Evaluating statistical methods to predict indoor black carbon in an urban birth cohort
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 a...
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| Language: | English |
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Elsevier
2025-06-01
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| Series: | Indoor Environments |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S295036202500013X |
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| author | Sherry WeMott Grace Kuiper Sheena E. Martenies Matthew D. Koslovsky William B. Allshouse John L. Adgate Anne P. Starling Dana Dabelea Sheryl Magzamen |
| author_facet | Sherry WeMott Grace Kuiper Sheena E. Martenies Matthew D. Koslovsky William B. Allshouse John L. Adgate Anne P. Starling Dana Dabelea Sheryl Magzamen |
| author_sort | Sherry WeMott |
| collection | DOAJ |
| description | 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 PM2.5, hard floors, and the presence of pets in the home, accounting for approximately 28 % of the variability in indoor BC concentrations measured in participant homes. In the absence of indoor monitoring, household characteristics like flooring and the presence of pets can help predict indoor levels of BC. |
| format | Article |
| id | doaj-art-43b6127f45e44248b60fc1f4dbbaa27e |
| institution | DOAJ |
| issn | 2950-3620 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Indoor Environments |
| spelling | doaj-art-43b6127f45e44248b60fc1f4dbbaa27e2025-08-20T03:16:46ZengElsevierIndoor Environments2950-36202025-06-012210008410.1016/j.indenv.2025.100084Evaluating statistical methods to predict indoor black carbon in an urban birth cohortSherry WeMott0Grace Kuiper1Sheena E. Martenies2Matthew D. Koslovsky3William B. Allshouse4John L. Adgate5Anne P. Starling6Dana Dabelea7Sheryl Magzamen8Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO, USADepartment of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO, USADepartment of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO, USA; University of Illinois Urbana-Champaign, Department of Health and Kinesiology, Urbana, IL, USADepartment of Statistics, Colorado State University, Fort Collins, CO, USADepartment of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado Anschutz Campus, Aurora, CO, USADepartment of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado Anschutz Campus, Aurora, CO, USA; Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USADepartment of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado Anschutz Campus, Aurora, CO, USA; Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USADepartment of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Lifecourse Epidemiology of Adiposity and Diabetes (LEAD Center), University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Department of Pediatrics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USADepartment of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO, USA; Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado Anschutz Campus, Aurora, CO, USA; Correspondence to: Department of Environmental and Radiological Health Sciences, Colorado State University, 1681 Campus Delivery, Fort Collins, CO 80523-1681, USA.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 PM2.5, hard floors, and the presence of pets in the home, accounting for approximately 28 % of the variability in indoor BC concentrations measured in participant homes. In the absence of indoor monitoring, household characteristics like flooring and the presence of pets can help predict indoor levels of BC.http://www.sciencedirect.com/science/article/pii/S295036202500013XIndoor air pollutionBlack carbonPredictive modeling |
| spellingShingle | Sherry WeMott Grace Kuiper Sheena E. Martenies Matthew D. Koslovsky William B. Allshouse John L. Adgate Anne P. Starling Dana Dabelea Sheryl Magzamen Evaluating statistical methods to predict indoor black carbon in an urban birth cohort Indoor Environments Indoor air pollution Black carbon Predictive modeling |
| title | Evaluating statistical methods to predict indoor black carbon in an urban birth cohort |
| title_full | Evaluating statistical methods to predict indoor black carbon in an urban birth cohort |
| title_fullStr | Evaluating statistical methods to predict indoor black carbon in an urban birth cohort |
| title_full_unstemmed | Evaluating statistical methods to predict indoor black carbon in an urban birth cohort |
| title_short | Evaluating statistical methods to predict indoor black carbon in an urban birth cohort |
| title_sort | evaluating statistical methods to predict indoor black carbon in an urban birth cohort |
| topic | Indoor air pollution Black carbon Predictive modeling |
| url | http://www.sciencedirect.com/science/article/pii/S295036202500013X |
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