Electrical resistivity tomography (ERT) and geochemical analysis dataset to delimit subsurface affected areas by livestock pig slurry ponds

Ximena Capa-Camacho; Pedro Martínez-Pagán; Marcos Martínez-Segura; María Gabarrón; Ángel Faz

doi:10.1016/j.dib.2022.108684

Electrical resistivity tomography (ERT) and geochemical analysis dataset to delimit subsurface affected areas by livestock pig slurry ponds

Data Brief. 2022 Oct 21:45:108684. doi: 10.1016/j.dib.2022.108684. eCollection 2022 Dec.

Authors

Ximena Capa-Camacho^{1

2}, Pedro Martínez-Pagán¹, Marcos Martínez-Segura¹, María Gabarrón², Ángel Faz²

Affiliations

¹ Department of Mining and Civil Engineering, Universidad Politécnica de Cartagena, 30203 Cartagena, Spain.
² Sustainable Use, Management, and Reclamation of Soil and Water Research Group, Escuela Técnica Superior de Ingeniería Agronómica, Universidad Politécnica de Cartagena, Paseo Alfonso XIII, 52, 30203 Cartagena, Spain.

Abstract

The electrical resistivity tomography (ERT) technique was employed with the support of geochemical analyses to delimit the affected surface area by slurry pig ponds. Data were taken in three selected slurry ponds located in Fuente Álamo municipality, Murcia region (SE Spain), to obtain electrical resistivity value-based 2D sections and 3D blocks. All ERT-based survey data were obtained in September 2020 using a SuperSting R8 resistivity meter from Advanced Geosciences Inc. and using the dipole-dipole array consisting of a total of twenty-eight electrodes. The soil samples were taken from drilling core sampling by boreholes at each slurry pond, and physical-chemical analyses of soil samples were obtained using standard laboratory testing methods. Electrical resistivity values and physical-chemical analysis data obtained from soil samples were contrasted, whose comparison showed a correlation between profiles-based electrical resistivity, laboratory-based electrical conductivity (EC) data, and nitrate (N-NO_3-) content from soil samples. The statistical analysis was run by SPSS Statistics v.23 software (IBM, Neconductivity York, NY, USA) to establish the non-parametric Spearman correlation. The dataset establishes a reliable methodology and provides insight and information to delimit the affected subsurface area by pig slurry. Data contained within this publication are presented concurrently with Capa-Camacho et al. 2022 [1].

Keywords: Electrical resistivity tomography; Geochemical techniques; Pig slurry; Pollutants infiltration.