Dataset Information

Quantitative estimation of meteorological impacts and the COVID-19 lockdown reductions on NO₂ and PM_2.5 over the Beijing area using Generalized Additive Models (GAM).

ABSTRACT: Unprecedented travel restrictions due to the COVID-19 pandemic caused remarkable reductions in anthropogenic emissions, however, the Beijing area still experienced extreme haze pollution even under the strict COVID-19 controls. Generalized Additive Models (GAM) were developed with respect to inter-annual variations, seasonal cycles, holiday effects, diurnal profile, and the non-linear influences of meteorological factors to quantitatively differentiate the lockdown effects and meteorology impacts on concentrations of nitrogen dioxide (NO₂) and fine particulate matters (PM_2.5) at 34 sites in the Beijing area. The results revealed that lockdown measures caused large reductions while meteorology offset a large fraction of the decrease in surface concentrations. GAM estim

SUBMITTER: Hua J

PROVIDER: S-EPMC8096144 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Dataset Information

Quantitative estimation of meteorological impacts and the COVID-19 lockdown reductions on NO₂ and PM_2.5 over the Beijing area using Generalized Additive Models (GAM).

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Quantitative estimation of meteorological impacts and the COVID-19 lockdown reductions on NO2 and PM2.5 over the Beijing area using Generalized Additive Models (GAM).

OmicsDI is part of the ELIXIR infrastructure

Tweets

Quantitative estimation of meteorological impacts and the COVID-19 lockdown reductions on NO₂ and PM_2.5 over the Beijing area using Generalized Additive Models (GAM).