Dietary exposure assessment of carbendazim residues in vegetables, fruits and mushrooms in Shanghai Market Ph.D Song Weiguo, Mao Xiang-dong, Zhao Zhi-hui Institute of Agro-food Standard and Testing Technology, Shanghai Academy of Agriculture Science Shanghai Engineer Research Center of Agro-food Quality and Safety Risk Assessment Lab of Agro-food Quality and Safety (Shanghai), Ministry of Agriculture 2014-3-18 Moscow Backgroud Issues 2011,carbendazim in juice 2012,in tea 2012年,in grape wine – Carbendazim(a.i.7% powder) was listed in Rotterdam Convention Annex III which is applicable to PIC(Prior Informed Consent)Procedure – Cancelled or restricted to use in UDA, AUS Unep, FAO: Chemicals and pesticides with high risks It is necessary to assess the risks of carbendazim to consumers in Shanghai by intaking vegetables, fruits and mushrooms for s c i e n t i f i c m o n i t o r i n g o f f o o d s a f e t y. >30 million consumers Global city Risk analysis Risk assessment Point assessment Probabilistic assessment Risk assessment Hazard identification epidemiological data、history、metablism to identify the possibility of hazard。 Doseresponse assessment Determine the endpoint or threshold of hazard(NOEC/NOAEL-ARfD&ADI) Exposure assessment Risk characteristic Residue data analysis;capital dietary; calculation of risk quotient Discribe the quality of risk and uncertainty Hazard identification chemical and physical parameters CAS 10605-21-7 General name Carbendazim Commd. name Chemical name Carbendazime、Carbendazol、MBC、BMC、BAS、 Bavistin、Delsene、Derosal、 Hoe1 methyl 1H-benzimidazol-2-ylcarbamate • Wide risk source – Carbendazim(MBC)、thiophanatemethyl(TM)、benomyl application – Widely used in crop(veget.,fruits,mushroom, grain,oil crops,and etc.) – Registered all over the world Benomyl TM C14H18N4O3 C12H14N4O4S2 Neutral or acid condition Leaf surface、 root MBC C9H9N3O2 (JMPR,1973) • • • • not dissolved in water(<0.1 g/100 mL 21 ºC) Hard to degrade(DT50 45d-2y) not easy to remove by cooking or heating Endocrine disrupting pesticides (European priority list, briefing, February 2001) • liver toxicity, developmental toxicity and reproductive (testicular) effects(Mantovani,1998) Potential chronic risks to consumer by intaking food Dose-response (hazard characteristic) Acute toxicity study Animal rat Rabbit Trials Maternal toxicity Developmental toxicity Acute toxicity,(Testicular effects) Maternal toxicity Developmentaltoxicity NOAEL 30 mg/kg bw/ d 10 mg/kg bw/ d 50 mg/kg bw 20 mg/kg bw/ d 10 mg/kg bw/ d Acute Referenced Dose,ARfD 0.1 mg/kg bw for special 0.5 mg/kg bw for general JMPR,2005 Carbendazim toxicology and NOAEL/NOEL levels: Rat: 500 mg/kg diet, equivalent to 25 mg/kg body weight (development toxicity) Dog: 100 mg/kg diet, equivalent to 2.5 mg/kg body weight development toxiciy) Rat: 30 mg/kg body weight per day (benomylteratology) The JMPR 1983 meeting (FAO/WHO, 1985a) estimated ADI for carbendazim to be 0-0.01 mg/kg body weight. Exposure assessment 工作基础 • Determination Car - 5.208 0.020 0.015 AU Thi - 7.206 – Methods were validated by adding standards into cucumber, pepper, pear, apple, mushroom – Extract by acetonitrile – MCX solid column clean – LC-Photo-diode arry detector(284nm) 0.010 0.005 0.000 1.00 2.00 3.00 4.0 0 5.00 Minutes 6.00 7.00 Figure 5 ug/ml carbendazim standards 8.00 9.00 10.00 Recovery experiments of carbendazim food A.C(mg/kg) Rec(%) RSD(%) Cuc. 0.25-0.75 91-113 4-9 Pep. 0.05-0.15 87-102 3-7 Pear 1.5-4.5 92-100 1-2 Apple 1.5-4.5 91-97 1-3 Musr.1 0.25-0.75 90-101 1-4 Musr.2 0.25-0.75 86-102 4-7 LOD: veg0.001, fr & msrm 0.0012 mg/kg Monitoring data 2008-2012,samples include: 41 kinds vegetable 2048 samples 32 kinds of fruits 2511 samples And 18 kinds of mushrooms 120 samples Covered Supermarkets, food shops, standard markets Monitoring data FOOD VEG. (LOD0.01mg/ kg) CROPS MRL(mg/kg) DEC.R% NOTE Veg.total / 4.3 68 samples detd. tomato cucumber 3 0.5 0.1 2 2 0 0 / 0 1.7 asparagus pepper chives MUSRM (LOD 0.0012mg/kg) 2.5 3 sample detd. food 水果 最低检出限 0.0012mg/kg crops MRL(mg/kg) Det.r% 备注 Fruit total / 20.1 397 samples detd. orange watermelon apple pear grape peach plum almond cherry date 5 0.5 3 3 3 2 0.5 2 0.5 0.5 1.1 0 24.7 12.5 28.3 13.2 27.3 0 50 12.5 strawberry 0.5 34.7 lemon olive pineapple Kiwi fruit litchi 0.5 0.5 0.5 0.5 0.5 12 / 0 12.1 37.0 mango 0.5 19.4 banana 0.1 6.7 According to method by GEMS/ FOOD-EURO Second Workshop on Reliable Evaluation of LowLevel Contamination of Food(GEMS,1995), If ND data are more than 60%,instead of ND, 0 and LOD will be used separately to get a statistics distribution by Monte-carlo method. Simulation of residue data-vegetables Residue data distr. (ND O) RiskExpon(0.011292,Ris kName(“VEG.DISTR-0")) Residue data distr. (ND LOD) RiskLoglogistic(0,0.010 191,6.3968) 95% percentile value(0.0162—0.0337) Simulation of residue data-fruits Residue data distr. (ND O) RiskExpon(0.031872,Ris kName(“FR.DISTR-0")) Residue data distr. (ND LOD) RiskPareto2(0.016424,1. 6972) 95% percentile value(0.077—0.094) Simulation of residue data-mushrooms Residue data distr. (ND O) RiskExpon(0.000302,Ris kName(“MSR.DISTR-0") ) Residue data distr. (ND LOD) RiskLoglogistic(0,0.005 004,21.435) 95% percentile value(0.00058—0.00089) Dietary data F(veg intake)= RiskLognorm(229.29,226.07,RiskS hift(-8.6937)) F(fr intake)= RiskLoglogistic(-3.6748,244.34,2.5 587) F(musr intake)=RiskExpon(44.265,RiskShift(2.4667)) + RiskPearson5(2.247,50.361,RiskShift(-3.9913)) Average intake(F+M): vegetables 219(0-3064)g/person/d、fruits 320(7-5238)g/person/d、mushrooms 82 (4-1553) g/person/d Average intake(M): 235(1-9609)、324(5-7764)、81(4-1417) Average intake(F): 224(0-2779)、326(2-9272)、83 (5-4884)g/ person/d。 From Shanghai FDA BODY WEIGHT Body weight of different consumers Age Male (kg) female(kg) 7-10y 38 33 11-14y 49 44 15-17y 65 55 ≥18y 70 59 Aver. 65 54 Total aver. 58 FAO/WHO guidelines Acute dietary risk quotient(ARQ): %RfD=HR×DIET/BW Chronic dietary risk quotient (CRQ): %ADI=MR×DIET/BW Parameters for point assessment of AR FOOD HR(mg/kg) MR(mg/kg) VEG 1.88 0.095 FR 3.96 0.022 MUSRM 0.18 0.18 ADI (mg/kg bw/d) RfD (mg/kg) 0.01 0.5 ARQ at simulatedhighest intake level age male female veg fr msrm Total food veg fr msrm Total food 7-‐10 13.31 6.50 0.08 19.89 15.24 7.44 0.09 22.77 11-‐14 10.30 5.03 0.06 15.39 11.59 5.66 0.07 17.32 15-‐17 7.86 3.84 0.05 11.74 9.27 4.53 0.05 13.85 >=18 7.31 3.57 0.04 10.92 8.69 4.25 0.05 12.99 Aver. 7.87 3.84 0.86 12.57 9.43 4.60 0.05 14.09 Total aver. 8.75 4.27 0.05 13.07 ARQ at average intake level age male female veg fr msrm Total food veg 7-‐10 187.08 108.43 1.46 214.23 124.17 11-‐14 144.80 83.93 1.13 162.89 94.41 15-‐17 110.40 63.99 0.86 130.33 75.54 >=18 102.70 59.53 0.80 122.18 70.82 Aver. 110.57 64.09 0.86 132.50 76.80 Total aver. 122.92 71.24 0.96 fr 1.67 1.27 1.02 0.95 1.03 Monte-carlo assessment CRQ:risk quotients of all consumers in Shanghai city exposed to 3 foods(veg, fr, msrm.) are lower than 100%,dietary exposure risks<(ADI)0.01mg/kg bw/d,99 percentile of risk quotient is 13.8-15.4%ADI,95 percentile 5.1-6.6%. Total food-total consumer veg fr Msr . Risk Characteristics Ø AR was assessed by point methods. At highest intake level of veg., fr. and msrm by @risk simulation, consumers 7-14 ages was exposed with highest risks, and fruit contribute most. Ø But possibility of actual exposure to highest level of 3 foods is too small to be more than 1 millionth. Ø By simulating by @RISK5.7.1,chronic dietary exposure of 3 foods is far below ADI, with 99 percentile to be 13.8-15.4%ADI。 Ø To make a final risk assessment decision that vegetables, fruits, and mushrooms in Shanghai markets are safe for consumers to intake. uncertainty u Residue data u The ND data selection has effects on the simulation distribution; u Limit data(based on monitoring,not cover all the vegetables, fruits and mushrooms) u Dietary data u Different people have different diets, and ultimate data not considered Conclusion (1)Carbendazim no risks (2)Risk assessment are open system and scientificity depend on the data. With the accumulation of data, the assessment will be more close to actual conditions. And technical innovation will increase the accuracy of the data. Welcome to Shanghai
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