Table 5.

Prediction models of personal exposure to PM_2.5 (Step 2)

	Model	Regression equation	R2m*	R2c**	Log-scale RMSE
Mothers	Baseline model	$\text{ln}\left(Y_{jk}\right)={\beta }_0+{\beta }_1{\left[P{M}_{2.5}\right]}_{\mathit{\text{community}}}+b_j+{\epsilon }_{jk}$	0.13	0.58	0.334
	Baseline model + seasonality + primary cooking fuel	$\text{ln}\left(Y_{jk}\right)={\beta }_0+{\beta }_1{\left[P{M}_{2.5}\right]}_{\mathit{\text{community}}}+{\beta }_2\left(\mathit{\text{harmattan}}\right)+{\beta }_3\left(\mathit{\text{fuel}}\right)+b_j+{\epsilon }_{jk}$	0.20	0.63	0.325
Children	Baseline model (with mother exposure as predictor)	$\text{ln}\left(Y_{jk}\right)={\beta }_0+{\beta }_1{\left[P{M}_{2.5}\right]}_{\mathit{\text{mother}}}+b_j+{\epsilon }_{jk}$	0.16	0.80	0.402
	Baseline model (with mother exposure as predictor) + seasonality	$\text{ln}\left(Y_{jk}\right)={\beta }_0+{\beta }_1{\left[P{M}_{2.5}\right]}_{\mathit{\text{mother}}}+{\beta }_2\left(\mathit{\text{harmattan}}\right)+b_j+{\epsilon }_{jk}$	0.23	0.81	0.385
	Baseline model (with community ambient as predictor) + seasonality	$\text{ln}\left(Y_{jk}\right)={\beta }_0+{\beta }_1{\left[P{M}_{2.5}\right]}_{\mathit{\text{community}}}+{\beta }_2\left(\mathit{\text{harmattan}}\right)+b_j+{\epsilon }_{jk}$	0.41	0.92	0.165

^*R²_m represents the proportion of the variance explained by the fixed effects alone (marginal R²).

^**R²_c represents the proportion of the variance explained by the fixed and random effects jointly (conditional R²).

^***Harmattan is a dry season characterized by dusty winds.