Establishment of background pollution levels and spatial analysis of moss data on a regional scale
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Elsevier
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The determination of background pollution levels is fundamental for the interpretation of the results obtained from environmental biomonitoring. In this paper we propose a new probabilistic method, based on a Gaussian mixture model, for determining the distribution of regional background levels of different pollutants. The distribution of the reference level is used to categorize the observations as “background” or “above-background” and spatial statistical techniques are then applied to determine the probability of the background level being exceeded. To exemplify its use, we applied the method to concentrations of five potentially toxic elements (Cd, Cu, Hg, Pb and Zn) measured in the moss Pseudoscleropodium purum. The proposed method was applied to data resulting from sampling at ca. 150 sampling stations in a regular grid (15 × 15 km) in Galicia (NW Spain). Sampling was carried out in June in 2000 and 2002, and in March and September in 2004, 2006, 2008 and 2014. The proposed method yielded consistent results for all of the different sampling surveys, and the pollution levels were found to be closely related to the sources of pollution identified in the study region. In short, although not an optimal solution, the proposed method seems to be suitable and realistic for the qualitative assessment of regional pollution
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Science of The Total Environment 839 (2022) 156182
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Science of The Total Environment 839 (2022) 156182Sponsors
J. R. Aboal, J. A. Fernández and P. Giráldez belong to the Grupo de Referencia Competitiva GRC GI-1252/GPC2020-23 (ED431C 2020/19) which is co-funded by ERDF (EU). Authors would like to thank RIAIDT-USC for the use of analytical facilities. P. Giráldez is grateful to the Spanish Ministerio de Ciencia, Innovación y Universidades for a grant awarded within the Programa de Formacion de Profesorado Universitario (FPU 2018 [grant number FPU18/04134]). Research of R. M. Crujeiras has been supported by MINECO (Grant PID2020-116587GB-100), and by Xunta de Galicia (Grupos de Referencia Competitiva ED431C 2021/24), all of them through the ERDF
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©2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4. 0/)
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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