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Multinational modelling of PM2.5 and CO exposures from household air pollution in peri-urban Cameroon, Ghana and Kenya
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Multinational modelling of PM2.5 and CO exposures from household air pollution in peri-urban Cameroon, Ghana and Kenya
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Journal Article
Multinational modelling of PM2.5 and CO exposures from household air pollution in peri-urban Cameroon, Ghana and Kenya
2025
Overview
In sub-Saharan Africa, approximately 85% of the population uses polluting cooking fuels (e.g. wood, charcoal). Incomplete combustion of these fuels generates household air pollution (HAP), containing fine particulate matter (PM
2.5
) and carbon monoxide (CO). Due to large spatial variability, increased quantification of HAP levels is needed to improve exposure assessment in sub-Saharan Africa. The CLEAN-Air(Africa) study included 24-h monitoring of PM
2.5
and CO kitchen concentrations (n
pm2.5
= 248/n
CO
= 207) and female primary cook exposures (n
pm2.5
= 245/n
CO
= 222) in peri-urban households in Obuasi (Ghana), Mbalmayo (Cameroon) and Eldoret (Kenya). HAP measurements were combined with survey data on cooking patterns, socioeconomic characteristics and ambient exposure proxies (e.g. walking time to nearest road) in separate PM
2.5
and CO mixed-effect log-linear regression models. Model coefficients were applied to a larger study population (n = 937) with only survey data to quantitatively scale up PM
2.5
and CO exposures. The final models moderately explained variation in mean 24-h PM
2.5
(R
2
= 0.40) and CO (R
2
= 0.26) kitchen concentration measurements, and PM
2.5
(R
2
= 0.27) and CO (R
2
= 0.14) female cook exposures. Primary/secondary cooking fuel type was the only significant predictor in all four models. Other significant predictors of PM
2.5
and CO kitchen concentrations were cooking location and household size; household financial security and rental status were only predictive of PM
2.5
concentrations. Cooking location, household financial security and proxies of ambient air pollution exposure were significant predictors of PM
2.5
cook exposures. Including objective cooking time measurements (from temperature sensors) from (n = 143) households substantially improved (by 52%) the explained variability of the CO kitchen concentration model, but not the PM
2.5
model. Socioeconomic characteristics and markers of ambient air pollution exposure were strongly associated with mean PM
2.5
measurements, while cooking environment variables were more predictive of mean CO levels.
