Abstract
Soil screening levels (SSLs) are reference threshold values required by environmental laws, established based on soil geochemical background data from often-extensive sampling areas. Such areas are often inappropriate for interpreting the true risk of pollution in small areas, since they overlook local factors (e.g., geology, industry, and traffic), which are unfeasible to encompass in large-scale samplings. To solve this issue, the calculation of local SSLs is proposed herein, performed on a major scale closer to the area of interest. To exemplify this proposal, a soil sampling campaign was performed in the Municipality of Langreo, one of the most industrialized areas in the Principality of Asturias (northwestern Spain). Sampling allowed the measurement of local soil screening levels for several inorganic contaminants. Afterwards, a soil pollution index was calculated, referred to both regional and local thresholds, to assess the degree of contamination. Both pollution indicators were subjected to a methodology based on a Bayesian network analysis, followed by a stochastic sequential Gaussian simulation approach. The methodologies used showed differences in the identification of potentially polluted areas depending on the soil screening levels (regional or local) used. It was concluded that, in urban/industrial cores, local soil screening levels facilitate the identification of polluted areas and also reduce the uncertainty associated with sampling density and diffuse contamination. Thus, use of local levels circumvents false-positive areas that would be classified as polluted were regional soil screening levels to be used.
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Acknowledgements
Carlos Boente obtained a grant (FPU014/02215) from the Formación del Profesorado Universitario program, financed by the Ministerio de E.C.D. de España. The authors thank Alicia Fernández-Braña for her support during the sampling campaign.
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Boente, C., Gerassis, S., Albuquerque, M.T.D. et al. Local versus Regional Soil Screening Levels to Identify Potentially Polluted Areas. Math Geosci 52, 381–396 (2020). https://doi.org/10.1007/s11004-019-09792-x
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DOI: https://doi.org/10.1007/s11004-019-09792-x