Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
Int J Environ Res Public Health. 2022 May 8;19(9):5724. doi: 10.3390/ijerph19095724.
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.
室内空气质量 (IAQ) 标准不断发展，以改善整体 IAQ 状况。为了提高使用替代参数进行 IAQ 筛选模型的性能，以识别不满意的 IAQ，并更新筛选模型，以便它们适用于新标准，本研究提出了一种使用机器学习方法更新筛选水平的新框架。所采用的分类模型是支持向量机 (SVM) 算法，具有不同的核函数（线性、多项式、径向基函数 (RBF) 和 sigmoid）、k-最近邻 (kNN)、逻辑回归、决策树 (DT)、随机森林 (RF) 和多层感知器人工神经网络 (MLP-ANN)。通过精心选择模型超参数，模型对 IAQ 的评估实现了 0.536-0.805 的平均测试准确率和 0.807-0.820 的最大测试准确率，表明机器学习模型适合筛选不满意的 IAQ。此外，以香港更新的 IAQ 标准为例，通过确定更新标准相对于旧标准的相对影响比，对新的 IAQ 标准进行了更新筛选模型。估计了相对影响比为 1.1-1.5，并且由于更新方案中暴露水平的收紧，更新方案中的相应似然比高于预期。所提出的框架展示了在采用新标准时更新机器学习 IAQ 模型的可行性，这将为与所有 IAQ 标准和暴露标准兼容的 IAQ 评估预测提供最终方法。
