Introduction: Elevated levels of indoor air pollutants may cause cardiopulmonary disease such as lower respiratory infection, chronic obstructive lung disease and lung cancer, but the association with tuberculosis (TB) is unclear. So far the risk estimates of TB infection or/and disease due to indoor air pollution (IAP) exposure are based on self-reported exposures rather than direct measurements of IAP, and these exposures have not been validated.
Objective: The aim of this paper was to characterize and develop predictive models for concentrations of three air pollutants (PM10, NO2 and SO2) in homes of children participating in a childhood TB study.
Methods: Children younger than 15 years living within the eThekwini Municipality in South Africa were recruited for a childhood TB case control study. The homes of these children (n=246) were assessed using a walkthrough checklist, and in 114 of them monitoring of three indoor pollutants was also performed (sampling period: 24h for PM10, and 2-3 weeks for NO2 and SO2). Linear regression models were used to predict PM10 and NO2 concentrations from household characteristics, and these models were validated using leave out one cross validation (LOOCV). SO2 concentrations were not modeled as concentrations were very low.
Results: Mean indoor concentrations of PM10 (n=105), NO2 (n=82) and SO2 (n=82) were 64μg/m3 (range 6.6-241); 19μg/m3 (range 4.5-55) and 0.6μg/m3 (range 0.005-3.4) respectively with the distributions for all three pollutants being skewed to the right. Spearman correlations showed weak positive correlations between the three pollutants. The largest contributors to the PM10 predictive model were type of housing structure (formal or informal), number of smokers in the household, and type of primary fuel used in the household. The NO2 predictive model was influenced mostly by the primary fuel type and by distance from the major roadway. The coefficients of determination (R2) for the models were 0.41 for PM10 and 0.31 for NO2. Spearman correlations were significant between measured vs. predicted PM10 and NO2 with coefficients of 0.66 and 0.55 respectively.
Conclusion: Indoor PM10 levels were relatively high in these households. Both PM10 and NO2 can be modeled with a reasonable validity and these predictive models can decrease the necessary number of direct measurements that are expensive and time consuming.
Keywords: Household characteristics; Indoor air pollution; Low socioeconomic status; Particulates; Passive smoke.
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