1. Case Study 1 Application of different tools: RBCA Tool Kit and APIDSS

2. Site location

3. Site map

4. Hazard assessment: Site investigation Reconstruction of the site industrial history: location of old plants; 4 location of old plants; 4 processes and technologies utilized; 4 wastes location and management. May affect sampling strategy and, consequently, the input data and the site conceptual model

5. Site investigation: Sampling strategy The common question “Where and how many samples may be representative of site contamination ?” depends:  on the horizontal and vertical distribution of contaminants;  on soil matrix nature. The common question “Where and how many samples may be representative of site contamination ?” depends:  on the horizontal and vertical distribution of contaminants;  on soil matrix nature.

6. Site investigation: Sampling strategy It is problematic to establish general rules and it is often appropriate to follow practical site-specific indication. A statistical approach can be very useful to quantify uncertainties even though it can lead to costly sampling design.

7. Data collection: Chemical analyses Data collection: Chemical analyses Choice of the most appropriate analytical method depends on the detection limit that will meet the concentration level of concern. In R.A. both sensitive and selective analysis are required (many chemicals show toxicity effects even at very low concentrations) since both toxicity assessment and risk evaluation are carried out for each CoC.

8. Site investigation hidrogeology Actual direction of contaminant plume during pumping period from nearby wells(1958 - 1978). Pumping from industrial wells produced local deviations of phreatic and semiconfined flows and hydraulic connections between aquifers. Actual direction of contaminant plume during pumping period from nearby wells(1958 - 1978). Pumping from industrial wells produced local deviations of phreatic and semiconfined flows and hydraulic connections between aquifers.

9. R.A. input R.A. input Quality and confidence of R.A. results strictly depend from these data and from the type of algorithms used for risk evaluation. Different assessment levels (tiers) can reduce uncertainties, moving from max. conservative assumptions to more site- specific and accurate investigations.

10. RBCA Tool Kit To compare RBCA with API-DSS, the same conceptual model and input parameters were used, except for input concentrations of CoCs that were derived from different statistical calculations. RBCA allows to calculate both risk to human health and site-specific remediation targets.

11. API-DSSAPI-DSS u Doesn’t directly calculate SSTLs, but uses fate & transport models for saturated and unsaturated zone contaminant migration simulation. u It estimates a time-dependent CoC concentration reaching the receptor and max values are used by the risk and HI calculation module. u The model by means of a MonteCarlo algorithm performs probabilistic F&T and Risk estimation, allowing to quantify uncertainties.

12. Flow diagram of site conceptual model Source Migration pathways Exposure points Targets Soil Atmospheric Dermal contact Commercial suspension & & ingestion activities dispersion workers/employees Atmospheric volatilisation & Air Remediation/ POLLUTED dispersion Particulate & Construction SOILS vapours workers inhalation Resident people Leaching & Groundwaters not connected groundwater Drinkable use with public transport water network

13. Third uncertainty (Chemicals of concern, generic) Third uncertainty (Chemicals of concern, generic) The selected CoCs were the same for the two applied R.A. models. The choice of a restricted number of pollutants may be an underestimation of total risk, and this might represent another uncertainty.

14. Fourth uncertainty Toxicological and chemical-physical data used for R.A. need to be continuously updated. To avoid uncertainties related to old data several data bases (IRIS, HEAST, WHO, NIOSH, etc.) can be used. One of the main problem is the estimation soil-water distribution coeff. (Kd) of heavy metals.

15. R.A. input concentrations Pollutants spatial distribution was represented by kriging interpolation contour plots. The UCL (95%) of the mean value of log-normal distribution, or max measured concentrations (in case of few available data) were retained as representative source concentrations.

16. Isoline map of lead concentration in surface soil (<1,5 m deep) of site area. 584620m584660m584700m584740m584780m 9 6 8 8 8 0 m 9 6 8 9 2 0 m 9 6 8 9 6 0 m 9 6 9 0 0 0 m 9 6 9 0 4 0 m 0 500 50000 300000 not interpolated area mg/kg d.w. 100 1000 100000

17. Estimated “sources” for API-DSS

18. Pb content in estimated “sources”for API-DSS Pb Surface soil<1m Subsurface Zonen. Samples conc.n. Samples conc. max (mg/kg) max (mg/kg) A 2 339 4 36 C 3 30 4 40 D 3 344 3 37 E 2 2700 3 179 F 1 30 1 23 G 13 29000 11 346 I 3 65 7 38 L 9 594830 7 2105 M 5 195000 2 36 N 3 113 5 398 O 4 6536 2 1080 P 4 40000 6 22500 Q 2 42 R 5 14600 3 12700 S 1 280 1 66 T 3 1709 6 8056

19. Chemicals and Site main features Main chem-phys. characters of COCs are: u solubility, Henry’s law constant, water diff., air diff., Kd (inorganics), Koc (organics); diff., Kd (inorganics), Koc (organics); u data from updated databases. Hydrogeological model of the site

20. RBCA vs. API-DSS results Substance Input conc. SSTL Input conc. SSTL Emilia Rom. RBCA RBCA API-DSS API-DSS region limits (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) Benzene 2,2 E-2 4,4 E-2 2,0 E-2 0,02 5 Benzo(b)fluorantene 9,2 E-1 1,8 7,2 2 10 Benzo(a)antracene 1,4 1,9 4,8 2 10 Benzo(a)pyrene 9,6 E-1 1,9 E-1 7 2,0 E-1 10 Crysene 1,8 2,21 8,9 1 n.f. Dibenzo(a,h)antracene 8,3 E-2 1,9 E-1 2,4 2,0 E-1 10 Ethylbenzene 2,8 E-2 9,1 E+1 1,0 E-1 1,0 E-1 50 Indeno(1,2,3,c,d)pyrene 8,3 E-1 1,9 7,1 2 10 Lead 3,4 E+4 9,59 E+2 1,2 E+5 1,7 E+3 1000 Naphtalene 7,2 E-1 29 2,5 2,5 50 Tetraethyl Lead 5,8 2,6 E-4 463 6,0 E-3 n.f. Toluene 4,1 E-1 58 1,5 1,5 30 Trichloroethylene 2,1 E-2 1,1 E-1 0,6 0,6 10 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Lisbon, 24-25 June 1999 International Conference on “ Investigation Methods on Soil Contamination” 22

21. Main uncertainties Uncertainties in modeling : u Affect the accuracy of R.A. u Require a model validation (often not feasible because of the predicting nature of R.A.) u Suggest a strictly conservative approach Uncertainties in input data u Can be quantified by probabilistic approaches

22. General conclusions u Characterisation of a contaminated site should provide data necessary for exposure analysis and provide an assessment of associated uncertainties u Geostatistical techniques allows to infer much more information from site and analytical data, and to quantify the uncertainties of estimated values u In this case R.A. provides a result in favour of remedial actions u Quite similar results for most CoC obtained by the two model lead to the conclusion that even if some lack of information exist (about site-specific parameters or features) a deeper level of risk calculation requiring more costs and time may be not useful

23. CASE STUDY 2 ROME