Abstract
Spatial interpolation method is the basis of soil heavy metal pollution assessment and remediation. The existing evaluation index for interpolation accuracy did not combine with actual situation. The selection of interpolation methods needs to be based on specific research purposes and research object characteristics. In this paper, As pollution in soils of Beijing was taken as an example. The prediction accuracy of ordinary kriging (OK) and inverse distance weighted (IDW) were evaluated based on the cross validation results and spatial distribution characteristics of influencing factors. The results showed that, under the condition of specific spatial correlation, the cross validation results of OK and IDW for every soil point and the prediction accuracy of spatial distribution trend are similar. But the prediction accuracy of OK for the maximum and minimum is less than IDW, while the number of high pollution areas identified by OK are less than IDW. It is difficult to identify the high pollution areas fully by OK, which shows that the smoothing effect of OK is obvious. In addition, with increasing of the spatial correlation of As concentration, the cross validation error of OK and IDW decreases, and the high pollution area identified by OK is approaching the result of IDW, which can identify the high pollution areas more comprehensively. However, because the semivariogram constructed by OK interpolation method is more subjective and requires larger number of soil samples, IDW is more suitable for spatial prediction of heavy metal pollution in soils.
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References
Aguilar FJ, Agüera F, Aguilar MA, Carvajal F (2005) Effects of terrain morphology, sampling density, and interpolation methods on grid Dem accuracy. Photogramm Eng Remote Sens 71(7):805–816. https://doi.org/10.14358/PERS.71.7.805
Bi X, Feng X, Yang Y, Li X, Shin GPY, Li F, Qiu G, Li G, Liu T, Fu Z (2009) Allocation and source attribution of lead and cadmium in maize (Zea Mays L.) impacted by smelting emissions. Environ Pollut 157(3):834–839. https://doi.org/10.1016/j.envpol.2008.11.013
Caruso C, Quarta F (1998) Interpolation methods comparison. Comput Math Appl 35(12):109–126. https://doi.org/10.1016/S0898-1221(98)00101-1
Chaplot V, Darboux F, Bourennane H, Leguédois S, Silvera N, Phachomphon K (2006) Accuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density. Geomorphology 77(1–2):126–141. https://doi.org/10.1016/j.geomorph.2005.12.010
Chen M, Ma LQ, Harris WG (2002) Arsenic concentrations in Florida surface soils approved for publication as Florida agricultural experiment station journal series no. R-07010. Soil Sci Soc Am J 66(2):632–640. https://doi.org/10.2136/sssaj2002.6320
Chen TB, Zheng YM, Chen H, Zheng GD (2004) Background concentrations of soil heavy metals in Beijing. J Environ Sci 25:117–122. https://doi.org/10.1065/jss2007.08.245
Facchinelli A, Sacchi E, Mallen L (2001) Multivariate statistical and Gis-based approach to identify heavy metal sources in soils. Environ Pollut 114(3):313–324. https://doi.org/10.1016/s0269-7491(00)00243-8
Goovaerts P (2001) Geostatistical modelling of uncertainty in soil science. Geoderma 103(1):3–26. https://doi.org/10.1016/S0016-7061(01)00067-2
Gotway CA, Ferguson RB, Hergert GW, Peterson TA (1996) Comparison of kriging and inverse-distance methods for mapping soil parameters. Soil Sci Soc Am J 60(4):1237–1247. https://doi.org/10.2136/sssaj1996.03615995006000040040x
Gozdowski D, Stępień M, Samborski S, Dobers ES, Szatyłowicz J, Chormański J (2015) Prediction accuracy of selected spatial interpolation methods for soil texture at farm field scale. J Soil Sci Plant Nutr 15(3):639–650. https://doi.org/10.4067/S0718-95162015005000033
Hu Y, Wang JF, Li XH, Ren D, Zhu J (2011) Geographical detector-based risk assessment of the under-five mortality in the 2008 Wenchuan earthquake, China. PLoS One 6(6):1–8. https://doi.org/10.1371/journal.pone.0021427
Huo XN, Li H, Sun DF, Zhou LD, Li BG (2010) Multi-scale spatial structure of heavy metals in agricultural soils in Beijing. Environ Monit Assess 164(1–4):605–616. https://doi.org/10.1007/s10661-009-0916-7
Joly D, Brossard T, Cardot H, Cavailhes J, Hilal M, Wavresky P (2011) Temperature interpolation based on local information: the example of France. Int J Climatol 31(14):2141–2153. https://doi.org/10.1002/joc.2220
Khan S, Cao Q, Zheng YM, Huang YZ, Zhu YG (2008) Health risks of heavy metals in contaminated soils and food crops irrigated with wastewater in Beijing, China. Environ Pollut 152(3):686–692. https://doi.org/10.1016/j.envpol.2007.06.056
Kuusisto-Hjort P, Hjort J (2013) Land use impacts on trace metal concentrations of suburban stream sediments in the Helsinki region, Finland. Sci Total Environ 456:222–230. https://doi.org/10.1016/j.scitotenv.2013.03.086
Lado LR, Hengl T, Reuter HI (2008) Heavy metals in European soils: a geostatistical analysis of the Foregs geochemical database. Geoderma 148(2):189–199. https://doi.org/10.1016/j.geoderma.2008.09.020
Li L, Wang Y, Zhang Q, Yu T, Zhao Y, Jin J (2007) Spatial distribution of aerosol pollution based on Modis data over Beijing, China. J Environ Sci 19(8):955–960. https://doi.org/10.1016/s1001-0742(07)60157-0
Li XW, Xie YF, Wang JF, Christakos G, Si JL, Zhao HN, Ding YQ, Li J (2013) Influence of planting patterns on fluoroquinolone residues in the soil of an intensive vegetable cultivation area in northern China. Sci Total Environ 458:63–69. https://doi.org/10.1016/j.scitotenv.2013.04.002
Li RK, Li ZP, Gao WJ, Ding WJ, Xu Q, Song XF (2015) Diurnal, seasonal, and spatial variation of PM2.5 in Beijing. Sci Bull 60(3):387–395. https://doi.org/10.1007/s11434-014-0607-9
Lough GC, Schauer JJ, Park JS, Shafer MM, Deminter JT, Weinstein JP (2005) Emissions of metals associated with motor vehicle roadways. Environ Sci Technol 39(3):826–836. https://doi.org/10.1021/es048715f
Lu GY, Wong DW (2008) An adaptive inverse-distance weighting spatial interpolation technique. Comput Geosci 34(9):1044–1055. https://doi.org/10.1016/j.cageo.2007.07.010
Lu A, Wang J, Qin X, Wang K, Han P, Zhang S (2012) Multivariate and geostatistical analyses of the spatial distribution and origin of heavy metals in the agricultural soils in Shunyi, Beijing, China. Sci Total Environ 425:66–74. https://doi.org/10.1016/j.scitotenv.2012.03.003
Nael M, Khademi H, Jalalian A, Schulin R, Kalbasi M, Sotohian F (2009) Effect of geo-pedological conditions on the distribution and chemical speciation of selected trace elements in forest soils of western Alborz, Iran. Geoderma 152(1–2):157–170. https://doi.org/10.1016/j.geoderma.2009.06.001
Panagopoulos T, Jesus J, Antunes MDC, Beltrão J (2006) Analysis of spatial interpolation for optimising management of a salinized field cultivated with lettuce. Eur J Agron 24(1):1–10. https://doi.org/10.1016/j.eja.2005.03.001
Pang S, Li TX, Wang YD, Yu HY, Li X (2009) Spatial interpolation and sample size optimization for soil copper (Cu) investigation in cropland soil at county scale using cokriging. Agric Sci China 8(11):1369–1377. https://doi.org/10.1016/s1671-2927(08)60349-1
Piccini C, Marchetti A, Francaviglia R (2014) Estimation of soil organic matter by geostatistical methods: use of auxiliary information in agricultural and environmental assessment. Ecol Indic 36:301–314. https://doi.org/10.1016/j.ecolind.2013.08.009
Reimann C, de Caritat P (2005) Distinguishing between natural and anthropogenic sources for elements in the environment: regional geochemical surveys versus enrichment factors. Sci Total Environ 337(1–3):91–107. https://doi.org/10.1016/j.scitotenv.2004.06.011
Salonen VP, Korkka-Niemi K (2007) Influence of parent sediments on the concentration of heavy metals in urban and suburban soils in Turku, Finland. Appl Geochem 22(5):906–918. https://doi.org/10.1016/j.apgeochem.2007.02.003
Shi W, Liu J, Du Z, Song Y, Chen C, Yue T (2009) Surface modelling of soil Ph. Geoderma 150(1–2):113–119. https://doi.org/10.1016/j.geoderma.2009.01.020
Spezia GR, Souza EG, Nóbrega LHP, Uribe-Opazo MA, Milan M, Bazzi CL (2012) Model to estimate the sampling density for establishment of yield mapping. Revista Brasileira de Engenharia Agrícola e Ambiental 16(4):449–457. https://doi.org/10.1590/S1415-43662012000400016
Susanto F, de Souza P, He J (2016) Spatiotemporal interpolation for environmental modelling. Sensors (Basel) 16(8):1245–1265. https://doi.org/10.3390/s16081245
U.S.EPA (1996) Method 3050B: acid digestion of sediments, sludges and soils, revision 2. Washington, DC
Wang JF, Hu Y (2012) Environmental health risk detection with Geogdetector. Environ Model Softw 33:114–115. https://doi.org/10.1016/j.envsoft.2012.01.015
Wang H, Lu S (2011) Spatial distribution, source identification and affecting factors of heavy metals contamination in urban-suburban soils of Lishui City, China. Environ Earth Sci 64(7):1921–1929. https://doi.org/10.1007/s12665-011-1005-0
Wang SS, Cao ZM, Lan DZ, Zheng ZC, Li GH (2008) Concentration distribution and assessment of several heavy metals in sediments of west-four Pearl River estuary. Environ Geol 55(5):963–975. https://doi.org/10.1007/s00254-007-1046-6
Webster R, Oliver MA (1992) Sample adequately to estimate variograms of soil properties. Eur J Soil Sci 43(1):177–192. https://doi.org/10.1111/j.1365-2389.1992.tb00128.x
Wollenhaupt NC, Wolkowski RP, Clayton MK (1994) Mapping soil test phosphorus and potassium for variable rate fertilizer application. J Prod Agric 7(4):441–448. https://doi.org/10.2134/jpa1994.0441
Wu L, Pan X, Chen L, Huang Y, Teng Y, Luo Y, Christie P (2013) Occurrence and distribution of heavy metals and tetracyclines in agricultural soils after typical land use change in East China. Environ Sci Pollut Res 20(12):8342–8354. https://doi.org/10.1007/s11356-013-1532-1
Xie YF, Chen TB, Lei M, Yang J, Guo QJ, Song B, Zhou XY (2011) Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: accuracy and uncertainty analysis. Chemosphere 82(3):468–476. https://doi.org/10.1016/j.chemosphere.2010.09.053
Yasrebi J, Saffari M, Fathi H, Karimian N, Mosazallahi M, Gazni R (2009) Evaluation and comparison of ordinary kriging and inverse distance weighting methods for prediction of spatial variability of some soil chemical parameters. Res J Biol Sci 4(1):93–102
Zheng YM, Chen TB, He JZ (2008) Multivariate geostatistical analysis of heavy metals in topsoils from Beijing, China. Heavy Metals Soil Miner 8(1):51–58. https://doi.org/10.1065/jss2007.08.245
Zhou J, Feng K, Li Y, Zhou Y (2016) Factorial kriging analysis and sources of heavy metals in soils of different land-use types in the Yangtze River Delta of eastern China. Environ Sci Pollut Res Int 23(15):14957–14967. https://doi.org/10.1007/s11356-016-6619-z
Zhu Q, Lin HS (2010) Comparing ordinary kriging and regression kriging for soil properties in contrasting landscapes. Pedosphere 20(5):594–606. https://doi.org/10.1016/S1002-0160(10)60049-5
Acknowledgements
The authors are grateful to Dongnan for language help and writing assistance.
Funding
This project was supported by the National High Technology Research and Development Program of China (“863” Program, 2014AA06A513) and the Project of Heavy Metal Risk Warning and Phytoremediation in Mining Concentrated Area (GJHZ201308).
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Qiao, P., Lei, M., Yang, S. et al. Comparing ordinary kriging and inverse distance weighting for soil as pollution in Beijing. Environ Sci Pollut Res 25, 15597–15608 (2018). https://doi.org/10.1007/s11356-018-1552-y
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DOI: https://doi.org/10.1007/s11356-018-1552-y