Bayesian-Based Probabilistic Risk Assessment of Fipronil in Food: A Case Study in Taiwan

Abstract

Fipronil, a broad-spectrum insecticide, is widely used in agriculture and veterinary practices. Fipronil-induced neurotoxicity and potential adverse effects on humans and aquatic organisms have raised health concerns. Monitoring programs have been implemented globally to assess fipronil residues in food, including fruits, vegetables, and animal products. However, previous exposure assessments have often focused on specific food categories or subsets of items, resulting in limited insights into the overall health risks. Additionally, the large number of non-detect fipronil residues in food has introduced uncertainties in exposure assessment. To address these issues, a probabilistic exposure assessment and dose-response analysis were adopted in this study, considering the sample distribution below the detection limit to better characterize uncertainties and population variability in health risk assessments. The estimated fipronil exposure to the general public ranges from 6.38 × 10^-6 ± 0.00017 mg/kg/day to 9.83 × 10^-6 ± 0.00034 mg/kg/day. Only one out of 200,000 simulated individuals had a fipronil dose exceeding the probabilistic reference dose (0.048 mg/kg/day, pRfD), which aims to protect 99% of the population with effects less than 10% extra risk. By incorporating uncertainties in exposure and dose-response data, a more comprehensive understanding of the health risks associated with fipronil exposure in the Taiwanese population has been achieved.

Keywords: Bayesian benchmark dose (BBMD); Markov Chain Monte Carlo simulation (MCMC); exposure assessment; fipronil; health risk assessment.

Figure 1

Comparative analysis of food intake and detection rate for each food category.

Figure 2

Reconstruction of fipronil residues using MCMC simulation: an example of root and stem vegetables (RV). (A) A scatter plot illustrates the difference between the original data and the data sampled from the posterior distribution of fipronil residues in root and stem vegetables (n =1760). In the original dataset, samples with non-detect values were shown as half of the detection limit (DL/2, n =1716). (B) Comparative analysis of the original data (red triangle) and the data sampled from the posterior distribution (black square), using a cumulative frequency plot (open circle, left y axis) and a relative frequency plot (right y axis). The black solid line denotes the detection limit (0.002 ppm) and the red dash line indicates the half of the detection limit (0.001 ppm).

Figure 3

Probabilistic exposure assessment of fipronil in Taiwan. The lower bound (LB) and upper bound (UB) exposure estimates are summarized by (A) age (all ages, toddlers (0–2 years old), preschoolers (3–5 years old), children (6–11 years old), pre-teenagers (12–15 years old), teenagers (16–18 years old), adults (19–65 years old), and elderly (≧66 years old)) or (B) sex. The red dash line indicates the acceptable daily intake (ADI) of fipronil (0.0002 mg/kg/day) proposed by the Joint FAO/WHO Meeting on Pesticide Residues (JMPR).

Figure 4

Probabilistic dose-response assessment of fipronil. (A) The probability density and cumulative probability distribution of BMDL₁₀ and (B) the probabilistic $H{D}_M^I$ plot for fipronil-induced convulsion in male SD rats. The probabilistic reference dose (pRfD) is defined as the 5th percentile $H{D}_{M=0.1}^{I=0.01}$, representing the estimated human dose, where the population has 1% incidence of the target magnitude of effect (BMR = 10%).

Figure 5

Probabilistic risk characterization of fipronil using lowerbound (A) and upperbound (B) exposure estimates in Taiwan. The dash line indicates the margin of safety (HQ = 1).