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Updated: Oct 1, 2026

Rearing the Cabbage White Butterfly (Pieris rapae) in Controlled Conditions: A Case Study with Heavy Metal Tolerance
Published on: August 18, 2023
Baseline Environmental Contamination Data and Model-based Screening of Children's Exposure-Risk Estimates from
Brian Turyahabwe1, Godswill J Udom2, Modinat A Adefisayo3
1Department of Pharmacology and Toxicology, School of Pharmacy, Kampala International University, Western Campus, Bushenyi, Uganda.
Abstract:
Potentially toxic metal(loid) contamination of agricultural environments may pose serious public health concerns, particularly for children. This study assessed contamination of agricultural soils, irrigation water, and commonly consumed vegetables in Kasese District, Uganda, an area influenced by mining and industrial activities. Agricultural soils (n = 9 composite samples), point-source irrigation water (n = 1), and vegetables (onion, cabbage, and amaranth; n = 36 composite samples) were analysed for 14 potentially toxic elements (PTEs) using Inductively Coupled Plasma Optical Emission Spectroscopy. Age-specific health risks for children were evaluated using the Agency for Toxic Substances and Disease Registry's Public Health Assessment Site Tool and complemented with Monte Carlo simulation (10,000 iterations) to account for exposure variability and uncertainty. Mean concentrations of arsenic (1.780-3.569 mg/kg), lead (2.017-9.670 mg/kg), mercury (0.025-0.700 mg/kg), nickel (0.523-3.655 mg/kg), and chromium (0.043-2.043 mg/kg) frequently exceeded recommended limits. Hazard index values were greater than one, indicating significant non-carcinogenic health risks. Deterministic cancer risk (6.4 × 10⁻⁴-2.1 × 10⁻³) and probabilistic estimates (5.79 × 10⁻⁴-3.86 × 10⁻³) exceeded acceptable priority screening thresholds (10⁻⁶-10⁻⁴), with arsenic and nickel as dominant contributors to probabilistic cancer-risk variability. However, the study is based on a single sampling campaign, and the exposure model relies partly on default parameters; therefore, the risk estimates should be interpreted as screening-level estimates rather than population-level incidence predictions. The findings support expanded spatial and seasonal monitoring, improved characterisation of dietary exposure and contaminant speciation, and targeted food-safety and agricultural risk-management interventions in the Mubuku irrigation scheme.
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