Ecological and carcinogenic risk assessment of potentially toxic elements in rangelands and croplands around Lake Junin (Peru): Integrating remote sensing, machine learning, and land cover segmentation
The Junín Lake basin, a critical high-altitude ecosystem in the central Peruvian Andes, faces severe contamination from potentially toxic elements (PTEs) driven by mining activities, agriculture, and urbanization. This study evaluates the spatial distribution, ecological risk, and human health impli...
| Main Authors: | , , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | Inglés |
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Elsevier
2025
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| Online Access: | http://hdl.handle.net/20.500.12955/2854 https://doi.org/10.1016/j.scitotenv.2025.180327 |
| _version_ | 1855028499014746112 |
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| author | Pizarro Carcausto, Samuel Edwin Requena Rojas, Edilson Jimmy Barboza, Elgar Peña Elme, Eunice Dorcas Arias Arredondo, Alberto Gilmer Ccopi Trucios, Dennis |
| author_browse | Arias Arredondo, Alberto Gilmer Barboza, Elgar Ccopi Trucios, Dennis Peña Elme, Eunice Dorcas Pizarro Carcausto, Samuel Edwin Requena Rojas, Edilson Jimmy |
| author_facet | Pizarro Carcausto, Samuel Edwin Requena Rojas, Edilson Jimmy Barboza, Elgar Peña Elme, Eunice Dorcas Arias Arredondo, Alberto Gilmer Ccopi Trucios, Dennis |
| author_sort | Pizarro Carcausto, Samuel Edwin |
| collection | Repositorio INIA |
| description | The Junín Lake basin, a critical high-altitude ecosystem in the central Peruvian Andes, faces severe contamination from potentially toxic elements (PTEs) driven by mining activities, agriculture, and urbanization. This study evaluates the spatial distribution, ecological risk, and human health implications of 14 heavy metals, metalloids, and trace elements in surface soils surrounding the lake. Using 211 soil samples, we integrated remote sensing, land cover classification, and Random Forest machine learning models with spectral, edaphic, topographic, and proximity-based environmental covariates to predict contamination patterns and assess risk. Results reveal extreme contamination, with arsenic (As), lead (Pb), cadmium (Cd), and zinc (Zn) concentrations exceeding ecological thresholds by over 100-fold in agricultural zones. Ecological risk assessments using contamination degree (mCD), pollution load index (PLI), and risk index (RI) indicated that over 99 % of the study area exhibits very high to ultra-high contamination levels. Human health risk analysis identified unacceptable carcinogenic risks from As, Pb, and Cr across adult and pediatric populations, with arsenic presenting the greatest concern. The integration of geospatial tools and machine learning enabled precise identification of contamination hotspots and vulnerable land cover types, demonstrating the value of AI approaches for monitoring contaminated territories. These findings underscore the urgent need for coordinated environmental management, targeted remediation strategies, and community-based monitoring to protect public health and preserve Andean ecosystem integrity. |
| format | info:eu-repo/semantics/article |
| id | INIA2854 |
| institution | Institucional Nacional de Innovación Agraria |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | INIA28542025-09-11T19:32:38Z Ecological and carcinogenic risk assessment of potentially toxic elements in rangelands and croplands around Lake Junin (Peru): Integrating remote sensing, machine learning, and land cover segmentation Pizarro Carcausto, Samuel Edwin Requena Rojas, Edilson Jimmy Barboza, Elgar Peña Elme, Eunice Dorcas Arias Arredondo, Alberto Gilmer Ccopi Trucios, Dennis Heavy metals Ecological risk assessment Human health risk Remote sensing Machine learning Soil contamination Andean wetlands Metales pesados Evaluación de riesgos ecológicos Riesgo para la salud humana Teledetección Aprendizaje automático Contaminación del suelo Humedales andinos https://purl.org/pe-repo/ocde/ford#4.01.04 Human health; Salud humana; Rangelands; Pastizales; Andean region; Región andina The Junín Lake basin, a critical high-altitude ecosystem in the central Peruvian Andes, faces severe contamination from potentially toxic elements (PTEs) driven by mining activities, agriculture, and urbanization. This study evaluates the spatial distribution, ecological risk, and human health implications of 14 heavy metals, metalloids, and trace elements in surface soils surrounding the lake. Using 211 soil samples, we integrated remote sensing, land cover classification, and Random Forest machine learning models with spectral, edaphic, topographic, and proximity-based environmental covariates to predict contamination patterns and assess risk. Results reveal extreme contamination, with arsenic (As), lead (Pb), cadmium (Cd), and zinc (Zn) concentrations exceeding ecological thresholds by over 100-fold in agricultural zones. Ecological risk assessments using contamination degree (mCD), pollution load index (PLI), and risk index (RI) indicated that over 99 % of the study area exhibits very high to ultra-high contamination levels. Human health risk analysis identified unacceptable carcinogenic risks from As, Pb, and Cr across adult and pediatric populations, with arsenic presenting the greatest concern. The integration of geospatial tools and machine learning enabled precise identification of contamination hotspots and vulnerable land cover types, demonstrating the value of AI approaches for monitoring contaminated territories. These findings underscore the urgent need for coordinated environmental management, targeted remediation strategies, and community-based monitoring to protect public health and preserve Andean ecosystem integrity. This 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. We would like to express our deepest gratitude to everyone who contributed to this research at the Santa Ana Experimental Station – Huancayo. 2025-09-11T19:32:38Z 2025-09-11T19:32:38Z 2025-08-27 info:eu-repo/semantics/article Pizarro, S., Requena-Rojas, E., Barboza, E., Peña-Elme, E., Arias-Arredondo, A., & Ccopi, D. (2025). Ecological and carcinogenic risk assessment of potentially toxic elements in rangelands and croplands around Lake Junin (Peru): integrating remote sensing, machine learning, and land cover segmentation. Science of the Total Environment, 999, 180327. https://doi.org/10.1016/j.scitotenv.2025.180327 0048-9697 http://hdl.handle.net/20.500.12955/2854 https://doi.org/10.1016/j.scitotenv.2025.180327 eng urn:issn:0048-9697 Science of the Total Environment info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf application/pdf Elsevier NL Instituto Nacional de Innovación Agraria Repositorio Institucional - INIA |
| spellingShingle | Heavy metals Ecological risk assessment Human health risk Remote sensing Machine learning Soil contamination Andean wetlands Metales pesados Evaluación de riesgos ecológicos Riesgo para la salud humana Teledetección Aprendizaje automático Contaminación del suelo Humedales andinos https://purl.org/pe-repo/ocde/ford#4.01.04 Human health; Salud humana; Rangelands; Pastizales; Andean region; Región andina Pizarro Carcausto, Samuel Edwin Requena Rojas, Edilson Jimmy Barboza, Elgar Peña Elme, Eunice Dorcas Arias Arredondo, Alberto Gilmer Ccopi Trucios, Dennis Ecological and carcinogenic risk assessment of potentially toxic elements in rangelands and croplands around Lake Junin (Peru): Integrating remote sensing, machine learning, and land cover segmentation |
| title | Ecological and carcinogenic risk assessment of potentially toxic elements in rangelands and croplands around Lake Junin (Peru): Integrating remote sensing, machine learning, and land cover segmentation |
| title_full | Ecological and carcinogenic risk assessment of potentially toxic elements in rangelands and croplands around Lake Junin (Peru): Integrating remote sensing, machine learning, and land cover segmentation |
| title_fullStr | Ecological and carcinogenic risk assessment of potentially toxic elements in rangelands and croplands around Lake Junin (Peru): Integrating remote sensing, machine learning, and land cover segmentation |
| title_full_unstemmed | Ecological and carcinogenic risk assessment of potentially toxic elements in rangelands and croplands around Lake Junin (Peru): Integrating remote sensing, machine learning, and land cover segmentation |
| title_short | Ecological and carcinogenic risk assessment of potentially toxic elements in rangelands and croplands around Lake Junin (Peru): Integrating remote sensing, machine learning, and land cover segmentation |
| title_sort | ecological and carcinogenic risk assessment of potentially toxic elements in rangelands and croplands around lake junin peru integrating remote sensing machine learning and land cover segmentation |
| topic | Heavy metals Ecological risk assessment Human health risk Remote sensing Machine learning Soil contamination Andean wetlands Metales pesados Evaluación de riesgos ecológicos Riesgo para la salud humana Teledetección Aprendizaje automático Contaminación del suelo Humedales andinos https://purl.org/pe-repo/ocde/ford#4.01.04 Human health; Salud humana; Rangelands; Pastizales; Andean region; Región andina |
| url | http://hdl.handle.net/20.500.12955/2854 https://doi.org/10.1016/j.scitotenv.2025.180327 |
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