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

Vertretungsplan SI Freitag 20.03.15