Assessing Public Health Risks from Trace Element Contamination in Common Leafy Vegetables from Ondo, Nigeria, Using PIXE and Multivariate Statistics

Abstract

The consumption of leafy vegetables is a critical pathway for human exposure to essential and toxic trace elements, posing significant public health risks in rapidly urbanizing environments. This study provides a detailed assessment of the elemental composition of six commonly consumed vegetables in Ondo Metropolis, Nigeria, a region experiencing increasing anthropogenic pressure. Ten composite samples from six vegetable types (Vernonia amygdalina, Talinum triangulare, Solanum macrocarpon, Amaranthus hybridus, Telfairia occidentalis, Solanecio biafrae) were analyzed using Proton Induced X-ray Emission (PIXE) spectroscopy. Rigorous quality control was implemented using Certified Reference Materials (CRMs). The obtained data were subjected to a suite of statistical analyses, including descriptive statistics, the KruskalWallis H test, Spearman's rank correlation, and Principal Component Analysis (PCA). Furthermore, health risk indices such as the Target Hazard Quotient (THQ) and Hazard Index (HI) were calculated for Ni and Co. Potassium was the most abundant macro-element (mean: 4541.8 ± 931.7 mg/kg). Alarmingly, Nickel (Ni) was detected in 60% of samples at concentrations ranging from 0.4 to 6.4 mg/kg, with a mean of 2.3 mg/kg, vastly exceeding the WHO/FAO safe limit of 0.3 mg/kg. Cobalt (Co) was ubiquitously present in all samples (0.8-6.1 mg/kg). Statistical analyses revealed significant (p < 0.05) inter-vegetable variation in elemental accumulation, with Vernonia amygdalina (Bitter Leaf) and Solanum macrocarpon (Garden Egg Leaf) identified as high accumulators. Strong positive correlations (ρ > 0.7) and PCA loadings identified a common source for Fe, Co, Ni, and Zn, indicative of a mixed lithogenic-anthropogenic origin. The health risk assessment indicated a THQ > 1 for Ni through consumption of Vernonia amygdalina, signaling potential non-carcinogenic health risks. This study innovatively integrates highly sensitive PIXE spectroscopy with advanced multivariate statistics and quantitative health risk assessment models. It provides a critical, datadriven baseline for policymakers, public health officials, and agricultural agencies, pinpointing specific contaminants (Ni, Co), identifying "high-risk" vegetables, and elucidating pollution sources for targeted monitoring and intervention strategies to enhance food safety in the region.