Environmental Science and Pollution Research

, Volume 24, Issue 3, pp 2578–2588 | Cite as

Comparison of four methods for spatial interpolation of estimated atmospheric nitrogen deposition in South China

  • Linglu Qu
  • Huayun Xiao
  • Nengjian Zheng
  • Zhongyi Zhang
  • Yu Xu
Research Article


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.


Estimated N deposition Moss biomonitoring Spatial distribution Interpolation Cross-validation 



This study was kindly supported by the National Key Research and Development Program of China through grant 2016YFA0601000 (H.Y. Xiao); the National Natural Science Foundation of China through grants 41425014, 41273027, and 41173027 (H.Y. Xiao); and the National Basic Research Program of China through grant 2013CB956703 (H.Y. Xiao).


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Linglu Qu
    • 1
    • 2
  • Huayun Xiao
    • 1
  • Nengjian Zheng
    • 1
    • 2
  • Zhongyi Zhang
    • 1
    • 2
  • Yu Xu
    • 1
    • 2
  1. 1.State Key Laboratory of Environmental Geochemistry, Institute of GeochemistryChinese Academy of ScienceGuiyangChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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