TY - JOUR
T1 - Updating Indoor Air Quality (IAQ) Assessment Screening Levels with Machine Learning Models
AU - Wong, Ling Tim
AU - Mui, Kwok Wai
AU - Tsang, Tsz Wun
N1 - Funding Information:
Funding: This research was jointly supported by a grant from the Collaborative Research Fund (CRF) COVID-19 and Novel Infectious Disease (NID) Research Exercise, Research Grants Council of the Hong Kong Special Administrative Region, China (Project no. PolyU P0033675/C5108-20G, HKPU P0033675/E-RB0P, PolyU 15217221 P0037773/Q-86B, PolyU 152088/17E P0005278/Q-59V) and the Research Institute for Smart Energy (RISE) Matching Fund (Project no. P0038532).
Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - Indoor air quality (IAQ) standards have been evolving to improve the overall IAQ situa-tion. To enhance the performances of IAQ screening models using surrogate parameters in identi-fying unsatisfactory IAQ, and to update the screening models such that they can apply to a new standard, a novel framework for the updating of screening levels, using machine learning methods, is proposed in this study. The classification models employed are Support Vector Machine (SVM) algorithm with different kernel functions (linear, polynomial, radial basis function (RBF) and sig-moid), k-Nearest Neighbors (kNN), Logistic Regression, Decision Tree (DT), Random Forest (RF) and Multilayer Perceptron Artificial Neural Network (MLP-ANN). With carefully selected model hyperparameters, the IAQ assessment made by the models achieved a mean test accuracy of 0.536– 0.805 and a maximum test accuracy of 0.807–0.820, indicating that machine learning models are suitable for screening the unsatisfactory IAQ. Further to that, using the updated IAQ standard in Hong Kong as an example, the update of an IAQ screening model against a new IAQ standard was conducted by determining the relative impact ratio of the updated standard to the old standard. Relative impact ratios of 1.1–1.5 were estimated and the corresponding likelihood ratios in the updated scheme were found to be higher than expected due to the tightening of exposure levels in the updated scheme. The presented framework shows the feasibility of updating a machine learning IAQ model when a new standard is being adopted, which shall provide an ultimate method for IAQ assessment prediction that is compatible with all IAQ standards and exposure criteria.
AB - Indoor air quality (IAQ) standards have been evolving to improve the overall IAQ situa-tion. To enhance the performances of IAQ screening models using surrogate parameters in identi-fying unsatisfactory IAQ, and to update the screening models such that they can apply to a new standard, a novel framework for the updating of screening levels, using machine learning methods, is proposed in this study. The classification models employed are Support Vector Machine (SVM) algorithm with different kernel functions (linear, polynomial, radial basis function (RBF) and sig-moid), k-Nearest Neighbors (kNN), Logistic Regression, Decision Tree (DT), Random Forest (RF) and Multilayer Perceptron Artificial Neural Network (MLP-ANN). With carefully selected model hyperparameters, the IAQ assessment made by the models achieved a mean test accuracy of 0.536– 0.805 and a maximum test accuracy of 0.807–0.820, indicating that machine learning models are suitable for screening the unsatisfactory IAQ. Further to that, using the updated IAQ standard in Hong Kong as an example, the update of an IAQ screening model against a new IAQ standard was conducted by determining the relative impact ratio of the updated standard to the old standard. Relative impact ratios of 1.1–1.5 were estimated and the corresponding likelihood ratios in the updated scheme were found to be higher than expected due to the tightening of exposure levels in the updated scheme. The presented framework shows the feasibility of updating a machine learning IAQ model when a new standard is being adopted, which shall provide an ultimate method for IAQ assessment prediction that is compatible with all IAQ standards and exposure criteria.
KW - assessment
KW - indoor air quality (IAQ) index
KW - machine learning model
KW - screening
UR - http://www.scopus.com/inward/record.url?scp=85129547126&partnerID=8YFLogxK
U2 - 10.3390/ijerph19095724
DO - 10.3390/ijerph19095724
M3 - Journal article
AN - SCOPUS:85129547126
SN - 1661-7827
VL - 19
JO - International Journal of Environmental Research and Public Health
JF - International Journal of Environmental Research and Public Health
IS - 9
M1 - 5724
ER -