west regions of the country, and the higher values in the south (1.3–1.7). Comparison of results, obtained with MODIS Terra and AERONET (Aerosol Robotic Network) at 550 nm, demonstrate a high interrelation (r = 0.8949), where 68.3% fall within the range of expected errors, set by MODIS over the land (± 0.05 ± 0.15 × AOD). The conducted Pearson's correlation analysis between various cities of the country showed that cities in one region with shortest distances from one another demonstrated higher correlations, suggesting distinct regional dependence in aerosols distribution. [ABSTRACT FROM AUTHOR]

The present paper has used a comprehensive approach to study atmosphere pollution sources including the study of vertical distribution characteristics, the epicenters of occurrence and transport of atmospheric aerosol in North-West China under intensive dust storm registered in all cities of the region in April 2014. To achieve this goal, the remote sensing data using Moderate Resolution Imaging Spectroradiometer satellite (MODIS) as well as model-simulated data, were used, which facilitate tracking the sources, routes, and spatial extent of dust storms. The results of the study have shown strong territory pollution with aerosol during sandstorm. According to ground-based air quality monitoring stations data, concentrations of PM 10 and PM 2.5 exceeded 400 μg/m 3 and 150 μg/m 3 , respectively, the ratio PM 2.5 /PM 10 being within the range of 0.123–0.661. According to MODIS/Terra Collection 6 Level-2 aerosol products data and the Deep Blue algorithm data, the aerosol optical depth (AOD) at 550 nm in the pollution epicenter was within 0.75–1. The vertical distribution of aerosols indicates that the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) 532 nm total attenuates backscatter coefficient ranges from 0.01 to 0.0001 km −1  × sr −1 with the distribution of the main types of aerosols in the troposphere of the region within 0–12.5 km, where the most severe aerosol contamination is observed in the lower troposphere (at 3–6 km). According to satellite sounding and model-simulated data, the sources of pollution are the deserted regions of Northern and Northwestern China. [ABSTRACT FROM AUTHOR]

Due to rapid economic growth of the country in the last 25 years, particulate matter (PM) has become a topic of great interest in China. The rapid development of industry has led to an increase in the haze created by pollution, as well as by high levels of urbanization. In 2012, the Chinese National Ambient Air Quality Standard (NAAQS) imposed 'more strict' regulation on the PM concentrations, i.e., 35 and 70 μg/m for annual PM and PM in average, respectively (Grade-II, GB3095-2012). The Pearson's correlation coefficient was used to determine the linear relationship of pollution between pollution levels and weather conditions as well as the temporal and spatial variability among neighbouring cities. The goal of this paper was to investigate hourly mass concentration of PM and PM from June 1 to August 31, 2015 collected in the 11 largest cities of Gansu Province. This study has shown that the overall average concentrations of PM and PM in the study area were 26 and 66 μg/m. In PM episode days (when concentration was more than 75 μg/m for 24 hrs), the average concentrations of PM was 2-3 times higher as compared to non-episode days. There were no observed clear differences during the weekday/weekend PM and other air pollutants (SO, NO, CO and O) in all the investigated cities. [ABSTRACT FROM AUTHOR]

• NDVI time series predictions for the Yellow River Basin, China. • Proposed multivariate Long-Short Term Memory neural network incorporating climate elements. • The common constraining effects of multiple climatic factors on NDVI are fully accounted for in the model proposed in the study. Accurate prediction of the trend of Normalized Difference Vegetation Index (NDVI) time series in the Yellow River Basin (YRB) is crucial for the assessment of the hydrological and ecological environment in this region. Currently, the NDVI time series prediction model is primarily based on traditional models and single-variable neural network models. Nevertheless, these models present challenges in considering the limitations of multiple factors, causing the NDVI time series prediction results to lack reliability. To predict NDVI time-series in the YRB of China, this study constructed a multilayer multivariate Long-Short Term Memory (LSTM) neural network model including climatic components. The initial important climatic elements in this region were identified using GeoDetector. Then, the relationship between NDVI and climatic factors in the YRB of China is established. Finally, numerical scale data are used to train and predict a multilayer multivariate LSTM model with climatic components. According to the results, the three-layer multivariate LSTM neural network NDVI time series prediction model developed in this study has the best performance among the evaluated indices. When compared to existing time series prediction models, the proposed model in this study takes into account the common constraint effect of various climate factors on NDVI. This leads to a significantly improved prediction accuracy, presenting new opportunities for enhancing the prediction model. By analyzing the NDVI time series prediction outcomes for the YRB, it has been determined that the ecological environment of the area will continuously improve in the future. This study offers significant technological and theoretical backing for assessing the hydrological and ecological environment of the YRB and comparable ecologically vulnerable regions in China. [ABSTRACT FROM AUTHOR]

A dust storm that formed in the north of China and the southeastern part of Mongolia in March 2021 significantly deteriorated air quality over a large area of East Asia. According to the synoptic pattern, the cause of the dust storm was a cyclone with a significant drop in pressure leading to high winds and dry components of the soil over parts of the Gobi Desert becoming airborne. Data obtained from ground-based air quality monitoring stations show that the observed hourly PM 10 concentration greatly exceeded the recommended maximum of 150 μg/m 3 with readings above 1500 μg/m 3 in the cities of Tianjin, Baoding, Zhengzhou, Luoyang, Zhoukou. In Shijiazhuang, Taiyuan, Jinnan, Xining, Baotou, and Jining. In Handan, it was over 2000 μg/m 3 . Cities where PM 10 concentration exceeded 3900 μg/m 3 included Lanzhou, Hohhot, Changzhou, Alashan, Yan'an, Yulin, Hami, Jiuquan, Heze, Hotan, and Baiyin. Concentrations exceeded 7000 μg/m 3 on March 15th over parts of the provinces of Inner Mongolia, Gansu and Ningxia, in the cities of Ordos, Jinchang, Wuwei and Zhongwei. According to satellite data, the area of dust covered approximately 450,000 km 2 . MODIS and TROPOMI data demonstrated high aerosol optical depth (AOD) (more than 1) with a high ultraviolet aerosol index (UVAI) (more than 2), confirming the predominance of dust particles during the storm. Data from CALIPSO show the presence of a dense layer of dust extending from the earth's surface to a height of about 8 km. The Dust Regional Atmospheric Model (BSC-DREAM8b) demonstrates high synchrony with the satellite's surface dust concentration data, ranging from 640 to 1280 μg/m 3 , and exceeding 2650 μg/m 3 in some areas. The purpose of this study is to analyze data from ground-based sensors, satellites, and atmospheric models to better understand the March 2021 dust storm event. The results may be useful for the implementation of protective and preventive measures for both the environment and human health, including air quality control., (Copyright © 2021 Elsevier Ltd. All rights reserved.)