Improvement and Predictability of Urban Air Quality Under Different Stages of the COVID-19 Pandemic

Abstract

This paper focuses the various impact of the severity of COVID-19 development on air quality in different types of cities. We analyze the different degrees of improvement of concentrations of six air pollutants (PM2.5, PM10, SO2, NO2, CO and O3) in different types of Chinese cities with difference method and ensemble empirical mode decomposition (EEMD), and then adopt the recursive plots (RPs) and recursive quantitative analysis (RQA) to discuss whether air quality is more difficult to predict during the outbreak. The empirical results indicate that: (1) After the initial outbreak, only the emissions of NO2, CO and PM2.5 declined for the first 1-3 months, and during the fourth to fifth months the emissions of six air pollutants were elevated in most cities; (2) For the cities with serious epidemic situations in Hubei, the air quality is improved significantly, but for the cities experiencing a second outbreak, the air quality was first enhanced and then deteriorated, and the sensitivity of air quality to COVID-19 re-outbreak is decreasing; (3) In comparison, the predictability of AQI has declined in cities with serious epidemic situations in Hubei, but AQI achieves a stable state sooner in cities with mild epidemic.

Keywords: Air quality, Predictability, COVID-19 pandemic, Ensemble empirical mode decomposition, Recursive quantitative analysis