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.     

A comparison of mesh-adaptive LES with wind tunnel data for flow past buildings: Mean flows and velocity fluctuations

In this paper we address two important aspects of micro-scale urban airflow model evaluation: (a) the identification of key flow features as dictated by the physics of the problem and as captured by the simulations, and (b) the comparison of important model output parameters (mean flows and fluctuations) with experimental data. A series of mesh-adaptive large eddy simulations (LES) was carried out for the study of air flows within two intersecting street canyons with varying building configurations. The novelty of the approach lies in the combination of LES with mesh adaptivity, which allows a variable-filter length and the implementation of an anisotropic eddy-viscosity model. Both coarse and fine-mesh simulations were carried out, using single and parallel-processor systems respectively. The simulations showed clearly that the expected flow patterns such as the street canyon recirculation and the street-mouth vortices, as well as the exchange of air flow at the street intersections, can readily be captured by the mesh-adaptive LES.In addition, the detailed comparisons of mean flows and fluctuations of the resolved velocity field with the measured data showed that the simulation results agreed well with the patterns and trends of the wind tunnel measurements. In most cases the finer-mesh simulations improved considerably the accuracy of the mean flows, especially for the symmetrical configuration. The improvement in the predicted fluctuations was less obvious, with several detector locations underpredicting the measured values, although the overall comparison was also satisfactory. The typical errors for the mean flows for all three building configurations were less than 30%, whilst for the velocity fluctuations less that 40%. Both the simulated means flows and turbulence levels were generally more accurate in the streets parallel to the wind (streamwise direction) than in the streets normal to the wind.     

Validation of ADMS against wind tunnel data of dispersion from chemical warehouse fires

Comparisons are presented of the predictions of the atmospheric dispersion modelling system (ADMS) and wind tunnel data for plume dispersion from chemical warehouse fires. The focus of the comparisons is dispersion from structurally intact buildings with open roofs and dispersion of plumes flush with the ground without obstacles, however, dispersion from building shells and doors is also considered. Both buoyancy driven and momentum driven flows are treated, although emphasis is on buoyancy driven flows as these are generally more likely to occur in warehouse fires. The study shows that the ADMS building module is able to reproduce many of the features of dispersion observed in the wind tunnel. These include a recirculating region behind the building in which material may be trapped, a main wake which brings material down towards the surface, and appropriate sensitivity to the buoyancy and momentum of the emitted material, and the location of sources on the building roof. The comparisons suggest that the ADMS building model can be used to predict dispersion from the stages of fire development studied. The precise level of agreement depends (but not in a systematic way) on the buoyancy flux parameter F_B, the momentum flux parameter F_M and the number of roof lights. There are some significant differences between the wind tunnel boundary layer and the simulated atmospheric boundary layer in ADMS which have to be considered when making wind tunnel model comparisons. These relate mainly to the near surface where the wind tunnel underestimates turbulent velocities, the boundary layer height which in the wind tunnel corresponds to an atmospheric boundary layer depth of 82.5 m (atmospheric boundary layers are frequently an order of magnitude deeper), and the boundary layer top where the ADMS boundary layer is capped by an inversion and has low turbulence levels whereas the wind tunnel boundary layer has higher levels of turbulence and no capping inversion.     

Tests with a portable wind tunnel for determining wind erosion threshold velocities

A portable open-floored wind tunnel was used to develop threshold wind speeds over two pebble covered desert soils and a sandy agricultural soil.     

A comparison of tailpipe, dilution tunnel, and wind tunnel data in measuring motor vehicle PM.

Comparison between particle size distributions recorded directly at the tailpipes of both diesel and gasoline vehicles and measurements made using a conventional dilution tunnel reveals two problems incurred when using the latter method for studying particle number emissions. One is the potential for particulate matter (PM) artifacts originating from hydrocarbon material stored in the transfer hose connecting the tailpipe to the dilution tunnel, and the other is the particle coagulation (as well as condensation and chemical changes) that occurs during the transport. Both are potentially generic to current PM emissions measurement practices. The artifacts typically occur as a nanoparticle mode (10-30 nm) that is 2-4 orders of magnitude larger than what is present in the vehicle exhaust and can easily be mistaken for a similar mode that can arise from the nucleation of hydrocarbon or SO4(2-) components in the exhaust under appropriate dilution rates. Wind tunnel measurements are in good agreement with those made directly from the tailpipe and substantiate the potential for artifacts. They reveal PM levels for the recent model port fuel injection (PFI) gasoline vehicles tested that are small compared with the ambient background particle level during steady-state driving. The PM emissions recorded for drive cycles such as the Federal Test Procedure (FTP) and US06 occur primarily during acceleration, as has been previously noted. Light-duty diesel vehicle emissions normally exhibit a single lognormal mode centered between 55 and 80 nm, although a nonartifact nanoparticle mode in some cases appears at a 70-mph cruise up a grade.     

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.     

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%.     

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.     

Air quality impact due to scrap-metal handling on a sea port: A wind tunnel experiment

Emissions from cargo unloading and handling activities in sea ports can be of special importance, particularly in case of proximity to dense urban populations. In this sense, the main purpose of this work was to develop and implement a set of technical solutions intended to reduce the emissions and dispersion of particles resulting from scrap-metal handling on one of the most important Portuguese sea ports.The analysis of the atmospheric flow within the discharging berth was carried out in the Wind Tunnel Laboratory of the Department of Environment and Planning, at the University of Aveiro (Portugal). Laser sheet and hot wire anemometry techniques were applied. Two possible solutions were proposed, consisting in the use of windbreaks located upwind of the scrap-metal piles and a barrier of containers positioned downwind. Both flow visualisation and velocity-measuring techniques confirm a higher efficiency in the reduction of suspended particulate matter by using the containers plus the windbreaks. This combined solution is responsible for an important reduction on the emissions from the scrap-metal piles under typical meteorological conditions for this area, offering a good compromise between cost, time needed for implementation and efficiency.     

Applicability of the Weibull distribution function to atmospheric radioactivity data

The Weibull distribution function is applied empirically to assemblages of atmospheric radioactivity data. Applications are presented in an exemplary fashion so as to acquaint the reader with the broad versatility of the treatment. The Weibull function is applicable to the temporal and spatial distributions of homogeneous atmospheric tritium oxide data, and to the temporal distributions of nonhomogeneous gross-beta and plutonium-239 atmospheric radioactivity data. Weibull distribution parameters are used to interpret different data sets in terms of physical mechanisms responsible for the observed distributions. Furthermore, the Weibull treatment provides an estimate of the minimum possible value of the quantity under investigation.     

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.     

Rate of pesticide volatilization from soil: an experimental approach with a wind tunnel system applied to trifluralin

Pesticide volatilization to the atmosphere may be a major pathway of dissipation closely linked with environmental, physico-chemical and technical factors. Understanding the volatilization process requires systems that make it possible to control some of these factors. Wind tunnels meet to these criteria. The volatilization flux is determined from a mass balance, using the difference in atmospheric pesticide concentration between the entrance and the exit of the tunnel and the airflow rate. An experiment was carried out in June 2000 to study the repeatability of this technique. Volatilization of trifluralin was measured in three wind tunnels for 8 days with a sampling period varying between 3 h and 2 days. Pesticide concentration was determined by trapping by XAD-2 resin in a two-stage cartridge, solvent extraction and analysis by gas chromatography. Cumulated losses through volatilization reached 30% of the measured application dose after 8 days, with a variability of less than 20% between the three tunnels. Approximately 20% remained in the topsoil (0–2 cm), with a variability of 14% between the three tunnels. The decrease in the volatilization flux over time is coherent with the expected theoretical evolution for a volatile pesticide such as trifluralin and with previous experimental works.     

Emission strength validation using four-dimensional data assimilation: application to primary aerosol and precursors to ozone and secondary aerosol.

Three-dimensional air quality models (AQMs) represent the most powerful tool to follow the dynamics of air pollutants at urban and regional scales. Current AQMs can account for the complex interactions between gas-phase chemistry, aerosol growth, cloud and scavenging processes, and transport. However, errors in model applications still exist due in part to limitations in the models themselves and in part to uncertainties in model inputs. Four-dimensional data assimilation (FDDA) can be used as a top-down tool to validate several of the model inputs, including emissions inventories, based on ambient measurements. Previously, this FDDA technique was used to estimate adjustments in the strength and composition of emissions of gas-phase primary species and O3 precursors. In this paper, we present an extension to the FDDA technique to incorporate the analysis of particulate matter (PM) and its precursors. The FDDA approach consists of an iterative optimization procedure in which an AQM is coupled to an inverse model, and adjusting the emissions minimizes the difference between ambient measurements and model-derived concentrations. Here, the FDDA technique was applied to two episodes, with the modeling domain covering the eastern United States, to derive emission adjustments of domainwide sources of NO., volatile organic compounds (VOCs), CO, SO2, NH3, and fine organic aerosol emissions. Ambient measurements used include gas-phase inorganic and organic species and speciated fine PM. Results for the base-case inventories used here indicate that emissions of SO2 and CO appear to be estimated reasonably well (requiring minor revisions), while emissions of NOx, VOC, NH3, and organic PM with aerodynamic diameter less than 2.5 microm (PM2.5) require more significant revision.     

Characterization of carbon fractions for atmospheric fine particles and nanoparticles in a highway tunnel

Fine particles (PM_2.5) and nanoparticles (PM_0.1) were sampled using Dichotomous sampler and MOUDI, respectively, in Xueshan Tunnel, Taiwan. Eight carbon fractions were analyzed using IMPROVE thermal-optical reflectance (TOR) method. The concentrations of different temperature carbon fractions (OC1–OC4, EC1–EC3) in both PM_2.5 and PM_0.1 were measured and the correlations between OC and EC were discussed. Results showed that the ratios of OC/EC were 1.26 and 0.67 for PM_2.5 and PM_0.1, respectively. The concentration of EC1 was found to be more abundant than other elemental carbon fractions in PM_2.5, while the most abundant EC fraction in PM_0.1 was found to be EC2. The variation of contributions for elemental carbon fractions was different among PM_2.5 and PM_0.1 samples, which was partly owing to the metal catalysts for soot oxidation. The correlations between char-EC and soot-EC showed that char-EC dominated EC in PM_2.5 while soot-EC dominated EC in PM_0.1. Using eight individual carbon fractions, the gasoline and diesel source profiles of PM_0.1 and PM_2.5 were extracted and analyzed with the positive matrix factorization (PMF) method.     

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.     

Estimation of atmospheric trace metal emissions in Vilnius City, Lithuania, using vertical concentration gradient and road tunnel measurement data

A new approach for the estimation of trace metal emissions in Vilnius city was implemented, using vertical concentration profiles in the urban boundary layer and road tunnel measurement data. Heavy metal concentrations were examined in fine and coarse particle fractions using a virtual impactor (cut-off size diameter 2.5 μm). Negative vertical concentration gradients were obtained for all metals (Ba, Pb, V, Sb, Zn) and both fractions. It was estimated that the vertical concentration gradient was formed due to emissions from an area of about 12 km². Road tunnel measurements indicated that trace metal concentrations on fine particles were lower than those on coarse particles, which suggested that re-emitted road dust was highly enriched in trace metal due to historic emissions within the tunnel. Emission rates of different pollutants in the road tunnel were calculated using pollutant concentration differences at the tunnel entrance and exit and traffic flow data. Heavy metal emission rates from the area of Vilnius city were estimated using the vertical gradient of heavy metal concentrations and the coefficient of turbulent mixing, as derived from meteorological measurement data. The emission values calculated by the two different methods coincided reasonably well, which indicated that the main source of airborne trace metals in Vilnius city is traffic. The potential of the vertical concentration gradient method for the direct estimation of urban heavy metal emissions was demonstrated.     

陶粒生物滤池低温处理黄河水研究

进行了利用陶粒生物滤池工艺低温生物预处理黄河微污染水的研究,结果表明,温度的降低对陶粒生物滤池去除CODMn、UV254和氨氮的影响不明显,其平均去除率分别为11%、22.2%和61.2%。水温低于5℃时出水中亚硝酸氮浓度升高。此外,低水温条件下陶粒上生物膜脱氢酶活性仍然较高。     

An index to measure consistency of the wind direction for periods around one day

A measure of the variability of wind direction for periods of around one day is frequently desired in air pollution studies. One such measure is the Consistency Index which may be applied to data measured either in degrees or compass points. The Index is compared with the resultant standard deviation of the wind direction to define limits of the Index for data screening purposes.     

Multi-model vs. EPS-based ensemble atmospheric dispersion simulations: A quantitative assessment on the ETEX-1 tracer experiment case

Several techniques have been developed over the last decade for the ensemble treatment of atmospheric dispersion model predictions. Among them two have received most of the attention, the multi-model and the ensemble prediction system (EPS) modeling. The multi-model approach relies on model simulations produced by different atmospheric dispersion models using meteorological data from potentially different weather prediction systems. The EPS-based ensemble is generated by running a single atmospheric dispersion model with the ensemble weather prediction members. In the paper we compare both approaches with the help of statistical indicators, using the simulations performed for the ETEX-1 tracer experiment. Both ensembles are also evaluated against measurement data. Among the most relevant results is that the multi-model median and the mean of EPS-based ensemble produced the best results, hence we consider a combination of multi-model and EPS-based approaches as an interesting suggestion for further research.