Search

The SARS-CoV-2 (Severe Acute Respiratory Syndrome-related Coronavirus 2) and its concomitant disease COVID-19 (Coronavirus Disease 2019), discovered at the end of December 2019, have spread around the world, becoming a global public health problem. To prevent the spread of COVID-19, the Chinese government took strict lockdown measures in early 2020 in many cities. The purpose of this study was to evaluate changes in air quality in Shanghai, China's most populous city during the lockdown. Lockdown measures led to a decrease in anthropogenic activity and a concomitant reduction in air pollutant emissions, which led to a temporary improvement in air quality. Mean daily concentrations of PM2.5, PM10, SO2 and NO2 during a 2-week portion of the lockdown period (24 January–6 February) were respectively reduced by −19.2%, −44.7%, −21.5% and −33.6% compared to the same period in 2019. Even with the decrease in PM2.5 and PM10 concentrations, they were still more than four times higher than the World Health Organization standards (10 μg/m3 and 20 μg/m3, respectively). This study provides data useful for evaluating existing environmental policy, as it showed how the control of pollution sources can affect air quality. [ABSTRACT FROM AUTHOR]

Air pollutants in nine cities of the Gobi Desert region are examined in this study. Mean concentrations of PM2.5 and PM10, retrieved from ground-based air quality monitoring stations, were 36.2±23.7 and 97.3±84.5 μg⋅m−3, respectively. The highest concentrations of pollutants were in spring and winter. This can be explained by wind-borne dust in the spring and emissions from domestic sources during the winter heating season. The highest PM2.5 and PM10 concentrations were registered in a period of dust-storm activity with values of 343 μg⋅m−3 and 1,642 μg⋅m−3. Generally, clean continental (CC) was the dominant aerosol type (73.9%), followed by mixed (MX) aerosol type (20.4%) with insignificant contribution of clean marine (CM) (2.1%), urban/industrial and biomass burning (UI/BB) (0.9%) and desert dust (DD) (2.7%) aerosol types. The various measures of pollution retrieved from AERONET stations, including aerosol optical depth (AOD), Ångström exponent (AE), asymmetry parameter (AP), single scattering albedo (SSA) and aerosol volume size distribution (AVSD), specify the content of coarse aerosol fractions in the period of dust activity. The aerosol radiative forcing at the bottom of the atmosphere (ARFBOA) and the top of the atmosphere (ARFTOA) during dust days were estimated to be −115.76 and−241.31 W⋅m−2, with a corresponding heating rate of 3.52 K⋅day−1. According to the backward and forward trajectories of air masses, the dust cloud moved to the east and southeast from the epicentre, affecting the eastern and the central parts of the country. This study may advise environmental policy, as it showed how controlling causes of pollution can improve air quality. [ABSTRACT FROM AUTHOR]

This study investigates the spatial–temporal evolution of aerosols, their optical properties (aerosol optical depth [AOD] and ångström exponent [AE]) and the trend over a period of 19 years in the Eastern European countries. The data used for the study are from moderate‐resolution imaging spectroradiometer (MODIS) Terra Collection 6.1 aerosol products with the use of Dark Target algorithm for the period from 2000 to 2018. The satellite‐based AOD products were validated against the AERONET AOD measurements in four stations, located in different regions. The results demonstrate high coherence in the setting of high values of correlation (R = 0.8–0.9) and low values of root‐mean‐square error (RMSE [0.066–0.130]) and mean absolute error (MAE [0.048–0.067]). However, of the total amount of data from AERONET stations, only 68.9% of available data fall within expected error (EE) over land. Generally, all countries recorded a low AOD (83.2% less than 0.2) and high AE (82.5% higher than 1). Mean AOD and AE values varied from 0.17 ± 0.12 and 1.31 ± 0.27 (Russia) to 0.24 ± 0.14 and 1.31 ± 0.33 (Czech Republic). There was a gradual decrease in aerosol load in all countries, the highest tendencies of AOD reduction are observed in Czech Republic, Bulgaria, Slovakia and Hungary (by −0.0028, −0.0027, −0.0026 and −0.0025 per year), while the lowest tendencies of reduction are observed in Russia and Moldova (by −0.001 and −0.0006 per year). Seasonal averaged AOD values have maximum values in summer and minimum values in winter. We studied predominant aerosol types, which are based on AOD and AE interrelation, the results showed the predominance of clean continental and mixed aerosols over all countries of the region. [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 survey addresses the comprehensive description of geographic locations, transport ways, size, and vertical aerosol distribution during four large dust events which occurred in the Northwest China. Based on the data from 35 ground-based air quality monitoring stations and the satellite data, emission flows for dust events within the period of 2014 to 2017 have been estimated. The data show that maximum peak daily average PM10 and PM2.5 concentrations exceeded 380 and 150 μg/m3, respectively, and the PM2.5/PM10 ratio was ranging within 0.12-0.66. Both satellite data and simulation data of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) coincide with location and extension of a dust cloud. The Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) found dust at 0 to 10 km altitude which remained at this level during the most part of its trajectory. The vertical aerosol distribution at a wave of 532 nm total attenuated backscatter coefficient range of 0.0025-0.003 km−1 × sr−1. Moderate Resolution Imaging Spectroradiometer (MODIS) (Terra) Collection 6 Level-3 aerosol products data show that aerosol optical depth (AOD) at pollution epicenters exceeds 1. A comprehensive data survey thus demonstrated that the main sources of high aerosol pollutions in the territory were deserted areas of North and Northwest China as well as the most part of the Republic of Mongolia, where one of the largest deserts, Gobi, extends. [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]

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/m3 with readings above 1500 μg/m3 in the cities of Tianjin, Baoding, Zhengzhou, Luoyang, Zhoukou. In Shijiazhuang, Taiyuan, Jinnan, Xining, Baotou, and Jining. In Handan, it was over 2000 μg/m3. Cities where PM 10 concentration exceeded 3900 μg/m3 included Lanzhou, Hohhot, Changzhou, Alashan, Yan'an, Yulin, Hami, Jiuquan, Heze, Hotan, and Baiyin. Concentrations exceeded 7000 μg/m3 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 km2. 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/m3, and exceeding 2650 μg/m3 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. [Display omitted] • Multi-satellite observation of an intense dust event. • One of the most heavy dust storm of the last 10 years. • Spatial and temporal variations of air pollutants and aerosol properties were analyzed. • PM 10 concentration was above 1500 μg/m3 in many cities. [ABSTRACT FROM AUTHOR]

The first case of COVID-19 in Poland was registered on 4 March 2020. Governmental measures significantly restricted social and economic activities. This study investigates the impact on air quality resulting from the preventive measures taken by the government to manage Covid-19. The study was carried out with use of aerosol optical depth (AOD) retrieved from Moderate Resolution Imaging Spectrometer (MODIS) satellite and tropospheric column NO 2 observed by Ozone Monitoring Instrument (OMI). Concentrations of atmospheric pollutants (PM 2.5 , PM 10 , NO 2 and SO 2) retrieved from ground-based air quality stations, located in five large cities of the country, were also used for quantitative assessment of air quality change. Ground-based and satellite data demonstrated the reduction of pollutants in the period of lockdown as compared to the same periods in 2018 and 2019. In particular, AOD data shows reductions of aerosol concentrations in the air column in April and May of approximately by −23% and −18% as compared to 2018–2019. The greatest contraction was for PM 2.5 in April and May with reductions of −11.1% to −26.4% and from −8.7 to −21.1% respectively. For PM 10, the reductions were from −8.6% to −33.9% and from −8.5% to −31.5% as compared to the same months in 2019. The results showed that restrictions imposed to prevent the spread of COVID-19 significantly improved Poland's air quality. • Impact of lockdown due to COVID-19 on air quality over Poland. • Decline of anthropogenic emissions during the lockdown. • Reductions of aerosol concentrations in April and May approximately by −23% and −18%. • Reductions of tropospheric NO2 approximately by −10 to 19%. [ABSTRACT FROM AUTHOR]

This study investigates columnar optical, microphysical and radiative properties of aerosols retrieved from the ground-based Aerosol Robotic Network (AERONET) station located in Minsk, Belarus. Mean values of aerosol optical depth (AOD), a measure of the amount of incoming light that aerosols prevent from reaching the surface, at a wavelength of 440 nm (AOD 440) and Ångström exponent at 440–870 (AE 440-870) between April 2002 and December 2019 were 0.22 ± 0.17 and 1.42 ± 0.29. A gradual decline of AOD 440 by −0.009 and increase of AE 440-870 by +0.0009 per year was noted. Seasonal change of AOD 440 (AE 440-870) demonstrates the highest values are in spring to summer and the lowest values are in winter for both indices. The data showed that the atmosphere over Minsk is under impact of various aerosol types with dominance of Continental Clean (СС) (52.21%), Mixed (MX) (24.30%) and Biomass burning/Urban-industrial (BUI) (20.54%) aerosol types, accounting for 97.05% of all aerosols. Aerosol volume size distribution (VSD), that defines the volume of all aerosol particles in the vertical atmosphere column, demonstrates a bimodal structure with clearly identified fine and coarse particles centered within a radius of 0.11–0.19 μm and 3.86–5.06 μm. Analysis of Asymmetry (ASY) parameter, Single scattering albedo (SSA), and Real and Imaginary Refractive Index (RRI and IRI) indicate the predominance of coarse-mode particles in autumn and winter; and fine-mode particles in spring and summer. The averaged aerosol direct radiative forcing (ARF) showed a cooling effect at the surface (SRF) and a significant warming in the atmosphere (ATM). The atmospheric heating rates varied from 0.40 ± 0.21 K day−1 (autumn) to 0.64 ± 0.38 K day−1 (winter). The results indicate definite changes in the aerosol optical, physical properties and types over Minsk during the study period. • Long term continuous data on columnar optical, microphysical and radiative properties over Minsk. • High AOD 440 (AE 440-870) in spring (summer) and the lowest values in winter for both indices. • Continental Clean, Mixed and Biomass burning/Urban-industrial is the dominant aerosols types. • Predominance of coarse-mode particles in winter and autumn; and fine-mode particles in summer and spring. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [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]