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A Novel Statistical Method of Defining Geochemical Baselines and Source Identification for Trace Metals in Soil in Zhangjiagang County, China

  • Soils, Sec 3 • Remediation and Management of Contaminated or Degraded Lands • Research Article
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Abstract

Purposes

Establishing accurate geochemical baselines by normalization procedures (NP) is important for distinguishing contaminated soils from the uncontaminated ones. In the areas with intensive anthropogenic influences, geochemical baselines are easily affected by the contaminated soils. The commonly used least squares method is not robust enough because it is sensitive to outliers, resulting in distorted regression relationship. A novel robust regression method, which can diminish the influences of outliers proposed, is proposed for defining more accurate geochemical baselines and the source of trace metals.

Methods

Zhangjiagang County, which is under with intensive anthropogenic influences and high pollution risks, was selected as the study area. Trace metal data obtained from subsoils were used as normalizers, and SMDM-estimators were used in the robust regression for defining the geochemical baselines and the sources of trace metals in this area.

Results

The robust statistical method can successfully be applied in normalization procedures for defining soil geochemical baselines. The SMDM robust regressions reduced the influence of outliers, building a more reasonable regression fitted to data points for the selected trace metals in topsoil and subsoil. Subsoil trace metals are less affected than topsoil, so they can be used as normalizers to replace general elemental normalizers.

Conclusions

It is found that the geochemical baselines defined by the novel robust regression method proposed in this study can fully reflect the natural variability of soil trace metals. Moreover, this method can also apparently reduce the effects of outliers on statistical analysis, improving the accuracy of geochemical baselines in the areas with intensive anthropogenic influences and high pollution risks.

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Acknowledgements

The authors are grateful of the financial support from Fundamental Research Funds for Central Public Welfare Research Institutes (Grant No. CKSF 2014022/TB), National Natural Science Foundation of China (Grant No. 41101191), and National Key R&D Program of China (Grant No. 2018YFC1801200).

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

Funding

Fundamental Research Funds for Central Public Welfare Research Institutes (Grant No. CKSF 2014022/TB) sponsored the design of the study and collection. National Natural Science Foundation of China (Grant No. 41101191) sponsored the analysis and interpretation of data of this study. National Key R&D Program of China (Grant No. 2018YFC1801200) financially supported manuscript writing.

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Authors and Affiliations

  1. Department of Soil and Water Conservation, Changjiang River Scientific Research Institute of Changjiang Water Resources Commission, Wuhan, 430010, China

    Zhigang Wang & Ke Wang

  2. Research Center on Mountain Torrent & Geologic Disaster Prevention of the Ministry of Water Resources, Wuhan, 430010, China

    Zhigang Wang & Ke Wang

  3. Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China

    Biao Huang, Jeremy Landon Darilek & Yongcun Zhao

Authors
  1. Zhigang Wang
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  2. Ke Wang
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  3. Biao Huang
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  4. Jeremy Landon Darilek
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  5. Yongcun Zhao
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Contributions

ZW carried out research plan, and collected and analyzed the data with KW and BH. ZW, KW, and JLD prepared the original draft. KW and YZ reviewed and edited the draft. BH supervised the entire process of this study.

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Correspondence to Zhigang Wang.

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Responsible editor: Yongtao Li

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Wang, Z., Wang, K., Huang, B. et al. A Novel Statistical Method of Defining Geochemical Baselines and Source Identification for Trace Metals in Soil in Zhangjiagang County, China. J Soils Sediments 21, 2619–2627 (2021). https://doi.org/10.1007/s11368-021-02959-2

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  • DOI: https://doi.org/10.1007/s11368-021-02959-2

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