Development of an air quality health index for respiratory disease mortality associated with air pollutant mixtures using weighted quantile sum regression
- VernacularTitle:大气污染物复合暴露对呼吸系统疾病死亡效应的空气质量健康指数构建：基于加权分位数和回归模型
- Author:
Yang NI
1
;
Jimian ZHANG
1
;
Qiang ZENG
1
Author Information
- Publication Type:Investigation
- Keywords: air pollution; weighted quantile sum regression; air quality index; air quality health index; respiratory disease
- From: Journal of Environmental and Occupational Medicine 2026;43(8):944-950
- CountryChina
- Language:Chinese
- Abstract: Background The air quality index (AQI), which is based on the concentration of a dominant pollutant, and the air quality health index (AQHI), which is constructed using excess risk estimates for individual pollutants, may not fully capture the health risks associated with combined exposure to air pollutants. These approaches also provide limited information on the relative contributions of different pollutants. Given the geographical heterogeneity in the composition and health effects of air pollutant mixtures, it is necessary to develop region- and disease-specific AQHIs for different population groups. Objective To develop and evaluate a health risk-based AQHI for respiratory disease mortality associated with air pollutant mixtures using the weighted quantile sum (WQS) regression model and to quantify the relative weight contributions of selected pollutants. Methods Daily data on six air pollutants, the AQI, meteorological factors, and deaths from respiratory diseases in the central urban districts of Tianjin from 2014 to 2019 were collected and integrated into a time-series database. First, generalized additive models (GAMs) were applied to identify indicator pollutants associated with respiratory disease mortality. Second, the WQS regression model was then applied to estimate the relative weight contributions of selected pollutants and to establish the exposure-response relationship between air pollutant mixtures and respiratory disease mortality for AQHI development. Finally, the exposure-response relationships of the AQHI and AQI with respiratory disease mortality were evaluated and compared across population subgroups and seasons. Results Fine particulate matter (PM2.5), inhalable particulate matter (PM10), sulfur dioxide (SO2), and ozone (O3) were identified as indicator pollutants for combined air pollution exposure. Particulate matter, including PM2.5 and PM10, and O3 contributed the most to the mixture index, followed by SO2. Each interquartile range (IQR) increase in the WQS-based AQHI was significantly associated with respiratory disease mortality in the total population, males, elderly individuals, non-elderly individuals, and during the warm season. The corresponding excess risks (ER) were 3.79% (95%CI: 1.24%, 6.27%), 4.37% (95%CI: 0.96%, 7.67%), 3.16% (95%CI: 0.47%, 5.77%), 9.36% (95%CI: 2.01%, 16.15%), and 7.13% (95%CI: 1.31%, 13.30%), respectively. Compared with the AQI, the AQHI showed stronger associations with respiratory disease mortality across different population subgroups and seasons. Conclusion PM2.5, PM10, and O3 contribute substantially to the association between air pollutant mixtures and respiratory disease mortality in Tianjin. Compared with the AQI, the WQS-based AQHI better characterizes the association between air quality and respiratory disease mortality across population subgroups.
