Search

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]

In this article, we analyzed the mass concentrations of particulate matter 2.5 micrometers (µm) or less in size (PM2.5), particulate matter 10 µm or less in size (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) in Lanzhou, the capital of Gansu province, China. We analyzed monitoring data collected from five air quality monitoring stations during the spring-summer period from 2014 to 2016. Our comparison of contaminant concentrations and average diurnal, daily, monthly, and annual concentrations revealed that the average concentrations of PM2.5 and PM10 amounted to 128.57 and 46.4 micrograms per cubic meters (µg/m3), respectively, exceeding the Chinese National Ambient Air Quality Standard (NAAQS). We used the Pearson correlation coefficient to establish connections between particulate matter and gaseous pollutants. The results show significant differences in the concentration levels of airborne pollutants. The Pearson correlation coefficient between PM2.5 and PM10 had the highest coefficient of r = 0.842. A correlation between the two particulate matter sizes (PM2.5 and PM10) and SO2 was PM2.5 and SO2 r = 0.313; PM10 and SO2 r = 0.279; and CO and the two particulate matter sizes, PM2.5 and CO r = 0.304; and PM10 and CO r = 0.203. The average monthly ratio for the study months of PM2.5 to PM10 was 0.361. In addition, we used the hybrid single particle Lagrangian integrated trajectory model for tracking sources and pathways of the air pollutants in Lanzhou. [ABSTRACT FROM AUTHOR]