Dioxin Exposure Modeling / Prof. Jean Francois Viel

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  • Dioxin Exposure Modeling / Prof. Jean Francois Viel

    1. 1. Dispersion modeling and use of GIS for dioxin exposure assessment in the vicinity of a municipal solid waste incinerator: a validation study. JF Viel, N Floret, E Lucot, JY Cahn, PM Badot, F Mauny University of Franche-Comté, France
    2. 2. Introduction <ul><li>Whether low environmental doses of dioxin affect the general population is the matter of intense debate and controversy. </li></ul><ul><li>In a previous study, we found a 2.3-fold risk for non-Hodgkin’s lymphoma associated with residence classified as highly exposed to dioxin emitted from a MSWI (Besançon, France). </li></ul><ul><li>Floret N, Mauny F, Challier B, Arveux P, Cahn JY, Viel JF. Dioxin emissions from a solid waste incinerator and risk of non-Hodgkin lymphoma. Epidemiology 2003;14:392-398. </li></ul><ul><li>The main limitation lay within the use of dispersion modeling as a proxy for dioxin exposure. </li></ul>
    3. 3. Aim <ul><li>The goal of this study was therefore to validate geographic based-exposure categories (derived from isopleths of predicted ground concentrations from a first-generation Gaussian-type dispersion model) through PCCD/F measurements from soil samples. </li></ul>
    4. 4. Materials and methods
    5. 5. The municipal solid waste incinerator of Besançon, France <ul><li>Began operation in 1971. </li></ul><ul><li>Located in an urbanized area. </li></ul><ul><li>Capacity: 7.2 metric tons/hour. </li></ul><ul><li>Stack: 40 m high. </li></ul><ul><li>Processing: 67,000 tons of waste (1998). </li></ul><ul><li>Emissions (1997): </li></ul><ul><ul><li>dioxin: 16.3 ng I-TEQ/m 3 , </li></ul></ul><ul><ul><li>dust: 315.6 mg/Nm 3 , </li></ul></ul><ul><ul><li>hydrogen chlorine: 803.5 mg/Nm 3 , </li></ul></ul><ul><ul><li>exhaust gases not maintained at temperatures ≥ 850°C for the legal time (> 2 s). </li></ul></ul>
    6. 6. Study area <ul><li>It exhibits a complex pattern: </li></ul><ul><ul><li>on the northeast side: </li></ul></ul><ul><ul><ul><li>the site is a mixed commercial/urban area, </li></ul></ul></ul><ul><ul><ul><li>with gentle hills of moderate slope, </li></ul></ul></ul><ul><ul><li>on the southwest side: </li></ul></ul><ul><ul><ul><li>the terrain is complex, </li></ul></ul></ul><ul><ul><ul><li>with more pronounced hills and valleys, </li></ul></ul></ul><ul><ul><ul><li>mainly covered with forest and urban patches. </li></ul></ul></ul>
    7. 7. Southwest of the MSWI, looking in the northeast direction
    8. 8. Northeast of the MSWI, looking in the southwest direction
    9. 9. Dioxin exposure modeling and GIS <ul><li>A first-generation Gaussian-type dispersion model (APC3) was performed in the framework of an environmental impact statement: </li></ul><ul><ul><li>to predict the future impact of dioxin emissions from new combustion chambers to be built. </li></ul></ul><ul><li>The respective contours of the modeled ground-level air concentrations were digitalised and contoured onto the surface of a map. </li></ul><ul><li>We assumed that contour shapes were reliable estimates of past dioxin exposure profiles: </li></ul><ul><ul><li>provided relative figures rather than absolute figures were used, </li></ul></ul><ul><ul><li>the contours were, therefore, classified as very low, low, intermediate, and high exposure areas. </li></ul></ul>
    10. 10. Modeled average ground-level dioxin concentrations < 0.0001 pg/m 3 0.0001 - 0.0002 pg/m 3 0.0002 - 0.0004 pg/m 3 0.0004 - 0.0016 pg/m 3 Dioxin concentrations Municipal solid waste incinerator Doubs river City boundary Soil samples 5 km N
    11. 11. Sampling <ul><li>75 sampling sites were determined in relation to homogeneous geological and topographical conditions. </li></ul><ul><li>Description of sampling points: </li></ul><ul><ul><li>altitude, geomorphology features, ecology features. </li></ul></ul><ul><li>Soil measurements: </li></ul><ul><ul><li>pH, </li></ul></ul><ul><ul><li>organic carbon concentration, </li></ul></ul><ul><ul><li>cation exchange capacity, </li></ul></ul><ul><ul><li>PCDD/Fs: </li></ul></ul><ul><ul><ul><li>17 congener concentrations, </li></ul></ul></ul><ul><ul><ul><li>pg WHO-TEQ (toxic equivalent)/g dry matter. </li></ul></ul></ul>
    12. 12. Statistical analyses <ul><li>Simple and multiple regression analyses were carried out to model the relation between: </li></ul><ul><ul><li>the natural logarithm of WHO-TEQ concentration in soil samples, as dependent variable, </li></ul></ul><ul><ul><li>independent variables: </li></ul></ul><ul><ul><ul><li>dioxin exposure categories derived from the dispersion model, </li></ul></ul></ul><ul><ul><ul><li>soil parameters, </li></ul></ul></ul><ul><ul><ul><li>geomorphology and ecology parameters. </li></ul></ul></ul><ul><li>Independent variables were included in the multivariate model, if they had a P -value of 0.20 or less in the univariate analysis. </li></ul>
    13. 13. Results
    14. 14. Dioxin soil concentrations <ul><li>Range = 0.25 - 28.06 pg WHO-TEQ/g dry matter. </li></ul><ul><li>Means (standard deviations), per geographic-based exposure and topography complexity categories. </li></ul>pg WHO-TEQ/g dry matter. 11.25 (12.39) 3.53 (2.30) 1.99 (1.37) 1.81 (1.14) Simple topography 1.37 (0.21) 1.91 (1.12) 2.44 (3.53) 1.09 (1.76) Complex topography High Intermediate Low Very low Geographic-based exposure
    15. 15. Adjusted means of log-transformed dioxin concen-tration per modeled dioxin exposure categories Dioxin exposure Ln I - TEQ (ln pg /g dry matter ) 0 1 2 - 1 Very low Low Intermediate High
    16. 16. Ln WHO-TEQ and independent variables Complex topography (r² = 30.5 %) P -value ß Variable 0.05 -0.01 altitude Geomorphology parameters 0.23 0.01 organic carbon concentration Soil parameters 0.19 0.47 high 0.02 0.87 intermediate 0.17 0.49 low - - very low Modeled dioxin exposure
    17. 17. Ln WHO-TEQ and independent variables Simple topography (r² = 52.2 %) P -value ß Variable 0.01 -0.01 altitude Geomorphology parameters 0.35 0.01 organic carbon concentration Soil parameters 0.001 1.21 high 0.08 0.56 intermediate 0.40 0.23 low - - very low Modeled dioxin exposure
    18. 18. Discussion
    19. 19. Exposure assessment (1) <ul><li>The two assumptions required to use geographic exposure indicators were met to the northeast of the MSWI : </li></ul><ul><ul><li>concentrations differed between areas in the manner expected, </li></ul></ul><ul><ul><li>pollutant levels within an area were relatively uniform. </li></ul></ul><ul><li>Therefore, contours of the modeled ground-level air concentrations, classified in four increasing exposure categories, adequately reflect past dioxin exposure in this area. </li></ul>
    20. 20. Exposure assessment (2) <ul><li>The first-generation Gaussian-type dispersion model revealed inappropriate for assessment of exposure on the southwest side. </li></ul><ul><li>This bias toward overprediction has also been described with ISC3 (a first-generation model similar to APC3). </li></ul><ul><li>Several limitations of APC3 software can explain these results: </li></ul><ul><ul><li>only a simplified topography has been introduced, </li></ul></ul><ul><ul><li>the turbulence boundary layer between surface and air was not considered, </li></ul></ul><ul><ul><li>surface roughness, which affects the vertical profiles of wind and temperature, was not accounted for. </li></ul></ul><ul><li>Moreover, the stack shortness (40 m) made the fraction of PCDD/F emissions that is deposited locally very sensitive to the treatment of dispersion. </li></ul>
    21. 21. Case-control study <ul><li>The subsequent question is whether this overprediction challenges the findings of our case-control study, since it entails a misclassification bias (although non-differential) for people living to the southwest of the MSWI. </li></ul><ul><ul><li>only 10.5% of cases and 9.3% of controls were concerned, </li></ul></ul><ul><ul><li>a logistic regression restricted to cases and controls residing on the northeast side yielded a slightly increased OR in the highest dioxin exposure area (OR = 2.5, 95% CI, 1.4-4.5), compared to our initial finding (OR = 2.3, 95% CI, 1.4-3.8). </li></ul></ul>
    22. 22. Conclusion
    23. 23. <ul><li>First-generation modeling provided a reliable proxy for dioxin exposure in simple terrain, reinforcing the results of our case-control study. </li></ul><ul><li>However, a more advanced atmospheric dispersion model should have been used for refined assessment in complex terrain. </li></ul>
    24. 24. <ul><li>Floret N, Viel JF, Lucot E, Dudermel PM, Cahn JY, Badot PM, Mauny F. Dispersion modeling as a dioxin exposure indicator in the vicinity of a municipal solid waste incinerator: a validation study. Environ Sci Technol 2006;40:2149-55. </li></ul>
    25. 25. Thank you for your kind attention.
    26. 27. Soil concentrations <ul><li>The current data show a notable PCDD/F contamination by the MSWI in the areas under its direct influence. </li></ul><ul><li>PCDD/F concentrations in soil samples at the Besançon site are comparable to levels found in different MSWI sites. </li></ul><ul><li>However, the dispersion of PCDD/F emissions in the atmosphere and their deposition onto soil being governed by numerous factors, soil concentrations in various locations around a MSWI must be compared with caution. </li></ul>
    27. 28. Other potential emissions sources <ul><li>There are no adjacent industrial sources, but a main road with heavy traffic runs near the plant. </li></ul><ul><li>To determine whether more than one potential emission source could explain the presence of PCDD/Fs in soil samples (and could challenge the ground-level concentration modeling), a principal component analysis (PCA) was carried out on the 17 congener concentrations. </li></ul><ul><li>The PCA provided a one-dimensional model (the first principal component explained 88% of the variance), reflecting high similarities in the congener profiles. </li></ul><ul><li>No other additional sources of PCDD/F contamination than the MSWI is, therefore, to be feared. </li></ul>

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