Abstract
BACKGROUND AND AIM: The human health risk assessment (HHRA) of groundwater around the Ariyamangalam open dumping site was conducted, assuming oral ingestion and dermal contact exposure scenarios. METHODS: Observed data of metal concentration in the leachate was used to compute non-carcinogenic hazard quotient (HQ) and cancer risk (CR) by integrating the leaching model with probabilistic HHRA framework. RESULTS:Results from 10,000 Monte Carlo simulations showed that the average value of cadmium (Cd) and lead (Pb) concentration from ground surface up to water table depth exceeded the safe permissible limit. The 95th percentile value of Cd concentration at the water table for sand and silty clay soil was estimated as 0.188 mg/L and 0.104 mg/L, respectively, much higher than safe limit of 0.005 mg/L. The 95th percentile value of HQ for all the subpopulations was found to be 2 - 4 orders higher than safe limit (1) via oral ingestion exposure to cadmium- and lead-contaminated groundwater. The child I (1-5 year) category was most affected by groundwater, as CR value of 1.86E-03 was observed for oral ingestion scenario, highest among all the considered cases. The 95th percentile and maximum value of cancer risk (CR) due to lead were found to be 10 to 1000 times higher than safe level (1E-06) for both the soil types. However, for the skin dermal contact scenario, only maximum value of CR for the child (6-10 year) and teen (11-18 year) exceeded the safe level. Variance attribution analysis showed that the metal concentration (80% variance contribution), as a primary contributor towards uncertainty in the risk estimates, while exposure duration (ED), ingestion rate (IR), and event duration (tevent) were observed as secondary contributors. CONCLUSIONS:The findings of this study highlighted the significance of model-driven HHRA approach and indicated that people residing in the vicinity of dumpsite should consume only treated groundwater. KEYWORDS: Unsaturated zone, Leaching Model, Contaminant Transport, Human Health Risk, Cancer Risk, Monte Carlo Simulations
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