Improved morbidity-based air quality health index development using Bayesian multi-pollutant weighted model

Huang, Wenxiu; He, Weiyun; Knibbs, Luke D.; Jalaludin, Bin; Lee, Hyewon; Morawska, Lidia; Heinrich, Joachim; Chen, Duo-Hong; Yu, Yunjiang; Zeng, Xiao‐Wen; Yu, Hongyao; Yang, Bo‐Yi; Hu, Liwen; Liu, Ru-Qing; Feng, Weiyue; Dong, Guang‐Hui

doi:10.1016/j.envres.2021.112397

Cited by 11 publications

(7 citation statements)

References 34 publications

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“…In a study in the United States, Wesson et al [38] compared the single-pollutant control strategy with the "Multi-pollutant, Risk-based" control strategy, and found that the latter greatly reduced the per-person emissions of PM 2.5 and O 3 , and had greater health benefits. In China, Huang et al [39] selected PM 2.5 , NO 2 , O 3 and SO 2 as the air pollutant mixture to examine the daily contribution of air pollutants to the risk of outpatient visits in Guangzhou and found that NO 2 and O 3 made prominent contribution. Zhu et al [40] noted that the primary type of high multi-pollutant air pollution in Tianjin in 2020 was PM 2.5 -NO 2 co-pollution.…”

Section: Discussionmentioning

confidence: 99%

Identification of Health Effects of Complex Air Pollution in China

Zhao

Sun

et al. 2022

IJERPH

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After the Chinese government introduced a series of policies to strengthen the control of air pollution, the concentration of particulate matter has decreased, but the concentration of ozone has increased, and the problem of complex air pollution still exists, posing a serious threat to public health. Therefore, disentangling the health effect of multi-pollutants has been a long-discussed challenge in China. To evaluate the adverse effects of complex air pollution, a generalized additive model was used to assess the health risks of different pollution types in eight metropolises in different climates in China from 2013 to 2016. Instead of directly introducing multiple pollutant concentrations, we integrated the concentration levels of PM2.5, NO2, and O3 into a set of predictors by grouping methods and divided air pollution into three high single-pollutant types and four high multi-pollutant types to calculate mortality risk in different types. The comprehensive results showed that the impact of high multi-pollutant types on mortality risk was greater than that of high single-pollutant types. Throughout the study period, the high multi-pollutant type with high PM2.5, NO2, and O3 and the high multi-pollutant type with high PM2.5 and NO2 were more associated with death, and the highest RRs were 1.129 (1.080, 1.181) and 1.089 (1.066, 1.113), respectively. In addition, the pollution types that most threaten people are different in different cities. These differences may be related to different pollution conditions, pollutant composition, and indoor–outdoor activity patterns in different cities. Seasonally, the risk of complex air pollution is greater in most cities in the warm season than in the cold season. This may be caused by the modifying effects of high temperature on pollutants in addition to different indoor–outdoor activity patterns in different seasons. The results also show that calculating the effect of individual air pollutants separately and adding them together may lead to an overestimation of the combined effect. It further highlights the urgency and need for air pollution health research to move towards a multi-pollutant approach that considers air pollution as a whole in the context of atmospheric abatement and global warming.

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Section: Discussionmentioning

confidence: 99%

Identification of Health Effects of Complex Air Pollution in China

Zhao

Sun

et al. 2022

IJERPH

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“…Because of differences in daily relative changes in the AQHI and AQI, the IQR was used as a measurement scale to examine the abilities of the AQHI and AQI to predict health hazards. The greater each IQR increase corresponding to an increase in total mortality, the stronger the correlation between the IQR and total mortality [26,30]. As illustrated in Figure 7, in Beijing, each IQR increase in the AQHI led to a 1.894% rise in daily mortality.…”

Section: Validity Analysis Of the Aqhimentioning

confidence: 95%

“…These activities can facilitate the construction of a reasonable evaluation index for providing public health recommendations. Some cities in China (e.g., Hong Kong, Shanghai, Tianjin, and Guangzhou) employ an AQHI [17,19,[23][24][25][26]. The air pollution in Beijing, the capital of China, has become severe due to the rapid development of China's economy, and this pollution has exacerbated citizens' health problems.…”

Section: Introductionmentioning

confidence: 99%

Construction of Multipollutant Air Quality Health Index and Susceptibility Analysis Based on Mortality Risk in Beijing, China

Wang

Ding

et al. 2022

Atmosphere

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Air pollution places a considerable disease burden on the public. Compared with the widely applied air quality index (AQI), the air quality health index (AQHI) provides a more comprehensive measure of multiple pollutants. In this study, AQHI was constructed using environmental data, meteorological data, and the daily mortality data of Beijing residents from 2018 to 2020. Factors increasing the susceptibility of the population to the health effects of air pollution were identified to aid the construction of a specific AQHI (S-AQHI) for susceptible populations. The findings indicated that older adults, women, and people with respiratory disease are more susceptible to the short-term health effects of air pollution. The relative deviation in the AQHI and S-AQHI for changes in daily mortality percentage of various specific populations ranged from only 1.4% to 10.3%, indicating the universality of the AQHI in its capacity to predict health risks. The Spearman coefficient of correlation between the AQHI and AQI was 0.78 (p < 0.01). Each increase in the interquartile range of the AQHI and AQI results in an increase of 1.894% and 1.029% in the total daily mortality, respectively, demonstrating the stronger capacity of the AQHI to predict daily mortality compared to the AQI.

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“…One such technique is the use of the cumulative risk index ( CRI ), which involves the linear combination of individual coefficients. This approach enables accurate estimation of cumulative effects, even in cases where there is a high correlation among variables ( 14 ), and has been recommended for joint estimates of multipollutant exposure effects on health outcomes ( 16 ). However, CRI -related studies are quite limited; most of these studies have been conducted in developed countries, and studies in developing countries are lacking ( 17 , 18 ).…”

Section: Introductionmentioning

confidence: 99%

Short-term joint effects of ambient PM2.5 and O3 on mortality in Beijing, China

Zhang,

Xin

et al. 2023

Front. Public Health

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IntroductionIn recent years, air pollution caused by co-occurring PM2.5 and O3, named combined air pollution (CAP), has been observed in Beijing, China, although the health effects of CAP on population mortality are unclear.MethodsWe employed Poisson generalized additive models (GAMs) to evaluate the individual and joint effects of PM2.5 and O3 on mortality (nonaccidental, respiratory, and cardiovascular mortality) in Beijing, China, during the whole period (2014–2016) and the CAP period. Adverse health effects were assessed for percentage increases (%) in the three mortality categories with each 10-μg/m3 increase in PM2.5 and O3. The cumulative risk index (CRI) was adopted as a novel approach to quantify the joint effects.ResultsThe results suggested that both PM2.5 and O3 exhibited the greatest individual effects on the three mortality categories with cumulative lag day 01. Increases in the nonaccidental, cardiovascular, and respiratory mortality categories were 0.32%, 0.36%, and 0.43% for PM2.5 (lag day 01) and 0.22%, 0.37%, and 0.25% for O3 (lag day 01), respectively. There were remarkably synergistic interactions between PM2.5 and O3 on the three mortality categories. The study showed that the combined effects of PM2.5 and O3 on nonaccidental, cardiovascular, and respiratory mortality were 0.34%, 0.43%, and 0.46%, respectively, during the whole period and 0.58%, 0.79%, and 0.75%, respectively, during the CAP period. Our findings suggest that combined exposure to PM2.5 and O3, particularly during CAP periods, could further exacerbate their single-pollutant health risks.ConclusionThese findings provide essential scientific evidence for the possible creation and implementation of environmental protection strategies by policymakers.

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Improved morbidity-based air quality health index development using Bayesian multi-pollutant weighted model

Cited by 11 publications

References 34 publications

Identification of Health Effects of Complex Air Pollution in China

Identification of Health Effects of Complex Air Pollution in China

Construction of Multipollutant Air Quality Health Index and Susceptibility Analysis Based on Mortality Risk in Beijing, China

Short-term joint effects of ambient PM2.5 and O3 on mortality in Beijing, China

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