Establishment of air quality health index based on cumulative health risk of air pollution in Tianjin
- VernacularTitle:基于大气污染累积健康风险的天津市空气质量健康指数研究
- Author:
Yang NI
1
;
Jimian ZHANG
1
;
Yu BAI
2
;
Qiang ZENG
2
Author Information
- Publication Type:Selectedarticle
- Keywords: air pollution; cumulative risk index; air quality health index; Tianjin
- From: Journal of Environmental and Occupational Medicine 2022;39(11):1242-1248
- CountryChina
- Language:Chinese
- Abstract: Background Cumulative risk index (CRI), as a commonly used approach to estimate the joint effects of multiple air pollutants on health, has been used by few studies to construct an air quality health index (AQHI). Objective To construct an AQHI based on the CRI of air pollution in Tianjin and evaluate the validity of the AQHI. Methods Daily data on air pollutants, meteorological factors, and non-accidental deaths during 2015–2019 in Tianjin were collected to create a time-series object. Descriptive statistical analyses were used to describe the characteristics of the data. To determine the best lag day and indicative pollutant, single-pollutant and two-pollutant generalized additive models were fitted to construct the exposure-response relationships between air pollutants and non-accidental deaths. After that we evaluated a CRI of air pollution using multi-pollutant models and constructed an AQHI and its classifications based on the CRI. Finally, we compared the exposure-response associations and coefficients of the AQHI and the conventional air quality index (AQI) with non-accidental deaths, and evaluated the health risk communication validity of the AQHI using generalized cross validation (GCV) values and R2 values. Results We selected lag1 as the best lag day and PM2.5, SO2, NO2 and O3 as the appropriate pollutants according to the unqualified rates of pollutants and significant statistical results. One μg·m−3 increase of PM2.5, SO2, NO2, and O3 was associated with −0.00002, 0.00079, 0.00015, and 0.00042 increase in effect size b of the non-accidental mortality, respectively. Based on these coefficients, we calculated the CRI and AQHI. According to a pre-determined classification scheme of the AQHI, the air quality of 63% study days was low risks and that of 34% study days was median risks. The associations of AQHI and AQI with non-accidental deaths in different populations were evaluated. The results showed that the excess risks of non-accidental deaths in total, female, and male populations for per interquartile range (IQR) increase in AQHI were higher than the corresponding values of AQI. The GCV values of the AQHI model (2.694, 1.819, and 1.938, respectively) were lower than those of the AQI model (2.747, 1.850, and 1.961, respectively), and the R2 values of the AQHI model (0.849, 0.780, and 0.820, respectively) were higher than those of the AQI model (0.846, 0.776, and 0.817, respectively). Conclusion Compared with AQI, the CRI-based AQHI may communicate the air pollution-related health risk to the public more effectively in Tianjin.
