Abstract
Accurate prediction of the spatial distribution of pollutants in soils based on applicable interpolation methods is often the basis for soil remediation in contaminated sites. However, the applicable interpolation method has not been determined for contaminated sites due to the complex spatial distribution characteristics and stronger local spatial variability of pollutants. In this research, the prediction accuracies of three interpolation methods (including the different values of their parameters) for the spatial distribution of benzo[b]fluoranthene (BbF) in four soil layers were compared. These included inverse distance weighting (IDW), radial basis function (RBF), ordinary kriging (OK). The results indicated: (1) IDW1 is applicable for the first layer, RBF-IMQ is applicable to the second, third, and fourth layers. (2) For IDW, the prediction error is bigger with high weight where high values and low values intersect, while the prediction error is smaller where high (or low) values aggregated distribution. (3) For RBF, if the pollutant concentration trend at the predicted location is consistent with the known points in its neighborhood, the prediction accuracy is higher. (4) IDW is suitable for fitting more drastic curved surfaces, while RBF is more effective for relatively gentle curved surfaces and OK is reasonable for curved surfaces without local outliers. (5) The interpolation uncertainty is positively associated with the contaminant concentration and local spatial variability. Therefore, we suggest the selection of the applicable interpolation model must be based on the principle of the model and the spatial distribution characteristics of the pollutants.
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Aguilar, F. J., Agüera, F., Aguilar, M. A., & Carvajal, F. (2005). Effects of terrain morphology, sampling density, and interpolation methods on grid DEM accuracy. Photogrammetric Engineering and Remote Sensing,71(7), 805–816. https://doi.org/10.14358/PERS.71.7.805.
Armiento, G., Cremisini, C., Nardi, E., & Pacifico, R. (2011). High geochemical background of potentially harmful elements in soils and sediments: implications for the remediation of contaminated sites. Chemistry and Ecology,27, 131–141. https://doi.org/10.1080/02757540.2010.534085.
Cao, S., Lu, A., Wang, J., & Huo, L. (2017). Modeling and mapping of cadmium in soils based on qualitative and quantitative auxiliary variables in a cadmium contaminated area. Science of the Total Environment,580, 430–439. https://doi.org/10.1016/j.scitotenv.2016.10.088.
Caruso, C., & Quarta, F. (1998). Interpolation methods comparison. Computers and Mathematics with Applications,35(12), 109–126. https://doi.org/10.1016/S0898-1221(98)00101-1.
Chaplot, V., Saleh, A., Jaynes, D. B., & Arnold, J. (2004). Predicting water, sediment and NO3–N loads under scenarios of land-use and management practices in a flat watershed. Water, Air, and Soil pollution,154(1–4), 271–293. https://doi.org/10.1023/b:wate.0000022973.60928.30.
Chen, Y., Shan, X., Jin, X., Yang, T., Dai, F., & Yang, D. (2016). A comparative study of spatial interpolation methods for determining fishery resources density in the Yellow Sea. Acta Oceanologica Sinica,35(12), 65–72. https://doi.org/10.1007/s13131-016-0966-y.
Ding, Q., Cheng, G., Wang, Y., & Zhuang, D. F. (2017). Effects of natural factors on the spatial distribution of heavy metals in soils surrounding mining regions. Science of the Total Environment,578, 577–585. https://doi.org/10.1016/j.scitotenv.2016.11.001.
Ding, Q., Wang, Y., & Zhuang, D. (2018). Comparison of the common spatial interpolation methods used to analyze potentially toxic elements surrounding mining regions. Journal of Environmental Management,212, 23–31. https://doi.org/10.1016/j.jenvman.2018.01.074.
Dlamini, P., & Chaplot, V. (2012). On the interpolation of volumetric water content in research catchments. Physics and Chemistry of the Earth,50–52(SI), 165–174. https://doi.org/10.1016/j.pce.2012.09.008.
Emadi, M., & Baghernejad, M. (2014). Comparison of spatial interpolation techniques for mapping soil pH and salinity in agricultural coastal areas, northern Iran. Archives of Agronomy and Soil Science,60(9), 1315–1327. https://doi.org/10.1080/03650340.2014.880837.
Fang, Y. Y., Nie, Z. Q., Die, Q. Q., Tian, Y. J., Liu, F., He, J., et al. (2017). Organochlorine pesticides in soil, air, and vegetation at and around a contaminated site in southwestern China: Concentration, transmission, and risk evaluation. Chemosphere,178, 340–349. https://doi.org/10.1016/j.chemosphere.2017.02.151.
Gan, S., Lau, E. V., & Ng, H. K. (2009). Remediation of soils contaminated with polycyclic aromatic hydrocarbons (PAHs). Journal of Hazardous Materials,172(2), 532–549. https://doi.org/10.1016/j.jhazmat.2009.07.118.
Gao, L., & Shao, M. (2011). The interpolation accuracy for seven soil properties at various sampling scales on the Loess Plateau, China. Journal of Soils and Sediments,12(2), 128–142. https://doi.org/10.1007/s11368-011-0438-0.
Gao, L., & Shao, M. G. (2012). The interpolation accuracy for seven soil properties at various sampling scales on the Loess Plateau, China. Journal of Soils and Sediments,12(2), 128–142. https://doi.org/10.1007/s11368-011-0438-0.
Girault, F., Perrier, F., Poitou, C., Isambert, A., Theveniaut, H., Laperche, V., et al. (2016). Effective radium concentration in topsoils contaminated by lead and zinc smelters. Science of the Total Environment,566, 865–876. https://doi.org/10.1016/j.scitotenv.2016.05.007.
Goovaerts, P. (1997). Geostatistics for natural resources evaluation (pp. 158–160). New York: Oxford University Press.
Gotway, C. A., Ferguson, R. B., Hergert, G. W., & Peterson, T. A. (1996). Comparison of kriging and inverse-distance methods for mapping soil parameters. Soil Science Society of America Journal,60(4), 1237–1247. https://doi.org/10.2136/sssaj1996.03615995006000040040x.
Gou, Y., Yang, S., Cheng, Y., Song, Y., Qiao, P., Li, P., et al. (2019). Enhanced anoxic biodegradation of polycyclic aromatic hydrocarbons (PAHs) in aged soil pretreated by hydrogen peroxide. Chemical Engineering Journal,356, 524–533. https://doi.org/10.1016/j.cej.2018.09.059.
Gubler, A., Wachter, D., Blum, F., & Bucheli, T. D. (2015). Remarkably constant PAH concentrations in Swiss soils over the last 30 years. Environmental Science-Processes Impacts,17(10), 1816–1828. https://doi.org/10.1039/c5em00344j.
Huo, X. N., Li, H., Sun, D. F., Zhou, L. D., & Li, B. G. (2010). Multi-scale spatial structure of heavy metals in agricultural soils in Beijing. Environmental Monitoring and Assessment,164(1–4), 605–616. https://doi.org/10.1007/s10661-009-0916-7.
Internal Agency for Research on Cancer. (2017). IARC monographs on cancer carcinogens list. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans, Lyon.
Jia, Z., Zhou, S., Su, Q., Yi, H., & Wang, J. (2017). Comparison study on the estimation of the spatial distribution of regional soil metal(loid)s pollution based on Kriging interpolation and BP neural network. International Journal of Environmental Research and Public Health,15(1), 1–14. https://doi.org/10.3390/ijerph15010034.
Jia, Z. Y., Zhou, S. L., Su, Q. L., Yi, H. M., & Wang, J. X. (2018). Comparison study on the estimation of the spatial distribution of regional soil metal(loid)s pollution based on Kriging interpolation and BP neural network. International Journal of Environmental Research and Public Health,15(1), 14. https://doi.org/10.3390/ijerph15010034.
Journel, A. G., & Huijbregts, C. J. (1978). Mining geostatistics. London: Academic Press.
Lado, L. R., Hengl, T., & Reuter, H. I. (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, J., & Heap, A. D. (2014). Spatial interpolation methods applied in the environmental sciences: A review. Environmental Modelling and Software,53, 173–189. https://doi.org/10.1016/j.envsoft.2013.12.008.
Liu, G., Niu, J. J., Zhang, C., Zhao, X., & Guo, G. L. (2014a). Spatial distribution prediction of surface soil Pb in a battery contaminated site. Environmetal Science,12, 4712–4719.
Liu, R. M., Chen, Y. X., Sun, C. C., Zhang, P. P., Wang, J. W., Yu, W. W., et al. (2014b). Uncertainty analysis of total phosphorus spatial-temporal variations in the Yangtze River Estuary using different interpolation methods. Marine Pollution Bulletin,86(1–2), 68–75. https://doi.org/10.1016/j.marpolbul.2014.07.041.
Ma, Y. G., Lei, Y. D., Xiao, H., Wania, F., & Wang, W. H. (2010). Critical review and recommended values for the physical-chemical property data of 15 polycyclic aromatic hydrocarbons at 25°C. Journal of Chemical and Engineering Data,55, 819–825.
Ma, Z. W., Chen, K., Li, Z. Y., Bi, J., & Huang, L. (2016). Heavy metals in soils and road dusts in the mining areas of Western Suzhou, China: A preliminary identification of contaminated sites. Journal of Soils and Sediments,16(1), 204–214. https://doi.org/10.1007/s11368-015-1208-1.
Mardikis, M. G., Kalivas, D. P., & Kollias, V. J. (2005). Comparison of interpolation methods for the prediction of reference evapotranspiration—An application in greece. Water Resources Management,19(3), 251–278. https://doi.org/10.1007/s11269-005-3179-2.
Monaco, D., Riccio, A., Chianese, E., Adamo, P., Di Rosa, S., & Fagnano, M. (2015). Chemical characterization and spatial distribution of PAHs and heavy hydrocarbons in rural sites of Campania Region, South Italy. Environmental Science and Pollution Research,22(19), 14993–15003. https://doi.org/10.1007/s11356-015-4733-y.
Nickel, S., Hertel, A., Pesch, R., Schröder, W., Steinnes, E., & Uggerud, H. T. (2014). Modelling and mapping spatio-temporal trends of heavy metal accumulation in moss and natural surface soil monitored 1990–2010 throughout Norway by multivariate generalized linear models and geostatistics. Atmospheric Environment,99, 85–93. https://doi.org/10.1016/j.atmosenv.2014.09.059.
Olea, R. E. (1991). Geostatistical glossary and multilingual dictionary. New York: Oxford University Press.
Panagopoulos, T., Jesus, J., Antunes, M. D. C., & Beltrão, J. (2006). Analysis of spatial interpolation for optimising management of a salinized field cultivated with lettuce. European Journal of Agronomy,24(1), 1–10. https://doi.org/10.1016/j.eja.2005.03.001.
Pang, S., Li, T. X., Wang, Y. D., Yu, H. Y., & Li, X. (2009). Spatial interpolation and sample size optimization for soil copper (Cu) investigation in cropland soil at county scale using cokriging. Agricultural Sciences in 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. Ecological Indicators,36, 301–314. https://doi.org/10.1016/j.ecolind.2013.08.009.
Qiao, P. W., Lei, M., Yang, S. C., Yang, J., Guo, G. H., & Zhou, X. Y. (2018). Comparing ordinary kriging and inverse distance weighting for soil as pollution in Beijing. Environmental Science and Pollution Research International,25(16), 15597–15608. https://doi.org/10.1007/s11356-018-1552-y.
Ren, L. X., Lu, H. W., He, L., & Zhang, Y. M. (2016). Characterization of monochlorobenzene contamination in soils using geostatistical interpolation and 3D visualization for agrochemical industrial sites in southeast China. Archives of Environmental Protection,42(3), 17–24. https://doi.org/10.1515/aep-2016-0025.
Rianawati, E., & Balasubramanian, R. (2009). Optimization and validation of solid phase micro-extraction (SPME) method for analysis of polycyclic aromatic hydrocarbons in rainwater and stormwater. Physics and Chemistry of the Earth,34(13–16), 857–865. https://doi.org/10.1016/j.pce.2009.07.003.
Robinson, T. P., & Metternicht, G. (2006). Testing the performance of spatial interpolation techniques for mapping soil properties. Computers and Electronics in Agriculture,50(2), 97–108. https://doi.org/10.1016/j.compag.2005.07.003.
Roslund, M. I., Gronroos, M., Rantalainen, A. L., Jumpponen, A., Romantschuk, M., Parajuli, A., et al. (2018). Half-lives of PAHs and temporal microbiota changes in commonly used urban landscaping materials. PeerJ,6, e4508. https://doi.org/10.7717/peerj.4508.
Sakalys, J., Kvietkus, K., Sucharova, J., Suchara, I., & Valiulis, D. (2009). Changes in total concentrations and assessed background concentrations of heavy metals in moss in Lithuania and the Czech Republic between 1995 and 2005. Chemosphere,76(1), 91–97. https://doi.org/10.1016/j.chemosphere.2009.02.009.
Shi, R., Xu, M., Liu, A., Tian, Y., & Zhao, Z. (2017). Characteristics of PAHs in farmland soil and rainfall runoff in Tianjin, China. Environmental Monitoring and Assessment,189(11), 558. https://doi.org/10.1007/s10661-017-6290-y.
Shi, W. J., Liu, J. Y., Du, Z. P., Song, Y. J., Chen, C. F., & Yue, T. X. (2009). Surface modelling of soil pH. Geoderma,150(1–2), 113–119. https://doi.org/10.1016/j.geoderma.2009.01.020.
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.
Wackernagel, H. (1995). Multivariate geostatistics. Berlin: Spring.
Wang, B., Huang, J., Lu, Y., Arai, S., Iino, F., Morita, M., et al. (2012). The pollution and ecological risk of endosulfan in soil of Huai’an city, China. Environmental Monitoring and Assessment,184(12), 7093–7101. https://doi.org/10.1007/s10661-011-2482-z.
Wang, S., Ni, H. G., Sun, J. L., Jing, X., He, J. S., & Zeng, H. (2013). Polycyclic aromatic hydrocarbons in soils from the Tibetan Plateau, China: Distribution and influence of environmental factors. Environmental Science-Processes and Impacts,15(3), 661–667. https://doi.org/10.1039/c2em30856h.
Wang, X. S., & Qin, Y. (2007). Some characteristics of the distribution of heavy metals in urban topsoil of Xuzhou, China. Environmental Geochemistry and Health,29(1), 11–19. https://doi.org/10.1007/s10653-006-9052-2.
Webster, R., & Oliver, M. A. (1992). Sample adequately to estimate variograms of soil properties. Journal of Soil Science,43(1), 177–192.
Wu, C. F., Wu, J. P., Luo, Y. M., Zhang, H. B., Teng, Y., & DeGloria, S. D. (2011). Spatial interpolation of severely skewed data with several peak values by the approach integrating kriging and triangular irregular network interpolation. Environmental Earth Sciences,63(5), 1093–1103. https://doi.org/10.1007/s12665-010-0784-z.
Xie, Y. F., Chen, T. B., Lei, M., Yang, J., Guo, Q. J., Song, B., et al. (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.
Xue, J. L., Zhi, Y. Y., Yang, L. P., Shi, J. C., Zeng, L. Z., & Wu, L. S. (2014). Positive matrix factorization as source apportionment of soil lead and cadmium around a battery plant (Changxing County, China). Environmental Science and Pollution Research,21(12), 7698–7707. https://doi.org/10.1007/s11356-014-2726-x.
Yang, S., Gou, Y., Song, Y., & Li, P. (2018). Enhanced anoxic biodegradation of polycyclic aromatic hydrocarbons (PAHs) in a highly contaminated aged soil using nitrate and soil microbes. Environmental Earth Sciences. https://doi.org/10.1007/s12665-018-7629-6.
Yao, R., & Yang, J. (2010). Quantitative evaluation of soil salinity and its spatial distribution using electromagnetic induction method. Agricultural Water Management,97(12), 1961–1970. https://doi.org/10.1016/j.agwat.2010.02.001.
Yao, X. L., Fu, B. J., Lu, Y. H., Sun, F. X., Wang, S., & Liu, M. (2013). Comparison of four spatial interpolation methods for estimating soil moisture in a complex Terrain catchment. PLoS ONE,8(1), 13. https://doi.org/10.1371/journal.pone.0054660.
Yuan, S. S., & Quiring, S. M. (2017). Comparison of three methods of interpolating soil moisture in Oklahoma. International Journal of Climatology,37(2), 987–997. https://doi.org/10.1002/joc.4754.
Zhang, Y. X., Li, B. L., Shi, Z., & Wan, G. S. (2014). The research on the spatial interpolation of heavy metals in soil by using an improved neural network. Environmental Monitoring and Assessment,30, 96–100.
Zhu, Q., & Lin, H. S. (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
This work was supported by the Beijing Postdoctoral Research Foundation, China Postdoctoral Science Foundation, Beijing Natural Science Foundation (16L00073) and Development and application demonstration of in situ precise remediation technology for soil and groundwater in organic contaminated sites (PXM2019_178203_00341400).
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Qiao, P., Li, P., Cheng, Y. et al. Comparison of common spatial interpolation methods for analyzing pollutant spatial distributions at contaminated sites. Environ Geochem Health 41, 2709–2730 (2019). https://doi.org/10.1007/s10653-019-00328-0
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DOI: https://doi.org/10.1007/s10653-019-00328-0