Fine particulate matter (PM2.5) is a leading public health risk factor globally. Indoor concentrations are an important determinant of exposure because people spend the majority of time indoors. I developed models for predicting PM2.5 concentrations inside the homes of pregnant women in Ulaanbaatar, Mongolia. The work was part of a randomized controlled trial of portable air cleaner use during pregnancy, fetal growth, and early childhood development. Multiple linear regression (MLR) and random forest regression (RFR) were used to model indoor PM2.5 concentrations using 7-day indoor PM2.5 measurements and potential predictors obtained from outdoor monitoring data, questionnaires, home assessments, and geographic data sets. The MLR (R2 = 50.5%) and RFR (R2 = 47.8%) models explained a moderate amount of variation in log-transformed indoor PM2.5. Model predictions can be used to evaluate associations between indoor PM2.5 concentrations during pregnancy and development in early life.
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Thesis advisor: Allen, Ryan
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