Author:
Wang Yu,Dan Mo,Dou Yan,Guo Ling,Xu Zhizhen,Ding Ding,Shu Mushui
Abstract
IntroductionAir pollution imposes a significant burden on public health. Compared with the popular air quality index (AQI), the air quality health index (AQHI) provides a more comprehensive approach to measuring mixtures of air pollutants and is suitable for overall assessments of the short-term health effects of such mixtures.MethodsWe established an AQHI and cumulative risk index (CRI)-AQHI for Tianjin using single–and multi-pollutant models, respectively, as well as environmental, meteorological, and daily mortality data of residents in Tianjin between 2018 and 2020.Results and discussionCompared with the AQI, the AQHI and CRI-AQHI established herein correlated more closely with the exposure-response relationships of the total mortality effects on residents. For each increase in the interquartile range of the AQHI, CRI-AQHI and AQI, the total daily mortality rates increased by 2.06, 1.69 and 0.62%, respectively. The AQHI and CRI-AQHI predicted daily mortality rate of residents more effectively than the AQI, and the correlations of AQHI and CRI-AQHI with health were similar. Our AQHI of Tianjin was used to establish specific (S)-AQHIs for different disease groups. The results showed that all measured air pollutants had the greatest impact on the health of persons with chronic respiratory diseases, followed by lung cancer, and cardiovascular and cerebrovascular diseases. The AQHI of Tianjin established in this study was accurate and dependable for assessing short-term health risks of air pollution in Tianjin, and the established S-AQHI can be used to separately assess health risks among different disease groups.
Subject
Public Health, Environmental and Occupational Health
Cited by
1 articles.
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1. IoT and Machine Learning-Based System for Predicting, Monitoring and Controlling Indoor Air Quality;2024 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR);2024-05-16