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<p>Office of Pesticide Programs (OPP) Conference Room 1126<br />
1801 S. Bell St. - Crystal Mall 2<br />
Arlington, VA  22202</p>

<p><strong>On this Page</strong></p>

<ul>
<li><a href="#attendees">Attendees</a></li>
<li><a href="#welcome">Welcome and Introductions</a></li>
<li><a href="#brief">Brief Updates</a></li>
<li><a href="#presentations">Major Presentations</a></li>
<li><a href="#wrap">Wrap up</a></li>
</ul>

<hr />

<h2 id="attendees">Attendees</h2>

<table class="table zebra" summary="Attendees">
<thead>
<tr><th scope="col">Name</th><th scope="col">Association</th></tr>
</thead>
<tbody>
<tr><td>Marietta Echeverria</td><td>		OPP/EFED</td></tr>
<tr><td>Ron Parker </td><td>			OPP/EFED</td></tr>
<tr><td>Betsy Behl</td><td>			OPP/EFED</td></tr>
<tr><td>Mark Corbin	</td><td>		OPP/EFED</td></tr>
<tr><td>Dirk Young	</td><td>		OPP/EFED</td></tr>
<tr><td>Mohamed Ruhman</td><td>		OPP/EFED</td></tr>
<tr><td>Nelson Thurman </td><td>		OPP/EFED</td></tr>
<tr><td>Lucy Shanaman</td><td>			OPP/EFED</td></tr>
<tr><td>Norm Birchfield</td><td>		OPP/EFED</td></tr>
<tr><td>Faruque Kahn</td><td>			OPP/EFED</td></tr>
<tr><td>Cheryl Sutton	</td><td>		OPP/EFED</td></tr>
<tr><td>Keara Moore	</td><td>		OPP/EFED</td></tr>
<tr><td>Subijoy Dutta	</td><td>		OPP/EFED</td></tr>
<tr><td>Holly Galavotti</td><td>			OPP/EFED</td></tr>
<tr><td>Lisa Eisenhauer</td><td>			OPP/EFED</td></tr>
<tr><td>Kathy Carr	</td><td>		Monsanto</td></tr>
<tr><td>Russell Jones 	</td><td>		Bayer CropScience</td></tr>
<tr><td>Nasser Assaff</td><td>			Valent</td></tr>
<tr><td>Paul Hendley 	</td><td>		Syngenta</td></tr>
<tr><td>Scott Jackson </td><td>			BASF</td></tr>
<tr><td>Wenlin Chen	</td><td>		Syngenta</td></tr>
<tr><td>Greg Leyes	</td><td>		ISKB</td></tr>
<tr><td>Mark Cheplick </td><td>			Waterborne Environmental</td></tr>
<tr><td>Amy Ritter	</td><td>		Waterborne Environmental</td></tr>
<tr><td>Chris Holmes	</td><td>		Waterborne Environmental</td></tr>
<tr><td>Cathleen Hapeman	</td><td>	USDA/ARS</td></tr>
<tr><td>Qingli Ma	</td><td>		Environment and Turf Services</td></tr>
<tr><td>LaJan Barnes	</td><td>		Environment and Turf Services</td></tr>
<tr><td>T. S. Ramanaryananan</td><td>		Bayer CropScience</td></tr>
<tr><td>Aldos Barefoot</td><td>			Dupont</td></tr>
<tr><td>Bruce H Stanley</td><td>		Dupont</td></tr>
<tr><td>Michelle M Thomson	</td><td>	Dupont</td></tr>
<tr><td>Adrian Wadley</td><td>			Stone Environmental</td></tr>
<tr><td>Cecil Dharmasri</td><td>		Syngenta</td></tr>
<tr><td>R. Srinivasan	</td><td>		Texas A&M University</td></tr>
<tr><td>Tom Nolan	</td><td>		USGS</td></tr>
<tr><td>Jim Ascough</td><td>			USDA</td></tr>
<tr><td>Jane Tang	</td><td>		Bayer</td></tr>
<tr><td>Kris Garber	</td><td>		Syracuse Research Corporation</td></tr>
<tr><td>Tim Negley	</td><td>		Syracuse Research Corporation</td></tr>
<tr><td>John Troiano	</td><td>		CDPR</td></tr>
<tr><td>Ann Pitchford	</td><td>		EPA/ORD</td></tr>
<tr><td>Elise McCoy	</td><td>		FMC</td></tr>
<tr><td>Kevin Armbrust</td><td>			Mississippi State University</td></tr>
</tbody>
</table>

<p class="pagetop"><a href="#content">Top of page</a></p>
<hr />

<h2 id="welcome">Welcome and Introductions</h2>

<p>The Exposure Modeling Work Group hosts quarterly meetings that are open to the public and provide a forum for cooperative exchange of facts and technical information on technical issues related to pesticide exposure modeling between EFED and stakeholders with similar technical expertise.  Marietta Echeverria (OPP/EFED) chaired the meeting in the capacity of co-chair of the EFED Water Quality Tech Team (WQTT). </p>

<p>This meeting includes presentations which focused on incorporation of geospatial tools and data in pesticide risk assessments.  All FIFRA EMWG agendas, minutes and presentations and be found at: </p>
<p><a href="http://www.epa.gov/oppefed1/models/water/emwg_top.htm"> http://www.epa.gov/oppefed1/models/water/emwg_top.htm</a></p>

<p class="pagetop"><a href="#content">Top of page</a></p>
<hr />

<h2 id="brief">Brief Updates</h2>

<ol>
<li><p><strong>PRZM 3.12.2 Evaluation</strong></p>

<p>Jim Hetrick reported that the QA/QC report for PRZM 3.12.2 is complete and has gone through management review.  Currently, they are addressing comments and providing clarification on issues such as irrigation and volatilization.  The report should be finished soon and will be made available at that time.</p>
</li>

<li><p><strong>EFED's Modeling Scenarios </strong></p>

<p>Mark Corbin reported on EFED's effort to respond to public comments on PRZM surface water runoff modeling scenarios. There has been a delay because EFED is using contractor support to update the scenarios and is now working out the contract for the new fiscal year.  The revised scenarios are expected to be ready by the end of the year.  The new version of PRZM, the new shell, and the updated scenarios will all be implemented at the same time.</p>
</li>

<li><p><strong>Spray Drift Update</strong></p>

<p>Al Barefoot of CLA presented a comparison of measured field data with values predicted by the 2004 beta version of the AGDISP ground model.  AGDISP ground uses many of the same inputs and tools as the aerial model.  Unlike AGDRIFT, it is predictive rather than empirical.  Two sets of field data were considered:  data from a 1993 study by the Spray Drift Task Force and data provided by Tom Wolf.  The comparison showed that AGDISP generally over predicted deposition values, more so for the SDTF data than for Wolf's data.  Possible explanations for the difference are that AGDISP may handle the coarse sprays used by Wolf better than fine sprays emphasized by the SDTF, that the different nozzles used in Wolf's study may be represented better by the model, or that AGDISP may perform better at locations closer to the edge of the field.  AGDISP also predicted higher deposition than AGDRIFT.</p>
</li>
</ol>

<p class="pagetop"><a href="#content">Top of page</a></p>
<hr />


<h2 id="presentations">Major Presentations</h2>

<p>All of the following presentations will be posted on the EMWG website.</p>

<p><strong>Applying Traditional Variance Component Methods For Quantifying and Predicting End-Points Under Spatial Variability (Bruce H. Stanley, Dupont Crop Protection)</strong></p>

<p>Dr. Bruce Stanley of Dupont Crop Protection (Dupont) gave the first presentation at the October 2005 Exposure Modeling Working Group (EMWG) titled "Applying Traditional Variance Component Methods For Quantifying and Predicting End-Points Under Spatial Variability".  Dr. Stanley's talk focused on the utilization of spatial variability to understand uncertainty and variation over a distance in the field of geostatistics. The importance of variograms and correlograms in predictions over geographic space was emphasized.  Geographic areas are almost always broken into discrete units with a clear boundary, such as a ditch, compacted road or stable geopolitical boundary.  Therefore, traditional statistical methods such as variance component analysis lend themselves readily to the summarization and prediction in the presence of spatial variation.</p>

<p>Dr Stanley discussed the importance of including covariates where there are known trends over space.  In addition, he noted that logical weights can be used to account for differing sizes and covariance's can be included for correlated adjacent areas.  Because of the discrete nature of the geographic units, these traditional variance component estimates can be tabled and "rolled-up" when the predictions are needed over a broader geographic area.  Further, because of the discrete geographic nature of the terrain, continuous model-based methods, such as many of the tools in geostatistics may give poor fits (i.e., predictions) due to inappropriate model specification.  The talk explored how traditional statistical methods can be applied to summarize data and make predictions in light of spatial variability.  The discussion and questions after the presentation focused on how to distinguish between variability and uncertainty and the importance of temporal changes in the analysis of variance.</p>

<p><strong>GIS Tool for Associating Estuarine/Marine Habitat with Agricultural Pesticide Uses</strong> (Kris Garber and Tim Negley, Syracuse Research Corporation)</p>
<p>Kris Garber of Syracuse Research Corporation (SRC) presented a new GIS tool, called Crop Mapping and Assessment Program (CMAP) 1.0 (beta version) for associating estuarine/marine habitat with agricultural pesticide applications. This tool was developed due to the need to define when pesticide uses should result in the assessment of risks to estuarine/marine habitats.  CMAP 1.0 includes two main sources of spatial data: NOAA National Estuary Inventory which defines the natural boundaries of the estuarine and costal drainage areas and the USDA/NASS Census of Agriculture 1997 and 2002 crop harvest data which quantifies crop yields on a county level. Using a MS Access database, the tool determines the intersection of the fraction of the area of a county that is within an estuarine/marine area and the crops harvested in each county. The output includes a U.S. (continental states) map and a calculated percent crop harvested in the estuarine/marine habitat.  CMAP could eventually be used by risk assessors to determine if applications of a pesticide to crops would potentially overlap with estuarine/marine areas, indicating a need to assess risks of a pesticide of concern to these habitats.  SRC is interested in distributing the tool to those participating in beta testing.  </p>

<p><strong>National Geo-spatial Data Policy (Kevin Kirby, EPA/OEI)</strong></p>

<p>Kevin Kirby of the Office of Environmental Information of the US Environmental Protection Agency presented an overview of EPA's new, integrated approach to geospatial policy, architecture, and investment.  The new National Geospatial Data Policy addresses the entire data life cycle from planning through collecting, processing, storing, access, maintenance and retirement.  It moves away from the previous policy's impractical and unnecessary 25-meter accuracy definition and, instead, allows for multiple accuracy tiers in order to support the varying program-specific geospatial data priorities; for example, Superfund needs may range from sub meter to 5-meter accuracy while environmental assessments of national scope may only need 1000-meter accuracy.  Default accuracy is 150 meters.  The new Geospatial Data Policy was released in August 2005, and is currently in its comment period with the expectation that it will be finalized by December 2005.  EPA is coordinating at the standards level with other federal agencies through the Federal Geographic Data Committee (FGDC) and with Europe and other countries through the International Organization for Standardization technical committee for geographical information/geomatics (ISO/TC 211).  Key stakeholder involvement in implementation of this Geospatial Policy will be essential.  Contact Kevin Kirby (kirby.kevin@epa.gov) for a copy of the Policy.</p>

<p><strong>The MARIA Spatial Water Quality Modeling Framework (James Ascough, USDA-ARS-NPA)</strong></p>

<p>The major products of the research facility in Ft. Collins include the Root Zone Water Quality Model (RZWQMP), Management of Agricultural Resources through Integrated Assessment and GIS (MARIA), Integrated Farm and Ranch Management (iFARM), Special Landscape Agricultural Model (SLAM), and the Object Modeling Systems (OMS).</p>

<p>The MARIA is a water quality tool that has been developed to predict space-time planning scenarios across spatially variable agricultural landscapes. The tool runs under the ArcGIS 9 environment, and consists of a multi-functional system for agricultural production and water quality simulation modeling; and spatial data storage, analysis, and display.  The major features of MARIA include the following:</p>

	<ul>
	<li><p>Offers a spatial framework for integrating a complex, agricultural system 	water quality model (modified USDA-ARS RZWQM)</p></li>
	<li><p>Provides interaction between simulated land areas via overland runoff and 	run on</p></li>
	<li><p>Provides increased interface sophistication necessary for distributed 	hydrologic modeling</p></li>
	<li><p>Works in a spatially distributive mode.</p></li>
	<li><p>Predicts space-time planning scenarios across spatially variable 	agricultural landscapes.  </p></li>
	<li><p>Significant progress in component model - soil erosion component, transport, 	and nutrient cycling</p></li>
	<li><p>Uses Tiger 2003 and other available data</p></li>
	<li><p>Works in a ArcGIS 9 environment and presently calls for a manually drawn 	vector in the GIS MAP</p></li>
	<li><p>Has a file builder wizard for guidance in creating input files</p></li>
	</ul>

<p>MARIA is applied to predict crop yield, nitrate (N) concentration, drainage volume, and nitrate loading.  Predicted results showed improved model performance considering spatially variable soil properties and overland flow generation within study plots in northeastern Iowa as compared to point-based model, such as RGWQM. Some of the assumptions used by the model include homogeneous soils with no flow routing.  Future work involves developing a Java-based object modeling framework which would allow code reuse and sharing.  Enhancing the Object Modeling System (OMS) for building new models for Field, Farm, and Watershed Scales is also in their agenda. For further information see 
<a href="http://www.epa.gov/epahome/exitepa.htm" title="EPA's External Link Disclaimer"><img src="http://www.epa.gov/epafiles/images/epafiles_misc_exitepadisc.gif" width="87" height="13" alt="Exit EPA Disclaimer" /></a>
<a href="http://www.ars.usda.gov/npa/ftcollins/gpsr">www.ars.usda.gov/npa/ftcollins/gpsr</a></p>

<p><strong>Utility of USGS Water Use Data on Drinking Water Exposure Assessment (T. S. Ramanarayanan, Bayer CropScience)</strong></p> 

<p>TS Ramanarayanan of Bayer Crop Science discussed the USGS' water use information program and related it to EFED's selection of sites for surface water scenarios.  Every five years, the USGS gathers source level data about water use nationwide.  Mr. Ramanarayanan presented maps displaying the distribution of drinking water from surface and groundwater sources, each of which provides about half of the country's drinking water.  The sites for EFED's surface water scenarios are chosen based on vulnerability, rather than water use.  A map overlaying the location of the standard scenarios with the surface water use distribution demonstrated that some scenarios are located in areas that are not significant sources of drinking water, including several Florida, Mississippi, Georgia, and Minnesota scenarios.  A more detailed analysis of Florida drinking water sources was presented indicating that less than 9% of the Florida population depends on surface water.  It was recommended that for drinking water assessments, surface water use distribution should be taken into account in selecting scenario locations.  Audience comments pointed out that the goal of EFED's assessments is to be protective even for small populations. </p>

<p><strong>Framework for Spatially Explicit Risk Assessments (Nelson Thurman and Mark Corbin, EPA/OPP)</strong></p>

<p>Nelson Thurman and Mark Corbin presented EFED's progress in making risk assessments increasingly spatially-explicit.  The presentation focused on the definition of "spatially-explicit" as it applies to risk assessments, fitting the use of spatially-explicit data and processes in the context of the Agency's guidelines for conducting Risk Assessments, and examples of works in progress which illustrate the use of spatially-explicit data and processes.  As defined at the 2003 Society for Risk Analysis workshop, spatially-explicit risk assessments consider the relative spatial distributions of the stressor (pesticide) and the receptor (non-target organism).  Assumptions of "equal and random" access of receptor to the stressor are broken down and explicit consideration is given to the heterogeneous distributions of receptors and contaminants.  In the context of the Agency's guidelines for conducting Risk Assessments, spatially-explicit data and approaches can be incorporated into the Problem Formulation phase which includes the development of the conceptual model, as well as the Analysis of Exposure and Effects and Risk Characterization phases of the Risk Assessment process.  In particular, while considering a screening approach to risk assessments if estimated exposure exceeds a level of concern it may be appropriate to consider the distribution in the stressor and receptor range in order to delineate the extent of the area to where the exceedance is likely to be limited.  </p>

<p>Nelson and Mark presented examples of spatially-explicit risk assessments currently underway in the division including the Pacific Northwest salmonid endangered species assessments and the N-methyl carbamate cumulative assessment.  For endangered species assessments, spatial data and analyses are being used to define the potential "Action Area".  In order to define the action area, the product label identifying potential use sites and Ag census crop location data are used to define the extent of the potential stressor range while delineated evolutionarily significant units (ESUs) and occupied stream segments are used to define the receptor range.  The intersection of the stressor and receptor ranges is the resultant action area.  For the N-methyl carbamate cumulative assessment, spatially-explicit pesticide use data, site vulnerability, and location of drinking water intakes are being used to identify scenarios of likely high-end exposure.   Monitoring data in Florida illustrated that detections of N-methyl carbamates in groundwater followed distinct spatial trends.  Specifically, detections were correlated to citrus use sites along the central ridge which is characterized by high potential for leaching soils.  These types of spatial analyses help identify characteristics that may indicate high exposure potential in other areas of the country that may not have robust monitoring data.</p>  

<p><strong>PLUS: Geospatial Leaching Assessment Tool (Mark Cheplick, Waterborne Environmental)</strong></p>

<p>Mark Cheplick gave this presentation (Scott Jackson and Paul Hendley are coauthors).  PLUS (<strong>P</strong>RZM-3 <strong>L</strong>eaching <strong>US</strong>) is a tool to geographically rank sites in the US for leaching potential for a pesticide of interest.  PLUS was presented as a possible Tier II national leaching assessment tool.  </p>

<p>Cheplick noted that PLUS has the following benefits:</p>

	<ol>
	<li><p>Provides soil vulnerability rankings with regards to leaching of a specific 	pesticide.</p></li>
	<li><p>Provides a spatial aspect to leaching vulnerability assessment.</p></li>
	<li><p>Allows for a probabilistic aspect to ground water exposure assessment.</p>	</li>
	</ol>

<p>PLUS allows selection of state, MLRA, soils, crops, and realistic weather scenarios (including irrigation water inputs, as appropriate).    With PLUS one can also quickly run PRZM for the pesticide of interest, as a benchmark for comparison, the <strong>F</strong>IFRA <strong>E</strong>xposure <strong>M</strong>odel <strong>V</strong>alidation <strong>T</strong>ask <strong>F</strong>orce (FEMVTF) prospective groundwater scenarios (based on some older Prospective Ground-Water Monitoring studies conducted for submission to EPA or other similarly detailed field-scale studies of pesticide fate).  The benchmark FEMVTF sites can be run either with their own-site specific weather or with weather from a site representative of the geographic region of interest for the pesticide being evaluated.  Specifics of how PRZM is calibrated to the field results at the FEMVTF sites were not discussed in this particular presentation.</p>

<p>User inputs for PLUS are fairly simple, the shell preselecting most of the inputs for the models.  For example:</p>

	<ol>
	<li><p>Pesticide Specific properties.  </p>
		<ol style="list-style-type:lower-alpha">
		<li><p>adsorption value (Koc/Kd) </p></li>
		<li><p>Half-life values by depth in the vadose zone (up to 12 feet?) and for 		ground water.</p></li>
		<li><p>Application rate and type.</p></li>
		</ol>
	<p>PLUS couples a ground-water dilution model (ADAM) to PRZM.  Inputs include:</p>
	</li>
	<li><p>Aquifer properties</p>
	<p>Screen length - depth of dilution zone in the aquifer</p>
	<p>Hydraulic conductivity</p>
	<p>Head gradient</p>
	<p>Porosity </p>
	<p>Bulk Density in the aquifer material</p>
	<p>Organic carbon content - for water and the bearing material</p>
	<p>Aquifer Mix - how much mixing is there between old residue and new flush of 	mass?</p>
	</li>
	</ol>

<p>The PLUS output automatically includes daily pesticide concentrations, mean annual shallow ground water concentrations, and 36-year mean concentrations by site (soil series).</p>

<p>PLUS has many embedded simplifying assumptions which enable automated modeling of a large number of sites.  Among these are assuming a standard sandy soil type below a specified depth, the same standard depth to ground water at all sites (i.e., 12 feet), selecting a standard set of aquifer characteristics for all sites run, and similar behavior of the pesticide at all sites (adsorption coefficients and half-lives are not varied by site). The only input variables that change by geographic location are topsoil characteristics and weather / irrigation.   Consequently, PLUS does not technically provide geographically specific estimates of the extent of pesticide leaching to ground water.  However, the authors propose that PLUS output does provide a useful generalized ranking of sites for pesticide leaching.</p>

<p><strong>National Hydrography Dataset (NHDplus) (Cindy McKay, Horizon Systems Corporation) </strong></p>

<p>Cindy McKay of Horizon Systems Corporation (HSC) presented NHD-Plus, a "reach file on steroids" developed in partnership between EPA OW, HSC, USGS WRD, and RTI Intl. that had EMWG members waving their credit cards.  NHD-Plus is the 1:100K National Hydrography Dataset (NHD) with enhanced analysis, navigation, and display functionality, including elevation-based catchments, catchment characteristics, flow direction grids, flow accumulation grids, flow line min/max elevations and slopes, cumulative drainage area characteristics, and flow volume and velocity estimates for every flow line in a stream network.  This analysis tool will integrate with nearly any landscape dataset and is designed for compatibility with a variety of analysis and modeling software.  Cindy gave some examples to demonstrate the power of NHD-Plus, such as adding dynamic definition to watersheds, overcoming quad density artifacts, and defining drainage areas with catchments, which leads to simple calculation and display of drainage area characteristics.  HUC-2 data are currently built in for the Pacific Northwest region and are coming soon for other regions of the U.S.  NHD-Plus will be available in a few months on the EPA OW website and the Reach Address Database.  Cindy has an initial distribution available from HSC upon request at 
<a href="http://www.epa.gov/epahome/exitepa.htm" title="EPA's External Link Disclaimer"><img src="http://www.epa.gov/epafiles/images/epafiles_misc_exitepadisc.gif" width="87" height="13" alt="Exit EPA Disclaimer" /></a>
<a href="mailto:LDM@horizon-systems.com">LDM@horizon-systems.com</a> .</p>

<p><strong>National Pyrethroid Aquatic Exposure Analysis; Estimation of Drift and Erosion Potential at the Watershed Level (Christopher Holmes, Waterborne Environmental)</strong></p>

<p>This presentation described a national aquatic exposure analysis undertaken to characterize watersheds across the conterminous US for relative risk to pyrethroid exposure via spray drift and erosion. Results were generated for each of approximately 62,000 watersheds (USGS Enhanced River Reach File dataset (ERF1_V2)) to create a nationwide distribution ranking potential aquatic exposure via spray drift and erosion.  Spatial proximity of agriculture to surface water was used as an indicator of potential exposure from agricultural spray drift. Surface water data from the National Hydrography Dataset (NHD) was combined with the National Land Cover Dataset (NLCD) to determine the spatial location of agricultural land proximate to surface water. These data were linked with 2002 Census of Agriculture to assess the likely occurrence of specific crops within marginal areas. Watersheds were also ranked based on potential edge of field pyrethroid transport via erosion. Pesticide Root Zone Model (PRZM) runs were made for each combination of soil, weather and crop, resulting in several hundred thousand simulations. The individual results were weighted (based upon area of occurrence) and then combined to provide an overall potential pyrethroid edge of field erosive loss per watershed. The relative sensitivity of pyrethroid exposure to both spray drift and erosion entry potential was examined. The linked EPA PRZM-EXAMS models were used to estimate the relative contribution of drift and runoff to pyrethroid sediment loads across representative crop-regions. Over 40,000 simulations were run for various pyrethroid/crop/drift/weather /soil/organic matter/slope length (LS) factor combinations under local weather conditions. Results of the proximity and erosion analyses, combined with estimated relative drift and erosion vulnerability, will be used to develop an optimum strategy for more detailed regional and crop-specific analyses of potential pyrethroid exposures in US water bodies.</p>

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<h2 id="wrap">Wrap up</h2>

<p><strong>Continuing Actions</strong></p>

<p>EFED continues to addressing PRZM Volatization routine and PRZM Temperature routines in the established QA/QC process.</p>

<p>Next EMWG Meeting to focus on model version control and quality assurance processes in exposure models</p>

<p><strong>New Actions</strong></p>

<p><strong>Action</strong>:  CLA to share detailed output from the review shown in Al Barefoot's presentation</p>
<p><strong>Action</strong>: Consider incorporating a detailed review of the AgDISP ground model at future EMWG meeting.</p>
<p><strong>Action</strong>:  Next meeting will be January 24<sup>th</sup> in room 1126 EPA offices, Washington DC</p>

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