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.     

AERMOD在国内环境影响评价中的实例验证与应用

AERMOD是美国环保局推出的新一代空气质量模式系统,它由AERMET(气象数据预处理器)、AERMAP(地形数据预处理器)和AERMOD(大气扩散模型)3部分组成.结合宁波市北仑区域大气环境影响评价,对该模式系统进行模式验证,并应用于实际预测评价.验证结果表明,在采用适当的模型参数时,该系统预测值与实际监测值具有很好的一致性,SO2、NO2日均最高浓度预测准确率分别达到64.3%和85.7%.最后结合实际预测评价工作,提出AERMOD模式系统在国内环境影响评价工作中的优势及不足.     

AERMOD performance evaluation for three coal-fired electrical generating units in Southwest Indiana

An evaluation of the steady-state dispersion model AERMOD was conducted to determine its accuracy at predicting hourly ground-level concentrations of sulfur dioxide (SO₂) by comparing model-predicted concentrations to a full year of monitored SO₂ data. The two study sites are comprised of three coal-fired electrical generating units (EGUs) located in southwest Indiana. The sites are characterized by tall, buoyant stacks, flat terrain, multiple SO₂ monitors, and relatively isolated locations. AERMOD v12060 and AERMOD v12345 with BETA options were evaluated at each study site. For the six monitor–receptor pairs evaluated, AERMOD showed generally good agreement with monitor values for the hourly 99th percentile SO₂ design value, with design value ratios that ranged from 0.92 to 1.99. AERMOD was within acceptable performance limits for the Robust Highest Concentration (RHC) statistic (RHC ratios ranged from 0.54 to 1.71) at all six monitors. Analysis of the top 5% of hourly concentrations at the six monitor–receptor sites, paired in time and space, indicated poor model performance in the upper concentration range. The amount of hourly model predicted data that was within a factor of 2 of observations at these higher concentrations ranged from 14 to 43% over the six sites. Analysis of subsets of data showed consistent overprediction during low wind speed and unstable meteorological conditions, and underprediction during stable, low wind conditions. Hourly paired comparisons represent a stringent measure of model performance; however, given the potential for application of hourly model predictions to the SO₂ NAAQS design value, this may be appropriate. At these two sites, AERMOD v12345 BETA options do not improve model performance.

Implications:

A regulatory evaluation of AERMOD utilizing quantile-quantile (Q–Q) plots, the RHC statistic, and 99th percentile design value concentrations indicates that model performance is acceptable according to widely accepted regulatory performance limits. However, a scientific evaluation examining hourly paired monitor and model values at concentrations of interest indicates overprediction and underprediction bias that is outside of acceptable model performance measures. Overprediction of 1-hr SO₂ concentrations by AERMOD presents major ramifications for state and local permitting authorities when establishing emission limits.     

Comparison of the industrial source complex and AERMOD dispersion models: case study for human health risk assessment

Air quality models are typically used to predict the fate and transport of air emissions from industrial sources to comply with federal and state regulatory requirements and environmental standards, as well as to determine pollution control requirements. For many years, the U.S. Environmental Protection Agency (EPA) widely used the Industrial Source Complex (ISC) model because of its broad applicability to multiple source types. Recently, EPA adopted a new rule that replaces ISC with AERMOD, a state-of-the-practice air dispersion model, in many air quality impact assessments. This study compared the two models as well as their enhanced versions that incorporate the Plume Rise Model Enhancements (PRIME) algorithm. PRIME takes into account the effects of building downwash on plume dispersion. The comparison used actual point, area, and volume sources located on two separate facilities in conjunction with site-specific terrain and meteorological data. The modeled maximum total period average ground-level air concentrations were used to calculate potential health effects for human receptors. The results show that the switch from ISC to AERMOD and the incorporation of the PRIME algorithm tend to generate lower concentration estimates at the point of maximum ground-level concentration. However, the magnitude of difference varies from insignificant to significant depending on the types of the sources and the site-specific conditions. The differences in human health effects, predicted using results from the two models, mirror the concentrations predicted by the models.     

CALPUFF and AERMOD Model Validation Study in the Near Field: Martins Creek Revisited

ABSTRACT

This paper describes a near-field validation study involving the steady-state, U.S. Environmental Protection Agency (EPA) guideline model AERMOD and the nonsteady-state puff model CALPUFF. Relative model performance is compared with field measurements collected near Martins Creek, PA—a rural, hilly area along the Pennsylvania-New Jersey border. The principal emission sources in the study were two coal-fired power plants with tall stacks and buoyant plumes. Over 1 yr of sulfur dioxide measurements were collected at eight monitors located at or above the two power plants' stack tops. Concurrent meteorological data were available at two sites. Both sites collected data 10 m above the ground. One of the sites also collected sonic detection and ranging measurements up to 420 m above ground. The ability of the two models to predict monitored sulfur dioxide concentrations was assessed in a four-part model validation. Each part of the validation applied different criteria and statistics to provide a comprehensive evaluation of model performance. Because of their importance in regulatory applications, an emphasis was placed on statistics that demonstrate the model's ability to reproduce the upper end of the concentration distribution. On the basis of the combined results of the four-part validation (i.e., weight of evidence), the performance of CALPUFF was judged to be superior to that of AERMOD.

IMPLICATIONS Use of the nonsteady-state CALPUFF model in the near field (<50 km) for regulatory applications has been limited because of the lack of appropriate model validation studies. Considered an alternative model by EPA, use of CALPUFF for regulatory purposes in the near field must be supported by a relevant performance evaluation using measured air quality data. This validation study should help address the lack of information on the performance of CALPUFF in near-field applications. The potential problem with the use of the robust high concentration as a metric in model validations is also examined.     

CALPUFF and AERMOD model validation study in the near field: Martins Creek revisited

This paper describes a near-field validation study involving the steady-state, U.S. Environmental Protection Agency (EPA) guideline model AERMOD and the nonsteady-state puff model CALPUFF. Relative model performance is compared with field measurements collected near Martins Creek, PA-a rural, hilly area along the Pennsylvania-New Jersey border. The principal emission sources in the study were two coal-fired power plants with tall stacks and buoyant plumes. Over 1 yr of sulfur dioxide measurements were collected at eight monitors located at or above the two power plants' stack tops. Concurrent meteorological data were available at two sites. Both sites collected data 10 m above the ground. One of the sites also collected sonic detection and ranging measurements up to 420 m above ground. The ability of the two models to predict monitored sulfur dioxide concentrations was assessed in a four-part model validation. Each part of the validation applied different criteria and statistics to provide a comprehensive evaluation of model performance. Because of their importance in regulatory applications, an emphasis was placed on statistics that demonstrate the model's ability to reproduce the upper end of the concentration distribution. On the basis of the combined results of the four-part validation (i.e., weight of evidence), the performance of CALPUFF was judged to be superior to that of AERMOD.     

Use of AERMOD to Determine a Hydrogen Sulfide Emission Factor for Swine Operations by Inverse Modeling

This study was conducted to determine both optimal settings applied to the plume dispersion model, AERMOD, and a scalable emission factor for accurately determining the spatial distribution of hydrogen sulfide concentrations in the vicinity of swine concentrated animal feeding operations (CAFOs). These operations emit hydrogen sulfide from both housing structures and waste lagoons. With ambient measurements made at 4 stations within 1 km of large swine CAFOs in Iowa, an inverse-modeling approach applied to AERMOD was used to determine hydrogen sulfide emission rates. CAFO buildings were treated as volume sources whereas nearby lagoons were modeled as area sources. The robust highest concentration (RHC), calculated for both measured and modeled concentrations, was used as the metric for adjusting the emission rate until the ratio of the two RHC levels was unity. Utilizing this approach, an average emission flux rate of 0.57 μg/m(2)-s was determined for swine CAFO lagoons. Using the average total animal weight (kg) of each CAFO, an average emission factor of 6.06 × 10(-7) μg/yr-m(2)-kg was calculated. From studies that measured either building or lagoon emission flux rates, building fluxes, on a floor area basis, were considered equal to lagoon flux rates. The emission factor was applied to all CAFOs surrounding the original 4 sites and surrounding an additional 6 sites in Iowa, producing an average modeled-to-measured RHC ratio of 1.24. When the emission factor was applied to AERMOD to simulate the spatial distribution of hydrogen sulfide around a hypothetical large swine CAFO (1M kg), concentrations 0.5 km from the CAFO were 35 ppb and dropped to 2 ppb within 6 km of the CAFO. These values compare to a level of 30 ppb that has been determined by the State of Iowa as a threshold level for ambient hydrogen sulfide levels.     

meteorological program for limiting power plant stack emission

This paper is directed to those individuals concerned with preserving the local air quality in areas affected by power plant operations. A meteorological forecast and field measurement program has been developed by the Tennessee Valley Authority for limiting stack emissions at the Paradise Steam Plant to preserve the air quality during adverse atmospheric dispersion conditions. Meteorological and plume dispersion criteria, developed from analysis of prior experience, govern the program. The criteria values are designed for limiting surface sulfur dioxide (SO₂) concentrations below an established threshold level.

Daily forecasts of vertical wind and temperature distribution, maximum surface temperature, and sky condition are issued each afternoon by the National Oceanic and Atmospheric Administration National Weather Service, Knoxville, Tennessee. Through use of power plant computer facilities, the forecast data are processed to determine quantitative criteria values. If the values indicate that the threshold level may be exceeded, an Air Pollution Control Notice (APCN) is issued that afternoon for the period 0900–1400 CST the following day, which is the expected period of maximum SO₂, surface concentrations. The APCN specifies the allowable SO₂ emission rate, in terms of megawatt load generation, which should prevent surface SO₂ concentrations from exceeding the established threshold level. Confirmation or cancellation of the APCN is made the following morning, based on plant-site meteorological field measurements taken at 0700–0730 CST. If confirmed, plant load generation is reduced to the designated level by 0900 CST and is continued no later than 1400 CST during the expected period of maximum SO2 surface concentrations.

The APCN conditions identified with the newer and larger TV A power plants with high stacks are associated with one principal regional weather pattern—a large surface high-pressure system, with weak-to-moderate anticyclonic circulation and pronounced stability throughout the lowest few thousand meters. With the limited mixing layer, or sometimes referred to as trapping- or capping-type dispersion associated with this weather condition, relatively high surface concentrations may persist 2–5 hours between 0900–1400 CST.     

海岸地区基于AERMOD的热力内边界层对大气污染物扩散影响的模拟研究

海岸地区热力内边界层(TIBL)对大气污染物扩散具有重要影响。选取杭州湾地区某区域为模拟区,采用一个TIBL高度的简单计算模式模拟模拟区的TIBL高度,将其耦合到空气质量模式AERMOD中,并对AERMOD的相关模块和参数进行了相应的修改,再分别利用原AERMOD和改进后的AERMOD,模拟了不同污染源情景下的大气污染物地面浓度分布。结果表明,在多数情况下,由于TIBL对于大气污染物扩散空间的限制,大气污染物的地面最大浓度有所升高,地面浓度的高值区范围也有所增加,具体影响特征取决于污染源与TIBL的相对高度以及污染源距离海岸的相对位置。     

Hot spots and plumes: Observation by meteorological satellite

Many hot spots can be detected by Channel 3 of the sun-synchronous meteorological satellites NOAA 6–9. This is because that channel is much more sensitive to radiation from bodies in the temperature range 500–1500 K than to scattered sunshine or earth-temperature radiation. Hot bodies occupying only a small fraction of a pixel can be detected. The other channels are designed to receive much greater power from earth radiation (300 K) or scattered sunshine (6000 K).Plumes from chimneys are very difficult to detect because they are small and rapidly diluted and dispersed. Some flukes of illumination make them detectable occasionally; and some infrequent meteorological circumstances make them persistent and large. Ship trails are quite exceptional and very obvious, but they are very rare.     

Terrain classification and derived meteorological parameters for interregional transport models

A procedure is outlined to incorporate surface characteristics in air pollution models. Deposition velocity, energy exchange and surface roughness are considered, because the knowledge of these parameters is essential in more advanced transport models, which no longer use the Pasquill stability scheme. An estimate of the terrain properties is obtained by inspection of land maps. For this purpose land maps with a scale 1: 250.000 are used. On areas of 10′ × 10′ (minutes of arc) the following terrain types are distinguished: water, open field, open field with scattered trees and hedges, wood, built-up areas and roads and railways. A procedure is outlined to derive from this classification the surface heat flux, deposition velocity and surface roughness. The method for the surface roughness is compared with a procedure of Smith and Carson (1977, Boundary-Layer Met. 12, 307–330) for the determination of the surface roughness in the United Kingdom.     

Sensitivity of two dispersion models (AERMOD and ISCST3) to input parameters for a rural ground-level area source

As of December 2006, the American Meteorological Society/U.S. Environmental Protection Agency (EPA) Regulatory Model with Plume Rise Model Enhancements (AERMOD-PRIME; hereafter AERMOD) replaced the Industrial Source Complex Short Term Version 3 (ISCST3) as the EPA-preferred regulatory model. The change from ISCST3 to AERMOD will affect Prevention of Significant Deterioration (PSD) increment consumption as well as permit compliance in states where regulatory agencies limit property line concentrations using modeling analysis. Because of differences in model formulation and the treatment of terrain features, one cannot predict a priori whether ISCST3 or AERMOD will predict higher or lower pollutant concentrations downwind of a source. The objectives of this paper were to determine the sensitivity of AERMOD to various inputs and compare the highest downwind concentrations from a ground-level area source (GLAS) predicted by AERMOD to those predicted by ISCST3. Concentrations predicted using ISCST3 were sensitive to changes in wind speed, temperature, solar radiation (as it affects stability class), and mixing heights below 160 m. Surface roughness also affected downwind concentrations predicted by ISCST3. AERMOD was sensitive to changes in albedo, surface roughness, wind speed, temperature, and cloud cover. Bowen ratio did not affect the results from AERMOD. These results demonstrate AERMOD's sensitivity to small changes in wind speed and surface roughness. When AERMOD is used to determine property line concentrations, small changes in these variables may affect the distance within which concentration limits are exceeded by several hundred meters.     

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.     

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.     

A review of statistical methods for the meteorological adjustment of tropospheric ozone

A variety of statistical methods for meteorological adjustment of ozone have been proposed in the literature over the last decade for purposes of forecasting, estimating ozone time trends, or investigating underlying mechanisms from an empirical perspective. The methods can be broadly classified into regression, extreme value, and space–time methods. We present a critical review of these methods, beginning with a summary of what meteorological and ozone monitoring data have been considered and how they have been used for statistical analysis. We give particular attention to the question of trend estimation, and compare selected methods in an application to ozone time series from the Chicago area. We conclude that a number of approaches make useful contributions to the field, but that no one method is most appropriate for all purposes and all meteorological scenarios. Methodological issues such as the need for regional-scale analysis, the nonlinear dependence of ozone on meteorology, and extreme value analysis for trends are addressed. A comprehensive and reliable methodology for space–time extreme value analysis is attractive but lacking.     

Coupling of the Weather Research and Forecasting Model with AERMOD for pollutant dispersion modeling. A case study for PM10 dispersion over Pune,India

The prediction of spatial variation of the concentration of a pollutant governed by various sources and sinks is a complex problem. Gaussian air pollutant dispersion models such as AERMOD of the United States Environmental Protection Agency (USEPA) can be used for this purpose. AERMOD requires steady and horizontally homogeneous hourly surface and upper air meteorological observations. However, observations with such frequency are not easily available for most locations in India. To overcome this limitation, the planetary boundary layer and surface layer parameters required by AERMOD were computed using the Weather Research and Forecasting (WRF) Model (version 2.1.1) developed by the National Center for Atmospheric Research (NCAR). We have developed a preprocessor for offline coupling of WRF with AERMOD. Using this system, the dispersion of respirable particulate matter (RSPM/PM10) over Pune, India has been simulated. Data from the emissions inventory development and field-monitoring campaign (13–17 April 2005) conducted under the Pune Air Quality Management Program of the Ministry of Environment and Forests (MoEF), India and USEPA, have been used to drive and validate AERMOD. Comparison between the simulated and observed temperature and wind fields shows that WRF is capable of generating reliable meteorological inputs for AERMOD. The comparison of observed and simulated concentrations of PM10 shows that the model generally underestimates the concentrations over the city. However, data from this single case study would not be sufficient to conclude on suitability of regionally averaged meteorological parameters for driving Gaussian models like AERMOD and additional simulations with different WRF parameterizations along with an improved pollutant source data will be required for enhancing the reliability of the WRF–AERMOD modeling system.     

An ozone budget for the UK: using measurements from the national ozone monitoring network; measured and modelled meteorological data,and a 'big-leaf' resistance analogy model of dry deposition

Data from the UK national air-quality monitoring network are used to calculate an annual mass budget for ozone (O3) production and loss in the UK boundary layer during 1996. Monthly losses by dry deposition are quantified from 1 km x 1 km scale maps of O(3) concentration and O(3) deposition velocities based on a big-leaf resistance analogy. The quantity of O(3) deposition varies from approximately 50 Gg-O(3) month(-1) in the winter to over 200 Gg-O(3) month(-1) in the summer when vegetation is actively absorbing O(3). The net O(3) production or loss in the UK boundary layer is found by selecting days when the UK is receiving "clean" Atlantic air from the SW to NW. In these conditions, the difference in O(3) concentration observed at Mace Head and a rural site on the east coast of the UK indicates the net O(3) production or loss within the UK boundary layer. A simple box model is then used to convert the concentration difference into a mass. The final budget shows that for most of the year the UK is a net sink for O(3) (-25 to -800 Gg-O(3) month(-1)) with production only exceeding losses in the photochemically active summer months (+45 Gg-O(3) month(-1)).     

Influence of meteorological factors on air pollution concentration for a coastal region in India

A long-term study of measurement of concentration of NO_x, SO₂ and TSP pollutants have been done in a port and harbour region in India. Monthly measurements of gaseous and particulate pollutants were made at six monitoring stations from January 1997 to December 2000. Meteorological data was also simultaneously collected. In this study, the relationship between monitored ambient air quality data and meteorological factors, such as wind speed, temperature, is statistically analysed, using the SPSS package. The monthly mean concentrations of NO_x, SO₂ and TSP were in the range of 19.5–59.0 μg/m3, 8.6–51.3 μg/m3 and 88.2–199.3 μg/m3, respectively. The results show that TSP is strongly correlated with NO_x and SO₂ with a correlation coefficient of 0.83 and 0.82, respectively. The correlation coefficients for TSP, NO_x, and SO₂ with wind are –0.78, –0.78, and –0.88, respectively.     

Variation of surface ozone in Campo Grande,Brazil: meteorological effect analysis and prediction

The effect of meteorological variables on surface ozone (O₃) concentrations was analysed based on temporal variation of linear correlation and artificial neural network (ANN) models defined by genetic algorithms (GAs). ANN models were also used to predict the daily average concentration of this air pollutant in Campo Grande, Brazil. Three methodologies were applied using GAs, two of them considering threshold models. In these models, the variables selected to define different regimes were daily average O₃ concentration, relative humidity and solar radiation. The threshold model that considers two O₃ regimes was the one that correctly describes the effect of important meteorological variables in O₃ behaviour, presenting also a good predictive performance. Solar radiation, relative humidity and rainfall were considered significant for both O₃ regimes; however, wind speed (dispersion effect) was only significant for high concentrations. According to this model, high O₃ concentrations corresponded to high solar radiation, low relative humidity and wind speed. This model showed to be a powerful tool to interpret the O₃ behaviour, being useful to define policy strategies for human health protection regarding air pollution.     

Extending the Kolmogorov-Zurbenko filter: application to ozone, particulate matter, and meteorological trends

Tropospheric ozone (O3) and particulate matter (PM) are pollutants of great concern to air quality managers. Federal standards for these pollutants have been promulgated in recent years because of the known adverse effects of the pollutants on human health, the environment, and visibility. Local meteorological conditions exert a strong influence over day-to-day variations in pollutant concentrations; therefore, the meteorological signal must be removed in order for air quality planners and managers to examine underlying emissions-related trends and make better air quality management decisions for the future. Although the Kolmogorov-Zurbenko (KZ) filter has been widely used for this type of trend separation in O3 studies in the eastern United States, this article aims to extend the method in three key ways. First, whereas the KZ filter is known as a useful tool for O3 analysis, this study also evaluates its effectiveness when applied to PM. Second, the method was applied to Tucson, AZ, a city in the semi-arid southwestern United States (Southwest), to evaluate the appropriateness of the method in a region with weaker synoptic weather controls on air quality than the eastern United States. Third, additional forms of output were developed and tailored to be more applicable to decision-makers' needs through a partnership between academic researchers and air quality planners and managers. Results of the study indicate that the KZ filter is a useful method for examining emissions-related PM trends, resulting in small, but potentially significant, differences after adjustment. For the Tucson situation with weaker synoptic controls, the KZ method identified mixing height as a more important variable than has been found in other cities.