Environmental modelling and validation

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1 Environmental modelling and validation Colin Brown University of York, UK 2 nd SETAC Europe Special Science Symposium

2 Overview Predicted environmental concentrations Models, validation and goodness-of-fit Exposure calculations for soil Validation status of current approaches Exposure calculations for surface water FOCUS tools Future developments Conclusions

3 Revision of Annex III requirements for fate and behaviour Currently Additionally Predicted environmental concentration in soil PEC in surface water PEC in ground water PEC in air PEC in sediment PEC s in other environmental compartments as a result of airborne transport

4 Directive text (unchanged) When models are used for estimation of predicted environmental concentrations they must: make a best-possible estimation of all relevant processes involved taking into account realistic parameters and assumptions where possible be reliably validated with measurements carried out under circumstances relevant for the use of the model be relevant to the conditions in the area of use. Additional processes Zonal registration Validation status

5 Surface water body Precipitation Evapotranspiration Interception Overland flow Plant uptake Infiltration Sublateral flow Unsaturated zone Drainage Drainflow Saturated zone Percolation Percolation Recharge

6 Surface water body Precipitation Evapotranspiration Interception Mixing layer Solid phase Sorption Liquid phase Volatilisation Gas phase Overland flow Leaching Degradation Sublateral flow Unsaturated zone Drainage Drainflow Saturated zone Percolation Percolation Recharge

7 Concentration (microg/l) Environmental behaviour of pesticides is inherently complex Chlorotoluron Atrazine Simazine Propyzamide /09/ /08/ /08/ /08/ /09/ /08/ /08/ /08/ /09/ /08/ /08/2005 Thames Water data for four herbicides in the River Thames at Walton

8 Why do we estimate exposure with mathematical models? We predict forwards in time What will happen if we use compound X on crop Y in country Z? We cannot measure behaviour under sufficient combinations of conditions Lack of control on experimental conditions Limitations on time and cost

9 Models are a key tool, but. All models are based on assumptions Don t fully understand the system Nearly always need to simplify the system Often we can t measure all the parameters So all models are just representations of reality We need to know: The range of validity The limitations imposed by the assumptions

10 Model validation Check that the model gives sufficiently accurate information under the conditions it s designed for Generally comparing model output with measured data Verification Sometimes comparing models with each other Comparison not always possible Extrapolation

11 Pesticide concentrations in drainflow (microg/l) How do we decide whether simulations match reality? 22/5 23/5 24/5 25/5 26/5 27/5 28/5 29/5 30/5 31/5 01/6 02/6 03/6 04/6 05/6 06/6 07/6 08/6 09/6 10/6 11/6 12/6 13/6 14/6 15/6 Pesticide concentrations in drainflow vs. simulations with MACRO Observed concentrations Interpolation MACRO simulation Date

12 How accurate do we want to be? Peak concentrations of pesticides in UK rivers vs. simulations with SWATCATCH Brown et al. (2002)

13 The myth of validation Often not possible to validate a model Can t measure all of the input and output variables Scale of model in space or time makes measurement impractical Cannot test all possible predictive situations Can invalidate a model or build sufficient comparisons that trust in the model grows See e.g. Köhne et al. (2009) for soil macropore flow models

14 Estimating exposure concentrations in soil EFSA WG Exposure Soil Organisms Tiered approach for annual crops and conventional tillage tier 1 conservative north-central-south scenarios for simple model Tiered approach for obtaining DegT50 tier 2 realistic worst-case scenarios for north-central-south for numerical models tier 3 product specific scenarios for numerical models

15 Walker (1974) model (later called PERSIST): # field dissipation only result of transformation in soil # first-order transformation rate function of - soil moisture content - soil temperature weather data field persistence of pesticide PERSIST model laboratory pesticide behaviour half-life as function of soil moisture content soil temperature

16 Simulated concentration (% of initial) % of initial simulated = 59% measured = 50% Time after application (d) 90 Simulation of 178 field trials on basis of laboratory degradation data Beulke et al. (1999) measured simulated Study no.

17 Frequency (%) Comparison at 50% residue in field study 35 Under-estimation Over-estimation Ratio observed / simulated Ratio simulated / observed

18 Aquatic exposure to pesticides STP discharge Spray drift Atmospheric deposition (wet & dry) Drain discharge Runoff Erosion Volatilisation Upstream input Hydrolysis Photolysis Degradation Dilution S-D Biota uptake & absorption S-D Groundwater S-D Degradation Central Science Laboratory, 2005

19 FOCUS Surface Water Scenarios Exposure Estimate Step 1: Initial estimate of aquatic exposure Step 2: Refined estimate of aquatic exposure Step 3: Deterministic estimate of aquatic exposure across a maximum range of ten scenarios Actual Range of Aquatic Exposure: Concentration Range

20 Validation status of FOCUS Surface Water Scenarios FOCUS SWS Group: a mechanism for assessing PECs in surface water and sediment with an acceptable degree of uncertainty Virtual scenarios but with real field basis Adjustments to data to make the scenarios broadly applicable mean none can be experimentally validated

21 FOCUS Step 1 FOCUS Step 2 FOCUS Step 3 (D2) UK Step 1 UKprob. DenchDry UKprob DenchMedium UKprob DenchWet WEBFRAM DenchDry WEBFRAM DenchMedium WEBFRAM DenchWet Brimstone Farm measured All European measured Predicted concentration (microg/l) Autumn application of isoproturon Mean DT50 ca. 20 d; mean Koc ca. 120 ml/g Measured data: n=

22 FOCUS exposure assessment: spray drift Overall 90 th percentile drift deposition profiles (e.g. Rautmann): Represented in crop regression tables Takes into account buffer and water body morphology Courtesy: Neil Mackay Air-curtain orchard sprayer uses multiple crossflow fans to disperse pesticide to apple trees. Under some conditions, the smoother, gentler flow of air can reduce spray drift by half.

23 Field validation of Ganzelmeier drift estimates Italian orchards Capri et al. ET&C 2005

24 Mechanistic drift simulation: the Silsoe spray drift model Courtesy Clare Butler-Ellis, TAG, UK Flexibility to adapt to spray conditions Original Silsoe spray drift model developed in 1980s and 1990s Droplet tracking model Ballistic near nozzle; random walk further downwind Now updated Includes multiple nozzles and a moving sprayer User-friendly interface for inputs

25 Airborne spray, ml/m2 Ground deposit, ml/m Ground deposit Distance downwind, m Comparison with experimental data Airborne spray 2 m downwind Standard flat fan nozzle Height above ground, m

26 Airborne spray, ml/m2 Ground deposit, ml/m2 Ground deposit Distance downwind, m Airborne spray 2 m downwind 25 XR nozzle Height above ground, m

27 Ground depost, % applied dose Rautmann et al. (2001) ground deposit data Distance downwind, m Rautmann mean values - error bars show 95th%ile

28 Ground depost, % applied dose Model simulation: 04 nozzle, 6 km/h speed, 0.5 m boom height, 2.0 m/s wind speed Distance downwind, m Rautmann mean values - error bars show 95th%ile Model simulation - typical Rautmann conditions

29 Ground depost, % applied dose Typical UK scenario: 03 nozzle, 12 km/h speed, 0.7 m boom height, 2 m/s wind speed Distance downwind, m Rautmann mean values - error bars show 95th%ile Model simulation - typical Rautmann conditions Model simulation - typical UK scenario

30 FOCUS exposure assessment Run-off Run-off is considered in four scenarios (1 NEU, 3 SEU) alongside drift Represented by PRZM (simplified mechanistic modelling) Very event driven Mitigation as recommended by FOCUS L&M) Severe rilling on a hillslope, UK South Downs

UK monitoring campaign for surface runoff Clusters of monitoring sites North Yorkshire (12 sites) Herefordshire (3 sites) Devon (4 sites) Range of winter and spring crops Sites selected as vulnerable

31 UK monitoring campaign for surface runoff Clusters of monitoring sites North Yorkshire (12 sites) Herefordshire (3 sites) Devon (4 sites) Range of winter and spring crops Sites selected as vulnerable to runoff and with direct connection to water Event-driven monitoring Grab samples at point of entry to water Multi-residue analysis Broad-brush campaign to assess frequency and magnitude of transfer to water

32 Pesticides in surface runoff at three sites in south-west England Site Date Compound Measured (µg/l) Simulated with PRZM (µg/l) Simulated with RZWQM (µg/l) HFD1 05/09/2008 Azoxystrobin HFD2 19/01/2009 Azoxystrobin <0.02 <0.02 <0.02 HFD2 19/01/2008 Chlorothalonil <0.02 < HFD1 05/09/2008 Isoproturon <0.02 <0.02 <0.02 HFD1 05/09/2008 Epoxiconazole HFD2 19/01/2009 Epoxiconazole HFD3 09/07/2008 Pirimicarb HFD1 05/09/2008 Tebuconazole HFD2 19/01/2008 Tebuconazole 0.02 <

33 Simulating the reduction in pesticide load as runoff passes through a vegetated buffer VFSMOD appears promising Simulating hydrologic response of buffer at time of event Infiltration, sedimentation from: Sabbagh et al.. (2009)

34 FOCUS exposure assessment Drainage Drainage is considered in six scenarios (5 NEU, 1 SEU) alongside drift Represented in MACRO (mechanistic modelling) Simulated losses generally greater for autumn applications, slightly less for spring, lowest for summer Mitigation options limited Field drain outlet

35 Many evaluation studies for MACRO vs. field drainage data - see Köhne et al. (2009) for review Isoproturon (mg L -1 ) 6 5 Measured Simulated Time from application (days from 13/11/90) Autumn application of isoproturon to winter cereals in northern England

36 Collation of European drainage studies No. of records Country No. of studies Max. concentration Seasonal loss United Kingdom Germany Denmark Netherlands France Italy Norway Total

37 Field DT50 (d) A representative range of compounds Circles are compounds with monitoring 10 Squares are FOCUS test compounds Koc (L kg -1 )

38 Load (% of applied) First-level comparison between FOCUS simulations and field drainage studies Concentration ( mg L -1 ) Seasonal loss in drainflow Maximum concentration in drainflow Literature FOCUS 0.01 Literature FOCUS

39 Concentration (µg/l) ng/l Exposure assessment outside of the FOCUS SW framework: FOCUS Landscape and Mitigation /01/ /06/ /10/ /03/ /07/ /12/ /04/ /09/2002 main stream (330 m) main stream (625 m) main stream (980 m) Main stream (1700 m) main stream (2090 m) main stream (2196 m) main stream (2647 m) main stream (2979 m) Inclusion of mitigation Catchment modelling Probabilistic approaches Use of monitoring data Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Date

Catchment-level simulation: all land contributing water to a given point Considers: Inter-relationship between agriculture and water Interactions between environmental compartments

40 Catchment-level simulation: all land contributing water to a given point Considers: Inter-relationship between agriculture and water Interactions between environmental compartments Land use and application patterns Maps out spatial and temporal variation in exposure When levels of concern are exceeded: Where? How often? For how long? SM Reaney Durham University

41 University of Ghent Coupling of SWAT and RWQM Tributary Input River Stretch Output Q i-1 C i-1 Q i C i +r i V i Q d C d

diuron (mg/l) crop % area pesticide SGBT 10.34 chloridazon CORN 15.

42 diuron (mg/l) crop % area pesticide SGBT chloridazon CORN atrazine WATR 0.03 WWHT isoproturon time (d)

43 µg/l Complexity in estimates of aquatic exposure µg/l High temporal variation Usage Runoff and drainage driven by rainfall events Raises numerous questions for ecotoxicologists and risk assessment See the presentation by Ralf Schulz ELINKstrobin Do wsimulated inter cereals - D2 Ditch patterns of exposure ELINKstrobin match winter cereals - R1 STREAM reality? DAYS DAYS

44 Concentration (microg/l) Peak concentration Peak1:Peak2 ratio Interval between pulses Background concentration 0 Pulse duration Time (d) More systematic comparison of FOCUS simulations vs. monitoring Isoproturon at scales from field to large river vs. D2 and D5

45 Mean peak duration [days] Duration of pulses of exposure 25 Field Data D2 FOCUS-SW D5 FOCUS-SW Cockle Park Cherwell culvert Cherwell culvert Cherwell flume Brimstone Plot 5 Brimstone Plot 9 Brimstone Plot 15 Brimstone Plot 20 Brimstone 1994 Plot 5 Brimstone 1994 Plot 9 Brimstone 1994 Plot 15 Brimstone 1994 Plot 20 Brimstone 1995 Plot 5 Brimstone 1995 Plot 9 Brimstone 1995 Plot 15 Brimstone 1995 Plot 20 Wytham 1994 Rosemaund Site 0 Rosemaund Site 0 Rosemaund Site 5 River Thames River Cherwell

46 Mean peak interval [days] Interval between peaks 25 Field Data D2 FOCUS-SW D5 FOCUS-SW Cockle Park Cherwell culvert Cherwell culvert Cherwell flume Brimstone Plot 5 Brimstone Plot 9 Brimstone Plot 15 Brimstone Plot 20 Brimstone 1994 Plot 5 Brimstone 1994 Plot 9 Brimstone 1994 Plot 15 Brimstone 1994 Plot 20 Brimstone 1995 Plot 5 Brimstone 1995 Plot 9 Brimstone 1995 Plot 15 Brimstone 1995 Plot 20 Wytham 1994 Rosemaund Site 0 Rosemaund Site 0 Rosemaund Site 5 River Thames 1995/96 River Thames 1996/97 River Thames 1997/98 River Thames 1998/99 River Thames 1999/00 River Thames 2000/01 River Thames 2001/02 River Thames 2002/03 River Thames 2003/04 River Thames 2004/05 River Thames 2005/06 River Cherwell 1996/97 River Cherwell 1997/98 River Cherwell 1998/99 River Cherwell 1999/00 River Cherwell 2000/01 River Cherwell 2001/02 River Cherwell 2002/03 River Cherwell 2003/04 River Cherwell 2004/05

47 DT50 end [days] Decrease in peak concentration for successive peaks Field Data D2 FOCUS-SW D5 FOCUS-SW Cockle Park Cherwell culvert Cherwell culvert Cherwell flume Brimstone Plot 5 Brimstone Plot 9 Brimstone Plot 15 Brimstone Plot 20 Brimstone 1994 Plot 5 Brimstone 1994 Plot 9 Brimstone 1994 Plot 15 Brimstone 1994 Plot 20 Brimstone 1995 Plot 5 Brimstone 1995 Plot 9 Brimstone 1995 Plot 15 Brimstone 1995 Plot 20 Wytham 1994 Rosemaund Site 0 Rosemaund Site 0 Rosemaund Site 5 River Thames 1995/96 River Thames 1996/97 River Thames 1997/98 River Thames 1998/99 River Thames 1999/00 River Thames 2000/01 River Thames 2001/02 River Thames 2002/03 River Thames 2003/04 River Thames 2004/05 River Thames 2005/06 River Cherwell 1996/97 River Cherwell 1997/98 River Cherwell 1998/99 River Cherwell 1999/00 River Cherwell 2000/01

48 EFSA validation activities General principle of validating approaches as guidance is issued/revised Evaluation of bird and mammal GD Holistic validation project for TERs under aquatic GD Presentation by Karin Nienstedt

49 Conclusion Exposure assessment for regulation has to be generalised Individual estimates cannot be validated Overall confidence in giving conservative estimate within tiered scheme Exposure modelling approaches continue to develop Mechanistic spray drift Dynamic runoff including fate in vegetated buffers Catchment-scale simulation

National exposure assessment for the authorization of plant protection products (PPP) in Austria:

National exposure assessment for the authorization of plant protection products (PPP) in Austria: Calculation of predicted environmental concentrations (PEC) in soil, groundwater, surface water, sediment