Characterizing Multiple Air Pollutant Indices Based on Their Effects on the Mortality in Tehran, Iran during 2012–2017

[Display omitted] •AAQI provides some advantages to adjust the AQI limitations•The indices significantly associated with the cardiovascular cause of deaths•The indices significantly associated with specific causes of death at shorter lags•AAQI was better in predicting the health outcomes of air poll...

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Veröffentlicht in:Sustainable cities and society 2020-08, Vol.59, p.102222, Article 102222
Hauptverfasser: Janjani, Hosna, Hassanvand, Mohammad Sadegh, Kashani, Homa, Yunesian, Masud
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Sprache:eng
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Zusammenfassung:[Display omitted] •AAQI provides some advantages to adjust the AQI limitations•The indices significantly associated with the cardiovascular cause of deaths•The indices significantly associated with specific causes of death at shorter lags•AAQI was better in predicting the health outcomes of air pollution compared to AQI The current limitations of Air Quality Indices (AQIs) have intrigued the attention on capturing the combined health effects of multiple air pollutants. Although previous studies have proposed the Aggregate Air Quality Index (AAQI) as the model of multiple air pollutants, there is no study evaluated the validity of the model considering the health effects related to air pollution. In this study, the three AAQI indices were proposed based on the variable parameter (ρ = 2, 2.5 and 3) and the Air Quality Index (AQI) was compared with AAQI to clarify which model predicts the mortality more effectively in the mega-city of Tehran. Time-series analysis was conducted to estimate the associations between air quality indices and cause - specific mortality at different lags in Tehran, Iran, and the fitted models were compared based on Akaike Information Criterion (AIC). Strongest associations were observed between deaths caused by Cardiovascular Diseases (CVD) and Respiratory Diseases (RPD) with AAQI ( = 2) by 2.01 % (95 % CI: 1.27–2.75) and 1.82 % (95 % CI: 1.10–2.54) per interquartile range (IQR) increase in the AAQI, respectively at moving average lags of 0–2 and 0–3. Finally, evaluation of the indices showed that AAQI (ρ = 2), AAQI (ρ = 2.5), AAQI (ρ = 3), and AQI, respectively were better in predicting the health outcomes of air pollution in the majority of lag times although, these differences were not statistically significant.
ISSN:2210-6707
2210-6715
DOI:10.1016/j.scs.2020.102222