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. 2025 Feb 26;15(1):6856.
doi: 10.1038/s41598-024-81413-y.

Multinational modelling of PM2.5 and CO exposures from household air pollution in peri-urban Cameroon, Ghana and Kenya

Harry Williams  1 , Miranda Baame  2 , Federico Lorenzetti  3 , Judith Mangeni  4 , Emily Nix  3 , Emmanuel Betang  2 , Ryan Chartier  5 , Edna Sang  4 , Daniel Wilson  6 , Theresa Tawiah  7 , Reginald Quansah  8 , Elisa Puzzolo  3 , Diana Menya  4 , Bertrand Hugo Mbatchou Ngahane  2 , Daniel Pope  3 , Kwaku Poku Asante  7 , Matthew Shupler  3
Affiliations

Multinational modelling of PM2.5 and CO exposures from household air pollution in peri-urban Cameroon, Ghana and Kenya

Harry Williams et al. Sci Rep. .

Abstract

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 (PM2.5 ) 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 PM2.5 and CO kitchen concentrations (npm2.5 = 248/nCO = 207) and female primary cook exposures (npm2.5 = 245/nCO = 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 PM2.5 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 PM2.5 and CO exposures. The final models moderately explained variation in mean 24-h PM2.5 (R2 = 0.40) and CO (R2 = 0.26) kitchen concentration measurements, and PM2.5 (R2 = 0.27) and CO (R2 = 0.14) female cook exposures. Primary/secondary cooking fuel type was the only significant predictor in all four models. Other significant predictors of PM2.5 and CO kitchen concentrations were cooking location and household size; household financial security and rental status were only predictive of PM2.5 concentrations. Cooking location, household financial security and proxies of ambient air pollution exposure were significant predictors of PM2.5 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 PM2.5 model. Socioeconomic characteristics and markers of ambient air pollution exposure were strongly associated with mean PM2.5 measurements, while cooking environment variables were more predictive of mean CO levels.

Keywords: CO; Household air pollution; PM2.5; Predictive modelling; Sub-Saharan Africa.

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Conflict of interest statement

Declarations. Competing interests: The authors have no competing interests to declare.

Figures

Fig. 1
Fig. 1
Predicted geometric mean kitchen PM2.5 concentration (µg/m3) by primary cooking fuel type and community.
Fig. 2
Fig. 2
Predicted geometric mean kitchen PM2.5 concentrations (µg/m3) by primary and secondary cooking fuel type, by community (where n ≥ 10).
Fig. 3
Fig. 3
Predicted geometric mean kitchen CO concentrations (ppm) by primary cooking fuel type and community.
Fig. 4
Fig. 4
Predicted geometric mean kitchen CO concentrations (ppm) by primary and secondary cooking fuel type, by community (where n ≥ 10).
Fig. 5
Fig. 5
Predicted geometric mean female cook PM2.5 exposures (µg/m3) by primary cooking fuel type and community.
Fig. 6
Fig. 6
Predicted geometric mean female cook PM2.5 exposures (µg/m3) by primary and secondary cooking fuel type, by community (where n ≥ 10).
Fig. 7
Fig. 7
Predicted geometric mean female cook CO exposures (ppm) by primary cooking fuel type and community.
Fig. 8
Fig. 8
Predicted geometric mean female cook CO exposures (ppm) by primary and secondary cooking fuel type, by community (where n ≥ 10).
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