ABSTRACT 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.
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