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Soil Sample Assay Uncertainty and the Geographic Distribution of Contaminants: Error Impacts on Syracuse Trace Metal Soil Loading Analysis Results

Author

Listed:
  • Daniel A. Griffith

    (School of Economic, Political and Policy Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX 75080, USA)

  • Yongwan Chun

    (School of Economic, Political and Policy Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX 75080, USA)

Abstract

A research team collected 3609 useful soil samples across the city of Syracuse, NY; this data collection fieldwork occurred during the two consecutive summers (mid-May to mid-August) of 2003 and 2004. Each soil sample had fifteen heavy metals (As, Cr, Cu, Co, Fe, Hg, Mo, Mn, Ni, Pb, Rb, Se, Sr, Zn, and Zr), measured during its assaying; errors for these measurements are analyzed in this paper, with an objective of contributing to the geography of error literature. Geochemistry measurements are in milligrams of heavy metal per kilogram of soil, or ppm, together with accompanying analytical measurement errors. The purpose of this paper is to summarize and portray the geographic distribution of these selected heavy metals measurement errors across the city of Syracuse. Doing so both illustrates the value of the SAAR software’s uncertainty mapping module and uncovers heavy metal characteristics in the geographic distribution of Syracuse’s soil. In addition to uncertainty visualization portraying and indicating reliability information about heavy metal levels and their geographic patterns, SAAR also provides optimized map classifications of heavy metal levels based upon their uncertainty (utilizing the Sun-Wong separability criterion) as well as an optimality criterion that simultaneously accounts for heavy metal levels and their affiliated uncertainty. One major outcome is a summary and portrayal of the geographic distribution of As, Cr, Cu, Co, Fe, Hg, Mo, Mn, Ni, Pb, Rb, Se, Sr, Zn, and Zr measurement error across the city of Syracuse.

Suggested Citation

  • Daniel A. Griffith & Yongwan Chun, 2021. "Soil Sample Assay Uncertainty and the Geographic Distribution of Contaminants: Error Impacts on Syracuse Trace Metal Soil Loading Analysis Results," IJERPH, MDPI, vol. 18(10), pages 1-28, May.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:10:p:5164-:d:553746
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    References listed on IDEAS

    as
    1. Michael F. Goodchild, 2004. "A general framework for error analysis in measurement-based GIS," Journal of Geographical Systems, Springer, vol. 6(4), pages 323-324, December.
    2. Daniel A. Griffith, 2013. "Better Articulating Normal Curve Theory for Introductory Mathematical Statistics Students: Power Transformations and Their Back-Transformations," The American Statistician, Taylor & Francis Journals, vol. 67(3), pages 157-169, August.
    3. Daniel A. Griffith & Yongwan Chun, 2016. "Evaluating Eigenvector Spatial Filter Corrections for Omitted Georeferenced Variables," Econometrics, MDPI, vol. 4(2), pages 1-12, June.
    4. Yee Leung & Jiang-Hong Ma & Michael F. Goodchild, 2004. "A general framework for error analysis in measurement-based GIS Part 1: The basic measurement-error model and related concepts," Journal of Geographical Systems, Springer, vol. 6(4), pages 325-354, December.
    5. Bernard Fingleton & Julie Le Gallo, 2008. "Estimating spatial models with endogenous variables, a spatial lag and spatially dependent disturbances: Finite sample properties," Papers in Regional Science, Wiley Blackwell, vol. 87(3), pages 319-339, August.
    6. Julie Le Gallo & Bernard Fingleton, 2008. "Estimating spatial models with endogenous variables, a spatial lag and spatially dependent disturbances : finite sample properties," Post-Print hal-00485035, HAL.
    7. Hyeongmo Koo & Yongwan Chun & Daniel A. Griffith, 2017. "Optimal Map Classification Incorporating Uncertainty Information," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(3), pages 575-590, May.
    8. Hyeongmo Koo & Yongwan Chun & Daniel A. Griffith, 2018. "Modeling Positional Uncertainty Acquired Through Street Geocoding," International Journal of Applied Geospatial Research (IJAGR), IGI Global, vol. 9(4), pages 1-22, October.
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    Cited by:

    1. Xuanxuan Zhao & Jiaxing Zhang & Ruijun Ma & Hui Luo & Tao Wan & Dongyang Yu & Yuanqian Hong, 2024. "Worldwide Examination of Magnetic Responses to Heavy Metal Pollution in Agricultural Soils," Agriculture, MDPI, vol. 14(5), pages 1-21, April.

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