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. 2022 May 8;19(9):5724.
doi: 10.3390/ijerph19095724.

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

Ling-Tim Wong  1 , Kwok-Wai Mui  1 , Tsz-Wun Tsang  1
Affiliations

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

Ling-Tim Wong et al. Int J Environ Res Public Health. .

Abstract

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.

Keywords: assessment; indoor air quality (IAQ) index; machine learning model; screening.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Pair plots of CO2, RSP, and TVOC grouped by assessed indoor air quality (IAQ) against assessment (a) Scheme 1 (b) Scheme 2.
Figure 2
Figure 2
Data processing for model training and evaluation.
Figure 3
Figure 3
Cross-validation accuracy of the SVM classifier. (a) Linear kernel, (b) rbf kernel, (c) sigmoid kernel, c0 = 0.01, (d) sigmoid kernel, c0 = 0.5, (e) polynomial kernel, c0 = 0, c1 = 2, (f) polynomial kernel, c0 = 1, c1 = 3.
Figure 4
Figure 4
Cross-validation accuracy of the kNN classifier. (a) W = 1/dk, (b) W = 1.
Figure 5
Figure 5
Cross-validation accuracy of the logistic classifier.
Figure 6
Figure 6
Cross-validation accuracy of the decision tree classifier. (a) Entropy impurity, nr = 6 (b) Gini impurity, nr = 2.
Figure 7
Figure 7
Cross-validation accuracy of the random forest classifier. (a) Entropy impurity, ns = 9, nf = 10 (b) Gini impurity, ns = 9, nf = 110, (c) Gini impurity, ns = 2, nf = 110.
Figure 8
Figure 8
Cross-validation accuracy of the MLP-ANN classifier. (a) 100 neurons, 1 hidden layer, (b) 200 neurons, 1 hidden layer, (c) 100 neurons, 6 hidden layers (d) 200 neurons, 6 hidden layers, (e) 100 neurons, 3 hidden layers.
Figure 9
Figure 9
Predicted IAQ satisfaction and dissatisfaction with an IAQ index with assessment criteria, (a) Scheme 1, (b) Scheme 2.
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