Updating Indoor Air Quality (IAQ) Assessment Screening Levels with Machine Learning Models

Bibliographic Data

ID15512682
AuthorsL T Wong (0000-0001-8449-4973, Hong Kong Polytechnic University), Kwok Wai Mui (0000-0003-2505-1261, Hong Kong Polytechnic University, corresponding author), Kwok-Wai Mui (Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong), Tsz-Wun Tsang (0000-0001-9866-4531, Hong Kong Polytechnic University)
Year2022
Volume19
Issue9
Pages5724-5724
Publication date2022-05-08
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph19095724
PMID35565119
OpenAlexW4229332262
LanguageEN
References cited33

Indoor air quality (IAQ) standards have been evolving to improve the overall IAQ situation. To enhance the performances of IAQ screening models using surrogate parameters in identifying 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 sigmoid), 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

Artificial neural network · Decision tree · Indoor air quality · Machine learning · Multilayer perceptron · Support vector machine · Air Quality and Health Impacts · Air Quality Monitoring and Forecasting · Computer Science · Engineering · Noise Effects and Management · Artificial Intelligence

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