TY - CONF
T1 - Ensembled models to improve residential indoor PM2.5 estimation for further Personal Exposure prediction
AU - Lu, Quang Oai
AU - Chang, Wei Hsiang
AU - Jung, Chien-Cheng
AU - Chu, Hone Jay
AU - Lee, Ching Chang
N1 - Funding Information:
Thank participants for their contributions, and our colleagues at the Research Center of Environmental Trace Toxic Substances for sampling and analytical support.
Publisher Copyright:
© 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.
PY - 2022
Y1 - 2022
N2 - The present study is aimed to predict the PM2.5 concentration with a spatial and temporal resolution based on the limitation of the monitoring station, IDW and machine learning were applied to estimate daily residential indoor PM2.5 in Tainan. 62 predictor variables from daily indoor and outdoor PM2.5 observations, questionnaires, and Taiwan Environmental Protection Administration (Taiwan EPA) database were obtained from 2018 to 2020. The correlation analysis and boosted tree (BS) were used to identify the important variables. Then, 10-fold cross-validation was used to validate these models. Among these models, the multilayer perceptron (MLP) model has outperformed when R2 exceeded 0.999 compared to other models. In addition, the higher fit in the predictive train-test was identified in the ensembled models and MLP model. The results demonstrated the ensembled approaches and deep learning show promise for modeling indoor air pollution and determined I/O ratio is an important factor. These models may apply to identify the indoor exposure assessment for epidemiology studies and serve as a model framework for other countries.
AB - The present study is aimed to predict the PM2.5 concentration with a spatial and temporal resolution based on the limitation of the monitoring station, IDW and machine learning were applied to estimate daily residential indoor PM2.5 in Tainan. 62 predictor variables from daily indoor and outdoor PM2.5 observations, questionnaires, and Taiwan Environmental Protection Administration (Taiwan EPA) database were obtained from 2018 to 2020. The correlation analysis and boosted tree (BS) were used to identify the important variables. Then, 10-fold cross-validation was used to validate these models. Among these models, the multilayer perceptron (MLP) model has outperformed when R2 exceeded 0.999 compared to other models. In addition, the higher fit in the predictive train-test was identified in the ensembled models and MLP model. The results demonstrated the ensembled approaches and deep learning show promise for modeling indoor air pollution and determined I/O ratio is an important factor. These models may apply to identify the indoor exposure assessment for epidemiology studies and serve as a model framework for other countries.
UR - http://www.scopus.com/inward/record.url?scp=85159204589&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85159204589&partnerID=8YFLogxK
M3 - Paper
AN - SCOPUS:85159204589
T2 - 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022
Y2 - 12 June 2022 through 16 June 2022
ER -