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

INTRODUCTION

Use of polluting cooking fuels generates household air pollution (HAP) containing health-damaging levels of fine particulate matter (PM_2.5). Many global epidemiological studies rely on categorical HAP exposure indicators, which are poor surrogates of measured PM_2.5 levels. To quantitatively characterize HAP levels on a large scale, a multinational measurement campaign was leveraged to develop household and personal PM_2.5 exposure models.

METHODS

The Prospective Urban and Rural Epidemiology (PURE)-AIR study included 48-hour monitoring of PM_2.5 kitchen concentrations (n = 2,365) and male and/or female PM_2.5 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 PM_2.5 exposures.

RESULTS

The final models explained half (R² = 54%) of the variation in kitchen PM_2.5 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 PM_2.5 kitchen concentrations. Average national PM_2.5 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_2.5 kitchen concentration, heating fuel type, season and secondhand smoke exposure were significant predictors of personal exposures. Modeled average PM_2.5 female exposures were lower than male exposures in upper-middle/high-income countries (India, China, Colombia, Chile).

CONCLUSION

Using survey data to estimate PM_2.5 exposures on a multinational scale can cost-effectively scale up quantitative HAP measurements for disease burden assessments. The modeled PM_2.5 exposures can be used in future epidemiological studies and inform policies targeting HAP reduction.

Exposure contrasts associated with a liquefied petroleum gas (LPG) intervention at potential field sites for the multi-country household air pollution intervention network (HAPIN) trial in India: results from pilot phase activities in rural Tamil Nadu

BACKGROUND

The Household Air Pollution Intervention Network (HAPIN) trial aims to assess health benefits of a liquefied petroleum gas (LPG) cookfuel and stove intervention among women and children across four low- and middle-income countries (LMICs). We measured exposure contrasts for women, achievable under alternative conditions of biomass or LPG cookfuel use, at potential HAPIN field sites in India, to aid in site selection for the main trial.

METHODS

We recruited participants from potential field sites within Villupuram and Nagapattinam districts in Tamil Nadu, India, that were identified during a feasibility assessment. We performed. (i) cross-sectional measurements on women (N = 79) using either biomass or LPG as their primary cookfuel and (ii) before-and-after measurements on pregnant women (N = 41), once at baseline while using biomass fuel and twice - at 1 and 2 months - after installation of an LPG stove and free fuel intervention. We involved participants to co-design clothing and instrument stands for personal and area sampling. We measured 24 or 48-h personal exposures and kitchen and ambient concentrations of fine particulate matter (PM2.5) using gravimetric samplers.

RESULTS

In the cross-sectional analysis, median (interquartile range, IQR) kitchen PM2.5 concentrations in biomass and LPG using homes were 134 μg/m3 [IQR:71-258] and 27 μg/m3 [IQR:20-47], while corresponding personal exposures were 75 μg/m3 [IQR:55-104] and 36 μg/m3 [IQR:26-46], respectively. In before-and-after analysis, median 48-h personal exposures for pregnant women were 72 μg/m3 [IQR:49-127] at baseline and 25 μg/m3 [IQR:18-35] after the LPG intervention, with a sustained reduction of 93% in mean kitchen PM2.5 concentrations and 78% in mean personal PM2.5 exposures over the 2 month intervention period. Median ambient concentrations were 23 μg/m3 [IQR:19-27). Participant feedback was critical in designing clothing and instrument stands that ensured high compliance.

CONCLUSIONS

An LPG stove and fuel intervention in the candidate HAPIN trial field sites in India was deemed suitable for achieving health-relevant exposure reductions. Ambient concentrations indicated limited contributions from other sources. Study results provide critical inputs for the HAPIN trial site selection in India, while also contributing new information on HAP exposures in relation to LPG interventions and among pregnant women in LMICs.

TRIAL REGISTRATION

ClinicalTrials.Gov. NCT02944682 ; Prospectively registered on October 17, 2016.

Imputation of missing values in a large job exposure matrix using hierarchical information

Job exposure matrices (JEMs) represent a useful and efficient approach for estimating occupational exposures. This study uses a large dataset of full-shift measurements and employs imputation strategies to develop noise exposure estimates for almost all broad level standard occupational classification (SOC) groups in the US. The JEM was constructed using 753,702 measurements from the government, private industry, and the published literature. Parametric Bayes imputation was used to take advantage of the hierarchical structure of the SOCs and the mean occupational noise exposures were estimated for all broad level SOCs, except those in major group 23-0000, for which no data were available. The estimated posterior mean for all broad SOCs was found to be 82.1 dBA with within- and between-major SOC variabilities of 22.1 and 13.8, respectively. Of the 443 broad SOCs, 85 were found to have an estimated mean exposure >85 dBA while 10 were >90 dBA. By taking advantage of the size and structure of the dataset, we were able to employ imputation techniques to estimate mean levels of noise exposure for nearly all SOCs in the US. Possible sources of errors in the estimates include misclassification of job titles due to limited data, temporal variations that were not accounted for, and variation in exposures within the same SOC. Our efforts have resulted in an almost completely populated noise JEM that provides a valuable tool for the assessment of occupational exposures to noise. Imputation techniques can lead to maximal use of available information that may be incomplete.

A modeling investigation of the impact of street and building configurations on personal air pollutant exposure in isolated deep urban canyons

This study evaluated the effectiveness of different configurations for two building design elements, namely building permeability and setback, proposed for mitigating air pollutant exposure problems in isolated deep canyons by using an indirect exposure approach. The indirect approach predicted the exposures of three different population subgroups (i.e. pedestrians, shop vendors and residents) by multiplying the pollutant concentrations with the duration of exposure within a specific micro-environment. In this study, the pollutant concentrations for different configurations were predicted using a computational fluid dynamics model. The model was constructed based on the Reynolds-Averaged Navier-Stokes (RANS) equations with the standard k-ε turbulence model. Fifty-one canyon configurations with aspect ratios of 2, 4, 6 and different building permeability values (ratio of building spacing to the building façade length) or different types of building setback (recess of a high building from the road) were examined. The findings indicated that personal exposures of shop vendors were extremely high if they were present inside a canyon without any setback or separation between buildings and when the prevailing wind was perpendicular to the canyon axis. Building separation and building setbacks were effective in reducing personal air exposures in canyons with perpendicular wind, although their effectiveness varied with different configurations. Increasing the permeability value from 0 to 10% significantly lowered the personal exposures on the different population subgroups. Likewise, the personal exposures could also be reduced by the introduction of building setbacks despite their effects being strongly influenced by the aspect ratio of a canyon. Equivalent findings were observed if the reduction in the total development floor area (the total floor area permitted to be developed within a particular site area) was also considered. These findings were employed to formulate a hierarchy decision making model to guide the planning of deep canyons in high density urban cities.