Alternative Tier 2 Process Model Refinement, Implementation and Validation PTAC: March 20, 2014

Research Providers: Dr. Melissa Whitfield Aslund Drs. Eric Lamb & Steve Siciliano Barry Zajdlik, PhD. Candidate Kathryn Bessie Robin Angell & Gladys Stephenson

Agenda 1 Background 2 Review Progress (Year 2) Vision for Aims & Objectives (Year 3)

Background 1 Weathered PHCs in soil = recalcitrant residuals less bioavailable and less toxic; Tier 1 SQGs generally overly conservative Direct eco-contact pathway = F2/F3 driver Provision for Tier 2 Pass/Fail approach Tier 2 Pass leads to closure Tier 2 Fail leads to further remediation or management measures; caveat on land title Develop a Tier 2 process that would allow the derivation of site-specific remedial objectives or clean-up values

Review (FY2; ) Project Goals 2 1 Develop models that could predict “effects” for soils with known properties and contaminant profiles 2 Use the distribution of predicted effects to guide management decisions 3 Establish a national database where ecotoxicological data (response variables) are linked with soil and contaminant characteristics

Review (FY2; ) 2 Feasibility of Four Approaches GeoMean Approach - Geo metric Mean of NOAEC and LOAECs PLS Approach – P artial L east S quares multiple regression analyses with leave one class out cross validation DRAMA Approach – D ata exploration, R eduction A nd M odel A veraging SEM Approach – S tructural E quation M odelling

Review (FY2; ) Conclusions (Year 2) 2 PLS and DRAMA showed that it was possible to link multivariate soil properties to biological responses These soil properties were as, or more, influential in explaining the variability in the response data than the contaminant variables Critical variables include – texture, OM, TOC, pH, Ca, Mg, fertility (P,K,N), EC, MC, SAR Final Report September 2013

Review (FY2; ) Conclusions (Year 2) 2 SEM demonstrated that confirmatory factor analyses to aggregate multiple endpoints into a single latent response variable could then be incorporated into standard non- linear modelling to estimate any ICp value Cross-site model predictions successfully explained the aggregate species responses (R 2 =0.70) SEM is “data hungry”; model adequacy questionable Final Report September 2013

Review (FY2; ) Summary (Year 2) 2 Tier 2 Pass/Fail is of limited value when site soils fail Failure to meet Tier 2 criteria is not necessarily a failure because of contaminant toxicity; substantial portion of the observed significant biological responses is attributable to non- contaminant variables Statistical tools can be applied to data to clarify interpretation of toxicity Final Report September 2013

Aims & Objectives (Year 3) 3 Collate data and expand the dataset Refine the models based on larger dataset Secure a site that failed the Tier 2 Pass/Fail Collect field soil samples and conduct an ecotoxicity assessment Apply the refined models (PLS, DRAMA, SEM) using the physical/chemical data for the site to predict effects across the site (independent) Compare the model predictions to the measured effects data – verification/validation

Status Update (Year 3) 4 Landowner cooperation for use of a site was secured Field soils and reference soils were collected and characterized An ecotoxicity assessment comprising 3 plant and 2 invertebrate species was conducted Tier 2 Pass/Fail Approach was applied Model refinement is still in progress Prediction of effects is pending Validation/verification is forthcoming

Status Update (Year 3) 4 Barley Northern Wheatgrass Alfalfa E. andrei F. candida

Tier 2 Pass/Fail Approach Site Soil# Criteria Pass:FailConclusion S1-11:3Fail S1-22:2Fail S1-32:2Fail S2-14:0Pass S2-24:0Pass S2-34:0Pass Status Update (Year 3) 4

4 % Effect Inhibition Relative to Control S1-1 S1-2 S1-3 S2-1 S2-2 S2-3

Status Update (Year 3) Model refinement Implementation - predictions Comparison (prediction vs response) Validation Further model refinement Reporting 4

Vision (FY4; ) 5 Implementation of models in site management Geospatial modelling of effects data Incorporation of predictive modelling into risk assessment Development of accessible database Publication of approach and models Additional applications

Questions?