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 (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 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 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.

Household and personal air pollution exposure measurements from 120 communities in eight countries: results from the PURE-AIR study.

BACKGROUND: Approximately 2·8 billion people are exposed to household air pollution from cooking with polluting fuels. Few monitoring studies have systematically measured health-damaging air pollutant (ie, fine particulate matter [PM2·5] and black carbon) concentrations from a wide range of cooking fuels across diverse populations. This multinational study aimed to assess the magnitude of kitchen concentrations and personal exposures to PM2·5 and black carbon in rural communities with a wide range of cooking environments. METHODS: As part of the Prospective Urban and Rural Epidemiological (PURE) cohort, the PURE-AIR study was done in 120 rural communities in eight countries (Bangladesh, Chile, China, Colombia, India, Pakistan, Tanzania, and Zimbabwe). Data were collected from 2541 households and from 998 individuals (442 men and 556 women). Gravimetric (or filter-based) 48 h kitchen and personal PM2·5 measurements were collected. Light absorbance (10-5m-1) of the PM2·5 filters, a proxy for black carbon concentrations, was calculated via an image-based reflectance method. Surveys of household characteristics and cooking patterns were collected before and after the 48 h monitoring period. FINDINGS: Monitoring of household air pollution for the PURE-AIR study was done from June, 2017, to September, 2019. A mean PM2·5 kitchen concentration gradient emerged across primary cooking fuels: gas (45 µg/m3 [95% CI 43-48]), electricity (53 µg/m3 [47-60]), coal (68 µg/m3 [61-77]), charcoal (92 µg/m3 [58-146]), agricultural or crop waste (106 µg/m3 [91-125]), wood (109 µg/m3 [102-118]), animal dung (224 µg/m3 [197-254]), and shrubs or grass (276 µg/m3 [223-342]). Among households cooking primarily with wood, average PM2·5 concentrations varied ten-fold (range: 40-380 µg/m3). Fuel stacking was prevalent (981 [39%] of 2541 households); using wood as a primary cooking fuel with clean secondary cooking fuels (eg, gas) was associated with 50% lower PM2·5 and black carbon concentrations than using only wood as a primary cooking fuel. Similar average PM2·5 personal exposures between women (67 µg/m3 [95% CI 62-72]) and men (62 [58-67]) were observed. Nearly equivalent average personal exposure to kitchen exposure ratios were observed for PM2·5 (0·79 [95% 0·71-0·88] for men and 0·82 [0·74-0·91] for women) and black carbon (0·64 [0·45-0·92] for men and 0·68 [0·46-1·02] for women). INTERPRETATION: Using clean primary fuels substantially lowers kitchen PM2·5 concentrations. Importantly, average kitchen and personal PM2·5 measurements for all primary fuel types exceeded WHO's Interim Target-1 (35 µg/m3 annual average), highlighting the need for comprehensive pollution mitigation strategies. FUNDING: Canadian Institutes for Health Research, National Institutes of Health.

Personal Exposure to PM2.5 Black Carbon and Aerosol Oxidative Potential using an Automated Microenvironmental Aerosol Sampler (AMAS).

Traditional methods for measuring personal exposure to fine particulate matter (PM2.5) are cumbersome and lack spatiotemporal resolution; methods that are time-resolved are limited to a single species/component of PM. To address these limitations, we developed an automated microenvironmental aerosol sampler (AMAS), capable of resolving personal exposure by microenvironment. The AMAS is a wearable device that uses a GPS sensor algorithm in conjunction with a custom valve manifold to sample PM2.5 onto distinct filter channels to evaluate home, school, and other (e.g., outdoors, in transit, etc.) exposures. Pilot testing was conducted in Fresno, CA where 25 high-school participants ( n = 37 sampling events) wore an AMAS for 48-h periods in November 2016. Data from 20 (54%) of the 48-h samples collected by participants were deemed valid and the filters were analyzed for PM2.5 black carbon (BC) using light transmissometry and aerosol oxidative potential (OP) using the dithiothreitol (DTT) assay. The amount of inhaled PM2.5 was calculated for each microenvironment to evaluate the health risks associated with exposure. On average, the estimated amount of inhaled PM2.5 BC (µg day-1) and OP [(µM min-1) day-1] was greatest at home, owing to the proportion of time spent within that microenvironment. Validation of the AMAS demonstrated good relative precision (8.7% among collocated instruments) and a mean absolute error of 22% for BC and 33% for OP when compared to a traditional personal sampling instrument. This work demonstrates the feasibility of new technology designed to quantify personal exposure to PM2.5 species within distinct microenvironments.

Solid-Phase Extraction Coupled to a Paper-Based Technique for Trace Copper Detection in Drinking Water.

Metal contamination of natural and drinking water systems poses hazards to public and environmental health. Quantifying metal concentrations in water typically requires sample collection in the field followed by expensive laboratory analysis that can take days to weeks to obtain results. The objective of this work was to develop a low-cost, field-deployable method to quantify trace levels of copper in drinking water by coupling solid-phase extraction/preconcentration with a microfluidic paper-based analytical device. This method has the advantages of being hand-powered (instrument-free) and using a simple "read by eye" quantification motif (based on color distance). Tap water samples collected across Fort Collins, CO, were tested with this method and validated against ICP-MS. We demonstrate the ability to quantify the copper content of tap water within 30% of a reference technique at levels ranging from 20 to 500 000 ppb. The application of this technology, which should be sufficient as a rapid screening tool, can lead to faster, more cost-effective detection of soluble metals in water systems.