Revealing the spatiotemporal response of air quality and public attention: a case study of Shandong province, China

Abstract Air quality plays a pivotal role in human health and well-being, with growing public awareness of its importance. However, the response relationship between air quality and public attention is unclear. Utilizing data from the Baidu Index, air quality, and demographic data, we developed the per capita public attention to air quality (PAAQ) index. Employing seasonal time series analysis, spatial analysis, spatial lag models, we examined the spatiotemporal response and driving factors of air quality and PAAQ index in Shandong province, China from 2014 to 2021. Our study focused on keywords analysis and spatiotemporal dimensions to uncover regional disparities in public response and concerns regarding air pollution. The results indicate that: (1) Consistent improvements in air quality have led to a decline in public concern, particularly since 2020. Monthly variations in PAAQ, AQI, PM 2.5 , PM 10 , CO, SO 2 , and NO 2 exhibit a U-shaped pattern, with lower levels in summer and higher levels in winter, while O 3 exhibits an inverse trend. (2) Air pollution in Shandong province demonstrates spatial clustering, with high pollution concentrations in the central, northwestern, and southern regions, whereas the Shandong Peninsula exhibits relatively better air quality. (3) Industrial cities in central and southern Shandong province experience high PAAQ levels due to severe winter pollution. PAAQ is more pronounced and forward-looking in developed areas, decreasing rapidly as air quality improves. (4) The proportion of the secondary industry, the urban population proportion, the number of Internet users per capita, and the AQI positively amplify PAAQ. The PAAQ index serves as a quantitative tool to gauge public feedback on air quality management, aiding governments in identifying key areas for intervention, optimizing spatiotemporal pollution control strategies, and promoting regional collaborative governance.