Residues on food items for birds and mammal. Robert Luttik, National Institute for Public Health and the Environment, NL

Transcription

1 Residues on food items for birds and mammal Robert Luttik, National Institute for Public Health and the Environment, NL Valencia workshop

2 History: Nomograms of Kenaga The EPA food-chain (Kenaga) nomogram used to predict maximum pesticide residues in ppm following application to different categories of plants and plant parts 2

3 History: Nomograms of Kenaga Premises are that the residues that one can expect after spraying are not the result of the compound but of the crop and that the initial concentration increases proportional with increasing dose. In 1973 Kenaga proposed, for lack of measurements, to use the residue data of forage crops and cereals for small and large insects, respectively. 3

4 History: European equivalent Plant/plant parts Short grass Long grass Leaves and leafy crops Small seeds / forage crops/small insects Pods Cereals / large insects Fruit Typical values 112 * D 82 * D 31 * D 29 * D 2.7 * D 2.7 * D 1.3 * D Maximum values 214 * D 98 * D 112 * D 52 * D 11 * D 8.9 * D 6.3 * D Relationship between "typical" and maximum residue concentrations on plants or parts of plants (in mg/kg fresh weight) and the dosage (D) of plant protection products (in kg active ingredient per hectare) immediately after spraying. 4

5 History: Guidance document 2002 Food type Short grass Long grass Leaves/leafy crops/forage crops/small seeds Fruit/pods/large seeds Small insects Large insects RUD Arithmetic mean RUD 90th percentile Residue unit doses to be used according to the Guidance document for birds and mammals (2002) after Fletcher et al. (1994). 5

6 History: Guidance document 2002 Note in guidance document: The residue estimate for small insects appears unsatisfactory, and as soon as better information becomes available this surrogate should be replaced. Research is highly desirable to develop more robust data for residues in insects, also with regard to the temporal pattern. 6

7 Revision of guidance document Residues on insects Both PSD and ECPA have funded much work in this area. PSD funded work has focused on working from the lab to the field and has concentrated on issues such as what invertebrates birds eat The industry approach has been to develop a generic protocol and produced many field based data. Residues on vegetation The existing dataset has been recently reviewed by Canada (Baril et al. 2005) MAF and TWA probably we will provide tables for MAF and a TWA calculator (excel) Guidance on how to carry out residue trials We will produce a guidance document including to decide on how much data are needed before the default values can be replaced. How to deal with studies on degradation time. 7

8 Revision of guidance document All following presented data are still under debate. No decision has been made what to propose for the guidance document. 8

9 Insects: ECPA / industry data set Analysis of field arthropod residue studies from different ECPA companies by RIFCON GmbH to extract suitable initial maximum residue levels => Initial residues measured as highest value within 7 days after application (worst-case) => Residues per unit doses (RUD) Consideration of different substance classes / application and sampling techniques etc. according to a questionnaire provided by industry. Consideration of available data from the literature and other sources. 9

10 Insects: ECPA / industry data set Database: Field studies conducted following GAP and standard agricultural practice 22 Studies (91% GLP) representing 25 different crop / pesticide scenarios from North America and Europe 67 datasets which differ in: study location, active ingredient, crop, method of arthropod sampling. 10

11 Insects: ECPA / industry data set Scenarios covered Crop types Orchards Vine Cotton Potatoes Miscellaneous Vegetables* Cereals Grassland No of trials (or data sets) Growth stage during application during or after flowering during or after flowering early / late early / late Bare soil / early / late late / post-harvest - * Miscellaneous vegetables contain alfalfa, peas, French beans, lentil, endive etc. 16 different active ingredients from 13 different chemical classes made up the dataset. insecticides (n=10), fungicides (n=4) or herbicides (n=2) Data were grouped within each of the major crop types (cereals, leafy crops/vegetables and orchards/vines) 11

12 Insects: ECPA / industry data set Arthropod categories and sampling methods Arthropods were classified as ground dwelling, foliage dwelling and flying arthropods according to sampling technique Arthropod class Ground dwelling arthropods Foliage dwelling arthropods Flying arthropods Sampling techniques -mainly pitfall traps -collected arthropods (aspirators, by hand) -crickets placed in exposure chambers at soil level -mainly inventory spray and beating method -collected arthropods (sweep nets) -exposure methods of larvae and adults at foliage level -car netting, light traps and malaise traps 12

13 Arthropod Sampling and Foraging Strata flying insects (6 data sets) car netting, light traps leaf dwelling arthropods (23 data sets) inventory spray, beating on vegetation ground dwelling arthropods (37 data sets) pitfall traps 13

14 Summary of Insect RUD values Preliminary results RUD [mg/(kg*h)] Arthropods Pesticide and Corp Median 90%tile Flying insects all pesticides all crops Ground dwellers foliage application insecticides and fungicides: orchards/vines, late growth stages of leafy crops 3.6* 9.8* Ground dwellers ground application herbicides (orchards/vines), all pesticides early growth stages of leafy crops Leaf dwellers foliage application all pesticides all crops * value for fungicides (covering insecticides) 14

15 ECPA / industry data set Summary Results and Conclusions RUD current SANCO vs. experimental residue data from ECPA database Difficult use of RUD for small and large arthropods (as recommended by current SANCO/4145) for refined risk assessments because no clear definition of what is small and large A differentiation into ecological groups (e.g. foliage vs. ground dwelling arthropods) is more appropriate instead of arbitrary and undefined size classes (e.g. small vs. large). The default values (52 / 29 mg/kg small insects) from the guidance document (SANCO/4145/2000) appear to overestimate actual arthropod residues. Proposed RUD values for ecological categories of arthropods from the ECPA database can be updated as new data become available 15

16 Residues on insects PSD study Extended lab studies for cereal and broad-leafed crops Pot grown plants (cereal, cabbage, beans) infested with invertebrates (aphids, syrphids, caterpillar identified as major food items for birds and mammals) and oversprayed using boom sprayer with pesticides Range of 9 pesticides- insecticides, herbicides, fungicides with range of formulations Samples taken immediately after drying 16

17 Residues on insects PSD study Preliminary results RUD Rhopalosiphum padi Acyrthosipho npisum Episyrphu s balteatus Pieris brassicae Mean Overall mean (range) on cereal 31 ( ) on bean 11 (0-40) on cereal 3.4 (0-8.5) on cabbage 13 (0-35) 4.6 Overall 90 th percentile (range) 109 (0-186) 27 (0-57) 18 (0-47.8) 37 (0-48) 38 17

18 Residues on insects PSD study Plot studies Cereal and cabbage grown on plots Infested with aphids and Pieris brassicae larvae (as highest residues in extended lab studies) Oversprayed with boom sprayer with prochloraz, chlorpyrifos, tebuconazole and L-cyhalothrin Samples taken when dry Beetles placed in plots and oversprayed 18

19 Residues on insects PSD study Preliminary results Pesticide Rate (kg ai/ha) Rhopalosiphum padi Rhopalosiphum padi Pieris brassicae Pieris brassicae mean 90 th Mean 90 th Prochloraz Chlorpyrifos Tebuconazole L-cyhalothrin Overall

20 Residues on insects PSD study Preliminary results Beetle Beetle Beetle All beetles bare soil cabbage Cereal Mean (range) (1.0-19) (1.3-56) (1.1-13) 90 th percentile (range) 15 (1.5-29) 28 ( ) 14 (1.4-17) 15 20

21 Residues on vegetation New research available: Baril, Whiteside and Boutin (2005) ETC 24: Database contains 1488 residue values originating from 314 sources From literature but also 25 field studies submitted by manufacturers in support of registration. Much larger database compared to Fletcher et al (1994), which was already much larger than the database of Kenaga. Still under discussion within the working group whether the values given in the paper are representative for the European agricultural practice. 21

22 Residues on vegetation Categories proposed by authors RUDS on fruits and seeds mean std n 50th 75th 90th 95th 99th Small fruits Large fruits Grains Pods Small fruits are berries and small fruits from orchards (apricot, cherry, plum, etc.) Large fruits are fruit from orchards like apples, tomato, eggplant and gourds. Pods are pulse crops and okra. 22

23 Residues on vegetation Other possibilities for food categories RUDS on fruits mean std n sign. Berries AB Small fruits from orchards A Large fruits from orchards B Tomato B Eggplant B Gourds B 23

24 Residues on vegetation Categories proposed by authors RUDs on leaves mean STD n 50th 75th 90th 95th 99th Shrubs Field crops Orchard/vineyard Short plants Oilseeds Vegetable row crops Tall grasses Thin plants Field crops: cereals, forage grasses, forages legumes and turf 24

25 Residues on vegetation Baril et al choice Shrubs Field crops Orchard/vineyard Short plants Oilseeds Vegetable row crops Tall grasses Thin plants Underlying crops Cereals Forage grasses Forages legumes Turf Fruit trees Woody vines Medium-sized plants Large whorled leaves Pulse crops significance A AB ABCD ABC ABC ABCD ABCD ABCD ABCD BCD CD CD D E 25

26 Residues on vegetation Other possible choices that can be made leaf type mean std n Turf ABC Forage grasses ABCD Forage legumes ABC Short leafy plants ABCD Oilseeds ABCD Small plants Fletcher values for leaves

27 MAF and TWA MAF = Multiple Application Factor Replaces a series of individual applications by one virtual total application. Underlying assumption: single 1 st order degradation kinetics (SFO) acute scenario: specific calculation to achieve 90 th percentile (or other percentile) long-term scenario: based on standard equation for SFO c = c 0 exp(-k t) with k = ln(2)/dt 50 TWA = time-weighted average Replaces a non-static concentration curve by a constant Underlying assumption: single 1 st order degradation kinetics (SFO) acute scenario: not applicable long-term scenario: based on standard equation for SFO c twa = c 0 (1 - exp(-k t))/(k t) with k = ln(2)/dt 50 27

28 MAF and TWA 2 concentration 1,75 1,5 1,25 1 0,75 working example: 3 applications interval 7 d DT 50 = 7 d 0,5 0, time 28

29 MAF and TWA 2 concentration 1,75 1,5 1,25 1 0,75 MAF transforms 3 single applications into one virtual total application here: MAF = ,5 0, time 29

30 MAF and TWA 2 1,75 twa concentration 1,5 1,25 1 0,75 areas match replaces a non-static concentration curve by a constant here: f twa = ,5 0, time 30

31 MAF and TWA 2 concentration 1,75 1,5 1,25 1 0,75 MAF twa stepwise calculation for multiple applications here: MAF f twa = ,5 0, time 31

32 MAF and TWA 2 concentration 1,75 1,5 1,25 1 0,75 MAF twa selection of appropriate time window 0,5 0, time 32

33 selection of input parameters a MAF can be derived read out MAF twa here 33

34 MAF and TWA Future use of degradation kinetics calculations to be based on single 1 st order kinetics (SFO) volatilisation, wash-off or photolysis of residues might result in non- SFO best fit kinetics, but use of SFO ensures conservative approach for the relevant time intervals shortly after application Future use of MAF gives maximum concentration to be expected for acute exposure scenario new GD will include equations to calculate 90 th percentile MAF (or other percentile when appropriate) no longer to be used alone for long-term exposure scenario Future use of f twa to be used in long-term exposure scenario as a combined factor MAF f twa calculation using a moving time-window check applicability of averaging for certain endpoints first 34

35 Guidance for field experiments It is intended to provide methodological recommendations how to perform arthropod residue studies under field conditions, considering: Study site selection, plot size, replicates Sampling methods for different strata (foliage, ground dwellers etc.) Sample size and frequency (for determination of residue decline data) Recommendations may be provided as an annex / supplement to the new GD Will this be helpful for authorities (study interpretation) and notifiers / CRO s (study performance)? 35