Assessing the Impact of COVID-19 Lockdown on Air Pollutants and Wildfires in China
Keywords:COVID-19, wildfires, air pollutants, machine learning
This research paper focuses on the impact of COVID-19 lockdown on air pollutants and wildfires in various provinces of China. The study uses machine learning techniques to analyze the association between lockdown restrictions and the subsequent decline in air pollution emissions, which in turn led to a reduction in fire count (FC). The frequency and severity of wildfires have been rising in China, with major negative effects on the country's air quality. Globally unprecedented responses to the COVID-19 epidemic have included lockdowns and limitations on human activity. The purpose of this study is to evaluate how the COVID-19 lockdown has affected air pollutants and wildfires in China. Specially, the focus is on the reduction in ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO) levels in the air. Satellite images were used before and during the lockdown to gauge the impact. The observed reduction in NO2, O3 and CO concentrations during the COVID-19 lockdown demonstrate the potential advantages of taking action to reduce air pollution. The study tries to find connections between the incidence of wildfires and the subsequent emission of air pollutants using machine learning methods. The study creates maps showing the influence of lockdown restrictions on the levels of air pollutants and the number of fires by analyzing historical data on air pollution measurements. These findings highlight the significance of sustainable practices, environmental regulations and offer insightful information regarding the interaction between human activities, air pollutants and wildfires in China.