A Generalized Additive Model Combining Principal Component Analysis for PM_2.5 Concentration Estimation

Author： Li Shuang Zhai Liang Zou Bin Sang Huiyong Fang Xin

Publisher： MDPI

E-ISSN： 2220-9964|6|8|248-248

ISSN： 2220-9964

Source： ISPRS International Journal of Geo-Information, Vol.6, Iss.8, 2017-08, pp. : 248-248

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Abstract

As an extension of the traditional Land Use Regression (LUR) modelling, the generalized additive model (GAM) was developed in recent years to explore the non-linear relationships between PM_2.5 concentrations and the factors impacting it. However, these studies did not consider the loss of information regarding predictor variables. To address this challenge, a generalized additive model combining principal component analysis (PCA–GAM) was proposed to estimate PM_2.5 concentrations in this study. The reliability of PCA–GAM for estimating PM_2.5 concentrations was tested in the Beijing-Tianjin-Hebei (BTH) region over a one-year period as a case study. The results showed that PCA–GAM outperforms traditional LUR modelling with relatively higher adjusted R² (0.94) and lower RMSE (4.08 µg/m³). The CV-adjusted R² (0.92) is high and close to the model-adjusted R², proving the robustness of the PCA–GAM model. The PCA–GAM model enhances PM_2.5 estimate accuracy by improving the usage of the effective predictor variables. Therefore, it can be concluded that PCA–GAM is a promising method for air pollution mapping and could be useful for decision makers taking a series of measures to combat air pollution.