Do transient hydrological processes explain the variability of strontium-90 activity in groundwater downstream of a radioactive trench near Chernobyl?

The Chernobyl Pilot Site (CPS) was created in 2000 in order to study radionuclide migration processes to the geosphere from radioactive material of the Red forest buried in a trench. In this article, the data collected in the CPS up to 2015 are analyzed to identify the links between hydrological conditions and release of strontium-90 (90Sr) from the trench. Then, a flow-and-transport model is used for simulating distribution of 90Sr both in the unsaturated and saturated zones downstream of the trench. The results show that the 90Sr activity in groundwater is strongly transient in time, due to the high inter-annual variability of both the recharge rate and the groundwater level (some particularly wet winters resulted in saturation of the bottom part of the trench). In addition, the parameters that govern the sorption of 90Sr in trench material appear to vary significantly in space (the retardation factor ranges from 10 to 50 depending on the location). This spatiotemporal variability could hide some critical processes, e.g., related to a long-term trend, and needs to be characterized through an appropriate sampling frequency.

Combining geostatistics and simulations of flow and transport to characterize contamination within the unsaturated zone.

Characterization of contamination in soils or groundwater resulting from industrial activities is critical for site remediation. In this study, geostatistics and physically-based simulations are combined for estimating levels of contamination within the unsaturated zone. First, a large number of flow and transport simulations are run and their outputs are used to compute empirical non stationary variograms. Then, these empirical variograms, called numerical variograms and which are expected to reproduce the spatial variability of the contaminant plume better than a usual variogram model based on observations only, are used for kriging. The method is illustrated on a two-dimensional synthetic reference test case, with a contamination due to a point source of tritium (e.g. tritiated water). The diversity among the simulated tritium plumes is induced by numerous sets of hydraulic parameter fields conditioned by samples from the reference test case. Kriging with numerical variograms is then compared to ordinary kriging and kriging with an external drift: the results show that kriging with numerical variograms improves the estimates, all the more that few observations are available, underlining the interest of the method. When considering a relatively dense sampling scenario, the mean absolute error with kriging with numerical variograms is reduced by 52% compared to ordinary kriging and by 45% compared to kriging with an external drift. For a scarcer sampling, those errors are respectively reduced of 73% and 34%. However, the performance of the method regarding the classification into contaminated or not contaminated zones depends on the pollution threshold. Yet, the distribution of contamination is better reproduced by kriging with numerical variograms than by ordinary kriging or kriging with an external drift.

A necessary distinction between spatial representativeness of an air quality monitoring station and the delimitation of exceedance areas.

The European legislation on ambient air quality introduces the concepts of spatial representativeness of a monitoring station and spatial extent of an exceedance zone. Spatial representativeness is an essential macro-scale siting criterion which should be evaluated before the setting-up and during the life of a monitoring point. As for the exceedance area, it has to be defined each time an environmental objective is exceeded in an assessment zone. No specific approach is prescribed to delimit such areas. A probabilistic methodology is presented, based on a preliminary kriging estimation of atmospheric concentrations at each point of the domain. It is applied to NO2 pollution on the urban scale. In the proposed approach, a point belongs to the area of representativeness of a station if its concentration differs from the station measurement by less than a given threshold. To take the estimation uncertainty into account, the standard deviation of the kriging error is used in a probabilistic framework. The choice of the criteria used to deal with overlapping areas is first tested on NO2 annual mean concentration maps of France, built by combining surface monitoring observations and outputs from the CHIMERE chemistry transport model. At the local scale, data from passive sampling surveys and high -resolution auxiliary variables are used to provide a more precise estimation of the background pollution in different French cities. The traffic-related pollution can also be accounted for in the map by additional predictors such as distance to the road, and traffic-related NOx emissions. Similarly, the proposed approach is implemented to identify the points, at a given statistical risk, where the NO2 concentration is above the annual limit value.

Analysis of the relationship binding in situ gamma count rates and soil sample activities: Implication on radionuclide inventory and uncertainty estimates due to spatial variability.

The paper strives to identify through geostatistical simulations the parameters which build up a correlation between radionuclide activity concentrations measured on core samples and corresponding in situ total gamma count rates measured into boreholes drilled within the contaminated soil. This numerical exercise demonstrates that a linear relationship should exist between logarithmic values of in situ count rates and logarithmic values of activity concentrations when the contamination is strongly structured through space. A sensitivity analysis to some parameters (geostatistical range of the contamination structure, core sampling method, soil water content, multiple gamma-emitter contamination, etc.) is undertaken to identify which situations may impede the use of such a correlation. Then this approach is applied on Chernobyl measurements undertaken in 2015 and compared to the co-kriging method which considers the localization of the measurements and the additional measurements. It appears that co-kriging is a better estimator than linear regression, but the latter remains an acceptable way of estimating activity from gamma emitters and presents better results than lognormal regression. Therefore, total gamma logging measurements performed into boreholes of porous media contaminated by gamma-emitting radionuclides can be used for characterizing contamination and dealing with its spatial variability with the use of co-kriging.

How to describe organic contamination in soils: A model comparison for chlorinated solvent concentrations at industrial site scale.

The heavy-tailed distribution of the data of organic pollution in soils can raise specific problems in estimating and mapping the concentrations. Some high values often highly impact the sample variogram and extend the pollution hot-spots on the estimation maps. Non-linear geostatistical models, such as the anamorphosed Gaussian model, have been proposed in the 70's. They allow a consistent estimate of the concentrations and the probability that the concentrations exceed a cut-off. These well-founded methods are rarely used by environmental consultants, mainly because of time constraints and because the hypotheses of the models are not always satisfied. To estimate the concentrations, an empirical method widely used by environmental consultants consists of truncating the high values to gain robustness in the variogram analysis. The truncation value is arbitrary, even if it has a strong influence on the estimates of the concentrations. Proposed to handle heavy-tailed distributions of ore grades, the top-cut model (Rivoirard et al., 2013) justifies the use of truncated values but corrects the underestimation of the mean caused by truncation. In this model, the decomposition of the variable into three components (the truncated value, a weighted indicator at the top-cut threshold and a residual) makes the variographic study more robust and guides choosing the top-cut threshold. In the case of a chlorinated solvent contamination, a detailed comparison between several estimation methods is performed: ordinary kriging, kriging after truncation of the highest concentrations and estimation within the top-cut model, with structured or pure nugget residual. A sensitivity study to the top-cut threshold is realized. The results of two implementations of the cross-validation are compared. The top-cut model with nugget residual appears to be robust, even if the hypotheses of the model are not perfectly satisfied.

Mapping the Centimeter-Scale Spatial Variability of PAHs and Microbial Populations in the Rhizosphere of Two Plants.

Rhizoremediation uses root development and exudation to favor microbial activity. Thus it can enhance polycyclic aromatic hydrocarbon (PAH) biodegradation in contaminated soils. Spatial heterogeneity of rhizosphere processes, mainly linked to the root development stage and to the plant species, could explain the contrasted rhizoremediation efficiency levels reported in the literature. Aim of the present study was to test if spatial variability in the whole plant rhizosphere, explored at the centimetre-scale, would influence the abundance of microorganisms (bacteria and fungi), and the abundance and activity of PAH-degrading bacteria, leading to spatial variability in PAH concentrations. Two contrasted rhizospheres were compared after 37 days of alfalfa or ryegrass growth in independent rhizotron devices. Almost all spiked PAHs were degraded, and the density of the PAH-degrading bacterial populations increased in both rhizospheres during the incubation period. Mapping of multiparametric data through geostatistical estimation (kriging) revealed that although root biomass was spatially structured, PAH distribution was not. However a greater variability of the PAH content was observed in the rhizosphere of alfalfa. Yet, in the ryegrass-planted rhizotron, the Gram-positive PAH-degraders followed a reverse depth gradient to root biomass, but were positively correlated to the soil pH and carbohydrate concentrations. The two rhizospheres structured the microbial community differently: a fungus-to-bacterium depth gradient similar to the root biomass gradient only formed in the alfalfa rhizotron.

Pluri-annual sediment budget in a navigated river system: the Seine River (France).

This study aims at quantifying pluri-annual Total Suspended Matter (TSM) budgets, and notably the share of river navigation in total re-suspension at a long-term scale, in the Seine River along a 225 km stretch including the Paris area. Erosion is calculated based on the transport capacity concept with an additional term for the energy dissipated by river navigation. Erosion processes are fitted for the 2007-2011 period based on i) a hydrological typology of sedimentary processes and ii) a simultaneous calibration and retrospective validation procedure. The correlation between observed and simulated TSM concentrations is higher than 0.91 at all monitoring stations. A variographic analysis points out the possible sources of discrepancies between the variabilities of observed and simulated TSM concentrations at three time scales: sub-weekly, monthly and seasonally. Most of the error on the variability of simulated concentrations concerns sub-weekly variations and may be caused by boundary condition estimates rather than modeling of in-river processes. Once fitted, the model permits to quantify that only a small fraction of the TSM flux sediments onto the river bed (<0.3‰). The river navigation contributes significantly to TSM re-suspension in average (about 20%) and during low flow periods (over 50%). Given the significant impact that sedimentary processes can have on the water quality of rivers, these results highlight the importance of taking into account river navigation as a source of re-suspension, especially during low flow periods when biogeochemical processes are the most intense.

Environmental statistics revisited: is the mean reliable?

The sample mean of data collected during critical pollution periods is a biased estimator of the annual mean. The bias can be corrected by weighting techniques, which take into account the measurement dates. The differences with standard calculations become important to characterize the temporal evolution when sampling changes with time. Sound statistical methods are therefore needed.

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.