Polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) soil contamination in Lausanne, Switzerland: Combining pollution mapping and human exposure assessment for targeted risk management.

In December 2020, high soil concentrations of polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) were discovered across large parts of Lausanne, Switzerland. Concentrations reached up to 640 ng TEQWHO-2005/kg dry weight. The most likely source was a former municipal waste incinerator. A three-step, multidisciplinary approach to human health risk assessment was conducted to determine the potential population exposure to PCDD/Fs and identify appropriate preventive measures. First, exposure scenarios were developed based on contaminated land uses. Second, the toxicological risks of different scenarios were evaluated using a toxicokinetic model estimating increases in blood serum PCDD/F concentrations over background concentrations from the general population's food consumption. Third, a detailed geostatistical mapping of PCDD/F soil contamination was performed. Stochastic simulations with an external drift and an anisotropic model of the variogram were generated to incorporate the effects of distance from emission source, topography, and main wind directions on the spatial distribution of PCDD/Fs in topsoil. Three main scenarios were assessed: i) direct ingestion of soil by children in playgrounds; ii) consumption of vegetables from private gardens by children and adults; and iii) consumption of food from livestock and poultry raised on contaminated soil. The worst exposure scenario involved the consumption of eggs from private hen houses, resulting in PCDD/F concentrations in serum an order of magnitude higher than might normally be expected. No relevant increases in serum concentrations were calculated for direct soil ingestion and vegetable consumption, except for cucurbitaceous vegetables. Combining mapping and exposure scenario assessment resulted in targeted protective measures for land users, especially concerning food consumption. The results also raised concerns about the potential unsafe consumption of products derived from animals raised on land with PCDD/F concentrations only moderately over environmental background levels.

Factorial Kriging Analysis as a tool for explaining the complex spatial distribution of metals in sediments.

Rivers flowing through urbanized and industrial areas are usually greatly damaged by anthropogenic activities discharging contaminants. Characterizing the spatial distribution of pollutants in sediments is of high importance for selecting a suitable remediation operation, but is a complex task because this spatial variability is the result of various physical and chemical mechanisms occurring at different scales. Factorial Kriging Analysis (FKA) was applied on data collected in a canalized river (Scarpe, France) for that purpose, because this geostatistical technique allows to decompose a given variable into components of different spatial correlations and map them separately. This decomposition is meaningful provided that it can be related to physical phenomena occurring at the identified spatial scales. FKA applied to Cd and Zn concentrations in sediments of the Scarpe river proved to be effective, allowing their mapping to be decomposed in a first map related to a short-range spatial correlation corresponding to hot spots interpreted as the impact of industrial and urban inputs located along the canal, and a second map related to a long-range spatial variability associated with long pollutant plumes interpreted as the effect of one major upstream pollutant input.

Geostatistical approach for assessing soil volumes requiring remediation: validation using lead-polluted soils underlying a former smelting works.

Assessing the volume of soil requiring remediation and the accuracy of this assessment constitutes an essential step in polluted site management. If this remediation volume is not properly assessed, misclassification may lead both to environmental risks (polluted soils may not be remediated) and financial risks (unexpected discovery of polluted soils may generate additional remediation costs). To minimize such risks, this paper proposes a geostatistical methodology based on stochastic simulations that allows the remediation volume and the uncertainty to be assessed using investigation data. The methodology thoroughly reproduces the conditions in which the soils are classified and extracted at the remediation stage. The validity of the approach is tested by applying it on the data collected during the investigation phase of a former lead smelting works and by comparing the results with the volume that has actually been remediated. This real remediated volume was composed of all the remediation units that were classified as polluted after systematic sampling and analysis during clean-up stage. The volume estimated from the 75 samples collected during site investigation slightly overestimates (5.3% relative error) the remediated volume deduced from 212 remediation units. Furthermore, the real volume falls within the range of uncertainty predicted using the proposed methodology.

Forecasting the number of soil samples required to reduce remediation cost uncertainty.

Sampling scheme design is an important step in the management of polluted sites. It largely controls the accuracy of remediation cost estimates. In practice, however, sampling is seldom designed to comply with a given level of remediation cost uncertainty. In this paper, we present a new technique that allows one to estimate of the number of samples that should be taken at a given stage of investigation to reach a forecasted level of accuracy. The uncertainty is expressed both in terms of volume of polluted soil and overall cost of remediation. This technique provides a flexible tool for decision makers to define the amount of investigation worth conducting from an environmental and financial perspective. The technique is based on nonlinear geostatistics (conditional simulations) to estimate the volume of soil that requires remediation and excavation and on a function allowing estimation of the total cost of remediation (including investigations). The geostatistical estimation accounts for support effect, information effect, and sampling errors. The cost calculation includes mainly investigation, excavation, remediation, and transportation. The application of the technique on a former smelting work site (lead pollution) demonstrates how the tool can be used. In this example, the forecasted volumetric uncertainty decreases rapidly for a relatively small number of samples (20-50) and then reaches a plateau (after 100 samples). The uncertainty related to the total remediation cost decreases while the expected total cost increases. Based on these forecasts, we show how a risk-prone decision maker would probably decide to take 50 additional samples while a risk-averse decision maker would take 100 samples.