Assessing air quality changes in heavily polluted cities during the COVID-19 pandemic: A case study in Xi'an, China

Chinese government has instated strict restrictions to halt the spread of COVID-19. Given the complete shutdown of emission-resources, like traffic, factories, restaurants, and construction sites, responses to this pandemic have wrought unintended consequences in air quality. We assessed air pollution during the COVID-19 pandemic in terms of PM2.5, PM10, and O3 in Xi'an, China, and revealed the relations between air quality and potential emission resources. We gleaned pollutant concentration data of O3, PM2.5, and PM10 from five monitoring sites and identified their trending during the observed periods. We also deployed ArcGIS to interpolate points among data detected by 130 monitoring sites and obtained spatial distribution of pollution during the observed periods. Correlation analysis helped us reveal the relations between pollutants and seven sources. The results showed that during the lockdown section, the concentration of O3 rose by 100.61 %, and those of PM2.5 and PM10 dropped by 22.4 % and 20.7 %, respectively; and during the recovering section, the concentration of PM10 increased by 12.8 %. The spatial distributions also helped us identify two high-polluted areas and two risky areas where PM10 increased sharply. The correlation analysis also implied that decreasing emission sources is the key to improve air quality. Our study also suggests that coordinated control on ozone and particles should be the focus in the future, and the two high-polluted and the two risky areas require immediate administrative interference. Our study can be a valuable reference for public propaganda on green life and governments' sustainable development strategies. The research method for Xi'an might also inspire similar studies on other cities.

Automated summary

This study analyzed air quality data during the COVID-19 pandemic in Xi'an, China, revealing changes in pollutant concentrations during lockdown and recovery periods. Spatial distribution showed two high-polluted areas and two risky areas. Correlation analysis indicated reducing emission sources is crucial for improving air quality.