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      Prediction of high-risk areas of soil heavy metal pollution with multiple factors on a large scale in industrial agglomeration areas

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      Science of The Total Environment
      Elsevier BV

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          Geographical Detectors‐Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China

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            The influence of pH and organic matter content in paddy soil on heavy metal availability and their uptake by rice plants.

            The experiments were done to investigate the effect of soil pH and organic matter content on EDTA-extractable heavy metal contents in soils and heavy metal concentrations in rice straw and grains. EDTA-extractable Cr contents in soils and concentrations in rice tissues were negatively correlated with soil pH, but positively correlated with organic matter content. The combination of soil pH and organic matter content would produce the more precise regression models for estimation of EDTA-Cu, Pb and Zn contents in soils, demonstrating the distinct effect of the two factors on the availability of these heavy metals in soils. Soil pH greatly affected heavy metal concentrations in rice plants. Furthermore, inclusion of other soil properties in the stepwise regression analysis improved the regression models for predicting straw Fe and grain Zn concentrations, indicating that other soil properties should be taken into consideration for precise predicting of heavy metal concentrations in rice plants. Copyright © 2010 Elsevier Ltd. All rights reserved.
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              Multivariate statistical and GIS-based approach to identify heavy metal sources in soils

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                Author and article information

                Journal
                Science of The Total Environment
                Science of The Total Environment
                Elsevier BV
                00489697
                February 2022
                February 2022
                : 808
                : 151874
                Article
                10.1016/j.scitotenv.2021.151874
                34826472
                df5dd8be-3de6-4b00-b2ae-4bba7d673240
                © 2022

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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