Dependence of the Wind Profile Power Law on Stability for Various Locations

Recent environmental regulations have increased the need for construction of meteorological towers at power generation facilities. Due to practical and economic considerations, tower heights are usually lower than effluent release heights. At heights where wind speed data are not available, the wind speed is usually estimated from the measured wind speed using the %th wind profile power law and assuming neutral stability conditions. This study examines published data for many locations and shows that the %th wind profile power law is often unrepresentative of actual conditions because the degree of variation of wind speed with height depends greatly on atmospheric stability. The frequency of neutral stability conditions also varies appreciably by site. These two considerations are especially important in dispersion models which extrapolate wind speed at stack height from low level wind speed data.     

A comparison of operational Lagrangian particle and adaptive puff models for plume dispersion forecasting

Transport and dispersion of pollutants in the lower atmosphere are predicted by using both a Lagrangian particle model (LPM) and an adaptive puff model (APM2) coupled to the same mesoscale meteorological prediction model PMETEO. LPM and APM2 apply the same numerical solutions for plume rise; but, for advection and plume growth, LPM uses a stochastic surrogate to the pollutant conservation equation, and APM2 applies interpolated winds and standard deviations from the meteorological model, using a step-wise Gaussian approach. The results of both models in forecasting the SO₂ ground level concentration (glc) around the 1400 MWe coal-fired As Pontes Power Plant are compared under unstable conditions. In addition, meteorological and SO₂ glc numerical results are compared to field measurements provided by 17 fully automated SO₂ glc remote stations, nine meteorological towers and one Remtech PA-3 SODAR, from a meteorological and air quality monitoring network located 30 km around the power plant.     

The Atmospheric Effects of Thermal Discharges into a Large Lake

Concern for the biological and ecological effects of heated water has resulted in legal actions that will prevent power companies from dumping the waste heat from the majority of their new generating units into rivers and lakes. Many nuclear- and fossil-fueled plants now under construction, and even some now online, are being required to change from once-through cooling systems to other methods, such as wet cooling towers, cooling ponds, and spray canals, despite higher costs and lower thermal efficiencies. Yet, these alternate cooling procedures are not without their own environmental problems.

The primary weather change due to once-through cooling on a large water body is a small local increase in fogginess at the plant outfall. But the relative probability of significant local meteorological effects is much higher with alternate cooling procedures, since these reduce the area of heat and moisture transfer. It is therefore concluded that, from a meteorological point of view, the least undesirable way to dispose of waste heat is by using once-through cooling on large water bodies.     

Spatial variability of unpaved road dust PM10 emission factors near El Paso, Texas

The testing re-entrained aerosol kinetic emissions from roads technique is compared with distance-based emission factors (EFs; g/VKT) measured downwind of a dirt road by using towers instrumented with real-time meteorological and particle sensors at multiple heights. The emission potential (EP), defined as the EF divided by the vehicle speed (m/sec), and weight index permits the intercomparison of emissions from multiple roadways surveyed by the TRAKER vehicle. A survey of 72 km of unpaved roads on the Ft. Bliss Military Base near El Paso, Texas, indicated that 60% of all measured EPs fell between 6.7 (g/VKT)/(m/sec) and 9.6 (g/VKT)/(m/sec). The EP measured across the base was approximately 50% lower than those collected in the vicinity of the instrumented towers. This implies that EFs measured for other vehicles on the same test section should be reduced by 50% to more accurately represent EFs for the entire military base. Using geographic information system-based soil maps, the inferred EFs are related to differences in soil types over the survey area. Variations among five different soil types accounted for <10% of variation in EP. Individual measurements using the testing re-entrained aerosol kinetic emissions from roads technique did show larger spatial variations in EP; however, these were not effectively captured by the soil classifications, partly because of the comparatively coarse spatial classification used in the soil survey data.     

Use of the Kit Fox field data to analyze dense gas dispersion modeling issues

The 1995 Kit Fox dense gas field data set consists of 52 trials where short-duration CO₂ gas releases were made at ground level over a rough surface during neutral to stable conditions. The experiments were intended to demonstrate the effects on dense gas clouds of relatively large roughnesses typical of industrial process plants. Fast response concentration observations were made by 80 samplers located on four downwind lines (25, 50, 100, and 225 m), including profile observations on three towers on each of the closest three arcs. Detailed meteorological measurements were made on several towers within and outside of the roughness arrays. The data analysis emphasized the variation of maximum concentration with surface roughness, the dependence of cloud advection speed on cloud depth, the variation of the three components of dispersion with ambient turbulence, and the dependence of vertical entrainment rate on ambient friction velocity and cloud Richardson number. The Kit Fox data were used to evaluate a specific dense gas dispersion model (HEGADAS 3+), with emphasis on whether it would be able to account for the increased roughness. The model was able to satisfactorily simulate the observed concentrations, with a mean bias of about 5% and with about 90% of the predictions within a factor of two of the observations.     

Using prognostic model-generated meteorological output in the AERMOD dispersion model: an illustrative application in Philadelphia, PA

In this study, we introduce the prospect of using prognostic model-generated meteorological output as input to steady-state dispersion models by identifying possible advantages and disadvantages and by presenting a comparative analysis. Because output from prognostic meteorological models is now routinely available and is used for Eulerian and Lagrangian air quality modeling applications, we explore the possibility of using such data in lieu of traditional National Weather Service (NWS) data for dispersion models. We apply these data in an urban application where comparisons can be made between the two meteorological input data types. Using the U.S. Environment Protection Agency's American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model (AERMOD) air quality dispersion model, hourly and annual average concentrations of benzene are estimated for the Philadelphia, PA, area using both hourly MM5 model-generated meteorological output and meteorological data taken from the NWS site at the Philadelphia International Airport. Our intent is to stimulate a discussion of the relevant issues and inspire future work that examines many of the questions raised in this paper.     

Directional biases in back trajectories caused by model and input data

Back trajectory analyses are often used for source attribution estimates in visibility and other air quality studies. Several models and gridded meteorological datasets are readily available for generation of trajectories. The Big Bend Regional Aerosol and Visibility Observational (BRAVO) tracer study of July to October 1999 provided an opportunity to evaluate trajectory methods and input data against tracer concentrations, particulate data, and other source attribution techniques. Results showed evidence of systematic biases between the results of different back trajectory model and meteorological input data combinations at Big Bend National Park during the BRAVO. Most of the differences were because of the choice of meteorological data used as input to the trajectory models. Different back trajectories also resulted from the choice of trajectory model, primarily because of the different mechanisms used for vertical placement of the trajectories. No single model or single meteorological data set was found to be superior to the others, although rawinsonde data alone are too sparse in this region to be used as the only input data, and some combinations of model and input data could not be used to reproduce known attributions of tracers and simulated sulfate.     

Neural network model for predicting peak photochemical pollutant levels

In this paper, an attempt is made for the 24-hr prediction of photochemical pollutant levels using a neural network model. For this purpose, a model is developed that relates peak pollutant concentrations to meteorological and emission variables and indexes. The analysis is based on measurements of O3 and NO2 from the city of Athens. The meteorological variables are selected to cover atmospheric processes that determine the fate of the airborne pollutants while special care is taken to ensure the availability of the required input data from routine observations or forecasts. The comparison between model predictions and actual observations shows a good agreement. In addition, a series of sensitivity tests is performed in order to evaluate the sensitivity of the model to the uncertainty in meteorological variables. Model forecasts are generally rather insensitive to small perturbations in most of the input meteorological data, while they are relatively more sensitive in changes in wind speed and direction.     

Neural Network Model for Predicting Peak Photochemical Pollutant Levels

ABSTRACT

In this paper, an attempt is made for the 24-hr prediction of photochemical pollutant levels using a neural network model. For this purpose, a model is developed that relates peak pollutant concentrations to meteorological and emission variables and indexes. The analysis is based on measurements of O₃ and NO₂ from the city of Athens. The meteorological variables are selected to cover atmospheric processes that determine the fate of the airborne pollutants while special care is taken to ensure the availability of the required input data from routine observations or forecasts. The comparison between model predictions and actual observations shows a good agreement. In addition, a series of sensitivity tests is performed in order to evaluate the sensitivity of the model to the uncertainty in meteorological variables. Model forecasts are generally rather insensitive to small perturbations in most of the input meteorological data, while they are relatively more sensitive in changes in wind speed and direction.     

Evaluation of the Meteo-France response in ETEX release 1

During ETEX Meteo-France applied part of its emergency response system for critical events developped in the framework of the World Meteorological Organization environmental emergency response program. The atmospheric transport model used to forecast the evolution of a passive tracer is an eulerian model called MEDIA. In real time this model is driven by meteorological data from ARPEGE, the operational numerical weather prediction model available at the Meteo-France operation center. The overall evaluation of the results show that the model can reproduce the cloud displacement, but there exists a stretching in the transport direction. In the ATMES-II phase, the results are closer to the observations when meteorological data from the European Center for Medium range Weather Forecast are used. A simulation using analyzed meteorological data from ARPEGE every 6 h slightly improve the results comparing with the real-time experiment. All the simulations we performed reveal that the quality of the atmospheric transport model is strongly dependent on the quality of the driving numerical weather prediction model.     

Formation of secondary inorganic aerosols by power plant emissions exhausted through cooling towers in Saxony

Background, aim, and scope  The fraction of ambient PM₁₀ that is due to the formation of secondary inorganic particulate sulfate and nitrate from the emissions of two large, brown-coal-fired power stations in Saxony (East Germany) is examined. The power stations are equipped with natural-draft cooling towers. The flue gases are directly piped into the cooling towers, thereby receiving an additionally intensified uplift. The exhausted gas-steam mixture contains the gases CO, CO₂, NO, NO₂, and SO₂, the directly emitted primary particles, and additionally, an excess of ‘free’ sulfate ions in water solution, which, after the desulfurization steps, remain non-neutralized by cations. The precursor gases NO₂ and SO₂ are capable of forming nitric and sulfuric acid by several pathways. The acids can be neutralized by ammonia and generate secondary particulate matter by heterogeneous condensation on preexisting particles. Materials and methods  The simulations are performed by a nested and multi-scale application of the online-coupled model system LM-MUSCAT. The Local Model (LM; recently renamed as COSMO) of the German Weather Service performs the meteorological processes, while the Multi-scale Atmospheric Transport Model (MUSCAT) includes the transport, the gas phase chemistry, as well as the aerosol chemistry (thermodynamic ammonium–sulfate–nitrate–water system). The highest horizontal resolution in the inner region of Saxony is 0.7 km. One summer and one winter episode, each realizing 5 weeks of the year 2002, are simulated twice, with the cooling tower emissions switched on and off, respectively. This procedure serves to identify the direct and indirect influences of the single plumes on the formation and distribution of the secondary inorganic aerosols. Results and conclusions  Surface traces of the individual tower plumes can be located and distinguished, especially in the well-mixed boundary layer in daytime. At night, the plumes are decoupled from the surface. In no case does the resulting contribution of the cooling tower emissions to PM₁₀ significantly exceed 15 μgm⁻³ at the surface. These extreme values are obtained in narrow plumes on intensive summer conditions, whereas different situations with lower turbulence (night, winter) remain below this value. About 90% of the PM₁₀ concentrations in the plumes are secondarily formed sulfate, mainly ammonium sulfate, and about 10% originate from the primarily emitted particles. Under the assumptions made, ammonium nitrate plays a rather marginal role. Recommendations and perspectives  The analyzed results depend on the specific emission data of power plants with flue gas emissions piped through the cooling towers. The emitted fraction of ‘free’ sulfate ions remaining in excess after the desulfurization steps plays an important role at the formation of secondary aerosols and therefore has to be measured carefully.     

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.     

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 Test Reference Year (TRY) approach to gas dispersion in the atmosphere

For environmental analysis such as the dispersion of pollutants in the atmosphere, it is essential to have meteorological data that are relevant for a long period. In this paper, we explore the possibility of using an environmental Test Reference Year (TRY), i.e., a set of real, contemporaneous and hourly meteorological variables, 'extracted' from a hourly series of at least 10 years, for modelling pollutant dispersion in the atmosphere. The classical approach, based on a statistical data set, implies the loss of important information such as the real correlation between the different meteorological variables, and this implies crude approximation in the simulation results. We compare the simulation results with the long hourly 10 years data set (which can be considered a 'brute force' approach, since it requires a huge amount of data and time processing, but it is here considered the most severe benchmark) and with the statistical data set commonly used. It is shown that the results obtained using the TRY have a good agreement with the ones obtained with the simulation of the 10 years and they are also much better than those obtained using the statistical data set.     

A new method to estimate air-quality levels using a synoptic-regression approach. Part I: Present-day O3 and PM10 analysis

In order to make projections for future air-quality levels, a robust methodology is needed that succeeds in reconstructing present-day air-quality levels. At present, climate projections for meteorological variables are available from Atmospheric-Ocean Coupled Global Climate Models (AOGCMs) but the temporal and spatial resolution is insufficient for air-quality assessment. Therefore, a variety of methods are tested in this paper in their ability to hindcast maximum 8 hourly levels of O₃ and daily mean PM₁₀ from observed meteorological data. The methods are based on a multiple linear regression technique combined with the automated Lamb weather classification. Moreover, we studied whether the above-mentioned multiple regression analysis still holds when driven by operational ECMWF (European Center for Medium-Range Weather Forecast) meteorological data. The main results show that a weather type classification prior to the regression analysis is superior to a simple linear regression approach. In contrast to PM₁₀ downscaling, seasonal characteristics should be taken into account during the downscaling of O₃ time series. Apart from a lower explained variance due to intrinsic limitations of the regression approach itself, a lower variability of the meteorological predictors (resolution effect) and model deficiencies, this synoptic-regression-based tool is generally able to reproduce the relevant statistical properties of the observed O₃ distributions important in terms of European air quality Directives and air quality mitigation strategies. For PM₁₀, the situation is different as the approach using only meteorology data was found to be insufficient to explain the observed PM₁₀ variability using the meteorological variables considered in this study.     

Bulgarian emergency response models–validation against ETEX first release

In the paper, the performance of two Bulgarian dispersion models is tested against European Tracer Experiment (ETEX) first release data base. The first one is the LED puff model which was the core of the Bulgarian Emergency Response System during all releases of ETEX. The second one is the newly created Eulerian dispersion model EMAP. These models have two important features: they are PC-oriented and they use quite a limited amount of input meteorological information. First, a number of runs with various source configurations are made on meteorological data produced by ECMWF. The aim of these runs is to verify the models’ ability to simulate reliably ETEX first release. To this end, a set of statistical criteria selected in ATMES (Atmospheric Transport Models Evaluation Study, see Klug et al., 1992 are used. The best runs for both models are obtained when the source is presented as a column towering from the ground to heights of 400–700 m. These runs took part in the second phase of ETEX (ETEX-II), the so called ATMES-type exercise where EMAP ranked ninth and LED - fourteenth among 34 models. Here, additional sets of EMAP are presented where in the first run the value of the horizontal diffusion coefficient is varied and in the other runs different meteorological data sets are tested. The results obtained from the first run show that the values of K_h=4–6×10⁴ m² s^-1 produce fields which fit experimental data best. The other sets of runs show that the higher the frequency of the meteorological data, the better the simulation. The results can be improved by linear interpolation of the meteorological parameters with time, the best fitting obtained with interpolation at each time step.     

Lagrangian dispersion model and its application to monitor nuclear power plants

The Lagrangian dispersion model and its advantages while applying it in monitoring nuclear power plants in complex terrain at varying meteorological conditions is explained. The software developed has been installed at the Bavarian State Authority to monitor its six nuclear power plants. Input data are routinely measured meteorological data as well as emission data for iodine, aerosols and noble gas.     

The State-of-the-Art of Measuring and Predicting Cooling Tower Drift and Its Deposition

One of the key elements in estimating the environmental effects associated with the deposition of airborne chemicals and, in particular, salt particles from cooling towers is the drift rate. Eight different experimental methods are currently employed to determine the drift rate from cooling towers. The difficulties associated with the various techniques vary from case to case, but they are mainly associated with collecting a representative sample, maintaining undisturbed air flow, determining the collection efficiency of the various sampling techniques and analyzing the samples for particle size.

Several approaches have been taken to predict the deposition of salt water drift droplets on ground surfaces. Some use a simple analogy with the deposition of industrial dust, others use a combination of plume rise theories in conjunction with the Gaussian diffusion model to predict the air concentration of water droplets from which the ground deposition is then calculated. Other methods calculate the trajectories of the drift droplets accounting for their evaporation or employ diffusion type equations. Estimates derived from the various models used to predict drift deposition appear to vary by a factor of ±10 from each other.

In contrast to the fast development in drift loss measurements, very few attempts have been made to measure actual drift deposition from fresh and salt water cooling towers and to compare experimental results with numerical models.

This paper presents a discussion on the state-of-the-art of measuring and computing drift losses. In particular, drift rate values, droplet size distribution and some typical measured and calculated ground deposition values are discussed.     

Episode selection for ozone modelling and control strategies analysis on the Swiss Plateau

An episode selection procedure was developed and applied to select sets of days representing characteristic meteorological conditions leading to high ozone episodes over the Swiss Plateau. The selection procedure was applied to data extending from January 1991 through December 1998, and is comprised of two steps: First, days were classified according to observed air quality and meteorological characteristics using classification and regression trees analysis (CART). Second, the CART results were used in conjunction with observed air quality data to identify sets of days characteristic of those leading to elevated ozone. These sets of days were selected to optimise how well a limited number of days represented seasonal air quality, and that formed longer episodes for use in the air quality modelling. CART analysis was performed for three zones of the Swiss Plateau that have different air quality and meteorological characteristics. The results for two zones were used together in the episode selection procedure in order to identify days representative for the whole Plateau. Meteorological analysis for a third zone suggested that it would be strongly impacted by pollutants transported in from outside the country. One thousand and eight hundred optimisation runs were performed to minimise the likelihood that the set of days was a local optimum, increasing the robustness for use in air quality modelling analysis. Fifteen days, grouped in four episodes ranging from 3 to 5 days were selected along with their calculated representativeness (or weight) to recreate a seasonal metric. The variety of local as well as regional meteorological characteristics showed that the episode selection procedure chose days representing a diverse set of meteorological situations which are associated with elevated ozone. This set of episodes can now be used to test air quality strategies.     

Factors influencing the variability of SO2 concentrations in Istanbul

The correlation between sulfur dioxide (SO2) concentrations measured at the European and Asian sides of Istanbul and meteorological parameters is investigated using principal component analysis (PCA) and multiple regression analysis techniques. Several meteorological parameters are selected to represent the atmospheric conditions during two winter periods: 1993-1994 and 1994-1995. Six principal components are found to explain the majority of the observed meteorological variability. Surface pressure, 850-mb temperature, and surface zonal (east-west) and meridional (north-south) winds show high loadings on separate factors identified by PCA. We seek dominant meteorological parameters that control the SO2 levels at each monitoring station. Several multiple regression analysis models are fitted to the data from each monitoring station using six principal components and previous-day SO2 concentrations as independent variables. Results suggest that the most important parameters, highly correlated with SO2 concentrations in the Istanbul metropolitan area, are atmospheric pressure and surface zonal and meridional winds. These components have more influence on the determination of the air pollution levels at the Asian side than at the European side.