Comprehensive spatial mapping of metals and metalloids in the Peruvian Mantaro Valley using advanced geospatial data Integration

dc.contributor.authorPizarro Carcausto, Samuel Edwin
dc.contributor.authorPricope , Narcisa G.
dc.contributor.authorVera Vilchez, Jesús Emilio
dc.contributor.authorCruz Luis, Juancarlos Alejandro
dc.contributor.authorLastra Paucar, Sphyros Roomel
dc.contributor.authorSolórzano Acosta, Richard Andi
dc.contributor.authorVerástegui Martínez, Patricia
dc.date.accessioned2025-04-01T20:37:16Z
dc.date.available2025-04-01T20:37:16Z
dc.date.issued2024-12-12
dc.description.abstractThe quality and safety of soil are crucial for ensuring social and economic development and providing contaminant-free food. The availability and quality of soil data, particularly for multiple metals and metalloids, are often insufficient for comprehensive analysis. Soil formation and the distribution of metals are shaped by various factors such as geology, climate, topography, and human activities, making accurate modeling highly challenging. Additionally, agricultural intensification, urban expansion, road construction, and mining activities frequently result in soil pollution, posing serious risks to ecosystems and human health. This study aims to integrate diverse geospatial datasets with machine learning for high resolution soil contamination mapping (10 m spatial resolution) in a major agricultural region of Peruvian highlands. This study mapped 25 elements (Ca, Mg, Sr, Ba, Be, K, Na, As, Sb, Se, Tl, Cd, Zn, Al, Pb, Hg, Cr, Ni, Cu, Mo, Ag, Fe, Co, Mn, V) in the Peruvian Mantaro Valley using a training dataset of 109 topsoil samples combined with various geospatial datasets (remote sensing, climate, topography, soil data, and distance). The model provided satisfactory results in predicting the spatial distribution of the selected elements, with R² values ranging from 0.6 to 0.9 for most elements. Edaphic, climate, and topographic covariates were the most significant predictors, particularly for croplands near rivers, whereas spectral variables were less important. The results reveal As, Pb, and Cd concentrations significantly above permissible limits, highlighting urgent health risks. These findings suggest that it is feasible to identify polluted soils and improve regulations based on widely available geospatial datasets with minimal training data. The study contributes to the development of models to assess the impact of pollutants on environmental and human health in the short-to-medium term, emphasizing the need for further research on the translocation of toxic metals into food crops and the implications for public health.
dc.description.sponsorshipThis research was funded by the INIA project "Mejoramiento de los servicios de investigación y transferencia tecnológica en el manejo y recuperación de suelos agrícolas degradados y aguas para riego en la pequeña y mediana agricultura en los departamentos de Lima, Áncash, San Martín, Cajamarca, Lambayeque, Junín, Ayacucho, Arequipa, Puno y Ucayali" CUI 2487112, of the Ministry of Agrarian Development and Irrigation (MIDAGRI) of the Peruvian Government.
dc.formatapplication/pdf
dc.identifier.citationPizarro et al., Comprehensive spatial mapping of metals and metalloids in the Peruvian Mantaro Valley using advanced geospatial data Integration, Geoderma 453 (2025) 117138
dc.identifier.doihttps://doi.org/10.1016/j.geoderma.2024.117138
dc.identifier.urihttp://hdl.handle.net/20.500.12955/2697
dc.publisherElsevier
dc.publisher.countryNL
dc.relation.ispartof0016-7061
dc.relation.ispartofseriesGeoderma
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceInstituto Nacional de Innovación Agraria
dc.source.uriRepositorio Institucional - INIA
dc.subjectRandom forest
dc.subjectSoil mapping
dc.subjectGoogle earth engine
dc.subjectMachine learning
dc.subjectCloud computing
dc.subject.agrovocMateria orgánica del suelo; Mapeo de suelos; Elementos traza; Metales; Valles; Perú
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#4.01.04
dc.titleComprehensive spatial mapping of metals and metalloids in the Peruvian Mantaro Valley using advanced geospatial data Integration
dc.typeinfo:eu-repo/semantics/article

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