基于数据稳健性的农用地土壤重金属空间分异研究
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摘要
研究目的:科学分析区域农用地表层土壤重金属空间分异特征,为准确追溯污染源,合理安全利用土地资源提供合理的数据支撑。研究方法:基于传统统计、数据稳健性、空间变异和空间插值构建土壤重金属空间分异研究方法体系。研究结果:以江苏省某市为例,探讨6种农用地表层土壤重金属As、Cd、Cu、Hg、Pb、Zn的统计特征、数据稳健性、空间变异特征和空间分布情况。结果表明:这6种重金属原始数据均不符合正态分布,呈右偏强变异,均受到自然和人为因素的共同作用。Normal Score Transformation(NST)稳健处理后的数据能保持与原始数据几乎相同的内部变异结构,据此利用反距离加权法(IDW)所做空间插值预测效果最佳。研究结论:该方法体系通过引入数据稳健性的概念补充了常规土壤重金属空间分异研究中对局部异常值的处理思路,还为后续该类研究提供了更系统的研究思路。
The purpose of the paper is to provide the important theoretical basis for tracing the source of regional soil heavy metal pollution accurately as well as to promote management and utilization of regional soil resource more appropriately and more efficiently, through scientific analysis of spatial variation characteristics of soil heavy metals. Taking a county of Jiangsu Province as an example, this paper characterizes the spatial variation of As, Cd, Cu, Hg, Pb and Zn in agricultural land topsoil by general statistical analysis, data robustness processing analysis, spatial variation analysis and spatial interpolation analysis. The robust basic descriptive statistics analysis results showed that these six heavy metals were all clustered spatial distributions with significant skewed characteristics. Their distributions were influenced by natural factors and human factors simultaneously based on the spatial variation analysis. The Inverse Distance Weighting(IDW) space interpolation prediction results were the best when using the Normal Score Transformation(NST) data of six heavy metals, which held almost the same data co-variance structure. The data robustness of spatial variation characteristics of soil heavy metals in this paper complemented the treatment methods of abnormal values in such pollution studies. Moreover, the research methodology also provides quantitative and reasonable theoretical support for a series of relevant studies in the future.
The purpose of the paper is to provide the important theoretical basis for tracing the source of regional soil heavy metal pollution accurately as well as to promote management and utilization of regional soil resource more appropriately and more efficiently, through scientific analysis of spatial variation characteristics of soil heavy metals. Taking a county of Jiangsu Province as an example, this paper characterizes the spatial variation of As, Cd, Cu, Hg, Pb and Zn in agricultural land topsoil by general statistical analysis, data robustness processing analysis, spatial variation analysis and spatial interpolation analysis. The robust basic descriptive statistics analysis results showed that these six heavy metals were all clustered spatial distributions with significant skewed characteristics. Their distributions were influenced by natural factors and human factors simultaneously based on the spatial variation analysis. The Inverse Distance Weighting(IDW) space interpolation prediction results were the best when using the Normal Score Transformation(NST) data of six heavy metals, which held almost the same data co-variance structure. The data robustness of spatial variation characteristics of soil heavy metals in this paper complemented the treatment methods of abnormal values in such pollution studies. Moreover, the research methodology also provides quantitative and reasonable theoretical support for a series of relevant studies in the future.
引文
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[2]谢云峰,陈同斌,雷梅,等.空间插值模型对土壤Cd污染评价结果的影响[J].环境科学学报,2010,30(4):847-854.
[3] VON STEIGER B, WEBSTER R, SCHULIN R, et al.Mapping heavy metals in polluted soil by disjunctive kriging[J]. Environmental Pollution, 1996, 94(2):205-215.
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[9] JOHNSON N L. Systems of frequency curves generated by methods of translation[J]. Biometrika, 1949, 36(1):149-176.
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[12]孙肖.基于空间统计学的高光谱降维后波段选择方法研究[D].北京:中国地质大学(北京),2016:31-33.
[13]钟晓兰,周生路,李江涛,等.长江三角洲地区土壤重金属污染的空间变异特征——以江苏省太仓市为例[J].土壤学报,2007,44(1):33-40.
[14]徐娅,陈红华.基于GIS的表层土壤重金属污染及空间特征研究——以玄武湖景区为例[J].环境工程学报,2015,9(8):4083-4089.
[15]邓欧平,周稀,黄萍萍,等.川中紫色丘区土壤养分空间分异与地形因子相关性研究[J].资源科学,2013,35(12):2434-2443.
[16] HANSEN L P, et al. Robustness[M]. Princeton:Princeton University Press, 2008:101-105.
[17] GOOVAERTS P. Geostatistics for Natural Resources Evaluation[M]. New York:Oxford University Press, 1997:266-271.
[18]王倩,尚月敏,冯锐,等.基于变异函数的耕地质量等别监测点布设分析——以四川省中江县和北京市大兴区为例[J].中国土地科学,2012,26(8):80-86.
[19] LIU X, WU J, XU J. Characterizing the risk assessment of heavy metals and sampling uncertainty analysis in paddy field by geostatistics and GIS[J]. Environmental Pollution,2006, 141(2):257-264.
[20]郭旭东,傅伯杰,陈利顶,等.河北省遵化平原土壤养分的时空变异特征——变异函数与Kringing插值分析[J].地理学报,2000,55(5):555-566.
[21]陈同斌,等.区域土壤环境质量[M].北京:科学出版社,2015:84-85.
[22] RAVANKHAH N, MIRZAEI R, MASOUM S. Human health risk assessment of heavy metals in surface soil[J]. Journal of Mazandaran University of Medical Sciences, 2016, 26(136):109-120.
[23]汤国安,杨昕. ArcGIS地理信息系统空间分析实验教程[M].北京:科学出版社,2012:330-336.
[24]吴春生,刘高焕,刘庆生,等.蒙古高原中北部土壤有机质空间分布研究[J].资源科学,2016,38(5):994-1002.
[2]谢云峰,陈同斌,雷梅,等.空间插值模型对土壤Cd污染评价结果的影响[J].环境科学学报,2010,30(4):847-854.
[3] VON STEIGER B, WEBSTER R, SCHULIN R, et al.Mapping heavy metals in polluted soil by disjunctive kriging[J]. Environmental Pollution, 1996, 94(2):205-215.
[4] YASREBI J, SAFFARI M, FATHI H, et al. Evaluation and comparison of ordinary kriging and inverse distance weighting methods for prediction of spatial variability of some soil chemical parameters[J]. Research Journal of Biological Sciences, 2009, 4(1):93-102.
[5] SIMON W H. Practical Geostatistics:Modeling and Spatial Analysis[M]. Berlin:Springer Verlag, 2002:147-148.
[6] ZHANG C, SELINUS O, SCHEDIN J. Statistical analyses for heavy metal contents in till and root samples in an area ofsoutheastern Sweden[J]. The Science of the Total Environment, 1998, 212(2-3):217-232.
[7] ZHANG C S, ZHANG S. A robust-symmetric mean:a new way of mean calculation for environmental data[J].GeoJournal, 1996, 40(1-2):209-212.
[8] BOX G E P, COX D R. An analysis of transformations[J].The Royal Statistical Society:Series B(Methodological),1964, 26(2):211-252.
[9] JOHNSON N L. Systems of frequency curves generated by methods of translation[J]. Biometrika, 1949, 36(1):149-176.
[10] HILL I D, HILL R, HOLDER R L. Algorithm as 99:fitting Johnson curves by moments[J]. Applied Statistics, 1976,25(2):180-189.
[11] CHOU Y M, POLANSKY A M, MASON R L. Transforming non-normal data to normality in statistical process control[J].Journal of Quality Technology, 1998, 30(2):133-141.
[12]孙肖.基于空间统计学的高光谱降维后波段选择方法研究[D].北京:中国地质大学(北京),2016:31-33.
[13]钟晓兰,周生路,李江涛,等.长江三角洲地区土壤重金属污染的空间变异特征——以江苏省太仓市为例[J].土壤学报,2007,44(1):33-40.
[14]徐娅,陈红华.基于GIS的表层土壤重金属污染及空间特征研究——以玄武湖景区为例[J].环境工程学报,2015,9(8):4083-4089.
[15]邓欧平,周稀,黄萍萍,等.川中紫色丘区土壤养分空间分异与地形因子相关性研究[J].资源科学,2013,35(12):2434-2443.
[16] HANSEN L P, et al. Robustness[M]. Princeton:Princeton University Press, 2008:101-105.
[17] GOOVAERTS P. Geostatistics for Natural Resources Evaluation[M]. New York:Oxford University Press, 1997:266-271.
[18]王倩,尚月敏,冯锐,等.基于变异函数的耕地质量等别监测点布设分析——以四川省中江县和北京市大兴区为例[J].中国土地科学,2012,26(8):80-86.
[19] LIU X, WU J, XU J. Characterizing the risk assessment of heavy metals and sampling uncertainty analysis in paddy field by geostatistics and GIS[J]. Environmental Pollution,2006, 141(2):257-264.
[20]郭旭东,傅伯杰,陈利顶,等.河北省遵化平原土壤养分的时空变异特征——变异函数与Kringing插值分析[J].地理学报,2000,55(5):555-566.
[21]陈同斌,等.区域土壤环境质量[M].北京:科学出版社,2015:84-85.
[22] RAVANKHAH N, MIRZAEI R, MASOUM S. Human health risk assessment of heavy metals in surface soil[J]. Journal of Mazandaran University of Medical Sciences, 2016, 26(136):109-120.
[23]汤国安,杨昕. ArcGIS地理信息系统空间分析实验教程[M].北京:科学出版社,2012:330-336.
[24]吴春生,刘高焕,刘庆生,等.蒙古高原中北部土壤有机质空间分布研究[J].资源科学,2016,38(5):994-1002.