Exploring associations between airborne pollen, air pollutants, and asthma-related public attention in Yan’an city: A digital epidemiology study based on Baidu index
Xiaoya Wang, Bo Liu, Jingjing Lei, Yujie Yang, Wu Yan, Yuxuan Li, Yuanxia Li, Jinwei HeBackground
Allergic asthma shows clear seasonal variation and is influenced by environmental factors. Internet search data provide a digital epidemiology tool for tracking asthma-related public attention at the population level.
Objective
To examine seasonal patterns and lagged temporal associations between airborne pollen, air pollutants, and asthma-related public attention in Yan’an City.
Methods
Airborne pollen monitoring data, meteorological parameters and concentrations of air pollutants were collected in the urban area of Yan’an City from March 1, 2023 to October 31, 2025. During the same period, Baidu Index data were retrieved for seven asthma-related search terms. Spearman correlation, Kruskal-Wallis H and cross-correlation function (CCF) analyses were used to assess temporal associations, group differences and lagged correlations. Generalized additive models (GAMs) were performed as sensitivity analyses to evaluate whether the observed associations remained after adjustment for meteorological variables and long-term temporal trends.
Results
Baidu Index showed clear seasonal fluctuations. Weak-to-moderate positive temporal associations were observed between asthma-related public attention and concentrations of dominant pollen taxa, including Asteraceae (particularly
Conclusion
This ecological time-series analysis suggests seasonal and lagged temporal co-variation between airborne pollen, air pollution, and asthma-related public attention in Yan’an City. Pollen-related associations were more consistent, whereas pollutant-related associations were less robust.