Improved Characterization of theImprovedCharacterizationoftheTemporal and Spatial Variability ofPotential Surface Water Drinking WaterE b Ui E i tl dExposurebyUsingEnvironmentalandHistoric Monitoring DatabasesPaul Hendley, Wenlin Chen, Robert Joseph,Ron Williams, Chris Harbourt & Paul MillerSyngenta Crop Protection LLC, Product Safety, and Waterborne Environmental Inc.Outline●Atrazine Historic Monitoring Database●Statistical Analysis of Monitoring Data●Understanding and Classifying Community Water Systems (CWS)●Approaches to Designing Sampling StrategiesApproachestoDesigningSamplingStrategies●Conclusions2Take Home Messages●Robust monitoring datasets definepotential extreme high centiles,chemograph patterns and year-to-year variation●Surface Water CWS are highly individual but can be logically grouped●Monitoringstudysamplingdesign must balance frequency/uncertaintyy pg g q y y- Various approaches exist to supplement moderate frequencymonitoring data sets for different risk assessment needs●Many conclusions from atrazine studies are potentially applicable togeneral agrochemical monitoring study design3Th At i W t M it iTheAtrazineWaterMonitoringDatabase is Extensive>340,500Surface waterl *The Atrazine Water Monitoring Database is Extensive*Does not include over206 000 SDWA GW samples>200,000surface watersamples>140,000*samplessamples*206,000SDWAGWsamplessamples14,875samples15,100samples57,600samples119,200samples6 CWS>500raw6 CWS>500finished~55,000finishedsamples48,530finished samplesrawsamplessamplesp46,500raw samples7-day frequencyin season5* Includes around 3260 samples of finished water from PDPinseasonDatabase AnalysesRich Temporal and Spatial Data Permit Detailed Analysis ofCompound Specific Trends Distributions and BehaviorCompoundSpecificTrends,DistributionsandBehaviorDatabase Size vs. ConfidenceConfidence Level (100 q)Centile (100 p)90.0% 95.0% 97.5% 99.0% 99.9%80.0%16 32 64 161 160990.0% 22 45 91 230 230295.0% 29 59 119 299 299597.5% 36 72 146 368 368899.0% 44 90 182 459 460399.9% 66 135 273 688 69057AMP/VMP dataprovide high confidence of highest centileatrazine concentrationsin raw & finished waterWaterSourceTypeWaterTypeCWSSampledDaysSampledCentile (ppb) 95% Confidence Interval (ppb)P95 P99 P99.9 P95 P99 P99.9AllRaw226 46264 4.50 9.76 27.524.18, 4.85 8.80 , 11.31 24.67, 35.37Finished201 464572.60 6.67 18.382.40, 2.80 5.84 , 7.73 16.00, 28.672232294161 614 11 66April toJulyRaw223229416.80 16.00 36.356.19,7.614.93,17.731.25,56.76Finished197 230843.95 11.00 31.553.56, 4.42 9.27,12.91 24.18, 44.84R118257914 6010 0028 204 03 5 168 80 12 9924 61 43 50StaticRaw118257914.6010.0028.204.03,5.168.80,12.9924.61,43.50Finished118 287302.80 7.50 20.352.47, 3.40 5.92,8.73 15.22, 42.11Mi dRaw14 30724.11 8.07 19.483.93, 5.50 6.32,18.85 15.00, 30.43MixedFinished14 33052.64 4.69 17.642.35, 3.20 4.00,7.07 9.16, 63.00FlowingRaw56 104742.88 8.49 23.632.67, 3.24 7.71,9.41 21.00, 29.10Finished56 105722.306.0017.742 00 2 605 33 6 6015 81 22 468Finished2.306.0017.742.00,2.605.33,6.6015.81,22.46Mosquin and Whitmore, 2010 (new enhanced version 2011)U d t di d Diff ti ti C itUnderstanding andDifferentiatingCommunityWater SystemsNew Database Characterizes CWS & Associated Watersheds●215 AMP/VMP CWS documented- Source water types, off-channel reservoirs etc-375 raw water intakes–324 take surface water for processing375rawwaterintakes324takesurfacewaterforprocessing- 224 static – 100 flowing- 188 static intakes are “on-channel” - 36 “off channel” reservoirs●Watersheds characterized by GISWatershedscharacterizedbyGIS- Extent, soils, hydrology, land use, curve number, corn sorghumplanting, estimates of atrazine use,time of concentration and metricsforpotential atrazine vulnerabilityfor surface water runoffy●Database includes CWS specific characteristics including atrazinemonitoring summaries●Allows categorization of CWS and subsequent analysis of categoryproperties10Many CWS Have Multiple Intakes – Some Are For Storage11CWS Categorization Example usingSource Type and Watershed Area188100143533116123312CWS Size Classes – Sizes and Finished Drinking Water AtrazineResidue CharacterizationFinishedWaterAtrazineConcentration(ug/L)AMPTypeSizeCategorySizeRangeTimeofConcentrationCount Min Median 90thPctile95thPctileMeanCategoryRangeRange(hrs)PctileFlowing Small 0‐50 0.6‐14.1 16<LOD 0.33 2.51 3.84 1.13Flowing Medium 50‐800 12.5‐127.1 31<LOD 0.24 1.42 2.40 0.64Flowing Large >800 25.4‐248.1 53<LOD 0.34 1.86 3.03 0.84Static Small 0‐50 0.0‐19.0 143<LOD 0.41 2.10 3.20 0.93Static Medium 50‐1000 11.0‐41.2 33<LOD 0.43 1.83 2.50 0.82Static Large >1000 50.4‐248.1 12<LOD 0.60 1.53 2.17 0.79●Chemograph shapes will differ-Statics/Flowing-Small/Large watersheds●Therefore optimal monitoring sampling strategies can differ13B l i S li F dBalancingSamplingFrequency andUncertainty in Monitoring Study DesignFactors Influencing Study Design●Endpoint of Interest- Duration, magnitude●Anticipated exposure patterns- Transport routesAgrochemical uses timings and locations-Agrochemicaluses,timingsandlocations- Water body types- Chemograph shapes and return frequenciesM d li t-Modeling outcomes●Cost– Sampling and Analysis●Availability of relevant EXISTING monitoring data-Can they address study objectives?NAWQA States earlier company programs15-NAWQA,States,earliercompanyprograms.....Study Design Options●Conduct new monitoring study at (current) desired frequency, # locations●Supplement existing data of lower-than-desired frequencySupplementexistingdataoflowerdesiredfrequency- Tiered approach -increasing realism -1. Apply conservative bias factors reflecting sample frequency uncertaintyM k ti ti b t b f “ i d” t-Make conservative assumptions aboutnumbers of“missed”events2. Use physically based model that conservatively estimates “missed” eventswhen a rainfall event happened in the watershed16Bias Factor Approach(see Wenlin Chen/Paul Mosquin presentation)DevelopingBias FactorsDevelop “daily”estimatedconcentrationsThen generate“simulated”4concentrationsby interpolation.Generate 7“sets”ofThree daily / near-daily data sets used to generate bias factors- 34 Atrazine eco monitoring sites (1-2 yrs data)1 large CWS on Missouri river (7 yrs data)simulated4,14, 28 dayRollingAverages fromGenerate7setsofsimulated 7-day samplingregimes-1largeCWSonMissouririver(7yrsdata)- 4 watersheds in Ohio (Heideleberg/NCWQR) (15+ years of data)Generate “true” 4, 14, 28dayRollingAveragesgeach resultingchemographbased on a 7day option95thcentile of error ratios “Bias Factors” forVariousWatershed AreaR i4-day Average 14-day Average 28-day Averageyggfrom Near-daily DatadayoptionAveraging PeriodsRange sq.mi.(site years)Sampling Interval 4 day 7 day 4 day 7 day 4 day 7 day34 AEMP Sites (Non drinking water source) 4.5 – 388 (1-2)1.82 3.04 1.52 2.14 1.39 1.90Rock Creek (Non drinking water source) 34 (15)2.00 2.47 1.50 1.92 1.45 1.82Compare each simulated rollingaverage with true values todevelop a set of 7 error ratios.thHoney Creek (Non drinking water source) 149 (16)1.68 2.73 1.44 1.87 1.35 1.56Maumee River (Potential raw water source) 6330 (16)1.30 1.91 1.19 1.50 1.13 1.34Sandusky River (Potential raw water source) 1251 (15)1.38 1.90 1.22 1.48 1.16 1.36St. Louis CWS (finished water only) 524,000 (7)1.54 1.68 1.12 1.20 1.11 1.20Estimate 95thcentile of pool ofdataAEMP Sites Demonstrate DecreasingBias Factors When Moving from 4- To 14-to 28-day Estimates Based on 7-daySi l t d M it iSimulatedMonitoringMO-01 2007 value3.4 from whitepaper19Error Ratios SimilarFor Raw andFinished WaterFinishedWater●Open circles reflect errorratios > group 95thcentile( f)Bias factors 2 60/2 61 for group of 6 CWS(i.e. biasfactor).●No significant differencesapparent between raw &finished samplesBiasfactors2.60/2.61forgroupof6CWSfinishedsamples.●Bias factors fall largelywithin range for watershedsof comparable area20Overall Variation Reducedin Larger NCWQRWatershed●Open circles reflecterror ratios > group 95thBias factor 2.73 for HoneyCreek (149 sq. mi.)Bias factor 1.91 for MaumeeRiver(6330 sq. mi.)centile (i.e. bias factor).●Shows that the largerwatershed has someyears with very tight( q)yearswithverytightdistributions21Apparent RelationshipBetween Group Bias Factorand Watershed AreasBias factor 3.04 forgroup of 34 AEMP sitesLarge open coloredsquares indicate 95thcentile (bias factor) forcorresponding sitesandWatershedAreas●Open circles reflecterror ratios > group 95thcentile (i e bias factor)Bias factor 2.60 forgroup of 6 CWScentile(i.e.biasfactor).●Supports expectationthat appropriate biasfactors for largerwatersheds will belower than for smallerwatersheds6 Daily 2011 CWSWatershed Areaa(mile2)680540553731161422722109Conservative Bias Factors Characterized For Flowing Water Bodies●Bias factors generated for 110 AEMP/NCWQR site-years and for 6 CWSsites with daily monitoring in 2011.- Systematic sampling is appropriate for AMP dataset- Raw and finished show similar ratios- Bias factors decrease as averaging period increases- Bias factors decrease when watershed area increases- Use bias factors appropriate for CWS based on watershed area:- <800 sq.mi.=2.6 and >800 sq. mi.=2.0 (4 day averages from 7 day samples)- Chemographs should not be extrapolated to valuesgreater than upperp p g ppbounds based upon atrazine database statistics●Bias factors defined for flowing water watershed, data do not addressstatic water sources- Generally agreed static sources are less “flashy” than flowing water- Flowing water bias factors will be conservative for static water bodies●Note that data used to generate biasfactors are mostly from small nond i kitd il CWS d t b i t d23drinking water sources–moredailyCWSdatabeing generatedBias Factors for Creating Synthetic ChemographsA t i ff t d b ti f li d●Assume atrazine runoffevents occurredbetweeneverypair ofsamplingdays- Generates unrealistically large number ofelevated atrazine peaks - distorts peak shapes●Magnitude 3 times higher than maximum of adjacent measured residues- Should consider applying upper bound “cap”based upon atrazine database statistics toavoid simulation of highly improbable values (see example below)Region of highly improbable estimates24Hi h Ti A hPh i ll b dHigherTierApproach–PhysicallybasedPRZM-Hybrid modelingPRZM-Hybrid is Site & Local Event Specific*●Uses PRZM code- Atrazine Efate data●Regional data- Crop reporting district basedatrazine application rate●Watershed & year specificlocal data- SSURGO soils- NEXRAD rainfall- % Crop (NASS CDL(NLCD))●Output- Uses directly available data -No model specific calibration- Conservative edge-of-fieldconcentrations26●Uncertainties definedPRZM-Hybrid Uses Local Soils/Rainfall to Drive RealisticSimulations of Potential Events between Monitored Data.AC

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FlTime(t)lPRZM‐HybInterpolatedLinePRZM‐HybConc.MonitoredSampleConc.FlowSampleInterpolatedLineRainfallEvent27How manyYears should aprogram last??pgYear-to-Year Variation Very Well Defined forAtrazine - Database Invaluable for GeneralMonitoring Design.Year-to-year Variation Very Well Defined for Atrazine●Atrazineisnear-ideale s eacase- High fraction of major crop treated every year at uniform rates- Large historical monitoring database-Wide range of Midwest agronomy/climatesWiderangeofMidwestagronomy/climates●Therefore atrazine data- Provide a direct answer for atrazineProvide optimal basis to assess options for other molecules-Provideoptimalbasistoassessoptionsforothermolecules●Atrazine residue and range of bias factors vary across years- Primary driver is inter-relationshipbetween runoff inducing rainfall event andapplication timingapplicationtiming●When extensive data do not already exist, medium-term prospectivemonitoring may not provide reasonable bounds on year-to-year variability ina reasonableperiod●Therefore, where monitoring is being planned, one approach would bevalidation of PRZM-Hybrid with 2 – 3 years of monitoring data followed byprobabilistic modeling of 30+ years of weather variation and crop rotation29-MODITORINGProbabilisticPRZM-Hybridto Evaluate Year-to-year Variability30Conclusions●Robust monitoring datasets definepotential extreme high centiles,chemograph patterns and year-to-year variation●Surface Water CWS are highly individual but can be logically grouped●Monitoringstudysamplingdesign must balance frequency/uncertaintyy pg g q y y●Various approaches exist to supplement existing moderate frequencymonitoring data to address different risk assessment needs- “Bias factor” approach- Conservative “synthetic chemograph” using worst case assumptions- Watershed specific “hybrid” modeling●Many conclusions from Atrazinestudies are applicable to generalagrochemical monitoring study design31Thank You.