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      Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network

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          Abstract

          Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As) and cadmium (Cd) pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE) of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loid)s in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid) pollution.

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          Most cited references34

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          Kriging: a method of interpolation for geographical information systems

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            A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors

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              Trace metal contamination in urban soils of China.

              The contamination of urban soils can affect the health of people living in urban areas, and the surrounding ecosystems. Urbanization in China has taken place at an unprecedented pace in the last three decades. This paper provides an overview of studies on the quality of urban soils in China with special reference to trace metal contamination. It summarizes the characteristics of accumulation, spatial and temporal distribution, and major sources of various toxic or potentially toxic trace metals in urban soils as reported in recent literature. Levels of pollution in urban soil and road dust were discussed using the concentrations, enrichment factors, pollution indexes, and chemical fractionation of trace metals in major cities of China, and compared with other countries. In future studies, more pollutants in urban environments need to be included in multi-compartmental environmental surveillance for potential risk assessments. In addition to routine urban soil surveys by a harmonized methodology, a comprehensive assessment of soil quality is needed for the control and management of many urban brownfield sites. Taking into consideration pathways of exposure and site characteristics, risk assessment frameworks for major pollutants in urban soils, which integrate land use type and environmental availability, may be developed in the future. Copyright © 2011 Elsevier B.V. All rights reserved.
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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                26 December 2017
                January 2018
                : 15
                : 1
                : 34
                Affiliations
                School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China; zhenyijay@ 123456smail.nju.edu.cn (Z.J.); njusuquanlong@ 123456163.com (Q.S.); 347806060@ 123456163.com (H.Y.); dz1427034@ 123456smail.nju.edu.cn (J.W.)
                Author notes
                [* ]Correspondence: zhousl@ 123456nju.edu.cn ; Tel.: +86-138-0517-1474
                Author information
                https://orcid.org/0000-0003-4494-3038
                https://orcid.org/0000-0001-8450-7658
                Article
                ijerph-15-00034
                10.3390/ijerph15010034
                5800134
                29278363
                f64f82b4-1186-4d09-8f56-1b18e5e3f269
                © 2017 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 27 November 2017
                : 22 December 2017
                Categories
                Article

                Public health
                soil metal(loid)s,spatial interpolation,bp neural network,cross validation
                Public health
                soil metal(loid)s, spatial interpolation, bp neural network, cross validation

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