Dataset Information

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

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.

SUBMITTER: Jia Z

PROVIDER: S-EPMC5800134 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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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.

Jia Zhenyi Z Zhou Shenglu S Su Quanlong Q Yi Haomin H Wang Junxiao J

International journal of environmental research and public health 20171226 1

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) ...[more]

PMID: 29278363

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Project description:Microbes are suitable candidates to recover and decontaminate different environments from soluble metal ions, either via reduction or precipitation to generate insoluble, non-toxic derivatives. In general, microorganisms reduce toxic metal ions generating nanostructures (NS), which display great applicability in biotechnological processes. Since the molecular bases of bacterial reduction are still unknown, the search for new -environmentally safe and less expensive- methods to synthesize NS have made biological systems attractive candidates. Here, 47 microorganisms isolated from a number of environmental samples were analyzed for their tolerance or sensitivity to 19 metal(loid)s. Ten of them were highly tolerant to some of them and were assessed for their ability to reduce these toxicants in vitro. All isolates were analyzed by 16S rRNA gene sequencing, fatty acids composition, biochemical tests and electron microscopy. Results showed that they belong to the Enterobacter, Staphylococcus, Acinetobacter, and Exiguobacterium genera. Most strains displayed metal(loid)-reducing activity using either NADH or NADPH as cofactor. While Acinetobacter schindleri showed the highest tellurite ( TeO32- ) and tetrachloro aurate ( AuCl4- ) reducing activity, Staphylococcus sciuri and Exiguobacterium acetylicum exhibited selenite ( SeO32- ) and silver (Ag+) reducing activity, respectively. Based on these results, we used these bacteria to synthetize, in vivo and in vitro Te, Se, Au, and Ag-containing nanostructures. On the other hand, we also used purified E. cloacae glutathione reductase to synthesize in vitro Te-, Ag-, and Se-containing NS, whose morphology, size, composition, and chemical composition were evaluated. Finally, we assessed the putative anti-bacterial activity exhibited by the in vitro synthesized NS: Te-containing NS were more effective than Au-NS in inhibiting Escherichia coli and Listeria monocytogenes growth. Aerobically synthesized TeNS using MF09 crude extracts showed MICs of 45- and 66- ?g/ml for E. coli and L. monocytogenes, respectively. Similar MIC values (40 and 82 ?g/ml, respectively) were observed for TeNS generated using crude extracts from gorA-overexpressing E. coli. In turn, AuNS MICs for E. coli and L. monocytogenes were 64- and 68- ?g/ml, respectively.

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Dataset Information

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

Publications

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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