Predicting PM in Well-Mixed Indoor Air for a Large Office Building Using Regression and Artificial Neural Network Models.

Although the exposure to PM has serious health implications, indoor PM monitoring is not a widely applied practice. Regulations on the indoor PM level and measurement schemes are not well established. Compared to other indoor settings, PM prediction models for large office buildings are particularly lacking. In response to these challenges, statistical models were developed in this paper to predict the PM concentration in well-mixed indoor air in a commercial office building. The performances of different modeling methods, including multiple linear regression (MLR), partial least squares regression (PLS), distributed lag model (DLM), least absolute shrinkage selector operator (LASSO), simple artificial neural networks (ANN), and long-short term memory (LSTM), were compared. Various combinations of environmental and meteorological parameters were used as predictors. The root-mean-square error (RMSE) of the predicted hourly PM was 1.73 μg/m for the LSTM model and in the range of 2.20-4.71 μg/m for the other models when regulatory ambient PM data were used as predictors. The LSTM models outperformed other modeling approaches across the performance metrics used by learning the predictors' temporal patterns. Even without any ambient PM information, the developed models still demonstrated relatively high skill in predicting the PM levels in well-mixed indoor air.