土壤污染调查加密布点区域优化及效率研究
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摘要
为探究土壤污染调查过程中,在不同区域及不同样本量情况下加密布点的效率问题,并提出高效的加密方式,结合案例验证,在初步调查的基础上,基于指示克里金预测土壤污染概率,在不同概率区间内进行加密布点,分析对比不同概率区间加密布点的效率,并在效率较高的区间内对比不同加密点位数量对结果准确性的影响。结果表明:在0.50～0.95概率区间的区域加密布点对土壤污染范围的估计精度有所提高,其他区域则产生负作用;而加密点数在11.4%(20个)以内时,会提高土壤污染范围的估计精度,加密点数超过11.4%(20个),反而会降低土壤污染范围的估计精度。在非均匀布点的前提下,基于插值估计面积的方法,加密布点不一定能提高土壤污染范围的估计精度,在大概率确定的清洁区域或污染区域加密布点,均会降低整个研究区土壤污染范围的估计精度,应该在疑似污染但不能确定的区域加密布点,才有可能提高土壤污染范围的估计精度;疑似污染区加密布点结果精度随加密点数增加呈先增加后降低的趋势,因此适当加密点位数才有助于估计精度的提高。
To explore the efficiency of detailed soil sampling in different areas with different sample sizes during the investigation of soil pollution, and to propose an efficient encryption method, based on the preliminary investigation, this study, combined with case validation,predicted the probability of soil contamination by indicator kriging, and compared the efficiency of different probability ranges and different soil sample sizes. The distribution of encrypted sites in the area with a pollution probability between 0.50～0.95 increased the estimation accuracy of contaminated soil, while in other areas it had a negative effect. In addition, when the number of encryption sites was less than 20,the estimation accuracy of the soil contamination range increased. If the number of encryption sites exceeded 20, then the estimation accuracy of the soil contamination range decreased. Under the premise of non-uniform distribution, encrypted sites did not necessarily improve the estimation accuracy of soil contamination. Encrypting sites in clean or contaminated areas and encrypting sites too densely in suspected contaminated areas reduced the estimation accuracy of soil contamination in the entire study area. Only encrypting the appropriate number of points in the suspected contaminated area helped improve the estimation accuracy.
To explore the efficiency of detailed soil sampling in different areas with different sample sizes during the investigation of soil pollution, and to propose an efficient encryption method, based on the preliminary investigation, this study, combined with case validation,predicted the probability of soil contamination by indicator kriging, and compared the efficiency of different probability ranges and different soil sample sizes. The distribution of encrypted sites in the area with a pollution probability between 0.50～0.95 increased the estimation accuracy of contaminated soil, while in other areas it had a negative effect. In addition, when the number of encryption sites was less than 20,the estimation accuracy of the soil contamination range increased. If the number of encryption sites exceeded 20, then the estimation accuracy of the soil contamination range decreased. Under the premise of non-uniform distribution, encrypted sites did not necessarily improve the estimation accuracy of soil contamination. Encrypting sites in clean or contaminated areas and encrypting sites too densely in suspected contaminated areas reduced the estimation accuracy of soil contamination in the entire study area. Only encrypting the appropriate number of points in the suspected contaminated area helped improve the estimation accuracy.
引文
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[21]杨奇勇,杨劲松,余世鹏.禹城市耕地土壤盐分与有机质的指示克里格分析[J].生态学报,2011,31(8):2196-2202.YANG Qi-yong,YANG Jin-song,YU Shi-peng.Evaluation on spatial distribution of soil salinity and soil organic matter by indicator Kriging in Yucheng City[J].Acta Ecologica Sinica,2011,31(8):2196-2202.
[22]Meirvenne M V,Goovaerts P.Evaluating the probability of exceeding1042385a site-specific soil cadmium contamination threshold[J].Geoderma,2001,102(1):75-100.
[23]CastrignanòA,Goovaerts P,Lulli L,et al.A geostatistical approach to estimate probability of occurrence of Tuber melanosporum in relation to some soil properties[J].Geoderma,2000,98(3/4):95-113.
[24]Lin Y P,Chang T K,Shih C W,et al.Factorial and indicator Kriging methods using a geographic information system to delineate spatial variation and pollution sources of soil heavy metals[J].Environmental Geology,2002,42(8):900-909.
[25]Cressie N.Statistics for spatial data[J].Technometrics,1992,35(3):321-323.
[26]Smith J L,Halvorson J J,Papendick R I.Using multiple-variable indicator Kriging for evaluating soil quality[J].Soil Science Society of America Journal,1993,57(3):743-749.
[27]Gao B,Liu Y,Pan Y,et al.Error index for additional sampling to map soil contaminant grades[J].Ecological Indicators,2017,77:129-138.
[28]Xie Y,Chen T B,Lei M,et al.Spatial distribution of soil heavy metal pollution estimated by different interpolation methods:Accuracy and uncertainty analysis[J].Chemosphere,2011,82(3):468-476.
[2]Theocharopoulos S P,Wagner G,Sprengart J,et al.European soil sampling delines for soil pollution studies[J].The Science of the Total Environment,2001,264(1/2):51-62.
[3]Gustavsson B,Luthbom K,Lagerkvist A.Comparison of analytical error and sampling error for contaminated soil[J].Journal of Hazardous Materials,2006,138(2):252-260.
[4]Jenkins T F,Grant C L,Brar G S,et al.Sampling error associated with collection and analysis of soil samples at TNT-contaminated sites[J].Field Analytical Chemistry&Technology,2010,1(3):151-163.
[5]姜成晟,王劲峰,曹志冬.地理空间抽样理论研究综述[J].地理学报,2009,64(3):368-380.JIANG Cheng-sheng,WANG Jin-feng,CAO Zhi-dong.A review of geo-spatial sampling theory[J].Acta Geographica Sinica,2009,64(3):368-380.
[6]Brus D J,Sp?tjens L E E M,Gruijter J J D.A sampling scheme for estimating the mean extractable phosphorus concentration of fields for environmental regulation[J].Geoderma,1999,89(1/2):129-148.
[7]刘庚,毕如田,张朝,等.某焦化场地苯并(a)芘污染空间分布范围预测的不确定性分析[J].环境科学学报,2013,33(2):587-593.LIU Geng,BI Ru-tian,ZHANG Chao,et al.Uncertainty analysis on spatial distribution prediction of BaP in a cokoing plant site[J].Acta Scientiae Circumstantiae,2013,33(2):587-593.
[8]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.XIE Yun-feng,CHEN Tong-bin,LEI Mei,et al.Impact of spatial interpolation methods on the estimation of regional soil Cd[J].Acta Scientiae Circumstantiae,2010,30(4):847-854.
[9]赵倩倩,赵庚星,姜怀龙,等.县域土壤养分空间变异特征及合理采样数研究[J].自然资源学报,2012,27(8):1382-1391.ZHAO Qian-qian,ZHAO Geng-xing,JIANG Huai-long,et al.Study on spatial variability of soil nutrients and reasonable sampling number at county scale[J].Journal of Natural Resources,2012,27(8):1382-1391.
[10]Marchant B P,Mcbratney A B,Lark R M,et al.Optimized multiphase sampling for soil remediation surveys[J].Spatial Statistics,2013,4(1):1-13.
[11]谢云峰,杜平,曹云者,等.基于地统计条件模拟的土壤重金属污染范围预测方法研究[J].环境污染与防治,2015,37(1):1-6.XIE Yun-feng,DU Ping,CAO Yun-zhe,et al.Estimating the area of heavy metal contaminated soil using geostatistical conditional simulation[J].Environmental Pollution Control,2015,37(1):1-6.
[12]D'Or D.Towards a real-time multi-phase sampling strategy optimization[M].Springer Berlin Heidelberg:Geostatistics for Environmental Applications,2005:355-366.
[13]Demougeot-Renard H,De F C,Renard P.Forecasting the number of soil samples required to reduce remediation cost uncertainty[J].Journal of Environmental Quality,2004,33(5):1694-1702.
[14]Juang K W,Lee D Y,Teng Y L.Adaptive sampling based on the cumulative distribution function of order statistics to delineate heavymetal contaminated soils using Kriging[J].Environmental Pollution,2005,138(2):268-277.
[15]Groenigen J W V,Siderius W,Stein A.Constrained optimisation of soil sampling for minimisation of the Kriging variance[J].Geoderma,1999,71(3):239-259.
[16]Tooren C F V,Mosselman M.A Framework for optimization of soil sampling strategy and soil remediation scenario decisions using moving window Kriging[M].Springer Netherlands:geoENVI-Geostatistics for Environmental Applications,1997:259-270.
[17]Verstraete S,Van M M.A multi-stage sampling strategy for the delin-1041eation of soil pollution in a contaminated brownfield[J].Environmental Pollution,2008,154(2):184-191.
[18]Webster R,Burgess T M.Optimal interpolation and isarithmic mapping of soil propertiesⅢchanging drift and universal kriging[J].European Journal of Soil Science,2010,31(3):505-524.
[19]Englund E J,Heravi N.Conditional simulation:Practical application for sampling design optimization[M].Springer Netherlands,1993:613-624.
[20]阎波杰,潘瑜春,赵春江.区域土壤重金属空间变异及合理采样数确定[J].农业工程学报,2008,24(增刊2):260-264.YAN Bo-jie,PAN Yu-chun,ZHAO Chun-jiang.Spatial variability and reasonable sampling number of regional soil heavy metal[J].Transactions of the CSAE,2008,24(Suppl 2):260-264.
[21]杨奇勇,杨劲松,余世鹏.禹城市耕地土壤盐分与有机质的指示克里格分析[J].生态学报,2011,31(8):2196-2202.YANG Qi-yong,YANG Jin-song,YU Shi-peng.Evaluation on spatial distribution of soil salinity and soil organic matter by indicator Kriging in Yucheng City[J].Acta Ecologica Sinica,2011,31(8):2196-2202.
[22]Meirvenne M V,Goovaerts P.Evaluating the probability of exceeding1042385a site-specific soil cadmium contamination threshold[J].Geoderma,2001,102(1):75-100.
[23]CastrignanòA,Goovaerts P,Lulli L,et al.A geostatistical approach to estimate probability of occurrence of Tuber melanosporum in relation to some soil properties[J].Geoderma,2000,98(3/4):95-113.
[24]Lin Y P,Chang T K,Shih C W,et al.Factorial and indicator Kriging methods using a geographic information system to delineate spatial variation and pollution sources of soil heavy metals[J].Environmental Geology,2002,42(8):900-909.
[25]Cressie N.Statistics for spatial data[J].Technometrics,1992,35(3):321-323.
[26]Smith J L,Halvorson J J,Papendick R I.Using multiple-variable indicator Kriging for evaluating soil quality[J].Soil Science Society of America Journal,1993,57(3):743-749.
[27]Gao B,Liu Y,Pan Y,et al.Error index for additional sampling to map soil contaminant grades[J].Ecological Indicators,2017,77:129-138.
[28]Xie Y,Chen T B,Lei M,et al.Spatial distribution of soil heavy metal pollution estimated by different interpolation methods:Accuracy and uncertainty analysis[J].Chemosphere,2011,82(3):468-476.