Data assimilation in the atmospheric dispersion model for nuclear accident assessments

Uncertainty factors in atmospheric dispersion models may influence the reliability of model prediction. The ability of a model in assimilating measurement data will be helpful to improve model prediction. In this paper, data assimilation based on ensemble Kalman filter (EnKF) is introduced to a Monte Carlo atmospheric dispersion model (MCADM) designed for assessment of consequences after an accident release of radionuclides. Twin experiment has been performed in which simulated ground-level dose rates have been assimilated. Uncertainties in the source term and turbulence intensity of wind field are considered, respectively. Methodologies and preliminary results of the application are described. It is shown that it is possible to reduce the discrepancy between the model forecast and the true situation by data assimilation. About 80% of error caused by the uncertainty in the source term is reduced, and the value for that caused by uncertainty in the turbulence intensity is about 50%.     

Estimating spatially-variable first-order rate constants in groundwater reactive transport systems

Numerical reactive transport models are often used as tools to assess aquifers contaminated with reactive groundwater solutes as well as investigating mitigation scenarios. The ability to accurately simulate the fate and transport of solutes, however, is often impeded by a lack of information regarding the parameters that define chemical reactions. In this study, we employ a steady-state Ensemble Kalman Filter (EnKF), a data assimilation algorithm, to provide improved estimates of a spatially-variable first-order rate constant λ through assimilation of solute concentration measurement data into reactive transport simulation results. The methodology is applied in a steady-state, synthetic aquifer system in which a contaminant is leached to the saturated zone and undergoes first-order decay. Multiple sources of uncertainty are investigated, including hydraulic conductivity of the aquifer and the statistical parameters that define the spatial structure of the parameter field. For the latter scenario, an iterative method is employed to identify the statistical mean of λ of the reference system. Results from all simulations show that the filter scheme is successful in conditioning the λ ensemble to the reference λ field. Sensitivity analyses demonstrate that the estimation of the λ values is dependent on the number of concentration measurements assimilated, the locations from which the measurement data are collected, the error assigned to the measurement values, and the correlation length of the λ fields.     

An ensemble Kalman filter for atmospheric data assimilation: Application to wind tunnel data

In the previous work (Zheng et al., 2007, Zheng et al., 2009), a data assimilation method, based on ensemble Kalman filter, has been applied to a Monte Carlo Dispersion Model (MCDM). The results were encouraging when the method was tested by the twin experiment and a short-range field experiment. In this technical note, the measured data collected in a wind tunnel experiment have been assimilated into the Monte Carlo dispersion model. The uncertain parameters in the dispersion model, including source term, release height, turbulence intensity and wind direction have been considered. The 3D parameters, i.e. the turbulence intensity and wind direction, have been perturbed by 3D random fields. In order to find the factors which may influence the assimilation results, eight tests with different specifications were carried out. Two strategies of constructing the 3D perturbation field of wind direction were proposed, and the result shows that the two level strategy performs better than the one level strategy. It is also found that proper standard deviation and the correlation radius of the perturbation field play an important role for the data assimilation results.     

Online update of model state and parameters of a Monte Carlo atmospheric dispersion model by using ensemble Kalman filter

For an atmospheric dispersion model designed for the assessment of nuclear accident consequences, some uncertain model parameters, such as source term and weather conditions, may influence the reliability of model predictions. In this respect, good estimations of both model state and uncertain parameters are required. In this paper, an ensemble Kalman filter (EnKF) based method for simultaneous state and parameter estimation, using off-site radiation monitoring data, is presented. This method is based on a stochastic state space model, which resembles the parameter errors with stochastic quantities. Three imperfect parameters, including the source release rate, wind direction and turbulence intensity were perturbed simultaneously, and multiple parameter estimation were performed. Having been tested against both simulated and real radiation monitoring data, the method was found to be able to realistically reconstruct the real scene of dispersion, as well as the uncertain parameters. The estimated parameters given by EnKF nicely converge to the true values, and the method also tracks the temporal variation of those parameters.     

Ensemble-based data assimilation and targeted observation of a chemical tracer in a sea breeze model

We study the use of ensemble-based Kalman filtering of chemical observations for constraining forecast uncertainties and for selecting targeted observations. Using a coupled model of two-dimensional sea breeze dynamics and chemical tracer transport, we perform three numerical experiments. First, we investigate the chemical tracer forecast uncertainties associated with meteorological initial condition and forcing error. We find that the ensemble variance and error builds during the transition between land and sea breeze phases of the circulation. Second, we investigate the effects on the forecast variance and error of assimilating tracer concentration observations extracted from a truth simulation for a network of surface locations. We find that assimilation reduces the variance and error in both the observed variable (chemical tracer concentrations) and unobserved meteorological variables (vorticity and buoyancy). Finally, we investigate the potential value to the forecast of targeted observations. We calculate an observation impact factor that maximizes the total decrease in model uncertainty summed over all state variables. We find that locations of optimal targeted observations remain similar before and after assimilation of regular network observations.     

Source function estimate by means of variational data assimilation applied to the ETEX-I tracer experiment

The ETEX data set opens new possibilities to develop data assimilation procedures in the area of long-range transport. This paper illustrates the possibilities using a variational approach, where the source term for ETEX-I was reconstructed. The MATCH model (Robertson et al., 1996) has been the basis for this attempt. The timing of the derived emission rates are in accordance with the time period for the ETEX-I release, and a cross validation, with observations beyond the selected assimilation period, shows that the source term gained holds for the entire ETEX-I experiment. A poor-man variational approach was shown to perform nearly as good as a fully variational data assimilation. The issue of quality control has not been considered in this attempt but will be an important part that has to be addressed in future work.     

Differences in uptake and translocation of selenate and selenite by the weeping willow and hybrid willow

BACKGROUND, AIM, AND SCOPE: Due to its essentiality, deficiency, and toxicity to living organisms and the extensive use in industrial activities, selenium (Se) has become an element of global environmental and health concern. Se removal from contaminated sites using physical, chemical, and engineering techniques is quite complicated and expensive. The goal of this study was to investigate uptake and translocation of Se in willows and to provide quantitative information for field application whether Se phytoremediation is feasible and ecologically safe. MATERIALS AND METHODS: Intact pre-rooted plants of hybrid willows (Salix matsudana Koidz x alba L.) and weeping willows (Salix babylonica L.) were grown hydroponically and treated with selenite or selenate at 24.0 +/- 1 degrees C for 144 h. Removal of leaves was also performed as a treatment to quantify the effect of transpiration on translocation and volatilization of Se. At the end of the study, total Se in the hydroponic solution and in different parts of plant tissues was analyzed quantitatively by hydride generation-atomic fluorescence spectrometry. The capacity of willows to assimilate both chemical forms of Se was also evaluated using detached leaves and roots in sealed glass vessels in vivo. Translocation efficiency of Se in both plants was estimated. RESULTS: Significant amounts of the applied selenite and selenate were eliminated from plant growth media by willows during the period of incubation. Both willows showed a significantly higher removal rate for selenate than for selenite (p < 0.05). Substantial differences existed in the distribution of both chemical forms of Se in plant materials: lower stems and roots were the major sites for accumulation of selenite and selenate, respectively. Translocation efficiency for selenite was significantly higher than that for selenate in both willow species (p < 0.01). Compared to the intact trees, remarkable decrease in the removal rate of both chemical forms of Se was found for willows without any leaves (p < 0.01). Volatilization of Se by plant leaves was estimated to be approximately 10% of the total applied selenite or selenate. Significant reduction (>20%) of selenate was observed in the sealed vessel with excised roots of willows, whereas trace amounts of selenite were eliminated from the hydroponic solution in the presence of roots. Detached leaves from neither of them reduced the concentration of selenite or selenate in the solution. DISCUSSION: Due to the significant difference in the removal rate and the distribution of the two chemical forms of Se in plant materials, the conversion of selenate to selenite in hydroponic solution prior to uptake and within plant tissues is unlikely. An independent uptake and translocation mechanisms are likely to exist for each Se chemical species. Uptake of selenate is mediated possibly through an active transport mechanism, whereas that of selenite may possibly depend on plant transpiration. Uptake velocities of selenite are linear (zero-order kinetics), while selenate removal processes obey first-order kinetics. In experiments with detached leaves in closed bottles, the cuticle of leaves was the major obstacle to extract both chemical forms of Se from the hydroponic solution. Phytovolatilization is a biological process playing an important role in Se removal. CONCLUSIONS: Although faster removal rates of selenate than selenite from plant growth media were observed by both willow species, selenite in plant materials was more mobile than selenate. Significant decrease in removal rates of both chemical forms of Se was detected for willows without any leaves. Significant differences in extraction, assimilation and transport pathways for selenite and selenate exist in willow trees. RECOMMENDATIONS AND PERSPECTIVES: Phytoremediation of Se is an attractive approach of cleaning up Se contaminated environmental sites. More detailed investigation on the assimilation of Se in plant roots and transport in tissues will provide further biochemical evidence to explain the differences in uptake and translocation mechanisms between selenite and selenate in willows. A relevant phytoremediation scheme can then be designed to clean up Se contaminated sites. Willows show a great potential for uptake, assimilation and translocation of both selenite and selenate. Phytotreatment of Se is potentially an efficient and practical technology for cleaning up contaminated environmental sites.     

The influence of metal speciation on the bioavailability and sub-cellular distribution of cadmium to the terrestrial isopod, Porcellio dilatatus

Cadmium is a non-essential toxic metal that is able to bioaccumulate in both flora fauna and has the potential to biomagnify in some food chains. However, the form in which cadmium is presented to consumers can alter the bioavailability and possibly the internal distribution of assimilated Cd. Previous studies in our laboratory highlighted differences in Cd assimilation among isopods when they were provided with a plant-based food with either Cd biologically incorporated into plant tissue or superficially amended with ionic Cd(2+). Cd is known for its high affinity for sulphur ligands in cysteine residues which form the basis for metal-binding proteins such as metallothionein. This study compares Cd assimilation efficiency (AE) in Porcellio dilatatus fed with food amended with either cadmium cysteinate or cadmium nitrate in an examination of the influence of Cd speciation on metal bioavailability followed by an examination of the sub-cellular distribution using a centrifugal fractionation protocol. As hypothesized the AE of Cd among isopods fed with Cd(NO(3))(2) (64%, SE=5%) was higher than AE for isopods fed with Cd(Cys)(2) (20%, SE=3%). The sub-cellular distribution also depended on the Cd species provided. Those isopods fed Cd(Cys)(2) allocated significantly more Cd to the cell debris and organelles fractions at the expense of allocation to metal-rich granules (MRG). The significance of the difference in sub-cellular distribution with regard to toxicity is discussed. This paper demonstrates that the assimilation and internal detoxification of Cd is dependent on the chemical form of Cd presented to the isopod.     

Assimilation of conventional and satellite wind observations in a mesoscale atmospheric model for studying atmospheric dispersion

A mesoscale atmospheric model PSU/NCAR MM5 is used to provide operational weather forecasts for a nuclear emergency response decision support system on the southeast coast of India. In this study the performance of the MM5 model with assimilation of conventional surface and upper-air observations along with satellite derived 2-d surface wind data from QuickSCAT sources is examined. Two numerical experiments with MM5 are conducted: one with static initialization using NCEP FNL data and second with dynamic initialization by assimilation of observations using four dimensional data assimilation (FDDA) analysis nudging for a pre-forecast period of 12 h. Dispersion simulations are conducted for a hypothetical source at Kalpakkam location with the HYSPLIT Lagrangian particle model using simulated wind field from the above experiments. The present paper brings out the differences in the atmospheric model predictions and the differences in dispersion model results from control and assimilation runs. An improvement is noted in the atmospheric fields from the assimilation experiment which has led to significant alteration in the trajectory positions, plume orientation and its distribution pattern. Sensitivity tests using different PBL and surface parameterizations indicated the simple first order closure schemes (Blackadar, MRF) coupled with the simple soil model have given better results for various atmospheric fields. The study illustrates the impact of the assimilation of the scatterometer wind and automated weather stations (AWS) observations on the meteorological model predictions and the dispersion results.     

Influence of dense surface meteorological data assimilation on the prediction accuracy of ozone pollution in the southeastern coastal area of the Korean Peninsula

In order to clarify the influence of surface meteorological data assimilation with various resolutions on the photochemical ozone concentration in the southeastern Korean Peninsula, several numerical experiments were conducted. The meteorological and photochemical reaction models used in this study are the fifth-generation mesoscale model (MM5) and the three-dimensional photochemical urban airshed model (UAM-V), respectively. Dense meteorological data make it easier to obtain accurate estimates and surface characteristics than coarse-resolution data. As a result, the estimated temperature obtained from high resolution surface data assimilation in the Busan and Ulsan metropolitan areas is higher than that obtained from coarse resolution surface data assimilation. These high temperatures resulted in strong winds from the sea and a significant dispersion of ozone. In analyses involving an index of agreement (IOA) and root mean square deviation (RMSD), the temperature and wind speed estimated with dense surface data assimilation agreed well with those obtained from observations.However, the influence of dense surface data assimilation tends to be stronger in the flat Ulsan metropolitan area than in the mountainous Busan metropolitan area. This is caused by the heterogeneity of the surface characteristics, including the topography. If the surface parameters induced by regional circulation, such as the topography and land use, are complex and heterogeneous, the efficiency of observational data on data assimilation has to be verified before air pollution is assessed.     

A multi-component data assimilation experiment directed to sulphur dioxide and sulphate over Europe

Fine particulate matter (PM) is relevant for human health and its components are associated with climate effects. The performance of chemistry transport models for PM, its components and precursor gases is relatively poor. The use of these models to assess the state of the atmosphere can be strengthened using data assimilation. This study focuses on simultaneous assimilation of sulphate and its precursor gas sulphur dioxide into the regional chemistry transport model LOTOS–EUROS using an ensemble Kalman filter. The process of going from a single component setup for SO₂ or SO₄ to an experiment in which both components are assimilated simultaneously is illustrated. In these experiments, solely emissions, or a combination of emissions and the conversion rates between SO₂ and SO₄ were considered uncertain. In general, the use of sequential data assimilation for the estimation of the sulphur dioxide and sulphate distribution over Europe is shown to be beneficial. However, the single component experiments gave contradicting results in direction in which the emissions are adjusted by the filter showing the limitations of such applications. The estimates of the pollutant concentrations in a multi-component assimilation have found to be more realistic. We discuss the behavior of the assimilation system for this application. The model uncertainty definition is shown to be a critical parameter. The increased complexity associated with the simultaneous assimilation of strongly related species requires a very careful specification of the experiment, which will be the main challenge in the future data assimilation applications.     

Particulate air quality model predictions using prognostic vs. diagnostic meteorology in central California

Comparisons were made between three sets of meteorological fields used to support air quality predictions for the California Regional Particulate Air Quality Study (CRPAQS) winter episode from December 15, 2000 to January 6, 2001. The first set of fields was interpolated from observations using an objective analysis method. The second set of fields was generated using the WRF prognostic model without data assimilation. The third set of fields was generated using the WRF prognostic model with the four-dimensional data assimilation (FDDA) technique. The UCD/CIT air quality model was applied with each set of meteorological fields to predict the concentrations of airborne particulate matter and gaseous species in central California. The results show that the WRF model without data assimilation over-predicts surface wind speed by ～30% on average and consequently yields under-predictions for all PM and gaseous species except sulfate (S(VI)) and ozone(O₃). The WRF model with FDDA improves the agreement between predicted and observed wind and temperature values and consequently yields improved predictions for all PM and gaseous species. Overall, diagnostic meteorological fields produced more accurate air quality predictions than either version of the WRF prognostic fields during this episode. Population-weighted average PM_2.5 exposure is 40% higher using diagnostic meteorological fields compared to prognostic meteorological fields created without data assimilation. These results suggest diagnostic meteorological fields based on a dense measurement network are the preferred choice for air quality model studies during stagnant periods in locations with complex topography.     

Top-down estimate of mercury emissions in China using four-dimensional variational data assimilation

An inverse modeling method using the four-dimensional variational data assimilation approach is developed to provide a top-down estimate of mercury emission inventory in China. The mercury observations on board the C130 aircraft during the Asian Pacific Regional Aerosol Characterization Experiment (ACE-Asia) campaign in April 2001 are assimilated into a regional chemical transport model, STEM. Using a 340 Mg of elemental mercury emitted in 1999, the assimilation results in an increase in Hg⁰ emissions for China to 1140 Mg in 2001. This is an upper limit amount of the elemental mercury required in China. The average emission-scaling factor is ∼3.4 in China. The spatial changes in the mercury emissions after the assimilation are also evaluated. The largest changes are estimated on the China north-east coastal areas and the areas of north-center China. The influences of the observation and inventory uncertainties and the initial and boundary conditions on the emission estimates are discussed. Increasing the boundary conditions of Hg from 1.2 to 1.5 ng m⁻³, results in a top-down estimate of Hg⁰ emissions for China of 718 Mg, and leads the average scaling factor from 3.4 to 2.1.     

Data assimilation in meteorological pre-processors: Effects on atmospheric dispersion simulations

In previous work [Kovalets, I., Andronopoulos, S., Bartzis, J.G., Gounaris, N., Kushchan, A., 2004. Introduction of data assimilation procedures in the meteorological pre-processor of atmospheric dispersion models used in emergency response systems. Atmospheric Environment 38, 457–467.] the authors have developed data assimilation (DA) procedures and implemented them in the frames of a diagnostic meteorological pre-processor (MPP) to enable simultaneous use of meteorological measurements with numerical weather prediction (NWP) data. The DA techniques were directly validated showing a clear improvement of the MPP output quality in comparison with meteorological measurement data. In the current paper it is demonstrated that the application of DA procedures in the MPP, to combine meteorological measurements with NWP data, has a noticeable positive effect on the performance of an atmospheric dispersion model (ADM) driven by the MPP output. This result is particularly important for emergency response systems used for accidental releases of pollutants, because it provides the possibility to combine meteorological measurements with NWP data in order to achieve more reliable dispersion predictions. This is also an indirect way to validate the DA procedures applied in the MPP. The above goal is achieved by applying the Lagrangian ADM DIPCOT driven by meteorological data calculated by the MPP code both with and without the use of DA procedures to simulate the first European tracer experiment (ETEX I). The performance of the ADM in each case was evaluated by comparing the predicted and the experimental concentrations with the use of statistical indices and concentration plots. The comparison of resulting concentrations using the different sets of meteorological data showed that the activation of DA in the MPP code clearly improves the performance of dispersion calculations in terms of plume shape and dimensions, location of maximum concentrations, statistical indices and time variation of concentration at the detectors locations.     

Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing

This two-part paper reports on the development, implementation, and improvement of a version of the Community Multi-Scale Air Quality (CMAQ) model that assimilates real-time remotely-sensed aerosol optical depth (AOD) information and ground-based PM_2.5 monitor data in routine prognostic application. The model is being used by operational air quality forecasters to help guide their daily issuance of state or local-agency-based air quality alerts (e.g. action days, health advisories). Part 1 describes the development and testing of the initial assimilation capability, which was implemented offline in partnership with NASA and the Visibility Improvement State and Tribal Association of the Southeast (VISTAS) Regional Planning Organization (RPO). In the initial effort, MODIS-derived aerosol optical depth (AOD) data are input into a variational data-assimilation scheme using both the traditional Dark Target and relatively new “Deep Blue” retrieval methods. Evaluation of the developmental offline version, reported in Part 1 here, showed sufficient promise to implement the capability within the online, prognostic operational model described in Part 2. In Part 2, the addition of real-time surface PM_2.5 monitoring data to improve the assimilation and an initial evaluation of the prognostic modeling system across the continental United States (CONUS) is presented.

Implications: Air quality forecasts are now routinely used to understand when air pollution may reach unhealthy levels. For the first time, an operational air quality forecast model that includes the assimilation of remotely-sensed aerosol optical depth and ground based PM_2.5 observations is being used. The assimilation enables quantifiable improvements in model forecast skill, which improves confidence in the accuracy of the officially-issued forecasts. This helps air quality stakeholders be more effective in taking mitigating actions (reducing power consumption, ride-sharing, etc.) and avoiding exposures that could otherwise result in more serious air quality episodes or more deleterious health effects.     

Magnesium and the regulation of lead in three populations of the garden snail Cantareus aspersus

Helicid snails appear to regulate Pb more closely than other toxic metals, though it is reported as the least toxic. No regulatory mechanism has been described in animals, and the possible role of Mg in limiting Pb assimilation is examined here for the first time. Three populations of Cantareus aspersus were fed Pb and Ca with three levels of Mg for up to 64 days. Metal assimilation and production efficiency was calculated for each of 108 snails. Populations differed in their pattern of uptake but soft tissue Pb was unaffected by dietary Mg. The proportion of Pb assimilated did not change as soft tissue concentrations increased, indicating no specific regulatory mechanism. The daily addition of Pb to the soft tissues increases with growth rate suggesting uptake is instead some function of growth or cell turnover. Bioconcentration factors varied with time and are unreliable indicators of an evolved regulatory mechanism for Pb.     

Effects of bisphenol A on ammonium assimilation in soybean roots

Bisphenol A (BPA), which is ubiquitous in the environment, is an example of an endocrine-disrupting compound (EDC). Ammonium assimilation has an important function in plant growth and development. However, insufficient information on the potential effect of BPA on ammonium assimilation in plants is available. In this study, the effects of BPA on ammonium assimilation in roots of soybean seedlings were investigated. During the stress period, 1.5 mg L^?1 of BPA improved glutamine synthetase (GS)/glutamate synthase (GOGAT) cycle and glutamate dehydrogenase (GDH) pathway in ammonium assimilation. The amino acid and the soluble protein contents increased in the soybeans. At 17.2 and 50.0 mg L^?1 of BPA, the GS/GOGAT cycle was inhibited and the GDH pathway was promoted. The amino acid content increased and the soluble protein content decreased. During the recovery period, the GS/GOGAT cycle and the GDH pathway recovered at 1.5 and 17.2 mg L^?1 of BPA but not at 50.0 mg L^?1 of BPA. The amino acid content continuously increased and the soluble protein content decreased compared with those in the control treatment. In summary, BPA treatment could affect the contents of soluble protein and amino acid in the soybean roots by regulating ammonium assimilation.     

Effects of metals on feeding rate and digestive enzymes in Gammarus fossarum: An in situ experiment

The feeding activity and afterward the assimilation of the products resulting of the food digestion, allow organisms to obtain energy useful for growth, maintenance and reproduction. These biological parameters may be studied to assess the impact of contaminants on the energy metabolism of organisms, which could induce potential effects at an individual level. The studied species was an amphipod Gammarus fossarum, which has a high ecological relevance since it is widespread in European streams and plays a major role in the breakdown of leaf litter. Thus some G. fossarum were transplanted in four sites of a river characterized by metal contamination (Amous River, France). The following parameters were studied: digestive enzymes activities (esterase, β-glucosidase, β-galactosidase, amylase and endoglucanase), feeding rate, metal bioaccumulation and survival. Results showed a strong relationship between digestive enzymes activities, feeding rate and metal contents.     

Targeting of observations for accidental atmospheric release monitoring

In the event of an accidental atmospheric release of radionuclides from a nuclear power plant, accurate real-time forecasting of the activity concentrations of radionuclides is acutely required by the decision makers for the preparation of adequate countermeasures. Yet, the accuracy of the forecasted plume is highly dependent on the source term estimation. Inverse modelling and data assimilation techniques should help in that respect. However the plume can locally be thin and could avoid a significant part of the radiological monitoring network surrounding the plant. Deploying mobile measuring stations following the accident could help to improve the source term estimation. In this paper, a method is proposed for the sequential reconstruction of the plume, by coupling a sequential data assimilation algorithm based on inverse modelling with an observation targeting strategy. The targeting design strategy consists in seeking the optimal locations of the mobile monitors at time t + 1 based on all available observations up to time t.The performance of the sequential assimilation with and without targeting of observations has been assessed in a realistic framework. It focuses on the Bugey nuclear power plant (France) and its surroundings within 50 km from the plant. The existing surveillance network is used and realistic observational errors are assumed. The targeting scheme leads to a better estimation of the source term as well as the activity concentrations in the domain. The mobile stations tend to be deployed along plume contours, where activity concentration gradients are important. It is shown that the information carried by the targeted observations is very significant, as compared to the information content of fixed observations. A simple test on the impact of model error from meteorology shows that the targeting strategy is still very useful in a more uncertain context.     

Polymer biodegradation: mechanisms and estimation techniques

Within the frame of the sustainable development, new materials are being conceived in order to increase their biodegradability properties. Biodegradation is considered to take place throughout three stages: biodeterioration, biofragmentation and assimilation, without neglect the participation of abiotic factors. However, most of the techniques used by researchers in this area are inadequate to provide evidence of the final stage: assimilation. In this review, we describe the different stages of biodegradation and we state several techniques used by some authors working in this domain. Validate assimilation (including mineralisation) is an important aspect to guarantee the real biodegradability of items of consumption (in particular friendly environmental new materials). The aim of this review is to emphasise the importance of measure as well as possible, the last stage of the biodegradation, in order to certify the integration of new materials into the biogeochemical cycles. Finally, we give a perspective to use the natural labelling of stable isotopes in the environment, by means of a new methodology based on the isotopic fractionation to validate assimilation by microorganisms.