Masselot Pierre, Kan Haidong, Kharol Shailesh K, Bell Michelle L, Sera Francesco, Lavigne Eric, Breitner Susanne, das Neves Pereira da Silva Susana, Burnett Richard T, Gasparrini Antonio, Brook Jeffrey R
Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom.
Department of Environmental Health, School of Public Health, Fudan University, Shanghai, China.
Environ Epidemiol. 2024 Oct 30;8(6):e342. doi: 10.1097/EE9.0000000000000342. eCollection 2024 Dec.
Fine particulate matter (PM) occurs within a mixture of other pollutant gases that interact and impact its composition and toxicity. To characterize the local toxicity of PM, 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 PM toxicity.
The PMCI is constructed as an index spanning seven different pollutants, relative to the PM 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 PM 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.
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 PM or its composition.
The PMCI represents an efficient and simple predictor of local PM-related mortality, providing evidence that PM 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.
细颗粒物（PM）存在于其他污染气体的混合物中，这些气体相互作用并影响其组成和毒性。为了表征PM的局部毒性，拥有一个考虑整个污染物混合物（包括气态污染物）的指标是很有用的。我们考虑使用最近提出的污染物混合物复杂性指数（PMCI）来评估它与PM毒性的相关程度。
PMCI被构建为一个相对于PM水平涵盖七种不同污染物的指数。我们使用来自北半球264个城市的数据进行标准的两阶段分析。第一阶段估计每日PM与全因死亡率之间特定城市的相对风险，然后将这些风险汇总到第二阶段的元回归模型中，我们用该模型估计PMCI的效应修正。
对于PMCI的四分位距增加（从1.09到1.95），我们估计相对超额风险为1.0042（95%置信区间：1.0023，1.0061）。PMCI预测了国内相对风险异质性的很大一部分，而解释的国家间异质性要少得多。主要模型的赤池信息准则和贝叶斯信息准则低于考虑PM氧化能力或其组成的替代元回归模型。
PMCI是局部PM相关死亡率的有效且简单的预测指标，提供了PM毒性取决于周围气态污染物混合物的证据。随着污染物遥感技术的出现，PMCI可以提供一个有用的指标来跟踪空气质量。
