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Indoor-to-outdoor particle concentration ratio model for human exposure analysis
https://orcid.org/0000-0002-4159-7099
Abstract This study presents an indoor-to-outdoor particle concentration ratio (IOR) model for improved estimates of indoor exposure levels. This model is useful in epidemiological studies with large population, because sampling indoor pollutants in all participants' house is often necessary but impractical. As a part of a study examining the association between air pollutants and atopic dermatitis in children, 16 parents agreed to measure the indoor and outdoor PM10 and PM2.5 concentrations at their homes for 48 h. Correlation analysis and multi-step multivariate linear regression analysis was performed to develop the IOR model. Temperature and floor level were found to be powerful predictors of the IOR. Despite the simplicity of the model, it demonstrated high accuracy in terms of the root mean square error (RMSE). Especially for long-term IOR estimations, the RMSE was as low as 0.064 and 0.063 for PM10 and PM2.5, respectively. When using a prediction model in an epidemiological study, understanding the consequence of the modeling error and justifying the use of the model is very important. In the last section, this paper discussed the impact of the modeling error and developed a novel methodology to justify the use of the model.
Graphical abstract Display Omitted
Highlights Concentrations of particulate matter were measured in 16 homes in Seoul, South Korea. A model for the indoor-to-outdoor particle concentration ratio (IOR) was developed. Multi-step multivariate linear regression analysis was performed to develop the model. Temperature and floor level were found to be powerful predictors of the IOR.
Indoor-to-outdoor particle concentration ratio model for human exposure analysis
https://orcid.org/0000-0002-4159-7099
Abstract This study presents an indoor-to-outdoor particle concentration ratio (IOR) model for improved estimates of indoor exposure levels. This model is useful in epidemiological studies with large population, because sampling indoor pollutants in all participants' house is often necessary but impractical. As a part of a study examining the association between air pollutants and atopic dermatitis in children, 16 parents agreed to measure the indoor and outdoor PM10 and PM2.5 concentrations at their homes for 48 h. Correlation analysis and multi-step multivariate linear regression analysis was performed to develop the IOR model. Temperature and floor level were found to be powerful predictors of the IOR. Despite the simplicity of the model, it demonstrated high accuracy in terms of the root mean square error (RMSE). Especially for long-term IOR estimations, the RMSE was as low as 0.064 and 0.063 for PM10 and PM2.5, respectively. When using a prediction model in an epidemiological study, understanding the consequence of the modeling error and justifying the use of the model is very important. In the last section, this paper discussed the impact of the modeling error and developed a novel methodology to justify the use of the model.
Graphical abstract Display Omitted
Highlights Concentrations of particulate matter were measured in 16 homes in Seoul, South Korea. A model for the indoor-to-outdoor particle concentration ratio (IOR) was developed. Multi-step multivariate linear regression analysis was performed to develop the model. Temperature and floor level were found to be powerful predictors of the IOR.
Indoor-to-outdoor particle concentration ratio model for human exposure analysis
https://orcid.org/0000-0002-4159-7099
Abstract This study presents an indoor-to-outdoor particle concentration ratio (IOR) model for improved estimates of indoor exposure levels. This model is useful in epidemiological studies with large population, because sampling indoor pollutants in all participants' house is often necessary but impractical. As a part of a study examining the association between air pollutants and atopic dermatitis in children, 16 parents agreed to measure the indoor and outdoor PM10 and PM2.5 concentrations at their homes for 48 h. Correlation analysis and multi-step multivariate linear regression analysis was performed to develop the IOR model. Temperature and floor level were found to be powerful predictors of the IOR. Despite the simplicity of the model, it demonstrated high accuracy in terms of the root mean square error (RMSE). Especially for long-term IOR estimations, the RMSE was as low as 0.064 and 0.063 for PM10 and PM2.5, respectively. When using a prediction model in an epidemiological study, understanding the consequence of the modeling error and justifying the use of the model is very important. In the last section, this paper discussed the impact of the modeling error and developed a novel methodology to justify the use of the model.
Graphical abstract Display Omitted
Highlights Concentrations of particulate matter were measured in 16 homes in Seoul, South Korea. A model for the indoor-to-outdoor particle concentration ratio (IOR) was developed. Multi-step multivariate linear regression analysis was performed to develop the model. Temperature and floor level were found to be powerful predictors of the IOR.
Indoor-to-outdoor particle concentration ratio model for human exposure analysis
Lee, Jae Young (author) / Ryu, Sung Hee (author) / Lee, Gwangjae (author) / Bae, Gwi-Nam (author)
Atmospheric Environment ; 127 ; 100-106
2015-12-07
7 pages
Article (Journal)
Electronic Resource
English
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