164 research outputs found

    Air pollution scenario over China during COVID-19

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    The unprecedented slowdown in China during the COVID-19 period of November 2019 to April 2020 should have reduced pollution in smog-laden cities. However, moderate resolution imaging spectrometer (MODIS) satellite retrievals of aerosol optical depth (AOD) show a marked increase in aerosols over the Beijing–Tianjin–Hebei (BHT) region and most of Northeast and Central China, compared with the previous winter. Fine particulate (PM2.5) data from ground monitoring stations show an increase of 19.5% in Beijing during January and February 2020, and no reduction for Tianjin. In March and April 2020, a different spatial pattern emerges, with very high AOD levels observed over 50% of the Chinese mainland, and including peripheral regions in the northwest and southwest. At the same time, ozone monitoring instrument (OMI) satellite-derived NO2 concentrations fell drastically across China. The increase in PM2.5 while NO2 decreased in BTH and across China is likely due to enhanced production of secondary particulates. These are formed when reductions in NOx result in increased ozone formation, thus increasing the oxidizing capacity of the atmosphere. Support for this explanation is provided by ground level air quality data showing increased volume of fine mode aerosols throughout February and March 2020, and increased levels of PM2.5, relative humidity (RH), and ozone during haze episodes in the COVID-19 lockdown period. Backward trajectories show the origin of air masses affecting industrial centers of North and East China to be local. Other contributors to increased atmospheric particulates may include inflated industrial production in peripheral regions to compensate loss in the main population and industrial centers, and low wind speeds. Satellite monitoring of the extraordinary atmospheric conditions resulting from the COVID-19 shutdown could enhance understanding of smog formation and attempts to control it

    Assessment of air quality in Northern China by using the COSMO-ART model in conjunction with satellite and ground-based data

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    Luftverschmutzung durch Aerosole ist eines der größten Umweltprobleme in der chinesischen Hauptstadt Peking. Insbesondere Mineralstaub, welcher oft aus den weitläufigen asiatischen Trockengebieten in das Stadtgebiet eingetragen wird, führt zu einer drastischen Verschlechterung der Luftqualität. Diese Arbeit ist eine detaillierte Studie über die raumzeitliche Dynamik dieses eingetragenen Mineralstaubs sowie dessen physikalische Interaktion mit lokal produzierten anthropogenen Partikeln

    Estimating PM2.5 in the Beijing-Tianjin-Hebei Region Using MODIS AOD Products from 2014 to 2015

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    Fine particulate matter with a diameter less than 2.5 μm (PM2.5) has harmful impacts on regional climate, economic development and public health. The high PM2.5 concentrations in China’s urban areas are mainly caused by combustion of coal and gasoline, industrial pollution and unknown/uncertain sources. The Beijing-Tianjin-Hebei (BTH) region with a land area of 218,000 km2, which contains 13 cities, is the biggest urbanized region in northern China. The huge population (110 million, 8% of the China’s population), local heavy industries and vehicle emissions have resulted in severe air pollution. To monitor ground-level PM2.5 concentration, the Chinese government spent significant expense in building more than 1500 in-situ stations (79 stations in the BTH region). However, most of these stations are situated in urban areas. Besides, each station can only represent a limited area around that station, which leaves the vast rural land out of monitoring. In this situation, geographic information system and remote sensing can be used as complementary tools. Traditional models have used 10 km MODIS Aerosol Optical Depth (AOD) product and proved the statistical relationship between AOD and PM2.5. In 2014, the 3 km MODIS AOD product was released which made PM2.5 estimation with a higher resolution became possible. This study presents an estimation on PM2.5 distribution in the BTH region from September 2014 to August 2015 by combining the MODIS satellite data, ground measurements of PM2.5, and meteorological documents. Firstly, the 3 km and 10 km MODIS AOD products were validated with AErosol RObotic NETwork (AERONET AOD. Then the MLR and GWR models were employed respectively to estimate PM2.5 concentrations using ground measurements and two MODIS AOD products, meteorological datasets and land use information. Seasonal and regional analyses were also followed to make a comparative study on strengths and weaknesses between the 3 km and 10 km AOD products. Finally, the number of non-accidental deaths attributed to the long-term exposure of PM2.5 in the BTH region was estimated spatially. The results demonstrated that the 10 km AOD product provided results with a higher accuracy and greater coverage, although the 3 km AOD product could provide more information about the spatial variations of PM2.5 estimation. Additionally, compared with the global regression, the geographically weighed regression model was able to improve the estimation results. Finally, it was estimated that more than 30,000 people died in the BTH region during the study period attributed to the excessive PM2.5 concentrations

    Reduction of solar photovoltaic resources due to air pollution in China

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    Solar photovoltaic (PV) electricity generation is expanding rapidly in China, with total capacity projected to be 400 GW by 2030. However, severe aerosol pollution over China reduces solar radiation reaching the surface. We estimate the aerosol impact on solar PV electricity generation at the provincial and regional grid levels in China. Our approach is to examine the 12-year (2003–2014) average reduction in point-of-array irradiance (POAI) caused by aerosols in the atmosphere. We apply satellite-derived surface irradiance data from the NASA Clouds and the Earth’s Radiant Energy System (CERES) with a PV performance model (PVLIB-Python) to calculate the impact of aerosols and clouds on POAI. Our findings reveal that aerosols over northern and eastern China, the most polluted regions, reduce annual average POAI by up to 1.5 kWh/m2 per day relative to pollution-free conditions, a decrease of up to 35%. Annual average reductions of POAI over both northern and eastern China are about 20–25%. We also evaluate the seasonal variability of the impact and find that aerosols in this region are as important as clouds in winter. Furthermore, we find that aerosols decrease electricity output of tracking PV systems more than those with fixed arrays: over eastern China, POAI is reduced by 21% for fixed systems at optimal angle and 34% for two-axis tracking systems. We conclude that PV system performance in northern and eastern China will benefit from improvements in air quality and will facilitate that improvement by providing emission-free electricity

    Evaluation and comparison of CMIP6 models and MERRA-2 reanalysis AOD against Satellite observations from 2000 to 2014 over China

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    Rapid industrialization and urbanization along with a growing population are contributing significantly to air pollution in China. Evaluation of long-term aerosol optical depth (AOD) data from models and reanalysis, can greatly promote understanding of spatiotemporal variations in air pollution in China. To do this, AOD (550 nm) values from 2000 to 2014 were obtained from the Coupled Model Inter-comparison Project (CIMP6), the second version of Modern-Era Retrospective analysis for Research, and Applications (MERRA-2), and the Moderate Resolution Imaging Spectroradiometer (MODIS; flying on the Terra satellite) combined Dark Target and Deep Blue (DTB) aerosol product. We used the Terra-MODIS DTB AOD (hereafter MODIS DTB AOD) as a standard to evaluate CMIP6 Ensemble AOD (hereafter CMIP6 AOD) and MERRA-2 reanalysis AOD (hereafter MERRA-2 AOD). Results show better correlations and smaller errors between MERRA-2 and MODIS DTB AOD, than between CMIP6 and MODIS DTB AOD, in most regions of China, at both annual and seasonal scales. However, significant under- and over-estimations in the MERRA-2 and CMIP6 AOD were also observed relative to MODIS DTB AOD. The long-term (2000–2014) MODIS DTB AOD distributions show the highest AOD over the North China Plain (0.71) followed by Central China (0.69), Yangtse River Delta (0.67), Sichuan Basin (0.64), and Pearl River Delta (0.54) regions. The lowest AOD values were recorded over the Tibetan Plateau (0.13 ± 0.01) followed by Qinghai (0.19 ± 0.03) and the Gobi Desert (0.21 ± 0.03). Large amounts of sand and dust particles emitted from natural sources (the Taklamakan and Gobi Deserts) may result in higher AOD in spring compared to summer, autumn, and winter. Trends were also calculated for 2000–2005, for 2006–2010 (when China introduced strict air pollution control policies during the 11th Five Year Plan or FYP), and for 2011–2014 (during the 12th FYP). An increasing trend in MODIS DTB AOD was observed throughout the country during 2000–2014. The uncontrolled industrialization, urbanization, and rapid economic development that mostly occurred from 2000 to 2005 probably contributed to the overall increase in AOD. Finally, China's air pollution control policies helped to reduce AOD in most regions of the country; this was more evident during the 12th FYP period (2011–2014) than during the 11th FYP period (2006–2010). Therefore this study strongly advises the authority to retain or extend these policies in the future for improving air quality

    Correlating Satellite Cloud Cover with Sky Cameras

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    The role of clouds is manifold in understanding the various events in the atmosphere, and also in studying the radiative balance of the earth. The conventional manner of such cloud analysis is performed mainly via satellite images. However, because of its low temporal- and spatial- resolutions, ground-based sky cameras are now getting popular. In this paper, we study the relation between the cloud cover obtained from MODIS images, with the coverage obtained from ground-based sky cameras. This will help us to better understand cloud formation in the atmosphere - both from satellite images and ground-based observations.Comment: Published in Proc. Progress In Electromagnetics Research Symposium (PIERS), 201
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