A spatial prioritization method for identifying potential eco-risk distributions of heavy metals in soil and birds

Geochemical approaches are popular for evaluations based on heavy metal concentrations in sediments or soils for eco-risk assessment. This study proposes a systematic geochemical approach (SymGeo) to explore six heavy metals in topsoils and bird tissues and organs of the target birds. We assume that the proposed approach based on field-collected heavy metals in topsoils and feathers can predict the areas with the potential risk of the heavy metals in birds. Finite mixture distribution modeling (FMDM) was used to identify background values of the heavy metal concentrations in topsoil. A spatial enrichment factor (EF), potential contamination index (PCI), contamination degree (Cod), and potential ecological risk index (PRI) based on FMDM results for topsoil, and a potential risk index (PRIbird) of heavy metals in the birds, were utilized for systematic prioritization of high ecorisk areas. Using multiple EF, PRI, and Cod results and multiple PRI-based maps of the heavy metals in feathers, we systematically prioritized risk areas where there is a high potential for heavy metal contamination in the birds. Our results indicate that heavy metal concentrations in the feather, liver, and kidney are not spatially cross-autocorrelated but are statistically significantly correlated with some heavy metals in topsoil due to external and internal depositions. Further, multiple EF, Cod, and RI distributions for topsoil, along with the PRI of the feather, showed that adequate coverages for potential risk for birds were greater than 71.05% in the top 30% and 84.69% in the top 20% potential eco-risk priority area of heavy metals in bird liver and kidney. Hence, our proposed approach suggests that assessments of heavy metals in bird feathers and topsoils without bird organs can be utilized to identify spatially high-risk areas. The proposed approach could be improved by incorporating water and sediment samples to enhance the crowdsourcing and the species-specific data.