Analysis of a database of pesticide residues on plants for wildlife risk assessment

被引:8
|
作者
Baril, A [1 ]
Whiteside, M [1 ]
Boutin, C [1 ]
机构
[1] Carleton Univ, Natl Wildlife Res Ctr, Canadian Wildlife Serv, Environm Canada, Ottawa, ON K1A 0H3, Canada
关键词
pesticides; wildlife; risk assessment; nomogram; plant residue;
D O I
10.1897/03-656.1
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Current methods to estimate exposure of wildlife to pesticides from diet depend on a database of published residue concentrations on crop plants normalized to a standard application rate to obtain a residue from a unit dosage (RUD). This database, first published in the early 1970s, was updated in the 1990s. For each category of crops, maximum and mean residues are determined and used to extrapolate concentrations on plants across application rates in calculations of risk. The present study aims to update the database, to examine the validity of extrapolating RUD values across application rates, and to improve the categorization of crops using crop morphology and cultivation methods. The slope of the linear regression of residue concentrations against application rate in 41 trials was significantly different from one in all but five cases. This supports the assumption that residue concentrations are directly proportional to the application rate, although less than half the variance in residue concentrations was explained by the linear model. Residues on leaves were partitioned into eight categories of crops using information regarding plant morphology and cultivation method. Fruit size was an additional variable useful for segregating residues into four categories: Small fruits, large fruits, pods, and grains. The proposed changes increase the amount of variance explained in the residue database from 19 to 32%. Depending on the crop category, residues on fruits were 2 - to 16-fold lower than those on leaves. Residue concentrations on leaves of short plants were more than fourfold higher than those on leaves of tall plants. Descriptive statistics are provided for each of the proposed crop categories.
引用
收藏
页码:360 / 371
页数:12
相关论文
共 50 条
  • [1] Pesticide residues in Cannabis: Pesticide exposure risk assessment
    Reibach, Paul
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2017, 254
  • [2] Pesticide Residues and Bees - A Risk Assessment
    Sanchez-Bayo, Francisco
    Goka, Koichi
    PLOS ONE, 2014, 9 (04):
  • [3] Uncertainty analysis of pesticide residues in drinking water risk assessment
    Tesfamichael, AA
    Kaluarachchi, JJ
    HUMAN AND ECOLOGICAL RISK ASSESSMENT, 2004, 10 (06): : 1129 - 1153
  • [4] Cumulative risk assessment of pesticide residues in food
    Boobis, Alan R.
    Ossendorp, Bernadette C.
    Banasiak, Ursula
    Hamey, Paul Y.
    Sebestyen, Istvan
    Moretto, Angelo
    TOXICOLOGY LETTERS, 2008, 180 (02) : 137 - 150
  • [5] Regulatory risk assessment of pesticide residues in air
    Gilbert, AJ
    WATER AIR AND SOIL POLLUTION, 1999, 115 (1-4): : 183 - 194
  • [6] Risk Assessment of Pesticide Residues in Litchi on the Market
    Jiang C.
    Lin S.
    He S.
    Zhou Y.
    Li Z.
    Su D.
    Shang X.
    Journal of Chinese Institute of Food Science and Technology, 2023, 23 (12) : 209 - 218
  • [7] Risk Assessment of Pesticide Residues in Chinese Litchis
    Kuang, Lixue
    Xu, Guofeng
    Tong, Yao
    Li, Haifei
    Zhang, Jianyi
    Shen, Youming
    Cheng, Yang
    JOURNAL OF FOOD PROTECTION, 2022, 85 (01) : 98 - 103
  • [8] Regulatory Risk Assessment of Pesticide Residues in Air
    A. J. Gilbert
    Water, Air, and Soil Pollution, 1999, 115 : 183 - 194
  • [9] EPA pesticide fate database for risk assessment
    Liu, SL
    Shamim, MT
    Holmes, J
    Nguyen, T
    Hoot, L
    Spatz, DS
    Shamim, AN
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2005, 230 : U97 - U97
  • [10] Risk Assessment and Ranking of Pesticide Residues in Xinjiang Apricot
    Jia Q.
    Wang X.
    Qin Q.
    Jia B.
    Hua Z.
    An J.
    Fan Y.
    He W.
    Wang C.
    Xu B.
    Liu F.
    Science and Technology of Food Industry, 2023, 44 (21) : 267 - 274