Logo Logo
Switch Language to German

Huang, Wen-Zhong; He, Wei-Yun; Knibbs, Luke D.; Jalaludin, Bin; Guo, Yu-Ming; Morawska, Lidia; Heinrich, Joachim; Chen, Duo-Hong; Yu, Yun-Jiang; Zeng, Xiao-Wen; Yu, Hong-Yao; Yang, Bo-Yi; Hu, Li-Wen; Liu, Ru-Qing; Feng, Wen-Ru and Dong, Guang-Hui (2021): Improved morbidity-based air quality health index development using Bayesian multi-pollutant weighted model. In: Environmental Research, Vol. 204, 112397

Full text not available from 'Open Access LMU'.


Background: The widely used Air Quality Index (AQI) has been criticized due to its inaccuracy, leading to the development of the air quality health index (AQHI), an improvement on the AQI. However, there is currently no consensus on the most appropriate construction strategy for the AQHI. Objectives: In this study, we aimed to evaluate the utility of AQHIs constructed by different models and health outcomes, and determine a better strategy. Methods: Based on the daily time-series outpatient visits and hospital admissions from 299 hospitals (January 2016-December 2018), and mortality (January 2017-December 2019) in Guangzhou, China, we utilized cumulative risk index (CRI) method, Bayesian multi-pollutant weighted (BMW) model and standard method to construct AQHIs for different health outcomes. The effectiveness of AQHIs constructed by different strategies was evaluated by a two-stage validation analysis and examined their exposure-response relationships with the cause-specific morbidity and mortality. Results: Validation by different models showed that AQHI constructed with the BMW model (BMW-AQHI) had the strongest association with the health outcome either in the total population or subpopulation among air quality indexes, followed by AQHI constructed with the CRI method (CRI-AQHI), then common AQHI and AQI. Further validation by different health outcomes showed that AQHI constructed with the risk of outpatient visits generally exhibited the highest utility in presenting mortality and morbidity, followed by AQHI constructed with the risk of hospitalizations, then mortality-based AQHI and AQI. The contributions of NO2 and O-3 to the final AQHI were prominent, while the contribution of SO2 and PM2.5 were relatively small. Conclusions: The BMW model is likely to be more effective for AQHI construction than CRI and standard methods. Based on the BMW model, the AQHI constructed with the outpatient data may be more effective in presenting short-term health risks associated with the co-exposure to air pollutants than the mortality-based AQHI and existing AQIs.

Actions (login required)

View Item View Item