Multinational prediction of household and personal exposure to fine particulate matter (PM2.5) in the PURE cohort study

Shupler, Matthew ORCID: 0000-0003-0259-9101, Hystad, Perry, Birch, Aaron, Li Chu, Yen, Jeronimo, Matthew, Miller-Lionberg, Daniel, Gustafson, Paul, Rangarajan, Sumathy, Mustaha, Maha, Heenan, Laura
et al (show 34 more authors) (2022) Multinational prediction of household and personal exposure to fine particulate matter (PM2.5) in the PURE cohort study. ENVIRONMENT INTERNATIONAL, 159. 107021-.

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<h4>Introduction</h4>Use of polluting cooking fuels generates household air pollution (HAP) containing health-damaging levels of fine particulate matter (PM<sub>2.5</sub>). Many global epidemiological studies rely on categorical HAP exposure indicators, which are poor surrogates of measured PM<sub>2.5</sub> levels. To quantitatively characterize HAP levels on a large scale, a multinational measurement campaign was leveraged to develop household and personal PM<sub>2.5</sub> exposure models.<h4>Methods</h4>The Prospective Urban and Rural Epidemiology (PURE)-AIR study included 48-hour monitoring of PM<sub>2.5</sub> kitchen concentrations (n = 2,365) and male and/or female PM<sub>2.5</sub> exposure monitoring (n = 910) in a subset of households in Bangladesh, Chile, China, Colombia, India, Pakistan, Tanzania and Zimbabwe. PURE-AIR measurements were combined with survey data on cooking environment characteristics in hierarchical Bayesian log-linear regression models. Model performance was evaluated using leave-one-out cross validation. Predictive models were applied to survey data from the larger PURE cohort (22,480 households; 33,554 individuals) to quantitatively estimate PM<sub>2.5</sub> exposures.<h4>Results</h4>The final models explained half (R<sup>2</sup> = 54%) of the variation in kitchen PM<sub>2.5</sub> measurements (root mean square error (RMSE) (log scale):2.22) and personal measurements (R<sup>2</sup> = 48%; RMSE (log scale):2.08). Primary cooking fuel type, heating fuel type, country and season were highly predictive of PM<sub>2.5</sub> kitchen concentrations. Average national PM<sub>2.5</sub> kitchen concentrations varied nearly 3-fold among households primarily cooking with gas (20 μg/m<sup>3</sup> (Chile); 55 μg/m<sup>3</sup> (China)) and 12-fold among households primarily cooking with wood (36 μg/m<sup>3</sup> (Chile)); 427 μg/m<sup>3</sup> (Pakistan)). Average PM<sub>2.5</sub> kitchen concentration, heating fuel type, season and secondhand smoke exposure were significant predictors of personal exposures. Modeled average PM<sub>2.5</sub> female exposures were lower than male exposures in upper-middle/high-income countries (India, China, Colombia, Chile).<h4>Conclusion</h4>Using survey data to estimate PM<sub>2.5</sub> exposures on a multinational scale can cost-effectively scale up quantitative HAP measurements for disease burden assessments. The modeled PM<sub>2.5</sub> exposures can be used in future epidemiological studies and inform policies targeting HAP reduction.

Item Type: Article
Uncontrolled Keywords: Household air pollution, PM2, 5, Kitchen concentrations, Personal exposures, Predictive modeling, Bayesian hierarchical modeling
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Population Health
Depositing User: Symplectic Admin
Date Deposited: 07 Jun 2022 10:02
Last Modified: 18 Jan 2023 21:00
DOI: 10.1016/j.envint.2021.107021
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