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摘要
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 PM2.5 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 PM2.5 concentrations in this study. The reliability of PCA-GAM for estimating PM2.5 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 R-2 (0.94) and lower RMSE (4.08 mu g/m(3)). The CV-adjusted R-2 (0.92) is high and close to the model-adjusted R-2, proving the robustness of the PCA-GAM model. The PCA-GAM model enhances PM2.5 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.
