Dispersion modeling and use of GISfor 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

Introduction

Whether low environmental doses of dioxin affect the general population is the matter of intense debate and controversy.

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).

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.

The main limitation lay within the use of dispersion modeling as a proxy for dioxin exposure.

Aim

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.

Materials and methods

The municipal solid waste incinerator of Besançon, France

Began operation in 1971.

Located in an urbanized area.

Capacity: 7.2 metric tons/hour.

Stack: 40 m high.

Processing: 67,000 tons of waste (1998).

Emissions (1997): dioxin: 16.3 ng I-TEQ/m3, dust: 315.6 mg/Nm3, hydrogen chlorine: 803.5 mg/Nm3, exhaust gases not maintained at temperatures ≥

850°C for the legal time (> 2 s).

Study area

It exhibits a complex pattern: on the northeast side:

the site is a mixed commercial/urban area,

with gentle hills of moderate slope,

on the southwest side: the terrain is complex,

with more pronounced hills and valleys,

mainly covered with forest and urban patches.

Southwest of the MSWI, looking in the northeast direction

Northeast of the MSWI, looking in the southwest direction

Dioxin exposure modeling and GIS

A first-generation Gaussian-type dispersion model (APC3) was performed in the framework of an environmental impact statement:

to predict the future impact of dioxin emissions from new combustion

chambers to be built.

The respective contours of the modeled ground-level air concentrations were digitalised and contoured onto the surface of a map.

We assumed that contour shapes were reliable estimates of past dioxin exposure profiles:

provided relative figures rather than absolute figures were used, the contours were, therefore, classified as very low, low, intermediate,

and high exposure areas.

Modeled average ground-level dioxin concentrations

< 0.0001 pg/m3

0.0001 - 0.0002 pg/m3

0.0002 - 0.0004 pg/m3

0.0004 - 0.0016 pg/m3

Dioxin concentrations

Municipal solid waste incineratorDoubs riverCity boundarySoil samples

5 km

N

Sampling

75 sampling sites were determined in relation to homogeneous geological and topographical conditions.

Description of sampling points: altitude, geomorphology features, ecology features.

Soil measurements: pH, organic carbon concentration, cation exchange capacity, PCDD/Fs:

17 congener concentrations, pg WHO-TEQ (toxic equivalent)/g dry matter.

Statistical analyses

Simple and multiple regression analyses were carried out

to model the relation between: the natural logarithm of WHO-TEQ concentration in soil

samples, as dependent variable,

independent variables: dioxin exposure categories derived from the dispersion model,

soil parameters,

geomorphology and ecology parameters.

Independent variables were included in the multivariate

model, if they had a P-value of 0.20 or less in the

univariate analysis.

Results

Dioxin soil concentrations

Range = 0.25 - 28.06 pg WHO-TEQ/g dry matter.

Means (standard deviations), per geographic-based exposure and topography complexity categories.

Geographic-based

exposure Very low Low

Intermediate

High

Complex topography

1.09 (1.76) 2.44 (3.53) 1.91 (1.12) 1.37 (0.21)

Simple topography

1.81 (1.14) 1.99 (1.37) 3.53 (2.30)11.25

(12.39)

pg WHO-TEQ/g dry matter.

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

Ln WHO-TEQ and independent variablesVariable ß P-value

Complex topography (r² = 30.5 %)

Modeled dioxin exposure

very low - -

low 0.49 0.17

intermediate 0.87 0.02

high 0.47 0.19

Soil parameters

organic carbon concentration

0.01 0.23

Geomorphology parameters

altitude -0.01 0.05

Ln WHO-TEQ and independent variablesVariable ß P-value

Simple topography (r² = 52.2 %)

Modeled dioxin exposure

very low - -

low 0.23 0.40

intermediate 0.56 0.08

high 1.21 0.001

Soil parameters

organic carbon concentration

0.01 0.35

Geomorphology parameters

altitude -0.01 0.01

Discussion

Exposure assessment (1)

The two assumptions required to use geographic

exposure indicators were met to the northeast of the

MSWI: concentrations differed between areas in the manner

expected,

pollutant levels within an area were relatively uniform.

Therefore, contours of the modeled ground-level air

concentrations, classified in four increasing exposure

categories, adequately reflect past dioxin exposure in

this area.

Exposure assessment (2)

The first-generation Gaussian-type dispersion model revealed inappropriate for assessment of exposure on the southwest side.

This bias toward overprediction has also been described with ISC3 (a first-generation model similar to APC3).

Several limitations of APC3 software can explain these results: only a simplified topography has been introduced, the turbulence boundary layer between surface and air was not

considered, surface roughness, which affects the vertical profiles of wind and

temperature, was not accounted for.

Moreover, the stack shortness (40 m) made the fraction of PCDD/F emissions that is deposited locally very sensitive to the treatment of dispersion.

Case-control study

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. only 10.5% of cases and 9.3% of controls were concerned,

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).

Conclusion

First-generation modeling provided a reliable proxy for

dioxin exposure in simple terrain, reinforcing the results

of our case-control study.

However, a more advanced atmospheric dispersion

model should have been used for refined assessment in

complex terrain.

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.

Thank you for your kind attention.

Soil concentrations

The current data show a notable PCDD/F contamination

by the MSWI in the areas under its direct influence.

PCDD/F concentrations in soil samples at the Besançon

site are comparable to levels found in different MSWI

sites.

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.

Other potential emissions sources

There are no adjacent industrial sources, but a main road with heavy traffic runs near the plant.

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.

The PCA provided a one-dimensional model (the first principal component explained 88% of the variance), reflecting high similarities in the congener profiles.

No other additional sources of PCDD/F contamination than the MSWI is, therefore, to be feared.