Evaluation of a long-range lagrangian dispersion model with ETEX

Performance of a Lagrangian dispersion model was examined in connection with its dependency on the boundary layer modelling and the input data resolution. The European Tracer Experiment (ETEX) data were used as reference. According to the sensitivity analysis of the model performance, the long-range dispersion model with the sparse input data was not noticeably different from that with the finer resolution data. The assumption of the prescribed constant mixing depth did not largely degrade the prediction results as compared with the simulation results with the temporally changing boundary layer. It is, therefore, concluded that the model is practical, considering the limited input data in the operational mode. However, it was also pointed out that the parameterization for the horizontal and vertical diffusion processes used in the present model enhanced the growth of plume. The improvement of input data resolution in time and space caused further dispersion of tracer deterministically. These resulted in the underestimation of the maximum concentration and the unfocussed concentration distribution map although the mean concentration was predicted fairly well.     

Development of a livestock odor dispersion model: part II. Evaluation and validation

A livestock odor dispersion model (LODM) was developed to predict odor concentration and odor frequency using routine hourly meteorological data input. The odor concentrations predicted by the LODM were compared with the results obtained from other commercial models (Industrial Source Complex Short-Term model, version 3, CALPUFF) to evaluate its appropriateness. Two sets of field odor plume measurement data were used to validate the model. The model-predicted mean odor concentrations and odor frequencies were compared with those measured. Results show that this model has good performance for predicting odor concentrations and odor frequencies.     

修订版大气导则与现行大气导则推荐模式实例对比验证分析

介绍了现行大气导则推荐预测模式的不足及修订版大气导则推荐模式AERMOD与ADMS的特点,并分析了不同推荐模式的功能及适用范围.以美国环境保护署两个试验场(Clifty Creek(简单地形)和Epri Bowline(建筑物下洗))的数据资料为基础,分别用修订版大气导则推荐模式AERMOD与ADMS及现行大气导则推荐模式计算小时浓度、日均浓度及年均浓度,并采用统计法(最大值比较法、高端值比较法、相对偏差比较法)和图形法(Q-Q对比图)进行模式比较分析.结果表明,在简单地形下,3种预测模式小时浓度预测精度接近,修订版大气导则推荐模式在可靠性上要优于现行大气导则推荐模式;在考虑建筑物下洗的复杂条件下,AERMOD、ADMS模式的预测结果普遍比现行大气导则推荐模式要好;现行大气导则推荐模式无法应用于计算建筑物下洗的模拟条件.     

Comparison of the complex terrain algorithms incorporated into two commonly used local-scale air pollution dispersion models (ADMS and AERMOD) using a hybrid model

ADMS and AERMOD are the two most widely used dispersion models for regulatory purposes. It is, therefore, important to understand the differences in the predictions of the models and the causes of these differences. The treatment by the models of flat terrain has been discussed previously; in this paper the focus is on their treatment of complex terrain. The paper includes a discussion of the impacts of complex terrain on airflow and dispersion and how these are treated in ADMS and AERMOD, followed by calculations for two distinct cases: (i) sources above a deep valley within a relatively flat plateau area (Clifty Creek power station, USA); (ii) sources in a valley in hilly terrain where the terrain rises well above the stack tops (Ribblesdale cement works, England). In both cases the model predictions are markedly different. At Clifty Creek, ADMS suggests that the terrain markedly increases maximum surface concentrations, whereas the AERMOD complex terrain module has little impact. At Ribblesdale, AERMOD predicts very large increases (a factor of 18) in the maximum hourly average surface concentrations due to plume impaction onto the neighboring hill; although plume impaction is predicted by ADMS, the increases in concentration are much less marked as the airflow model in ADMS predicts some lateral deviation of the streamlines around the hill.     

Dispersion of carbon dioxide released from buried high-pressure pipeline over complex terrain

Environmental Science and Pollution Research - To quantitatively assess the risks associated with Carbon Capture and Storage (CCS) technology, a better understanding of the dispersion...     

High-resolution simulation of volcanic sulfur dioxide dispersion over the Miyake Island

After severe eruptions of the volcano at Miyake Island in August 2000, a large amount of volcanic gas was released into the atmosphere. To simulate flows and dispersion of sulfur dioxide (SO₂) over Miyake Island, a set of numerical models was developed. The multi-nesting method was adopted to reflect a realistic meteorological field and to sufficiently resolve the flow over the island with a diameter of 8 km. The outermost model was the Regional Spectral Model (RSM) of the Japan Meteorological Agency (JMA) with a horizontal grid size of 10 km. Finer atmospheric structure was simulated with the nonhydrostatic model jointly developed by the Meteorological Research Institute and the Numerical Prediction Division of JMA (MRI/NPD-NHM) with grid intervals of 2 km, 400 m and 100 m. Realistic topography of the island was represented in the innermost model. The Lagrangian particle method was applied to the dispersion model, which is driven by the meteorological field of the 100 m grid MRI/NPD-NHM. The random walk procedure was used to represent the turbulent diffusion. The model was verified in four cases. Simulated SO₂ concentrations agreed well with observed concentrations at a monitoring station including temporal variation. Under a large synoptic change, however, accurate prediction became difficult. Further numerical experiments have been done to investigate characteristics of the flow and the distribution of SO₂. Steady inflows, classified according to the surface wind speed and direction, were assumed. Simulated SO₂ distribution on the ground apparently depends on the surface wind. Under relatively weak inflow, there is a large diurnal change in SO₂ distribution, affected by the thermally induced flow. SO₂ gas is widely spread downstream in the nighttime but hardly reaches the coastal area in the daytime. On the other hand, SO₂ gas steadily reached the downstream coast with little diurnal variation under the stronger inflow. Ground temperature, as well as the static stability of the inflow, also influences downstream wind, turbulent diffusivity and SO₂ distribution.     

Community Odours in the Vicinity of a Petrochemical Industrial Complex

Community odours in the vicinity of an oil refinery and petrochemical industrial area were studied by an observer network throughout an entire year. Odour episodes occurred during 0.86 percent of the time of the study. Odour reports increased closer to the refinery. Within a range of 12.5 km, more than 50 percent of the odours were estimated to originate from the refinery. The most typical odour was that of reduced sulphur compounds (35.2 percent of all observations). Odour episodes occurred during low wind velocities and high humidity. Odours were most frequent during January-April, and were usually reported early in the morning. The odours were evidently due to the general operation of the plants, as no single activity or in-plant factor could be identified as the source of the odours.     

浅谈工业污水处理厂和接管企业的环保管理

对以工业废水处理为主的工业污水处理厂和接管企业的环保管理提出了建议，有利于工业污水处理厂的健康发展，促进环境保护。     

Evaluation of historical CMIP6 model simulations and future projections of temperature over the Pan-Third Pole region

Environmental Science and Pollution Research - The Pan-Third Pole (PTP) region, which encompasses the Eurasian highlands and their surroundings, has experienced unprecedented, accelerated warming...     

Spatial soil zinc content distribution from terrain parameters: A GIS-based decision-tree model in Lebanon

Heavy metal contamination has been and continues to be a worldwide phenomenon that has attracted a great deal of attention from governments and regulatory bodies. In this context, our study proposes a regression-tree model to predict the concentration level of zinc in the soils of northern Lebanon (as a case study of Mediterranean landscapes) under a GIS environment. The developed tree-model explained 88% of variance in zinc concentration using pH (100% in relative importance), surroundings of waste areas (90%), proximity to roads (80%), nearness to cities (50%), distance to drainage line (25%), lithology (24%), land cover/use (14%), slope gradient (10%), conductivity (7%), soil type (7%), organic matter (5%), and soil depth (5%). The overall accuracy of the quantitative zinc map produced (at 1:50.000 scale) was estimated to be 78%. The proposed tree model is relatively simple and may also be applied to other areas.     

Performance of various sub-grid scale models in large-eddy simulations of turbulent flow over complex terrain

Turbulent flow over a two-dimensional steep hill was analyzed by large-eddy simulations (LES). Here, six LES computations were carried out using four different sub-grid scale (SGS) models and two different ground surface conditions. The accuracy of these computations was assessed by comparing the results with those from an experiment by Ishihara et al. (An experimental study of turbulent boundary layer over a steep hill, Proceedings of the 15th National Symposium on Wind Engineering, 1998, pp. 61–66; J. Wind Eng. 89 (2001) 573). The results of the dynamic SGS models were in very poor agreement with those of the experiment. The poor prediction accuracy was mainly caused by the inaccurate estimation of the model coefficient near the ground surface. In order to improve the prediction accuracy of the dynamic SGS models, a hybrid SGS model, i.e., a combination of the standard Smagorinsky model and the dynamic Smagorinsky model, was introduced. The hybrid model provided very accurate predictions and produced the best results of the four SGS models compared here.     

Solutions and verification of a scale-dependent dispersion model

In this paper, analytical solutions are derived for a one-dimensional scale-dependent dispersion model (SDM), considering linear equilibrium sorption and first-order degradation for continuous and pulse contaminant sources, with a constant input concentration in a semi-infinite uniform porous medium. In the SDM model, dispersivity alpha(x) is replaced with a constant epsilon multiplied by the transport distance x. The solution for a pulse source is verified experimentally in the analysis of tritium data obtained from an 8-m-long homogenous pea-gravel column with multiple sampling locations, and the results are compared with those analysed by a commonly used solution of a constant dispersion model (CDM). The SDM predicts concentrations satisfactorily at all sampling locations, while the CDM fits the experimental data well for only one location. Both models are then calibrated for each individual concentration breakthrough curve, using local values for either epsilon in the SDM or alpha(x) in the CDM. Both models give equally good fits for appropriate choices of individual epsilon and alpha(x) values, and both indicate a linear increase in alpha(x) with distance. The epsilon values tend to change little as x increases and are expected to approach a constant at relatively large distances downstream. Hence, predictions from the SDM should become more accurate as x increases.     

Development of a low-rise industrial source dispersion model

In Japan, with amendment of the Air Pollution Control Law in May 1996, various substances, including benzene and trichloroethylene, were newly designated as hazardous air pollutants, and environmental standards were established. In this situation, it is necessary to develop a dispersion model that is applicable to environmental impact assessment of industrial areas with a complex of factory buildings. To overcome this problem, modification of the ISC downdraught model was undertaken based on datasets from wind tunnel experiments by the Ministry of International Trade and Industries and Japan Environmental Management Association for Industry. This new model is called the METI-LIS model, and comparison shows that the performance of the model is better than that of the original ISC model.     

Spatial variability of sulfur dioxide and sulfate over complex terrain in East Tennessee,USA

In 2004 and 2005, the East Tennessee Ozone Study (ETOS) enhanced its regional measurement program with annular denuder systems to quantify sulfur dioxide (SO₂) and PM_2.5 sulfate (SO₄^2?) at five sampling sites that were representative of the complex terrain and physiographic features of East Tennessee. Intersite spatial variability was more defined for SO₂ than for SO₄^2?, which showed a fairly uniform structure in both daytime and nighttime measurements. Pollution roses indicated that two sites may have been influenced by the proximity of SO₂ emission sources. The data suggest that SO₂ is affected by nearby sources in the study area while the sources of SO₄^2? are regionally distributed.     

Modeling the ETEX plume dispersion with the Canadian emergency response model

The CANadian Emergency Response Model (CANERM) was used to simulate the dispersion resulting from the ETEX release of 23 October 1994. Dispersion simulations were done using three different data sets as meteorological input: the ECMWF/ETEX Data Set, data from the CMC Global Data Assimilation System, and results from a diagnostic execution of the Global Environmental Multiscale (GEM) model. Comparisons of the dispersion simulations are made with observed surface concentration data provided by the Joint Research Centre (JRC) of the European Commission. It is found that CANERM can simulate fairly well the main features of the cloud dispersion. The spatial and temporal evolution of the simulated cloud appear quite plausible, but a tendency to overestimate surface concentrations is apparent. The simulations provide a credible explanation for the two peaks observed at station NL01; the first peak appears to be associated with the passage of the head portion of the plume, while the second seems to be associated with the tail part. Verification scores indicate that the simulations using the ECMWF/ETEX data set and CMC global data are of equivalent quality. However, the simulations obtained using the GEM diagnostic fields are significantly better.     

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%.