1 research outputs found
Counterfactual time series analysis for the air pollution during the outbreak of COVID-19 in Wuhan
Environmental issues are becoming one of the main topics of concern for
society, and the quality of air is closely linked to people's lives. Previous
studies have examined the effects of abrupt interventions on changes in air
pollution. For example, researchers used an interrupted time series design to
quantify the impact of the 1990 Dublin coal ban; and a regression discontinuity
to determine the arbitrary spatial impact of the Huaihe River policy in China.
An important feature of each of these studies is that they investigated abrupt
and localized changes over relatively short time spans (the Dublin coal ban)
and spatial scales (the Huaihe policy). Due to the abrupt nature of these
interventions, defining a hypothetical experiment in these studies is
straightforward. In response to the novel coronavirus outbreak, China
implemented 'the largest quarantine in human history' in Wuhan on January 23,
2020. Similar measures were implemented in other Chinese cities. Since then,
the movement of people and associated production and consumption activities
have been significantly reduced. This provides us with an unprecedented
opportunity to estimate the changes in air pollution brought about by this
sudden "silent" move. We speculate that the initiative will lead to a
significant reduction in regional air pollution. Thus, we performed
counterfactual time series analysis on Wuhan air quality data from 2017-2022
based on three models, SARIMA, LSTM and XGBOOST, and compared the excellence of
different models. Finally, we conclude that 'silent' measures will
significantly reduce air pollution. Using this conclusion to further
investigate the extent of air pollution reduction will help the country to
better designate environmental policies.Comment: 9 pages, 8 figure