Repositorio
Institucional

Shupler, Matthew

Hystad, Perry

Birch, Aaron

Li Chu, Yen

Jeronimo, Matthew

Miller-Lionberg, Daniel

Gustafson, Paul

Rangarajan, Sumathy

Mustaha, Maha

Heenan, Laura

Seron, Pamela

Lanas, Fernando

Cazor, Fairuz

Jose Oliveros, Maria

Lopez-Jaramillo, Patricio

Camacho, Paul A.

Otero, Johnna

Perez, Maritza

Yeates, Karen

West, Nicola

Ncube, Tatenda

Ncube, Brian

Chifamba, Jephat

Yusuf, Rita

Khan, Afreen

Liu, Zhiguang

Wu, Shutong

Wei, Li

Tse, Lap Ah

Mohan, Deepa

Kumar, Parthiban

Gupta, Rajeev

Mohan, Indu

Jayachitra, K. G.

Mony, Prem K.

Rammohan, Kamala

Nair, Sanjeev

Lakshmi, P. V. M.

Sagar, Vivek

Khawaja, Rehman

Iqbal, Romaina

Kazmi, Khawar

Yusuf, Salim

Brauer, Michael

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

PERGAMON-ELSEVIER SCIENCE LTD

ENVIRONMENT INTERNATIONAL

en

Introduction: Use of polluting cooking fuels generates household air pollution (HAP) containing health-damaging levels of fine particulate matter (PM2.5). Many global epidemiological studies rely on categorical HAP exposure indicators, which are poor surrogates of measured PM2.5 levels. To quantitatively characterize HAP levels on a large scale, a multinational measurement campaign was leveraged to develop household and personal PM2.5 exposure models. Methods: The Prospective Urban and Rural Epidemiology (PURE)-AIR study included 48-hour monitoring of PM2.5 kitchen concentrations (n = 2,365) and male and/or female PM2.5 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 PM2.5 exposures. Results: The final models explained half (R2 = 54%) of the variation in kitchen PM2.5 measurements (root mean square error (RMSE) (log scale):2.22) and personal measurements (R2 = 48%, RMSE (log scale):2.08). Primary cooking fuel type, heating fuel type, country and season were highly predictive of PM2.5 kitchen concentrations. Average national PM2.5 kitchen concentrations varied nearly 3-fold among households primarily cooking with gas (20 mu g/m3 (Chile), 55 mu g/m3 (China)) and 12-fold among households primarily cooking with wood (36 mu g/ m3 (Chile)), 427 mu g/m3 (Pakistan)). Average PM2.5 kitchen concentration, heating fuel type, season and secondhand smoke exposure were significant predictors of personal exposures. Modeled average PM2.5 female exposures were lower than male exposures in upper-middle/high-income countries (India, China, Colombia, Chile). Conclusion: Using survey data to estimate PM2.5 exposures on a multinational scale can cost-effectively scale up quantitative HAP measurements for disease burden assessments. The modeled PM2.5 exposures can be used in future epidemiological studies and inform policies targeting HAP reduction.

artículo original

10.1016/j.envint.2021.107021

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Household air pollution

PM2

5

Kitchen concentrations

Personal exposures

Predictive modeling

Bayesian hierarchical modeling

INDOOR AIR-POLLUTION

SOLID-FUEL USE

BLOOD-PRESSURE

COOKSTOVE INTERVENTION

BIOMASS COMBUSTION

SMOKE EXPOSURE

RISK-FACTOR

COOKING

IMPACT

WOMEN

Ciencias Ambientales

Contaminación del aire en el hogar

PM2

5

Concentraciones en la cocina

Exposiciones personales

Modelado predictivo

Modelado jerárquico bayesiano

CONTAMINACIÓN DEL AIRE INTERIOR

USO DE COMBUSTIBLE SÓLIDO

PRESIÓN ARTERIAL

INTERVENCIÓN DE ESTUFA

COMBUSTIÓN DE BIOMASA

EXPOSICIÓN AL HUMO

FACTOR DE RIESGO

COCINAR

IMPACTO

MUJERES

Ciencias del Medio Ambiente

Epidemiología

Salud Pública y Ambiental

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

artículo original

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CC BY-NC-ND 4.0

CC BY-NC-ND 4.0

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acceso abierto

WOS:000733713000002