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

Abstract:

Introduction: Use of polluting cooking fuels generates household air pollution (HAP) containing health-damaging levels of fine particulate matter (PM<inf>2.5</inf>). Many global epidemiological studies rely on categorical HAP exposure indicators, which are poor surrogates of measured PM<inf>2.5</inf> levels. To quantitatively characterize HAP levels on a large scale, a multinational measurement campaign was leveraged to develop household and personal PM<inf>2.5</inf> exposure models. Methods: The Prospective Urban and Rural Epidemiology (PURE)-AIR study included 48-hour monitoring of PM<inf>2.5</inf> kitchen concentrations (n = 2,365) and male and/or female PM<inf>2.5</inf> 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<inf>2.5</inf> exposures. Results: The final models explained half (R² = 54%) of the variation in kitchen PM<inf>2.5</inf> 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 PM<inf>2.5</inf> kitchen concentrations. Average national PM<inf>2.5</inf> kitchen concentrations varied nearly 3-fold among households primarily cooking with gas (20 μg/m³ (Chile); 55 μg/m³ (China)) and 12-fold among households primarily cooking with wood (36 μg/m³ (Chile)); 427 μg/m³ (Pakistan)). Average PM<inf>2.5</inf> kitchen concentration, heating fuel type, season and secondhand smoke exposure were significant predictors of personal exposures. Modeled average PM<inf>2.5</inf> female exposures were lower than male exposures in upper-middle/high-income countries (India, China, Colombia, Chile). Conclusion: Using survey data to estimate PM<inf>2.5</inf> exposures on a multinational scale can cost-effectively scale up quantitative HAP measurements for disease burden assessments. The modeled PM<inf>2.5</inf> exposures can be used in future epidemiological studies and inform policies targeting HAP reduction.

Año de publicación:

2022

Keywords:

• Bayesian hierarchical modeling
• Kitchen concentrations
• Household air pollution
• Personal exposures
• Pbkp_redictive modeling
• PM 2.5
• PM2.5
• predictive modeling

Fuente:

scopus

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