Methods of Land Use Regression (LUR) modeling and Ordinary Kriging (OK) interpolation have been widely used to offset the shortcomings of PM2.5 data observed at sparse monitoring sites. However, traditional point-based performance evaluation strategy for these methods remains stagnant, which could cause unreasonable mapping results. To address this challenge, this study employs ‘information entropy’, an area-based statistic, along with traditional point-based statistics (e.g. error rate, RMSE) to evaluate the performance of LUR model and OK interpolation in mapping PM2.5 concentrations in Houston from a multidimensional perspective. The point-based validation reveals significant differences between LUR and OK at different test sites despite the similar end-result accuracy (e.g. error rate 6.13% vs. 7.01%). Meanwhile, the area-based validation demonstrates that the PM2.5 concentrations simulated by the LUR model exhibits more detailed variations than those interpolated by the OK method (i.e. information entropy, 7.79 vs. 3.63). Results suggest that LUR modeling could better refine the spatial distribution scenario of PM2.5 concentrations compared to OK interpolation. The significance of this study primarily lies in promoting the integration of point- and area-based statistics for model performance evaluation in air pollution mapping.

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Abstract. The Kobe earthquake of January 17, 1995, which had a weathered granite fault wall as shown by arrow B in Figure lb; magnitude of 7.2, was accompanied by earthquake hghtning and 3) the clayey fault gouge under the part where the charred (EQL). The fault gouge near ground level at the Nojima fault, roots were found was highly lithified and showed a lamellar near where the EQL was witnessed, was highly lithified and structure. anomalously mag-netized. The characterization of the fault gouge and the mudstone near the fault suggests that the anomahes might have been induced by an intense EQL current which passed through the fault plane.

Satellite-based PM 2.5 concentration estimation is growing as a popular solution to map the PM 2.5 spatial distribution due to the insufficiency of ground-based monitoring stations. However, those applications usually suffer from the simple hypothesis that the influencing factors are linearly correlated with PM 2.5 concentrations, though non-linear mechanisms indeed exist in their interactions. Taking the Beijing-Tianjin-Hebei (BTH) region in China as a case, this study developed a generalized additive modeling (GAM) method for satellite-based PM 2.5 concentration mapping. In this process, the linear and non-linear relationships between PM 2.5 variation and associated contributing factors, such as the aerosol optical depth (AOD), industrial sources, land use type, road network, and meteorological variables, were comprehensively considered. The reliability of the GAM models was validated by comparison with typical linear land use regression (LUR) models. Results show that GAM modeling outperforms LUR modeling at both the annual and seasonal scale, with obvious higher model fitting-based adjusted R 2 and lower RMSEs. This is confirmed by the cross-validation-based adjusted R 2 with values of GAM-based spring, summer, autumn, winter, and annual models, which are 0.92, 0.78, 0.87, 0.85, and 0.90, respectively, while those of LUR models are 0.87, 0.71, 0.84, 0.84, and 0.85, respectively. Different to the LUR-based hypothesis of the "straight line" relations, the "smoothed curves" from GAM-based apportionment analysis reveals that factors contributing to PM 2.5 variation are unstable with the alternate linear and non-linear relations. The GAM model-based PM 2.5 concentration surfaces clearly demonstrate their superiority in disclosing the heterogeneous PM 2.5 concentrations to the discrete observations. It can be concluded that satellite-based PM 2.5 concentration mapping could be greatly improved by GAM modeling given its simultaneous considerations of the linear and non-linear influencing mechanisms of PM 2.5 .