Predicting PM_2.5 in Well-Mixed Indoor Air for a Large Office Building Using Regression and Artificial Neural Network Models

Citations Over TimeTop 10% of 2020 papers

Abstract

Although the exposure to PM_2.5 has serious health implications, indoor PM_2.5 monitoring is not a widely applied practice. Regulations on the indoor PM_2.5 level and measurement schemes are not well established. Compared to other indoor settings, PM_2.5 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_2.5 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_2.5 was 1.73 μg/m3 for the LSTM model and in the range of 2.20-4.71 μg/m3 for the other models when regulatory ambient PM_2.5 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_2.5 information, the developed models still demonstrated relatively high skill in predicting the PM_2.5 levels in well-mixed indoor air.

Related Papers

• → Partial least-squares regression: a tutorial(1986)6,874 cited
• → Comparison of principal components regression, partial least squares regression, multi-block partial least squares regression, and serial partial least squares regression algorithms for the analysis of Fe in iron ore using LIBS(2011)96 cited
• → Canal-LASSO: A sparse noise-resilient online linear regression model(2020)4 cited
• → A Starting Note: A Historical Perspective in Lasso(2021)1 cited
• → LASSO, the(2015)