Korean J Nutr. 2010 Aug;43(4):404-412. Korean.
Published online Aug 31, 2010.
© 2010 The Korean Nutrition Society
Original Article

Dietary Risk Assessment for Pesticide Residues of Vegetables in Seoul, Korea

Mira Jang,1 Hyunkyung Moon,2 Taerang Kim,1 Donghyun Yuk,1 Junghun Kim,1 and Seoggee Park1
    • 1Gangnam Agro-marin Products Inspection Center, Seoul Metropolitan Government Research Institute of Public Health and Environment, Seoul 138-701, Korea.
    • 2Department of Food Science & Nutrition, Dankook University, Yongin 448-701, Korea.
Received June 10, 2010; Revised June 22, 2010; Accepted June 24, 2010.

Abstract

This paper specifically discusses the risk assessment on the pesticide residues in vegetables collected from traditional markets, big marts and departments in the southern part of Seoul. Vegetable samples were 6,583 cases from January to December in 2009. Monte-Carlo simulation was used to calculate the uncertainty for the risk index using pesticide residues, average dietary intake for vegetables and acceptable daily intake. Deterministic risk indexes were 7.33% of diethofencarb, 5.13% of indoxacarb, 3.96% of EPN, 3.92% of diniconazole and 3.09% of chlorothalonil, respectively. And other pesticides were below 3%. Distributions of risk indexes obtained by the Monte-Carlo simulations were similar to the deterministic values, even though the confidence intervals for 95% were very wide. We confirmed that health risks caused by eating vegetables exceeded maximum residue limits of pesticide are very low and the population is generally safe, judging from the risk indexes located between 0.07 to 9.49%.

Keywords
pesticide residues; dietary risk assessment; vegetables

Figures

Fig. 1
Frequency and probability density function for measured perilla leaves data.

Fig. 2
Uncertainty analysis for endosulfan using Monte Carlo simulation.

Tables

Table 1
Analyzed pesticides in vegetables

Table 2
GC-NPD and GC-µECD conditions used in the determination of the residual pesticides

Table 3
GC-MSD conditions used in the determination of the residual pesticides

Table 4
HPLC-UV conditions used in the determination of the residual pesticides

Table 5
ADIs of pesticides from chronic dietary exposure

Table 6
Vegetables violated Maximum Residue Limits (MRLs) for pesticides

Table 7
Residual pesticides violated Maximum Residue Limits (MRLs)

Table 8
Ranking distributions for selecting probability density function of endosulfan in perilla leaves

Table 9
Comparison between deterministic and stochastic results for the risk index

Notes

This work was supported by Seoul Metropolitan Government Research Institute of Public Health and Environment.

References

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