ArticleIndoor environments2025
Evaluating statistical methods to predict indoor black carbon in an urban birth cohort.
Article in Indoor environments, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Most air pollution epidemiology studies rely on outdoor exposure data from various sources, such as reference monitors, low-cost monitors, models, or Earth observations. However, people spend 90 % of their time indoors, with 70 % of that time spent at home, which may result in misclassification of air pollution exposure when using data reflecting ambient concentrations. In this study, we evaluated methods to predict residential indoor black carbon (BC) from outdoor BC, PM2.5, and housing characteristics to support future efforts in estimating personal air pollution exposure. Households from the Healthy Start cohort in Denver, CO hosted paired indoor/outdoor low-cost air samplers for one-week periods during spring 2018, summer 2018, and winter 2019. Participants completed questionnaires about housing characteristics like building type, flooring, and heating and cooling methods. Filters were analyzed for BC using transmissometry. Ridge, LASSO and ordinary least squares regression (OLS) techniques were used to build predictive models of indoor BC given the available set of covariates. Leave-one-out cross-validation was used to assess the predictive accuracy of each model. We hypothesized that Ridge and LASSO will obtain improved predictive performance over the OLS model due to regularization. A total of 27 households participated, with 39 paired measurements available after data cleaning. All winter data were excluded due to high variability and incomplete sampling times for outdoor measurements. Performance issues suggested insufficient weatherproofing of monitors for low temperatures. The Ridge regression showed the best predictive performance. The final inference model included outdoor PM2
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