Display options
Cite
Boente C, Albuquerque MTD, Gallego JR, et al. Compositional baseline assessments to address soil pollution: An application in Langreo, Spain. Sci Total Environ. 2021;812:152383doi: 10.1016/j.scitotenv.2021.152383.
Boente, C., Albuquerque, M. T. D., Gallego, J. R., Pawlowsky-Glahn, V., & Egozcue, J. J. (2021). Compositional baseline assessments to address soil pollution: An application in Langreo, Spain. The Science of the total environment, 812152383. https://doi.org/10.1016/j.scitotenv.2021.152383
Boente, C, et al. "Compositional baseline assessments to address soil pollution: An application in Langreo, Spain." The Science of the total environment vol. 812 (2021): 152383. doi: https://doi.org/10.1016/j.scitotenv.2021.152383
Boente C, Albuquerque MTD, Gallego JR, Pawlowsky-Glahn V, Egozcue JJ. Compositional baseline assessments to address soil pollution: An application in Langreo, Spain. Sci Total Environ. 2021 Dec 21;812:152383. doi: 10.1016/j.scitotenv.2021.152383. Epub 2021 Dec 21. PMID: 34952083.
Copy Download .nbib Format: NLM
Share it on

Sci Total Environ. 2021 Dec 21;812:152383. doi: 10.1016/j.scitotenv.2021.152383. Epub 2021 Dec 21.

Compositional baseline assessments to address soil pollution: An application in Langreo, Spain.

The Science of the total environment

C Boente, M T D Albuquerque, J R Gallego, V Pawlowsky-Glahn, J J Egozcue

Affiliations

  1. Department of Mining, Mechanic, Energetic and Construction Engineering, ETSI, University of Huelva, 21071 Huelva, Spain; CIQSO-Center for Research in Sustainable Chemistry, Associate Unit CSIC-University of Huelva, Atmospheric Pollution, Campus El Carmen s/n, 21071 Huelva, Spain. Electronic address: [email protected].
  2. CERNAS | QRural, Instituto Politécnico de Castelo Branco and ICT, Universidade de Évora, Portugal. Electronic address: [email protected].
  3. Environmental Biogeochemistry & Raw Materials Group and INDUROT, Campus de Mieres, University of Oviedo, C/Gonzalo Gutiérrez Quirós, S/N, 33600 Mieres, Spain. Electronic address: [email protected].
  4. Dpt. Computer Science, Applied Mathematics and Statistics, University of Girona, Spain. Electronic address: [email protected].
  5. Dpt. Civil and Environmental Engineering, Technical University of Catalonia, Barcelona, Spain. Electronic address: [email protected].

PMID: 34952083 DOI: 10.1016/j.scitotenv.2021.152383

Abstract

Potentially Toxic Elements (PTEs) are contaminants with high toxicity and complex geochemical behaviour and, therefore, high PTEs contents in soil may affect ecosystems and/or human health. However, before addressing the measurement of soil pollution, it is necessary to understand what is meant by pollution-free soil. Often, this background, or pollution baseline, is undefined or only partially known. Since the concentration of chemical elements is compositional, as the attributes vary together, here we present a novel approach to build compositional indicators based on Compositional Data (CoDa) principles. The steps of this new methodology are: 1) Exploratory data analysis through variation matrix, biplots or CoDa dendrograms; 2) Selection of geological background in terms of a trimmed subsample that can be assumed as non-pollutant; 3) Computing the spread Aitchison distance from each sample point to the trimmed sample; 4) Performing a compositional balance able to predict the Aitchison distance computed in step 3.Identifying a compositional balance, including pollutant and non-pollutant elements, with sparsity and simplicity as properties, is crucial for the construction of a Compositional Pollution Indicator (CI). Here we explored a database of 150 soil samples and 37 chemical elements from the contaminated region of Langreo, Northwestern Spain. There were obtained three Cis: the first two using elements obtained through CoDa analysis, and the third one selecting a list of pollutants and non-pollutants based on expert knowledge and previous studies. The three indicators went through a Stochastic Sequential Gaussian simulation. The results of the 100 computed simulations are summarized through mean image maps and probability maps of exceeding a given threshold, thus allowing characterization of the spatial distribution and variability of the CIs. A better understanding of the trends of relative enrichment and PTEs fate is discussed.

Copyright © 2021. Published by Elsevier B.V.

Keywords: Compositional indicators; Potentially toxic elements; Sequential Gaussian simulation; Soil pollution

Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this pa

Publication Types