Assessing Exposure to Household Air Pollution: A Systematic Review and Pooled Analysis of Carbon Monoxide as a Surrogate Measure of Particulate Matter

Abstract

Background:Household air pollution from solid fuel burning is a leading contributor to disease burden globally. Fine particulate matter (PM2.5) is thought to be responsible for many of these health impacts. A co-pollutant, carbon monoxide (CO) has been widely used as a surrogate measure of PM2.5 in studies of household air pollution.Objective:The goal was to evaluate the validity of exposure to CO as a surrogate of exposure to PM2.5 in studies of household air pollution and the consistency of the PM2.5–CO relationship across different study settings and conditions.Methods:We conducted a systematic review of studies with exposure and/or cooking area PM2.5 and CO measurements and assembled 2,048 PM2.5 and CO measurements from a subset of studies (18 cooking area studies and 9 personal exposure studies) retained in the systematic review. We conducted pooled multivariate analyses of PM2.5–CO associations, evaluating fuels, urbanicity, season, study, and CO methods as covariates and effect modifiers.Results:We retained 61 of 70 studies for review, representing 27 countries. Reported PM2.5–CO correlations (r) were lower for personal exposure (range: 0.22–0.97; median=0.57) than for cooking areas (range: 0.10–0.96; median=0.71). In the pooled analyses of personal exposure and cooking area concentrations, the variation in ln(CO) explained 13% and 48% of the variation in ln(PM2.5), respectively.Conclusions:Our results suggest that exposure to CO is not a consistently valid surrogate measure of exposure to PM2.5. Studies measuring CO exposure as a surrogate measure of PM exposure should conduct local validation studies for different stove/fuel types and seasons. https://doi.org/10.1289/EHP767

Household air pollution and measures of blood pressure, arterial stiffness and central haemodynamics

“…In Ghana, a 1 ppm increase in CO exposure was associated with 0.4 mm Hg higher bSBP and bDBP in pregnant women with mean CO exposure of 1.6 ppm 20. Compared with our study, the smaller magnitude of association may be due to their younger participants (mean age=27 years) or measurement error with use of CO as a surrogate of PM 21. No previous studies have assessed HAP and central haemodynamics or arterial stiffness, preventing comparison.…”

Prenatal exposure to particulate matter (PM2.5) and low birth weight in a Sri Lankan birth cohort

“…Most studies to date have been conducted in LMICs including India, Guatemala, Pakistan, and Ghana, countries where other competing risk factors for adverse birth outcomes such as malnutrition, poverty, infections and poor access to health care, are highly prevalent (21,26). Further, in-home deliveries and limited access to prenatal care in rural regions may often result in gaps in information related to fetal growth and pregnancy-related complications making it difficult to control for residual confounding factors to estimate the true effect of HAP on fetal health.…”

Monitoring and modeling of household air quality related to use of different Cookfuels in Paraguay

“…Some studies reported a relatively strong correlation (Pearson's r > 0.8) between both pollutants, 35,36 while others found a weaker correlation, 18,37 especially at the level of personal exposure. 38 Based on our regression analysis, we can indicate that CO was strongly and significantly associated with the variation in PM 2.5 levels (P-value = 0.006) in the kitchen area. This would support the methodology used in other studies of household air pollution, which have estimated PM 2.5 from measurements made in CO concentrations.…”

“…From the measurements described in this paper, a baseline for indoor air quality and two predictive models were developed for rural kitchens in Paraguay. Some studies reported a relatively strong correlation (Pearson's r > 0.8) between both pollutants, 35,36 while others found a weaker correlation,18,37 especially at the level of personal exposure 38. Regression analysis showed that variables such as the kitchen structure and construction materials were not significant, while other factors, such as the community, cookstove usage, and the type of fuel used for cooking, were strong predictors of the indoor PM 2.5 and CO concentrations.…”