LAUSR

To understand the temporal variation, spatial distribution and factors influencing the public’s sensitivity to air pollution in China, this study collected air pollution data from 2210 air pollution monitoring sites from around China and used keyword-based filtering to identify individual messages related to air pollution and health on Sina Weibo during 2017–2021. By analyzing correlations between concentrations of air pollutants (PM2.5, PM10, CO, NO2, O3 and SO2) and related microblogs (air-pollution-related and health-related), it was found that the public is most sensitive to changes in PM2.5 concentration from the perspectives of both China as a whole and individual provinces. Correlations between air pollution and related microblogs were also stronger when and where air quality was worse, and they were also affected by socioeconomic factors such as population, economic conditions and education. Based on the results of these correlation analyses, scientists can survey public concern about air pollution and related health outcomes on social media in real time across the country and the government can formulate air quality management measures that are aligned to public sensitivities.