Air pollution mixture complexity and its effect on PM2.5-related mortality: A multicountry time-series study in 264 cities.

IF 3.3 Q2 ENVIRONMENTAL SCIENCES Environmental Epidemiology Pub Date : 2024-10-30 eCollection Date: 2024-12-01 DOI:10.1097/EE9.0000000000000342
Pierre Masselot, Haidong Kan, Shailesh K Kharol, Michelle L Bell, Francesco Sera, Eric Lavigne, Susanne Breitner, Susana das Neves Pereira da Silva, Richard T Burnett, Antonio Gasparrini, Jeffrey R Brook
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Abstract

Background: Fine particulate matter (PM2.5) occurs within a mixture of other pollutant gases that interact and impact its composition and toxicity. To characterize the local toxicity of PM2.5, it is useful to have an index that accounts for the whole pollutant mix, including gaseous pollutants. We consider a recently proposed pollutant mixture complexity index (PMCI) to evaluate to which extent it relates to PM2.5 toxicity.

Methods: The PMCI is constructed as an index spanning seven different pollutants, relative to the PM2.5 levels. We consider a standard two-stage analysis using data from 264 cities in the Northern Hemisphere. The first stage estimates the city-specific relative risks between daily PM2.5 and all-cause mortality, which are then pooled into a second-stage meta-regression model with which we estimate the effect modification from the PMCI.

Results: We estimate a relative excess risk of 1.0042 (95% confidence interval: 1.0023, 1.0061) for an interquartile range increase (from 1.09 to 1.95) of the PMCI. The PMCI predicts a substantial part of within-country relative risk heterogeneity with much less between-country heterogeneity explained. The Akaike information criterion and Bayesian information criterion of the main model are lower than those of alternative meta-regression models considering the oxidative capacity of PM2.5 or its composition.

Conclusions: The PMCI represents an efficient and simple predictor of local PM2.5-related mortality, providing evidence that PM2.5 toxicity depends on the surrounding gaseous pollutant mix. With the advent of remote sensing for pollutants, the PMCI can provide a useful index to track air quality.

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空气污染混合物的复杂性及其对 PM2.5 相关死亡率的影响:264 个城市的多国时间序列研究。
背景:细颗粒物(PM2.5)出现在其他污染气体的混合物中,这些气体相互作用,影响其成分和毒性。要描述 PM2.5 的局部毒性,最好能有一个考虑到整个污染物混合物(包括气态污染物)的指数。我们考虑了最近提出的污染物混合物复杂性指数(PMCI),以评估它在多大程度上与 PM2.5 的毒性有关:污染物混合物复杂性指数是相对于PM2.5水平而构建的一个涵盖七种不同污染物的指数。我们使用北半球264个城市的数据进行了标准的两阶段分析。第一阶段估算城市每日PM2.5与全因死亡率之间的相对风险,然后将其汇集到第二阶段的元回归模型中,利用该模型我们估算了PMCI的效应修正:我们估计 PMCI 在四分位数范围内(从 1.09 到 1.95)增加的相对超额风险为 1.0042(95% 置信区间:1.0023,1.0061)。PMCI 预测了国家内相对风险异质性的很大一部分,而对国家间异质性的解释要少得多。主要模型的阿凯克信息标准和贝叶斯信息标准低于考虑PM2.5氧化能力或其组成的其他元回归模型:PMCI 是预测当地 PM2.5 相关死亡率的有效而简单的方法,它提供了 PM2.5 的毒性取决于周围气体污染物组合的证据。随着污染物遥感技术的出现,PMCI 可以为跟踪空气质量提供有用的指数。
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来源期刊
Environmental Epidemiology
Environmental Epidemiology Medicine-Public Health, Environmental and Occupational Health
CiteScore
5.70
自引率
2.80%
发文量
71
审稿时长
25 weeks
期刊最新文献
Air pollution mixture complexity and its effect on PM2.5-related mortality: A multicountry time-series study in 264 cities. Short-term, lagged association of airway inflammation, lung function, and asthma symptom score with PM2.5 exposure among schoolchildren within a high air pollution region in South Africa. Associations of per- and polyfluoroalkyl substances with human milk metabolomic profiles in a rural North American cohort. Association between fine particulate matter (PM2.5) and infant mortality in a North Carolina birth cohort (2003-2015). Impacts of land-use and land-cover changes on temperature-related mortality.
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