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Quantitative assessment of changes in surface particulate matter concentrations and precursor emissions over China during the COVID-19 pandemic and their implications for Chinese economic activity
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2021
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Source: Atmospheric Chemistry and Physics, 21(13), 10065-10080
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Journal Title:Atmospheric Chemistry and Physics
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Description:Sixty days after the lockdown of Hubei Province, where the coronavirus was first reported, China's true recovery from the pandemic remained an outstanding question. This study investigates how human activity changed during this period using observations of surface pollutants. By combining surface data with a three-dimensional chemistry model, the impacts of meteorological variations and variations in yearly emission control are minimized, demonstrating how pollutant levels over China changed before and after the Lunar New Year from 2017 to 2020. The results show that the reduction in NO2 concentrations, an indicator of emissions in the transportation sector, was clearly greater and longer in 2020 than in normal years and started to recover after 15 February. By contrast, PM2.5 emissions had not yet recovered by the end of March, showing a reduction of around 30 % compared with normal years. SO2 emissions were not affected significantly by the pandemic. An additional model study using a top–down emission adjustment still confirms a reduction of around 25 % in unknown surface PM2.5 emissions over the same period, even after realistically updating SO2 and NOx emissions. This evidence suggests that different economic sectors in China may be recovering at different rates, with the fastest recovery in transportation and a slower recovery likely in agriculture. The apparent difference between the recovery timelines of NO2 and PM2.5 implies that monitoring a single pollutant alone (e.g., NOx emissions) is insufficient to draw conclusions on the overall recovery of the Chinese economy.
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Source:Atmospheric Chemistry and Physics, 21(13), 10065-10080
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ISSN:1680-7324
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Rights Information:CC BY
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Compliance:Library
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