土壤重金属Cd污染指数的适宜插值方法和合理采样数量研究
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摘要
对局部存在重金属污染地区采用适宜的插值方法和布设合理的采样点对重金属污染状况监测具有重要的意义。运用单因子指数法得到土壤重金属Cd污染指数,并在镇域内布设2033个采样点的基础上,通过随机抽样法抽取1830、1423、1017、610、203五个采样点样本子集。首先,运用普通克里金法(OK)、径向基函数法(RBF)和反距离权重法(IDW)对该地区土壤重金属Cd污染指数进行插值预测,并通过交叉验证法进行精度检验。然后,在反距离权重法的基础上,对五个采样子集进行插值精度分析,得到大致合理采样数量。结果表明:(1)利用全集2033个采样点对3种插值方法进行交叉验证分析可知,RMSE表现为IDW(3.018)>RBF(2.942)>OK(2.837),ME表现为OK(-0.0736)>IDW(0.0214)>RBF(0.0096),MAE表现为IDW(0.5668)>RBF(0.5575)>OK(0.5227),3种插值方法在整体预测精度上差异不明显。而对于污染区域的识别,IDW在轻度污染区、中度污染区和重度污染区预测上表现出较大的优势,能较好的反应污染区域的空间变异特征。因此,认为IDW为较适宜的空间插值方法。(2)对不同采样数量的样本进行交叉验证分析可知,RMSE、ME和MAE在1017个采样点到610个采样点误差变化幅度分别为29.84%、71.31%和36.99%,误差增加幅度较前三个子样本间明显增大。在空间特征识别方面,2033、1830、1423和1017个采样点反映的污染区的空间分布特征非常相似,610和203个采样点预测的污染区域面积明显扩大,对各级污染区域的空间特征细节表现能力较差。因此,对于该镇域内的土壤重金属Cd污染指数的研究,1017个左右采样点是比较合理的采样数量。
Using appropriate interpolation method and setting reasonable sampling point is of great importance for monitoring condition of heavy metal pollution. In this paper, we used the single factor index method to obtain soil pollution index of Cd and extract random samples of 1830, 1423, 1017, 610 and 203 from a total of 2033 samples.First of all, we predicted the spatial distribution of Cd pollution index using ordinary Kriging(OK), radial basis function(RBF) and Inverse distance weighting(IDW) methods and checked the accuracy by the cross validation method. Secondly, we analyzed the interpolation accuracy for the five subsets using inverse distance weighting method to get a general reasonable number of samples. The results showed that(1) The complete 2033 sampling points were used to make cross validation analysis for three interpolation methods. The RMSE result decreased as follows: IDW(3.018) > RBF(2.942) > OK(2.837), the ME result OK(-0.0736) > IDW(0.0214) > RBF(0.0096), and the MAE result IDW(0.5668) > RBF(0.5575) > OK(0.5227). The prediction accuracy showed no obvious difference among three interpolation methods. Taking the identification of polluted area as another standard, IDW method showed a greater advantage to better reflect spatial variability characteristics of regional pollution in districts of slight pollution,moderate pollution and severe pollution.(2) The cross validation about different sampling sizes showed that the error variation amplitude of RMSE, ME and MAE from 1017 to 610 sampling points was 29.84%, 71.31% and 36.99%,respectively. And the amplitude of ME was obvious bigger compared with the first three sub samples. From the spatial feature recognition, 2033, 1830, 1423 and 1017 sampling points reflected the very similar spatial distribution characteristics in contaminated area, but the area of regional pollution was significantly expanded and the detail of spatial characteristics at all pollution levels was considerably decreased from 610 and 203 sampling points. Therefore,the reasonable sampling quantity was about 1017 sampling points in this town for the study of Cd pollution index in soil.
Using appropriate interpolation method and setting reasonable sampling point is of great importance for monitoring condition of heavy metal pollution. In this paper, we used the single factor index method to obtain soil pollution index of Cd and extract random samples of 1830, 1423, 1017, 610 and 203 from a total of 2033 samples.First of all, we predicted the spatial distribution of Cd pollution index using ordinary Kriging(OK), radial basis function(RBF) and Inverse distance weighting(IDW) methods and checked the accuracy by the cross validation method. Secondly, we analyzed the interpolation accuracy for the five subsets using inverse distance weighting method to get a general reasonable number of samples. The results showed that(1) The complete 2033 sampling points were used to make cross validation analysis for three interpolation methods. The RMSE result decreased as follows: IDW(3.018) > RBF(2.942) > OK(2.837), the ME result OK(-0.0736) > IDW(0.0214) > RBF(0.0096), and the MAE result IDW(0.5668) > RBF(0.5575) > OK(0.5227). The prediction accuracy showed no obvious difference among three interpolation methods. Taking the identification of polluted area as another standard, IDW method showed a greater advantage to better reflect spatial variability characteristics of regional pollution in districts of slight pollution,moderate pollution and severe pollution.(2) The cross validation about different sampling sizes showed that the error variation amplitude of RMSE, ME and MAE from 1017 to 610 sampling points was 29.84%, 71.31% and 36.99%,respectively. And the amplitude of ME was obvious bigger compared with the first three sub samples. From the spatial feature recognition, 2033, 1830, 1423 and 1017 sampling points reflected the very similar spatial distribution characteristics in contaminated area, but the area of regional pollution was significantly expanded and the detail of spatial characteristics at all pollution levels was considerably decreased from 610 and 203 sampling points. Therefore,the reasonable sampling quantity was about 1017 sampling points in this town for the study of Cd pollution index in soil.
引文
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[17]张贝尔,黄标,赵永存,等.采样数量与空间插值方法对华北平原典型区土壤质量评价空间预测精度的影响[J].土壤,2013,45(3):540-547.
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[2]唐发静,祖艳群.土壤重金属空间变异的研究方法[J].云南农业大学学报,2008,34(3):558-561.
[3]殷飞,王海娟,李燕燕,等.不同钝化剂对重金属复合污染土壤的修复效应研究[J].农业环境科学学报,2015,36(3):438-448.
[4]卫泽斌,郭晓方,吴启堂,等.混合螯合剂的不同施加方式对重金属污染土壤套种修复效果的影响[J].华南农业大学学报,2016,1:29-34.
[5]杨洋,陈志鹏,黎红亮,等.两种农业种植模式对重金属土壤的修复潜力[J].生态学报,2016,37(3):688-695.
[6]钱春香,王明明,许燕波.土壤重金属污染现状及微生物修复技术研究进展[J].东南大学学报(自然科学版),2013,43(3):669-674.
[7]王超,王纪华,陆安祥,等.土壤重金属分布空间插值方法的优选——以湖南某地土壤重金属Cd的分布为例[J].吉林农业,2015,21:74-75.
[8]陈思萱,邹滨,汤景文.空间插值方法对土壤重金属污染格局识别的影响[J].测绘科学,2015,40(1):63-67.
[9]孙义祥,吴传洲,朱克保,等.插值方法与样点数对县域土壤有效磷空间变异特征评价的影响[J].应用生态学报,2009,20(3):673-678.
[10]王建军,王长松,殷朝珍,等.不同采样密度对土壤养分空间插值结果的影响[J].江西农业学报,2010,22(7):87-90.
[11]R KERRY&M A OLIVER.Comparing sampling needs f or variograms of soil properties computed by the method of moments and residual maximum likelihood[J].Geoderma,2007,140:383-396
[12]DE SOUZA,ZM,et al.Number of samples in geostatistical analysis and kriging maps of soil properties[J].Ciência Rural,2014,44:261-268.
[13]CHRISTOPHER L SHOPE&GANGA RAM MAHAR JAN.Modeling Spatiotemporal Precipitation:Effects of Density,Interpolation,and Land Use Distribution[J].Hindawi,2015,16:1-16.
[14]李湘凌,张颖慧,杨善谋,等.合肥义城地区土壤重金属污染评价中典型插值方法的对比[J].吉林大学学报(地球科学版),2011,41(1):222-227.
[15]凌辉,武伟,王润,等.小尺度下土壤重金属铬含量的空间插值方法比较研究[J].西南大学学报(自然科学版),2007,29(11):93-99.
[16]樊燕,刘洪斌,武伟.土壤重金属污染现状评价及其合理采样数的研究[J].土壤通报,2008,39(2):369-374.
[17]张贝尔,黄标,赵永存,等.采样数量与空间插值方法对华北平原典型区土壤质量评价空间预测精度的影响[J].土壤,2013,45(3):540-547.
[18]BURROUGH,PETER A.Prineiples of Geographie Information Systems for Land Resoures Assessment[M].Oxford University Press,1986.
[19]PAUL A,LONGLEY,et al.Information System,Volumel,Principles and Technical Issues,Second Edition[M].电子工业出版社,2004.
[20]袁峰,白晓宇,周涛发,等.元素空间分布插值方法的对比研究:以铜陵地区土壤中的重金属元素为例[J].地学前缘,2008,15(5):103-109.
[21]CLARK I.Practical geostatistics[M].London:Applied Science Publishers,1979.
[22]ROBINSON T P,METTEMICHT G.Testing the performance of spatial interpolation techniques for mapping soil properties[J].Computers and Electronics in Agriculture,2006,50(2):97-108.
[23]王学军,邓宝山,张泽浦.北京东郊污灌区表层土壤微量元素的小尺度空间结构特征[J].环境科学学报,1997,17(4):414-416.