PMID- 33725536
OWN - NLM
STAT- MEDLINE
DCOM- 20210420
LR  - 20210420
IS  - 1873-6424 (Electronic)
IS  - 0269-7491 (Linking)
VI  - 278
DP  - 2021 Jun 1
TI  - A lognormal model for evaluating maximum residue levels of pesticides in crops.
PG  - 116832
LID - S0269-7491(21)00412-7 [pii]
LID - 10.1016/j.envpol.2021.116832 [doi]
AB  - To evaluate pesticide regulatory standards in agricultural crops, we introduced a 
      regulatory modeling framework that can flexibly evaluate a population's aggregate 
      exposure risk via maximum residue levels (MRLs) under good agricultural practice 
      (GAP). Based on the structure of the aggregate exposure model and the nature of 
      variable distributions, we optimized the framework to achieve a simplified 
      mathematical expression based on lognormal variables including the lognormal sum 
      approximation and lognormal product theorem. The proposed model was validated 
      using Monte Carlo simulation, which demonstrates a good match for both head and 
      tail ends of the distribution (e.g., the maximum error = 2.01% at the 99th 
      percentile). In comparison with the point estimate approach (i.e., theoretical 
      maximum daily intake, TMDI), the proposed model produced higher simulated daily 
      intake (SDI) values based on empirical and precautionary assumptions. For 
      example, the values at the 75th percentile of the SDI distributions simulated 
      from the European Union (EU) MRLs of 13 common pesticides in 12 common crops were 
      equal to the estimated TMDI values, and the SDI values at the 99th percentile 
      were over 1.6-times the corresponding TMDI values. Furthermore, the model was 
      refined by incorporating the lognormal distributions of biometric variables 
      (i.e., food intake rate, processing factor, and body weight) and varying the 
      unit-to-unit variability factor (VF) of the pesticide residues in crops. This 
      ensures that our proposed model is flexible across a broad spectrum of pesticide 
      residues. Overall, our results show that the SDI is significantly reduced, which 
      may better reflect reality. In addition, using a point estimate or lognormal PF 
      distribution is effective as risk assessments typically focus on the upper end of 
      the distribution.
CI  - Copyright (c) 2021 Elsevier Ltd. All rights reserved.
FAU - Guo, Yuan
AU  - Guo Y
AD  - School of Civil Engineering, Sun Yat-sen University, Guangdong, 510275, China.
FAU - Li, Zijian
AU  - Li Z
AD  - School of Public Health (Shenzhen), Sun Yat-sen University, Guangdong, 510275, 
      China. Electronic address: lizijian3@mail.sysu.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20210301
PL  - England
TA  - Environ Pollut
JT  - Environmental pollution (Barking, Essex : 1987)
JID - 8804476
RN  - 0 (Pesticide Residues)
RN  - 0 (Pesticides)
SB  - IM
MH  - Agriculture
MH  - Crops, Agricultural
MH  - Food Contamination/analysis
MH  - *Pesticide Residues/analysis
MH  - *Pesticides/analysis
OTO - NOTNLM
OT  - Agriculture
OT  - Crop contamination
OT  - Health risks
OT  - Pesticide regulation
COIS- Declaration of competing interest The authors have declared that is no conflict 
      of interest in this paper.
EDAT- 2021/03/17 06:00
MHDA- 2021/04/21 06:00
CRDT- 2021/03/16 20:13
PHST- 2020/12/05 00:00 [received]
PHST- 2021/02/19 00:00 [revised]
PHST- 2021/02/22 00:00 [accepted]
PHST- 2021/03/17 06:00 [pubmed]
PHST- 2021/04/21 06:00 [medline]
PHST- 2021/03/16 20:13 [entrez]
AID - S0269-7491(21)00412-7 [pii]
AID - 10.1016/j.envpol.2021.116832 [doi]
PST - ppublish
SO  - Environ Pollut. 2021 Jun 1;278:116832. doi: 10.1016/j.envpol.2021.116832. Epub 
      2021 Mar 1.