Authors

1. Minneapolis/St. Paul Metropolitan Surface Soils Study: The Importance of Background Surface Soils Data  

2. Authors • Minnesota Department of Transportation David Belluck Bruce Johnson Diana Wong (currently International Paper) Julie Whitcher • Cooperators Sally Benjamin, Risk Writers, Ltd. Steve David, HDR Engineering Mark Collins, HDR Engineering Beth Regan, HDR Engineering Howard Mielke, Xavier University Paul Bloom, University of Minnesota

3. When is a Local Surface Soil Contaminated Above Acceptable Limits? Simple Answers: • When a government agency tells you it is. • When a government agency numerical soil cleanup is exceeded. • When a risk assessment shows unacceptable risk to receptors. • When lending institutions tell you it is. • When a court tells you it is.

4. When is a Local Surface Soil Contaminated Above Acceptable Limits? Difficult Answer: • When local background levels are exceeded.  Why is this a difficult answer? • Poor understanding of local background levels for most chemicals.  • Variation in background levels from natural or anthropogenic causes.  • No existing local background data for most chemicals.

5. Why does a Transportation Agency Need Background Soils Concentrations for Common Chemical Contaminants? Short Answer: • To protect the department and state from toxic tort or environmental liability in property transactions, litigation, and waste recycling.

6. Mn/DOT is Vulnerable to Contaminant Claims and Regulatory Actions Facts: • Mn/DOT is the largest single administrator of land in the State of Minnesota.  Minnesota Road and Street Mileage – 2000 State Total: 132,251 Percent of US Total: 3.36% Rank: 5 State Jurisdiction: 13,203 Local Jurisdiction: 117,024 

7. Mn/DOT is Vulnerable to Contaminant Claims and Regulatory Actions (Continued) • Mn/DOT purchases large parcels of land, many in urban corridors and industrial zones, for construction purposes. • Mn/DOT is encouraged by federal and state government agencies to recycle former industrial wastes into its soils, concrete and bituminous asphalt.

8. Mn/DOT is Vulnerable to Contaminant Claims and Regulatory Actions (Continued) • Mn/DOT is required to meet all federal and state laws, rules, regulations, policies and executive orders (e.g., Federal E.O. 13101). • Mn/DOT can be a responsible party if its actions cause soil contamination that exceeds state or federal cleanup levels. • Highway projects involve complex regulatory compliance activities and analyses (e.g. EIS, EAW, Environmental Justice).

9. Mn/DOT is Vulnerable to Contaminant Claims and Regulatory Actions (Continued) Take-home messages: • To reduce environmental liability exposures, Mn/DOT must perform due diligence and compliance reviews for infrastructure inputs and wastes. • Background soils chemistry is an important part of this activity.

10. How does Mn/DOT use Background Soil Chemistry Data? • Background soils chemistry data allows Mn/DOT to determine whether: • waste recycling into infrastructure projects have the potential to produce superfund sites; • soil sample exceedence of a numerical limit is a contamination incident or just local background (reduce false positive contaminated site declarations); • new property for a road should be purchased or avoided; and • site contaminant assessments are needed. This is not just a transportation agency issue…

11. Why Public and Private Sector Organizations Need Background Soils Chemical Information • Respond to citizen general questions about soil contaminant levels in their area.  • Evaluate Brownfield sites for investigation and remediation. • Confirm that site chemistry is within normal or acceptable ranges to allow for loan application acceptance or property transfer.

12. Why Public and Private Sector Organizations Need Background Soils Chemical Information (Continued) • Demonstrate no significant soil contamination as part of property contaminant disclosures during real estate transactions. • Verify that completed soil cleanup is at background soil chemistry levels. • Assure ability to expand housing stock without excessive environmental cleanup costs and risks.

13. Facts • By 2030 the Twin Cities will need up to 84,000 new housing units on developed land and 391,000 new housing units on undeveloped land (Metropolitan Council. Blueprint 2030 Adopted December 18, 2002) (http://www.metrocouncil.org/planning/blueprint2030/BPAppD-AccommodatingAreaGrowth.pdf)

14. Facts • Population pressures and urban sprawl will bring more people into contact with soils that have industrial, commercial, or agricultural contamination. This trend will continue and increase in the coming years.

15. Facts • New roads will be built and old roads expanded to meet future infrastructure needs. Due diligence investigations will be required at all these locations prior to road construction.

16. Fact • Given current problems with elevated levels of arsenic in surface soils where CCA-treated wood or cancelled arsenic based pesticides have been used in the past, the need to quantify background levels of soil contaminants will increase in an effort by people not to buy or live in contaminated environments.

17. Minnesota Lessons Learned • If you don’t know the surface soil background levels in a given area, you cannot: • Determine contaminated from uncontaminated soils; • Define what chemical contaminant levels are normal or expected; • Know that you are in or out of regulatory compliance; • Calculate background hazards from soils exposure; • Calculate levels of recycled wastes that can be used as soil amendments without exceeding numerical soil cleanup criteria; • Perform due diligence when purchasing land or allowing a land use that will increase surface soil contaminant levels; and • Perform EIS, EAW, or permit risk assessments with soil impact and risk components

18. Minnesota Experience • Minnesota has been using 1980’s background surface soils chemical data from non-metropolitan sites since the inception of the Superfund and RCRA programs. • Background surface soils studies have apparently been conducted at some sites but that data has not been incorporated in any database or made readily accessible to interested parties.

19. Minnesota Experience (Continued) • Available background data may not be usable because of potential analytical chemistry problems or incompatibility between analytical methods (e.g. “total total”) • Minnesota has never conducted a systematic study of surface soil contaminant chemistry. • Minnesota has numerous types of soil types and soil types vary dramatically in different parts of the Minneapolis/St. Paul metropolitan area.

20. Minnesota Experience (Continued) • Given the Minnesota focus on waste recycling into soils, Mn/DOT determined that understanding local background soils was critical to its mission of building and maintaining infrastructure

21. Metropolitan Soils Study • 36 Infrastructure Sample Locations • Surface samples adjacent to existing roadways • 5-sample transect perpendicular to roadway (A-E) • A, D and E samples evaluated for metals • All 5 samples evaluated for physical soil parameters by Mn/DOT Maplewood Laboratory • 24 “Background” Locations • Parklands and other relatively undeveloped properties • Could be affected to some degree by urban pollutants • 1 sample per site, composited from 8 subsamples along a 200 ft transect

22. Sample Collection Locations

23. Sample Collection Locations

24. Infrastructure Sampling Locations

25. Infrastructure Sampling Locations

26. Background Sampling Locations

27. Background Sampling Locations

28. Multi-Sample Positions • 5 Sample Positions • Adjacent to Roadway where vegetation was present* • Ditch downslope, above apparent water line • Bottom of Ditch • Ditch upslope, above apparent water line* • Edge of Right-of-Way* All samples evaluated by Mn/DOT Maplewood Laboratory for soil physical/chemical parameters • Additionally analyzed for specific metals

29. Multi-Sample Positions • 5 Sample Positions • Adjacent to Roadway where vegetation was present* • Ditch downslope, above apparent water line • Bottom of Ditch • Ditch upslope, above apparent water line* • Edge of Right-of-Way* All samples evaluated by Mn/DOT Maplewood Laboratory for soil physical/chemical parameters • Additionally analyzed for specific metals

30. Terminology • ‘Ring’ refers to three concentric rings covering the Minneapolis-St. Paul Metropolitan Area, centered on the downtown Minneapolis post office • ‘Radius’ refers to 12 radii, spaced 30 degrees apart, intersecting the rings. Infrastructure samples were collected along the Mn/DOT administered highway nearest the intersections • ‘Position’ refers to the location of the sample relative to the roadway (i.e., the A-B-C-D-E samples)

31. Summary of Analytical Results

32. Comparisons to Risk Criteria • Individual surface soils metals concentrations were compared to Minnesota risk-based soil remediation guidelines, i.e., the Tier I and Tier II Soil Reference Values (direct exposure pathway) and Soil Leaching Values (leaching to groundwater pathway) • No median value aggregated by ring, radius or position exceeded the risk criteria

33. Data Evaluation • Statistical Methods Used to Evaluate Infrastructure and Background Surface Soils Samples • Non-parametric Statistical Methods: • Mann-Whitney test for comparison of two data subsets (e.g., a subset of the infrastructure data versus background) • Kruskall-Wallace test for comparison of more than two data subsets (e.g., rings 1, 2 and 3 versus each other) Since non-parametric methods were used, censored data were assigned concentrations = 0 • Multi-parameter Correlations • Correlation coefficients calculated to evaluate the linear dependence of two data subsets

34. Comparisons to Background • Statistically compare infrastructure soil concentrations to the concentrations of the background samples • Aggregate infrastructure samples by ring, radius or position • Statistically evaluate the hypotheses that the resulting subsets of the infrastructure data are each statistically similar to background • e.g., Is the Ring 1 data similar to background?

35. Background Comparison Results • Empirical Observations • Higher hypothesis failure rates with smaller diameter ring and nearer to roadway • Higher hypothesis failure rates to the north for Pb and to the west for Zn; other patterns not apparent Failures of Null Hypothesis (i.e., the number of infrastructure data subsets that are statistically different than the background data)

36. Pooled Comparisons • Aggregate the infrastructure data by ring, radius or position and calculate the median for each group • Evaluate the hypothesis that each of the resulting subsets is statistically similar to the others of the same variable • e.g., Are the data from each ring similar to the data from each of the other rings?

37. Pooled Comparison Results • Empirical Observations • Cu, Pb and Zn median concentrations decreased with increasing distance from center (ring) • Radii 3 and 4 had generally higher concentrations for both Cd and Pb (likely related to downtown St. Paul) • Cd, Cu, Pb and Zn concentrations decrease with increasing distance from roadway Hypothesis failed, e.g., Cadmium concentrations along at least one radius are statistically different than Cadmium concentrations on other radii

38. Position-Ring Comparisons • Aggregate by position for each ring, and vice versa • Compare statistically to evaluate the hypothesis that the subsets are similar Pos. A Pos. D Pos. E • For Example (Column 1, left) • Subset the data to select only the Pos. A results. • Then aggregate the Pos. A group into subsets for Rings 1, 2 and 3. • Compare the Ring subsets for statistical similarity. Ring 1 Similar? Similar? Ring 2 Ring 3 Similar? Similar? Similar? Similar?

39. Position-Ring Comparison Results • Test the hypothesis that the data from the three sampling positions were the same for each ring • Empirical Observations • The medians indicate increasing concentrations inward for the three rings at Position A for Cu • The medians indicated increasing concentrations inward for Pb and Zn for all positions (A, D and E) Hypothesis failed, e.g., Copper concentrations from Pos. A statistically differed between the rings

40. Position-Ring Comparison Results • Test the hypothesis that the data from each ring were the same for each sampling position • Empirical Observations • Data indicate increasing concentrations with decreasing distance from the roadway for Cd, Cu, Pb and Zn in Ring 1 • Data indicate increasing concentrations with decreasing distance from the roadway for Cd and Cu in Ring 2 • Data indicate increasing concentrations with decreasing distance from the roadway for Cu and Pb in Ring 3 Hypothesis failed, e.g., Cadmium concentrations from Rings 1 and 2 statistically differed between the sampling positions

41. Interparameter Correlations • Explores potential relationships between parameters, i.e., can we predict the concentration of one chemical from a given concentration of another chemical • Cd, Cu, Pb and Zn show the strongest correlations with each other • Future work will evaluate the reasons for these findings Correlation Coefficients Note: Correlation coefficients close to 1 or –1 represent strong correlations, where coefficients close to 0 indicate independence

42. What’s Next? • Roll in Neutron Activation Analysis (NAA) and other results from work done to date (provides additional parameters) • Background Concentration Study • 15-20 cities outside of metropolitan area, southern 2/3 of Minnesota • ~9 sites per city • 1 sample per site, composited from 8 randomly selected sample locations along a 200 ft transect • Analysis by NAA and wet chemistry where NAA is not appropriate • Work completed by June 30