Assessing heavy metal contamination in agricultural soils: a new GIS-based Probabilistic Pollution Index (PPI) – case study: Guarda Region, Portugal
Soil contamination by heavy metals is a global agricultural problem, as these elements can be absorbed by plants and transferred to humans through the food pathway. Current contamination assessment methods rely on in situ sample collection, which restricts the evaluation process due to the number of...
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| Format: | Article |
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Taylor & Francis Group
2025-01-01
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| Series: | Annals of GIS |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19475683.2025.2452256 |
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| author | Silvia Aparisi-Navarro Maria Moncho-Santonja Beatriz Defez Carla Candeias Fernando Rocha Guillermo Peris-Fajarnés |
| author_facet | Silvia Aparisi-Navarro Maria Moncho-Santonja Beatriz Defez Carla Candeias Fernando Rocha Guillermo Peris-Fajarnés |
| author_sort | Silvia Aparisi-Navarro |
| collection | DOAJ |
| description | Soil contamination by heavy metals is a global agricultural problem, as these elements can be absorbed by plants and transferred to humans through the food pathway. Current contamination assessment methods rely on in situ sample collection, which restricts the evaluation process due to the number of samples that can be analysed and the associated costs. This study addresses the need for a more efficient and cost-effective approach to identifying areas at risk of heavy metal contamination without the logistical constraints of physical sampling. To meet this challenge, we developed a new Probabilistic Pollution Index (PPI), calculated by integrating GIS tools with an 8-parameter probability-risk matrix to identify agricultural areas potentially contaminated by heavy metals. The factors considered included roads, industrial sites, pH levels, soil organic matter content, terrain slope, soil texture, mining areas, and drainage. Each parameter was classified and reclassified to produce a contamination risk map, categorizing each pixel into five levels of risk. To test the PPI, data analysis, parameter classification, and reclassification were applied in the district of Guarda, Portugal. The PPI map revealed that the central portion of the Guarda municipality, along with specific zones in Celorico da Beira, Sabugal, Mêda, and Pinhel, exhibited a high-probability risk of contamination. Given the agricultural expanse within each municipality, enhanced monitoring of heavy metal levels was recommended for Mêda, Pinhel, and Vila Nova de Foz Côa. This index provides a scalable, cost-effective, and easily replicable tool for identifying potential contamination hotspots and the sources and processes of contamination. Designed as a first-line method for large-scale assessments, it supports governmental decision-making and facilitates targeted risk mitigation strategies through a low-cost approach. |
| format | Article |
| id | doaj-art-77e86cf2ee8346e280f3fc43307c3a04 |
| institution | OA Journals |
| issn | 1947-5683 1947-5691 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Annals of GIS |
| spelling | doaj-art-77e86cf2ee8346e280f3fc43307c3a042025-08-20T02:34:29ZengTaylor & Francis GroupAnnals of GIS1947-56831947-56912025-01-0131114316210.1080/19475683.2025.2452256Assessing heavy metal contamination in agricultural soils: a new GIS-based Probabilistic Pollution Index (PPI) – case study: Guarda Region, PortugalSilvia Aparisi-Navarro0Maria Moncho-Santonja1Beatriz Defez2Carla Candeias3Fernando Rocha4Guillermo Peris-Fajarnés5Centro de Investigación en Tecnologias Gráficas, Universitat Politècnica de Valencia, Valencia, SpainCentro de Investigación en Tecnologias Gráficas, Universitat Politècnica de Valencia, Valencia, SpainCentro de Investigación en Tecnologias Gráficas, Universitat Politècnica de Valencia, Valencia, SpainGeoBioTec Research Unit, Geosciences Department, University of Aveiro, Aveiro, PortugalGeoBioTec Research Unit, Geosciences Department, University of Aveiro, Aveiro, PortugalCentro de Investigación en Tecnologias Gráficas, Universitat Politècnica de Valencia, Valencia, SpainSoil contamination by heavy metals is a global agricultural problem, as these elements can be absorbed by plants and transferred to humans through the food pathway. Current contamination assessment methods rely on in situ sample collection, which restricts the evaluation process due to the number of samples that can be analysed and the associated costs. This study addresses the need for a more efficient and cost-effective approach to identifying areas at risk of heavy metal contamination without the logistical constraints of physical sampling. To meet this challenge, we developed a new Probabilistic Pollution Index (PPI), calculated by integrating GIS tools with an 8-parameter probability-risk matrix to identify agricultural areas potentially contaminated by heavy metals. The factors considered included roads, industrial sites, pH levels, soil organic matter content, terrain slope, soil texture, mining areas, and drainage. Each parameter was classified and reclassified to produce a contamination risk map, categorizing each pixel into five levels of risk. To test the PPI, data analysis, parameter classification, and reclassification were applied in the district of Guarda, Portugal. The PPI map revealed that the central portion of the Guarda municipality, along with specific zones in Celorico da Beira, Sabugal, Mêda, and Pinhel, exhibited a high-probability risk of contamination. Given the agricultural expanse within each municipality, enhanced monitoring of heavy metal levels was recommended for Mêda, Pinhel, and Vila Nova de Foz Côa. This index provides a scalable, cost-effective, and easily replicable tool for identifying potential contamination hotspots and the sources and processes of contamination. Designed as a first-line method for large-scale assessments, it supports governmental decision-making and facilitates targeted risk mitigation strategies through a low-cost approach.https://www.tandfonline.com/doi/10.1080/19475683.2025.2452256Heavy metalsProbabilistic Pollution Index (PPI)soil contamination mappingpollution risk assessmentspatial risk modelling |
| spellingShingle | Silvia Aparisi-Navarro Maria Moncho-Santonja Beatriz Defez Carla Candeias Fernando Rocha Guillermo Peris-Fajarnés Assessing heavy metal contamination in agricultural soils: a new GIS-based Probabilistic Pollution Index (PPI) – case study: Guarda Region, Portugal Annals of GIS Heavy metals Probabilistic Pollution Index (PPI) soil contamination mapping pollution risk assessment spatial risk modelling |
| title | Assessing heavy metal contamination in agricultural soils: a new GIS-based Probabilistic Pollution Index (PPI) – case study: Guarda Region, Portugal |
| title_full | Assessing heavy metal contamination in agricultural soils: a new GIS-based Probabilistic Pollution Index (PPI) – case study: Guarda Region, Portugal |
| title_fullStr | Assessing heavy metal contamination in agricultural soils: a new GIS-based Probabilistic Pollution Index (PPI) – case study: Guarda Region, Portugal |
| title_full_unstemmed | Assessing heavy metal contamination in agricultural soils: a new GIS-based Probabilistic Pollution Index (PPI) – case study: Guarda Region, Portugal |
| title_short | Assessing heavy metal contamination in agricultural soils: a new GIS-based Probabilistic Pollution Index (PPI) – case study: Guarda Region, Portugal |
| title_sort | assessing heavy metal contamination in agricultural soils a new gis based probabilistic pollution index ppi case study guarda region portugal |
| topic | Heavy metals Probabilistic Pollution Index (PPI) soil contamination mapping pollution risk assessment spatial risk modelling |
| url | https://www.tandfonline.com/doi/10.1080/19475683.2025.2452256 |
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