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- W4385543687 endingPage "103452" @default.
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- W4385543687 abstract "COVID-19 cases surged in late 2019, leading to worldwide lockdowns that closed non-essential places and activities, industries, and businesses to halt the spread of the virus. Many studies suggested improved air quality during lockdowns. However, these findings often focused on core city limits and did not account for heavy pollution sources outside cities (around the fringe areas), such as factories, power plants, and coal mines, which operated continuously for energy needs even during lockdowns. Therefore, this study quantified and re-analyzed the air quality data using a top-down approach. This study analyzed six major air quality parameters namely SO2, O3, NO2, PM2.5, AOD500, and UAI. The time-averaged approach was adopted to analyze the data followed by ground validation. High variability and anomalies in air quality parameters were observed at different levels of observations (i.e. city level, country level, etc.). However, it was found that during the lockdown period, PM2.5 and NO2 significantly dropped at the country level with few exceptions. Changes were also observed in AOD500, O3, and UAI concentrations from city to country scale. Mixed behaviors among the atmospheric pollutants were observed with changes in scale and time. This makes the claim about air quality improvements during COVID-19 lockdowns very relative to the scale of observation and the pollutant indicators being referred to. Multi-layered analyses of pollutant concentrations extending beyond the city limits to the meso-regional levels with varying space-time observations made the present work unique from existing literature that claimed a global air quality improvement during the COVID-19 lockdown period." @default.
- W4385543687 created "2023-08-04" @default.
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- W4385543687 date "2023-12-01" @default.
- W4385543687 modified "2023-10-02" @default.
- W4385543687 title "Have COVID lockdowns really improved global air quality? –Hierarchical observations from the perspective of urban agglomerations using atmospheric reanalysis data" @default.
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- W4385543687 doi "https://doi.org/10.1016/j.pce.2023.103452" @default.
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