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%.     

A model study for ambient air quality analysis using routine meteorological observations

In the present study, more realistic and easily adaptable input parameters have been used with a view to investigating the long-range air quality analysis for the dispersion of air pollutants emitted from an area source with a multiple box model. The model formulation has been discussed at length for the ground level sources when convective conditions prevail. The routine meteorological observations have been used for the computation of sensible surface heat flux, friction velocity and mixing depth. A radiation model provides the estimates of the sensible surface heat flux. Based on the similarity theory, an iterative procedure has been adopted for the estimation of friction velocity which provides a coupling of radiation computation and the surface layer of the planetary boundary layer through surface heat flux expression. The important parameters—wind speed and eddy diffusivity profiles—have been derived and have been used to obtain the concentration patterns as hourly averages. The procedure could be easily adopted where observed meteorological parameters may be used for studying the dispersal of pollutants from the ground level sources.     

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.     

A hybrid plume model for local-scale atmospheric dispersion

This paper presents a local-scale dispersion model, based on atmospheric boundary layer scaling theory. In the vicinity of the source, Gaussian equations are used in both the horizontal and vertical directions. After a specified transition distance, gradient transfer theory is applied in the vertical direction, while the horizontal dispersion is still assumed to be Gaussian. The dispersion parameters and eddy diffusivity are modelled in a form, which facilitates the use of a meteorological pre-processor. We present a novel model of the vertical eddy diffusivity (K_z), which is a continuous function of height in various atmospheric scaling regions. The model also includes a treatment of the dry deposition of gases and particulate matter. The accuracy of the numerical model was analysed by comparing the model predictions with two analytical solutions; the numerical deviations from these solutions were less than 2% for the computational regime. The model has been tested against the Kincaid experimental field data. The agreement of the predictions and the data is good on the average, although the internal variation of the predictions versus data scatter plot is substantial.     

A stochastic-advective transport model for NAPL dissolution and degradation in non-uniform flows in porous media

Remediation schemes for contaminated sites are often evaluated to assess their potential for source zone reduction of mass, or treatment of the contaminant between the source and a control plane (CP) to achieve regulatory limits. In this study, we utilize a stochastic stream tube model to explain the behavior of breakthrough curves (BTCs) across a CP. At the local scale, mass dissolution at the source is combined with an advection model with first-order decay for the dissolved plume. Field-scale averaging is then employed to account for spatial variation in mass within the source zone, and variation in the velocity field. Under the assumption of instantaneous mass transfer from the source to the moving liquid, semi-analytical expressions for the BTC and temporal moments are developed, followed by derivation of expressions for effective velocity, dispersion, and degradation coefficients using the method of moments. It is found that degradation strongly influences the behavior of moments and the effective parameters. While increased heterogeneity in the velocity field results in increased dispersion, degradation causes the center of mass of the plume to shift to earlier times, and reduces the dispersion of the BTC by lowering the concentrations in the tail. Modified definitions of effective parameters are presented for degrading solutes to account for the normalization constant (zeroth moment) that keeps changing with time or distance to the CP. It is shown that anomalous dispersion can result for high degradation rates combined with wide variation in velocity fluctuations. Implications of model results on estimating cleanup times and fulfillment of regulatory limits are discussed. Relating mass removal at the source to flux reductions past a control plane is confounded by many factors. Increased heterogeneity in velocity fields causes mass fluxes past a control plane to persist, however, aggressive remediation between the source and CP can reduce these fluxes.     

A statistical approach for estimating uncertainty in dispersion modeling: An example of application in southwestern USA

A method based on a statistical approach of estimating uncertainty in simulating the transport and dispersion of atmospheric pollutants is developed using observations and modeling results from a tracer experiment in the complex terrain of the southwestern USA. The method takes into account the compensating nature of the error components by representing all terms, except dispersion error and variance of stochastic processes. Dispersion error and the variance of the stochastic error are estimated using the maximum likelihood estimation technique applied to the equation for the fractional error. Mesoscale Model 5 (MM5) and a Lagrangian random particle dispersion model with three optional turbulence parameterizations were used as a test bed for method application. Modeled concentrations compared well with the measurements (correlation coefficients on the order of 0.8). The effects of changing two structural components (the turbulence parameterization and the model grid vertical resolution) on the magnitude of the dispersion error also were examined. The expected normalized dispersion error appears to be quite large (up to a factor of three) among model runs with various turbulence schemes. Tests with increased vertical resolution of the atmospheric model (MM5) improved most of the dispersion model statistical performance measures, but to a lesser extent compared to selection of a turbulence parameterization. Method results confirm that structural components of the dispersion model, namely turbulence parameterizations, have the most influence on the expected dispersion error.     

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.     

Interval estimation of urban ozone level and selection of influential factors by employing automatic relevance determination model

In this work, we focus on simulating the ground-level ozone (O3) time series and its daily maximum concentration in Hong Kong urban air by employing the multilayer perceptron (MLP) model combined with the automatic relevance determination (ARD) method (for simplicity, we name it as MLP-ARD model). Two air quality monitoring sites in Hong Kong, i.e., Tsuen Wan and Tung Chung, are selected for the numerical experiments. The MLP-ARD model based on Bayesian evidence framework can provide reliable interval estimation of real observation as well as offering efficient strategy to avoid over-fitting. The performance comparisons between MLP-ARD model and traditional artificial neural network (ANN) model based on maximum likelihood indicate that MLP-ARD model is more powerful to capture the wild fluctuation of O3 level especially during O3 episodes than the traditional model. Furthermore, it can assess and rank the input variables for the prediction according to their relative importance to the output variable, i.e., the daily maximum O3 concentration in this study. The preliminary experimental results indicate that nitric oxide (NO) and solar radiation are the most important input variables for O3 prediction at both selected sites. In addition, the previous daily maximum O3 level is also important for Tung Chung site. In this regard, MLP-ARD model is a feasible tool to interpret the real physical and chemical process of urban O3 variation.     

Prediction of atmospheric dispersion of pollutants in an airport environment

In this article we discuss the development of a methodology to predict atmospheric turbulent dispersion of pollutants generated from air traffic in an airport. It is based on the Lagrangian stochastic model (LSM), developed by Das and Durbin [2005. A Lagrangian stochastic model for dispersion in stratified turbulence, Physics of Fluids 17, 025109]. The approach is via the backward trajectory formulation of the model. The sources and receptors in an airport type problem are modeled as spheres and procedures have been derived for concentration calculation by both forward and backward trajectory methods. Some tests are performed to highlight certain features of the method. The turbulence statistics that are required as input are provided in terms of similarity profiles. The airport domain is partitioned to make the required search algorithms efficient. Pollutant concentration profiles are calculated over a range of meteorological data.     

Assessing sensitivity of source term estimation

Source term estimation algorithms compute unknown atmospheric transport and dispersion modeling variables from concentration observations made by sensors in the field. Insufficient spatial and temporal resolution in the meteorological data as well as inherent uncertainty in the wind field data make source term estimation and the prediction of subsequent transport and dispersion extremely difficult. This work addresses the question: how many sensors are necessary in order to successfully estimate the source term and meteorological variables required for atmospheric transport and dispersion modeling?The source term estimation system presented here uses a robust optimization technique – a genetic algorithm (GA) – to find the combination of source location, source height, source strength, surface wind direction, surface wind speed, and time of release that produces a concentration field that best matches the sensor observations. The approach is validated using the Gaussian puff as the dispersion model in identical twin numerical experiments. The limits of the system are tested by incorporating additive and multiplicative noise into the synthetic data. The minimum requirements for data quantity and quality are determined by an extensive grid sensitivity analysis. Finally, a metric is developed for quantifying the minimum number of sensors necessary to accurately estimate the source term and to obtain the relevant wind information.     

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.     

工业点源大气污染扩散空间信息系统

开发了一个基于高斯扩散的大气污染扩散空间信息系统,用于模拟工业点源污染对区域大气质量的影响。该工业点源污染模型包括工业点源数据库、扩散参数、气象条件和大气质量评价4个主要数据库。用该模型计算上海市主要工业区的SO2排放,结果表明,该模型为模拟SO2污染扩散提供了一个有效便捷的方法。     

Review of indoor emission source models. Part 2. Parameter estimation

This review consists of two parts. Part 1 provides an overview of 52 indoor emission source models. Part 2--this paper-focuses on parameter estimation, a topic that is critical to modelers but has never been systematically discussed. A perfectly valid model may not be a useful one if some of its parameters are difficult to estimate in the absence of experimental data. This is true for both statistical and mass transfer models. Forty-eight methods are compiled and reviewed in this paper. Overall, developing methods for parameter estimation has fallen behind the development of models. Such imbalance is the main reason that many models have been left on the shelf since they were published.     

Use of a reactive transport model to describe reductive dechlorination (RD) as a remediation design tool: application at a CAH-contaminated site

In this paper, a numerical model is presented that is capable of describing the complex set of biochemical processes that occur in chlorinated aliphatic hydrocarbon (CAH)-contaminated groundwater when an exogenous electron donor is added. The reactive pattern is based on the degradation pathways of both chlorinated ethanes and ethenes, and it includes electron donor production (H₂ and acetate) from the fermentation of an organic substrate as well as rate-limiting processes related to electron acceptor competition. Coupling of the kinetic model to a convection–dispersion module is described. The calibration phase was carried out using data obtained at a real CAH-contaminated site in the north of Italy. Model simulations of different application scenarios are presented to draw general conclusions on the effectiveness of reductive dechlorination (RD) as a possible cleanup strategy. Early outcomes indicate that cleanup targets can only be achieved if source longevity is reduced. Therefore, metabolic RD is expected to produce beneficial effects because it is known to induce bioenhanced degradation and transformation of CAHs.     

Development of a fuzzy-stochastic nonlinear model to incorporate aleatoric and epistemic uncertainty

Site uncertainties significantly influence groundwater flow and contaminant transport predictions. Aleatoric and epistemic uncertainty are both identified in site characterization and represented using proper uncertainty theories. When one theory best represents one parameter whereas a different theory may be more suitable for another parameter, the hybrid propagation of aleatoric (random) and epistemic (nonrandom) uncertainties will occur. The computational challenges of joint propagation of aleatoric and epistemic uncertainty through groundwater flow and contaminant transport models are significant. A fuzzy-stochastic nonlinear model was developed in this paper to incorporate these two types of uncertain site information and reduce the computational cost. The results show that (1) the computational cost using the nonlinear model is reduced compared with that of using the sparse grid algorithm and Monte Carlo methods; (2) the uncertainty of hydraulic conductivity (K) significantly influences the water head and solute distribution at the observation wells compared to other uncertain parameters, such as the storage coefficient and the distribution coefficient (K_d); and (3) the combination of multiple uncertain parameters substantially affects the simulation results. Neglecting site uncertainties may lead to unrealistic predictions.     

Assessment of ensemble-based chemical data assimilation in an idealized setting

Data assimilation is the process of integrating observational data and model predictions to obtain an optimal representation of the state of the atmosphere. As more chemical observations in the troposphere are becoming available, chemical data assimilation is expected to play an essential role in air quality forecasting, similar to the role it has in numerical weather prediction (NWP). Considerable progress has been made recently in the development of variational tools for chemical data assimilation. In this paper, we assess the performance of the ensemble Kalman filter (EnKF). Results in an idealized setting show that EnKF is promising for chemical data assimilation.     

Estimation of Methyl tert -Butyl Ether Plume Length Using the Domenico Analytical Model

Methyl tert -butyl ether (MTBE) plume is controlled by many factors, primarily by groundwater flow velocity, dispersion, natural attenuation. This study employed an analytical model introduced by Domemico (1987, J. Hydrol 91 , 49-58.) to describe the MTBE concentration distribution horizontal pattern and estimated the MTBE plume length. The model was applied to 90 leaking underground storage tank cases in Los Angeles, CA, U.S.A. The analytical model was calibrated with field data for each ease using a Microsoft Excel spreadsheet program. Methyl tert -butyl ether concentrations in one source monitoring well and one to two downgradient centerline monitoring wells were used for each case study. When the centerline well is not available, the closest off-centerline wells were projected to the centerline using an ellipse trigonometry method. The model parameter values for longitudinal dispersivity, groundwater velocity, and degradation rate constant were calibrated using the field data and then used to estimate the maximum distance between source well and the plume edge. This study demonstrates that the Domenico model can be applied to MTBE plume investigation when adequate field data are available. The correlation coefficients calculated based on the results of the 90 case studies indicate that MTBE plume length has a poor correlation with MTBE concentration at the source well, and a moderate negative correlation with the degradation rate constant ( m 0.65) and u / v ratio (0.64). Furthermore, MTBE plume length has a poor correlation with the longitudinal dispersivity ( m 0.4), hydraulic gradient ( m 0.1), and groundwater velocity (0.17).     

Bayesian inference for source determination with applications to a complex urban environment

The problem of determining the source of an emission from the limited information provided by a finite and noisy set of concentration measurements obtained from real-time sensors is an ill-posed inverse problem. In general, this problem cannot be solved uniquely without additional information. A Bayesian probabilistic inferential framework, which provides a natural means for incorporating both errors (model and observational) and prior (additional) information about the source, is presented. Here, Bayesian inference is applied to find the posterior probability density function of the source parameters (location and strength) given a set of concentration measurements. It is shown how the source–receptor relationship required in the determination of the likelihood function can be efficiently calculated using the adjoint of the transport equation for the scalar concentration. The posterior distribution of the source parameters is sampled using a Markov chain Monte Carlo method. The inverse source determination method is validated against real data sets acquired in a highly disturbed flow field in an urban environment. The data sets used to validate the proposed methodology include a water-channel simulation of the near-field dispersion of contaminant plumes in a large array of building-like obstacles (Mock Urban Setting Trial) and a full-scale field experiment (Joint Urban 2003) in Oklahoma City. These two examples demonstrate the utility of the proposed approach for inverse source determination.     

A multimedia environmental model of chemical distribution: fate, transport, and uncertainty analysis

This paper presented a framework for analysis of chemical concentration in the environment and evaluation of variance propagation within the model. This framework was illustrated through a case study of selected organic compounds of benzo[alpha]pyrene (BAP) and hexachlorobenzene (HCB) in the Great Lakes region. A multimedia environmental fate model was applied to perform stochastic simulations of chemical concentrations in various media. Both uncertainty in chemical properties and variability in hydrometeorological parameters were included in the Monte Carlo simulation, resulting in a distribution of concentrations in each medium. Parameters of compartmental dimensions, densities, emissions, and background concentrations were assumed to be constant in this study. The predicted concentrations in air, surface water and sediment were compared to reported data for validation purpose. Based on rank correlations, a sensitivity analysis was conducted to determine the influence of individual input parameters on the output variance for concentration in each environmental medium and for the basin-wide total mass inventory. Results of model validation indicated that the model predictions were in reasonable agreement with spatial distribution patterns, among the five lake basins, of reported data in the literature. For the chemical and environmental parameters given in this study, parameters associated to air-ground partitioning (such as moisture in surface soil, vapor pressure, and deposition velocity) and chemical distribution in soil solid (such as organic carbon partition coefficient and organic carbon content in root-zone soil) were targeted to reduce the uncertainty in basin-wide mass inventory. This results of sensitivity analysis in this study also indicated that the model sensitivity to an input parameter might be affected by the magnitudes of input parameters defined by the parameter settings in the simulation scenario. Therefore, uncertainty and sensitivity analyses for environmental fate models was suggested to be conducted after the model output was validated based on an appropriate input parameter settings.