Environmental and Occupational Health

INTRODUCTION: Use of polluting cooking fuels generates household air pollution (HAP) containing health-damaging levels of fine particulate matter (PM). Many global epidemiological studies rely on categorical HAP exposure indicators, which are poor surrogates of measured PM levels. To quantitatively characterize HAP levels on a large scale, a multinational measurement campaign was leveraged to develop household and personal PM exposure models.

METHODS: The Prospective Urban and Rural Epidemiology (PURE)-AIR study included 48-hour monitoring of PM kitchen concentrations (n = 2,365) and male and/or female PM 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 exposures.

RESULTS: The final models explained half (R = 54%) of the variation in kitchen PM measurements (root mean square error (RMSE) (log scale):2.22) and personal measurements (R = 48%; RMSE (log scale):2.08). Primary cooking fuel type, heating fuel type, country and season were highly predictive of PM kitchen concentrations. Average national PM kitchen concentrations varied nearly 3-fold among households primarily cooking with gas (20 μg/m (Chile); 55 μg/m (China)) and 12-fold among households primarily cooking with wood (36 μg/m (Chile)); 427 μg/m (Pakistan)). Average PM kitchen concentration, heating fuel type, season and secondhand smoke exposure were significant predictors of personal exposures. Modeled average PM female exposures were lower than male exposures in upper-middle/high-income countries (India, China, Colombia, Chile).

CONCLUSION: Using survey data to estimate PM exposures on a multinational scale can cost-effectively scale up quantitative HAP measurements for disease burden assessments. The modeled PM exposures can be used in future epidemiological studies and inform policies targeting HAP reduction.