Unsupervised PM_2.5 anomalies in China induced by the COVID-19 epidemic.

ABSTRACT: To stop the spread of COVID-19 (2019 novel coronavirus), China placed lockdown on social activities across China since mid-January 2020. The government actions significantly affected emissions of atmospheric pollutants and unintentionally created a nationwide emission reduction scenario. In order to assess the impacts of COVID-19 on fine particular matter (PM_2.5) levels, we developed a "conditional variational autoencoder" (CVAE) algorithm based on the deep learning to discern unsupervised PM_2.5 anomalies in Chines cities during the COVID-19 epidemic. We show that the timeline of changes in number of cities with unsupervised PM_2.5 anomalies is consistent with the timeline of WHO's response to COVID-19. Using unsupervised PM_2.5 anomaly as a time n

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC8247192 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature