RPIC Montreal Presented by: Francois Lauzon Prepared by: David Wilson Stantec Consulting Ltd.

Transcription

1 Managing Risk at Northern Contaminated Sites: Differentiating Assessment-derived Uncertainty from Risk Assessment Conservatism in Remedial Action Planning RPIC Montreal 2016 Presented by: Francois Lauzon Prepared by: David Wilson Stantec Consulting Ltd. April 27, 2016

2 Agenda 1 Uncertainty in Site and Risk Assessment 2 Role of Risk Assessment in Remedial Action Planning 3 The Uncertainty (Risk) Budget 4 Working with Risk 5 Case Example

3 1 Uncertainty in Site and Risk Assessment Risk is like fire: If controlled it will help you; if uncontrolled it will rise up and destroy you - Theodore Roosevelt

4 Definitions Risk Treasury Board (2010b): the effect of uncertainty on objectives or the expression of likelihood and impact of an event [4] Uncertainty Treasury Board (2010a): the state, even partial, of deficiency of information related to understanding or knowledge of an event, its consequence, or likelihood [3] Environment Canada (2012): the state of having limited knowledge where it is impossible to exactly describe an existing state or future outcome [1] Types of uncertainty: Aleatory or Exogenous Uncertainty - statistical variability and heterogeneity of the system (e.g., standard deviation of sample results) Epistemic Uncertainty - model and parameter uncertainty (e.g., infiltration rate) Deep Uncertainty : - uncertainty about fundamental processes or assumptions [2] Often forgotten: also includes scenario and decision-rule uncertainty

5 Building the Conceptual Site Model Phase CSM COCs* Terrestrial/ Land Use Climate/ Hydrology Hydrogeology Aquatic ESA I I(G) Historical land use/sara/ terrestrial species ESA II C(G) Characterize soils (impacted and background) ESA III D(G) Delineate soil impacts/ background HHERA C(SS) Define human health and eco exposures Identify surface water (SW) bodies Characterize SW (impacted and background) Characterize SW transport Consider seasonality and trends Water wells Stratigraphy, groundwater (GW) Porosity/ Gradients/ GW Transport Characterize GW exposure SARA/aquatic species Characterize SW and sediment Delineate SW and sediment impacts Characterize SW/sediment exposure RAP D(SS) Risk mitigation (remedial options) / management measures * I(G) = Identify (generic guideline) C(G) = Characterize (generic guideline) D(G) = Delineate (generic guideline) C(SS) = Characterize (site-specific guideline) D(SS) = Delineate (site-specific guideline)

6 Uncertainty and the CSM How should it be developed? [5] Dealing with uncertainty: Understand the data by doing exploratory analysis Identify and quantify uncertainty revisions to the CSM are expected Question assumptions Supplement data where needed, re-analyze, update the CSM [6] SAP = sampling and analysis plan

7 Sources of Uncertainty SOURCE e.g.s: Background conditions poorly defined CSM not fully developed Some COCs not identified Impacts not delineated Decision criteria not defined NORTHERN SITE MAGNIFIERS Limited time/samples Limited site-specific physical system data Limited historical data Risk assessment exposure scenarios not typical Identifying/engaging stakeholders challenging Quantification Parameter inclusion as COC: Y/N/Unk. Parameter inclusion as COC: Y/N/Unk. Impacted soil volume range: x m3 ± y m3 Identify reliance of decisions on site objectives Uncertainty is generally identified within a phase but is often not carried to a subsequent phase (e.g., from ESA to ROA/RAP; from RA to ROA/RAP; from ROA to RAP; from ROA/RAP to Cost) [7]

8 Characterizing Uncertainty Aleatory or Exogenous Uncertainty Measures of statistical variability: o standard deviation (parametric and non-parametric)/ standard error; variogram Bayesian probabilities Epistemic Uncertainty Model sensitivity analysis Monte Carlo simulation Deep Uncertainty Expert judgment Pairwise comparison of significance

9 2 Role of Risk Assessment in Remedial Action Planning "Reality is that which, when you stop believing in it, doesn t go away - Philip K. Dick

10 Remedial Action Planning Objective: Reduce risk to acceptable levels as effectively and efficiently as possible Inputs: Site hazards posing significant human health or ecological risks Outputs: Recommended remedial/risk management options that eliminate risk or reduce to acceptable levels ESA RAP

11 Risk Assessment Reduce Uncertainty of Using Generic Criteria: Screen out/in COCs Eliminate non-existing pathways and receptors Define Site-specific Criteria: Based upon site COC concentrations For site receptors Dependencies: Exposure scenarios: land use assumptions Pathways: CSM Pathways COPCs & Media Receptors Risk resides here

12 Remedial Options Analysis How do uncertainties affect the ROA? Attributes: Uncertainties: ESA-CSM or RA: Toxicity TRVs; Exposure duration RA Hazard Location Size/volume Pathways/receptors + / - Both ESA-CSM Stability Trend ESA-CSM Significant or not: elimination, reduction or management RO Technology RO Cost/Duration RO Technology/Cost/Duration

13 3 The Uncertainty (Risk) Budget There are those who are so scrupulously afraid of doing wrong that they seldom venture to do anything - Vauvenargues

14 The ESA Risk Budget Component Phase of Definition Nature of Uncertainty Management of Uncertainty Background Conditions II, III Nat. Var. mean, S.D. Epistemic - 95% UCLM; High natural CSM: Land use I, II, III Epistemic - generic criteria Deep - future intentions Additional sampling Conservatism (lowest criteria) CSM: Terrestrial Environment CSM: Aquatic Environment COCS: Identification I, II (Preliminary) II, III (Mature) I, II (Preliminary) II, III (Mature) Nat. Var. - soil depth, grain size, FOC, groundwater Epistemic - porosity Nat. Var. seasonality, TDS/ TSS, ph, species Epistemic flows (e.g., 7Q10) I, II Epistemic - COCs > Tier 1 criteria not identified Conservatism (e.g., coarse grained); additional sampling Conservatism (e.g., max values); additional sampling Gap analysis; additional sampling COCs: Characterization COCs: Delineation II Nat. Var. - mean, S.D. Epistemic - generic vs. sitespecific criteria III Nat. Var. - extent in x, y, z, duration in t Epistemic - qualifiers (L, M, H) Gap analysis; additional sampling Gap analysis; additional sampling; contingency volume

15 The RA Risk Budget Component Nature of Uncertainty Management of Uncertainty Inputs Background Conditions As per ESA Reference sites 95% UCLM CSM: Land use Human health exposure scenario; Applicable pathways Conservatism (chronic vs. acute exposure) CSM: Terrestrial and aquatic environments Ecological exposure scenario; Applicable pathways Modeled vs. measured concentrations ESA COCs Nat. Var. mean, S.D., extent in x, y, z, duration in t 95% UCLM = EPC OR max. conc. Models Human health dose model TRV Conservatism (e.g. published uncertainty factors for TRVs) Ecological dose model Species; Exposure area and duration Conservatism (e.g. most sensitive species; max. concentrations)

16 ESA and RA Risk Budgets Compared How much of the uncertainty is due to natural variability, to ESA-CSM models used, to RA models used, or to deeper issues? Find out: predict expected case, best case, and worst case values E.g. area of soil impacted with arsenic COC arsenic Volume 100 m 3 +/- 20 m 3 Soil texture undefined Sample results: # samples: 7 95% UCLM = N/a Log-norm. mean = 136 mg/kg Max. concentration = 367 mg/kg HH chronic TRVs: Ingestion 1.8 (mg/kg-d) -1 Inhalation 27 (mg/kg-d) -1 HH HQ 1.6 (FN toddler) Eco subchronic TRVs: Dog 0.55 (mg/kg-d) UF: 3 Eco HQ 10 (Masked shrew) ESA RA 20% - 10% - 10% % - 20% - 5% - 10% - 50% - 10%

17 The Conservatism Cascade Component Background CSM: Land Conditions use CSM: Terrestrial Environment CSM: Aquatic Environment COCS: Identification COCs: Characterization COCs: Delineation ESA to RA Not enough samples Max value vs. mean Future use unknown Most sensitive use GW pathway undefined GW pathway assumed Aquatic species undefined Species assumed present COCs not identified COC list assumed complete COCs not character -ized Characterization assumed COCs not delineated Delineation assumed Impact on Risk Error Type* * Assuming a site is contaminated, Type 1 = false negative (i.e., incorrectly assuming site is clean, and Type 2 = false positive (i.e., incorrectly assuming site is contaminated)

18 4 Working with Risk Fail to plan, plan to fail

19 Revisiting the CSM Post-RA The Dynamic CSM Before beginning the ROA, update the CSM based upon the RA results Investments in Uncertainty Reduction Best approach for a given hazard still unclear (i.e., remediate vs. risk manage)? Is the information needed for ROA (application of selection criteria) available? Managing Residual Uncertainty (Risk) Define triggers and thresholds in the LTMP

20 Mitigating Uncertainty Exogenous Uncertainty Variability is due to spatial, temporal, or individual randomness and cannot be decreased by further data collection: it s impact can only be (and should be) managed Epistemic and Deep Uncertainties Rank first by risk significance (e.g., hazardous vs. non-hazardous, COC HQs), then by magnitude Work down ranking, and answer the questions: Does the uncertainty span a decision threshold? [e.g., remediate or risk manage; on-site or off-site disposal] Worth investing in reduction of uncertainty (mitigate), or better to manage? [Cost-benefit analysis] What is the source of uncertainty? [Where should the investment in uncertainty reduction be made?]

21 Reducing and Managing Uncertainty based upon Source ESA (COCs): Additional sampling Advanced analysis CSM: Additional characterization (e.g., pump tests) Additional analysis (e.g., time series analysis) Monitor inputs to remedial option (RO) or risk management approach (RMA) and compare to design criteria RA: Development of short-term TRVs Use of measured vs. modeled concentrations in vegetation and animal tissues Uncertainty in Technology Performance: Monitor outputs of RO or RMA and compare to performance prediction

22 5 Case Study Everything that can be counted does not necessarily count; everything that counts cannot necessarily be counted - Albert Einstein

23 Case Study Example Scenario: gold mining site decommissioned to the standard of the day in the late 1980 s: arsenic impacts in sediments and surface water adjacent to a tailings containment area (TCA) and flooded underground workings. Best option for the TCA? Assessment History: Phase Scope Ph I/II ESA - historical identification of APECs - limited test pits/boreholes - soil. tailings and WR samples Ph III ESA - characterization of AECs additional boreholes - SW, GW and sediment samples - background soil samples HHERA - regional background soil 2014 analysis; additional samples - COPC screening-to-cocs - calculation of soil SSTLs Suppl. ESA - additional soil, SW, GW, 2015 sediment samples - benthic organism toxicity sampling and analysis 1988

24 Case Study Example cont d ESA Uncertainty Budget: Background [As] mg/kg: - Geo. Mean/S.D. = 5.1±2.5-95% UCLM = 22.4 TCA [As] mg/kg: - 09 Mean/S.D. = 150± Mean/S.D. = 131±101 - All Mean/S.D. = 136±124-95% UCLM = 340 CSM: - Tier 1 crit. (Agric.) = 12 mg/kg - Tier 1 crit. (Res.)* = 31 mg/kg - SSRT = 69 mg/kg - SW (ug/l) and sediment: SW Mean/S.D. (#sm 3) = 16.1±3.6 mg/kg Sed. Mean/S.D. (#sm 14) = 38±33 mg/kg - Groundwater regime unknown - Impacted area volume: 12 (#sm 84) = 42,000±15,000 m 3 15 (#sm 106) = 44,800±9,400 m * Carcinogenic

25 Case Study Example cont d ESA Uncertainty Analysis: - is a decision threshold spanned? - background exceeded? Y - volume estimate: - Tier 1 = 66,000±20,000 m 3 - SSRTs = 44,800±9,400 m 3 - remedial action required - can options be evaluated? N Cont d: - need to know if the aquatic environment is being impacted - additional assessment of aquatic environment required RA Uncertainty Budget: undertake a similar process - Uncertainty in inputs - Uncertainty in models Conclude options analysis: - assessment shows impacts above Tier 1 but RA shows nontoxic to benthic invertebrates - Class A cost estimate achievable within uncertainty? Y 2012

26 References References 1. Environment Canada Federal Contaminated Sites Action Plan (FCSAP) Environmental Risk Assessment Guidance. ISBN no Cat. no. En14-19/1-2013E-PDF. 2. Committee on Decision Making Under Uncertainty (CDMU) Environmental Decisions in the Face of Uncertainty. Board on Population Health and Public Health Practice. National Academy of Sciences. ISBN TBS. 2010a. Framework for the Management of Risk. 4. TBS. 2010b. Guide to Integrated Risk Management. 5. Maheux, P., Lauzon, F., Wilson, D., Sundaram, S., Bouchard, M Developing a Good Conceptual Model for Federal Contaminated Sites Common Shortfalls and Data Needs. Pres. at the RPIC Federal Contaminated Sites National Workshop, Toronto, Ontario. 6. Evolving Conceptual Site Models (CSMs) in Real-time for Cost Effective Projects, Kira P. Lynch, US Army Corps Seattle District. 7. Wilson, D Advancements in Managing Uncertainty in Remedial Options Analysis and Remedial Action Plan Development for Northern Sites. Pres. at the RPIC Federal Contaminated Sites Regional Workshop, Edmonton, Alberta.

27 Questions? David Wilson, M.A.Sc., P.Eng. Senior Associate Stantec Ottawa (613)