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Spatial interpolation methods have been applied in many environmental research studies. However, it is still a controversial issue to select an appropriate interpolation method for the conversion of discrete sampling sites into continuous maps. This study aimed at selecting an optimal interpolation method to analyze the spatial pattern of atmospheric N deposition in South China. N deposition was calculated by 259 moss sample data. Four spatial interpolation methods, including inverse distance weighting (IDW), radial basis function (RBF), ordinary kriging (OK), and universal kriging (UK), were utilized for modeling the spatial distribution of N deposition. It is the first time that these methods were applied to analyze N deposition in South China. Validation method was used to evaluate the interpolation precision of the various methods, and the cross-validation method was used to evaluate their interpolation accuracy. Comparison of predicted values with measured values indicated that OK was the optimal method for analyzing the spatial distribution of N deposition in this study; it had the highest precision (mean error (ME) = −0.059, root-mean-square error (RMSE) = 5.240, mean relative error (MRE) = 0.129, mean absolute error (MAE) = 4.007) and the lowest uncertainties (standard deviation (SD) = 5.47, coefficient of variation (CV) = 0.15). RBF produced similar results as good as OK, while the worst performed interpolation method was UK. By using the OK method for analyzing N deposition, this work revealed systematic temporal and spatial variations in atmospheric N deposition in South China. [ABSTRACT FROM AUTHOR]

In recent years, implementation of aggressive and strict clean air policies has resulted in significant decline in observed PM 2.5 concentration in the Beijing-Tianjin-Hebei (BTH) region and its surrounding areas (i.e., the "2 + 26" region). To eliminate the effects of interannual and seasonal meteorological variation, and to evaluate the effectiveness of emission abatement policies, we applied a boosted regression tree model to remove confounding meteorological factors. Results showed that the annual average PM2.5 concentration normalized by meteorology for the "2 + 26" region declined by 38% during 2014-2019 (i.e., from 96 to 60 μg/m 3 ); however, the BTH region exhibited the most remarkable decrease in PM 2.5 concentration (i.e., a 60% reduction). Certain seasonal trend in normalized PM 2.5 level remained for four target subregions owing to the effects of anthropogenic emissions in autumn and winter. Although strong interannual variations of meteorological conditions were unfavorable for pollutant dispersion during the heating seasons of 2016-2018, the aggressive abatement policies were estimated to have contributed to reductions in normalized PM 2.5 concentration of 19%, 10%, 19%, and 17% in the BTH, Henan, Shandong, and Shanxi subregions, respectively. Our study eliminated the meteorological contribution to concentration variation and confirmed the effectiveness of the implemented clean air policies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)