Assessing the behaviour of heavy metals in abandoned phosphogypsum deposits combining electrical resistivity tomography and multivariate analysis

• Published in: Journal of environmental management Vol. 278; no. Pt 1; p. 111517
• Main Authors: Vásconez-Maza, Marco D., Bueso, María C., Faz, Angel, Acosta, J.A., Martínez-Segura, Marcos A.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15.01.2021
• Subjects: Calcium Sulfate; Correlation; Electrical resistivity tomography; Heavy metals; Metals, Heavy; Multivariate Analysis; Phosphogypsum; Phosphorus; Tomography; Electrical resistivity tomography; Correlation; Phosphogypsum; Multivariate analysis; Heavy metals
• ISSN: 0301-4797; 1095-8630
• DOI: 10.1016/j.jenvman.2020.111517

This study presents the results of a physical-chemical characterisation of phosphogypsum deposits generated with hydrochloric and sulphuric acid during the wet acid process. The paper aims to establish an efficient methodology based on electrical resistivity tomography (ERT), chemical analysis and multivariate analysis identifying the areas most contaminated by heavy metals in an abandoned factory where fertiliser was derived from phosphoric rock. This fertiliser has provided many benefits to agriculture; however, it generates a vast amount of waste (5 tonnes phosphoric rock/1 tonne fertiliser). The chemical composition of this by-product varies according to the industrial process performed. Hydrochloric acid (HCl) recovers more than 90% of phosphorus, while sulphuric acid (H2SO4) recovers around 30%. Therefore, a chemical assessment of the remaining waste is a necessary step prior to initiating any remediation process. ERT provided the geometry of the deposits and the distribution of the phosphogypsum. The chemical analyses consistently validated the electrical contrast found within the deposits. We employed a correlation analysis combined with multivariate analysis to identify the relationships among the metal concentrations and resistivity. Principal component analysis (PCA) reduced the information contained in all the variables to a few principal components. The first three principal components accounted for 74% of the variability of all the studied variables. Partial least squares-discriminant analysis (PLS-DA) for classification allowed the discrimination of the two populations. Electrical resistivity was the most influential variable for separating HCl waste from that of H2SO4. The use of ERT saves time and reduces costs yielding a methodology which facilitates the environmental assessment of large areas. [Display omitted] •Identification of heavy metals with electrical resistivity tomography.•Correlation of metallic concentrations with electrical resistivity data.•Multivariate analysis to discriminate chemical populations.•Methodology for gauging heavy metal extent of phosphogypsum.•Accelerating environmental assessment of large areas.