Source estimation methods for atmospheric dispersion

Both forward and backward transport modeling methods are being developed for characterization of sources in atmospheric releases of toxic agents. Forward modeling methods, which describe the atmospheric transport from sources to receptors, use forward-running transport and dispersion models or computational fluid dynamics models which are run many times, and the resulting dispersion field is compared to observations from multiple sensors. Forward modeling methods include Bayesian updating and inference schemes using stochastic Monte Carlo or Markov Chain Monte Carlo sampling techniques. Backward or inverse modeling methods use only one model run in the reverse direction from the receptors to estimate the upwind sources. Inverse modeling methods include adjoint and tangent linear models, Kalman filters, and variational data assimilation, among others.This survey paper discusses these source estimation methods and lists the key references. The need for assessing uncertainties in the characterization of sources using atmospheric transport and dispersion models is emphasized.     

Assimilating concentration observations for transport and dispersion modeling in a meandering wind field

Assimilating concentration data into an atmospheric transport and dispersion model can provide information to improve downwind concentration forecasts. The forecast model is typically a one-way coupled set of equations: the meteorological equations impact the concentration, but the concentration does not generally affect the meteorological field. Thus, indirect methods of using concentration data to influence the meteorological variables are required. The problem studied here involves a simple wind field forcing Gaussian dispersion. Two methods of assimilating concentration data to infer the wind direction are demonstrated. The first method is Lagrangian in nature and treats the puff as an entity using feature extraction coupled with nudging. The second method is an Eulerian field approach akin to traditional variational approaches, but minimizes the error by using a genetic algorithm (GA) to directly optimize the match between observations and predictions. Both methods show success at inferring the wind field. The GA-variational method, however, is more accurate but requires more computational time. Dynamic assimilation of a continuous release modeled by a Gaussian plume is also demonstrated using the genetic algorithm approach.     

Improving pollutant source characterization by better estimating wind direction with a genetic algorithm

In homeland security applications, it is often necessary to characterize the source location and strength of a potentially harmful contaminant. Correct source characterization requires accurate meteorological data such as wind direction. Unfortunately, available meteorological data is often inaccurate or unrepresentative, having insufficient spatial and temporal resolution for precise modeling of pollutant dispersion. To address this issue, a method is presented that simultaneously determines the surface wind direction and the pollutant source characteristics. This method compares monitored receptor data to pollutant dispersion model output and uses a genetic algorithm (GA) to find the combination of source location, source strength, and surface wind direction that best matches the dispersion model output to the receptor data. A GA optimizes variables using principles from genetics and evolution.The approach is validated with an identical twin experiment using synthetic receptor data and a Gaussian plume equation as the dispersion model. Given sufficient receptor data, the GA is able to reproduce the wind direction, source location, and source strength. Additional runs incorporating white noise into the receptor data to simulate real-world variability demonstrate that the GA is still capable of computing the correct solution, as long as the magnitude of the noise does not exceed that of the receptor data.     

A Simulation Model for Air Pollution over Connecticut

A different approach to mathematically modeling large-scale atmospheric processes is presented. Whereas past approaches have been to develop a model based on an accumulation of information from a specific geographical area, resulting in a model applicable to that area only, we have developed a general mathematical model applicable to any geographical area. The model’s applicability is controlled by specifying the input information describing the meteorological situation and pollution source configuration. A rectangular array of grid points is used to specify both the wind field, by using stream functions, and the average source strength of some pollutant for the area represented by the grid. The diffusion problem is divided into two areas: transport by the mean wind field, and dispersion based on travel time and distance as described by empirical equations. Trajectories of pollutants are traced backwards from the points of interest in the course of the calculations and the contributions of all sources that affect the points of interest are accumulated. The model requires an array of source strength information. An inventory of pollution sources in the State of Connecticut was compiled and maps of source strengths were prepared for five pollutants on a 5000-ft grid-square array. Maps of sulfur dioxide and carbon monoxide source strengths are presented with the resulting concentration distribution for “typical” meteorological conditions. The model permits the changing of meteorological or source values at predetermined intervals so that diurnal changes are incorporated in the calculations. The model has not been verified, but the values of pollution concentration are the right order of magnitude and the resulting patterns are as expected.     

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.     

Transport and dispersion during wintertime particulate matter episodes in the San Joaquin Valley, California

Data analysis and modeling were performed to characterize the spatial and temporal variability of wintertime transport and dispersion processes and the impact of these processes on particulate matter (PM) concentrations in the California San Joaquin Valley (SJV). Radar wind profiler (RWP) and radio acoustic sounding system (RASS) data collected from 18 sites throughout Central California were used to estimate hourly mixing heights for a 3-month period and to create case studies of high-resolution diagnostic wind fields, which were used for trajectory and dispersion analyses. Data analyses show that PM episodes were characterized by an upper-level ridge of high pressure that generally produced light winds through the entire depth of the atmospheric boundary layer and low mixing heights compared with nonepisode days. Peak daytime mixing heights during episodes were -400 m above ground level (agl) compared with -800 m agl during nonepisodes. These episode/nonepisode differences were observed throughout the SJV. Dispersion modeling indicates that the range of influence of primary PM emitted in major population centers within the SJV ranged from -15 to 50 km. Trajectory analyses revealed that little intrabasin pollutant transport occurred among major population centers in the SJV; however, interbasin transport from the northern SJV and Sacramento regions into the San Francisco Bay Area (SFBA) was often observed. In addition, this analysis demonstrates the usefulness of integrating RWP/RASS measurements into data analyses and modeling to improve the understanding of meteorological processes that impact pollution, such as aloft transport and boundary layer evolution.     

The impact of nudging coefficient for the initialization on the atmospheric flow field and the photochemical ozone concentration of Seoul,Korea

In order to incorporate correctly the large or local scale circulation in the model, a nudging term is introduced into the equation of motion. Nudging effects should be included properly in the model to reduce the uncertainties and improve the air flow field. To improve the meteorological components, the nudging coefficient should perform the adequate influence on complex area for the model initialization technique which related to data reliability and error suppression. Several numerical experiments have been undertaken in order to evaluate the effects on air quality modeling by comparing the performance of the meteorological result with variable nudging coefficient experiment. All experiments are calculated by the upper wind conditions (synoptic or asynoptic condition), respectively. Consequently, it is important to examine the model response to nudging effect of wind and mass information. The MM5–CMAQ model was used to assess the ozone differences in each case, during the episode day in Seoul, Korea and we revealed that there were large differences in the ozone concentration for each run.These results suggest that for the appropriate simulation of large or small-scale circulations, nudging considering the synoptic and asynoptic nudging coefficient does have a clear advantage over dynamic initialization, so appropriate limitation of these nudging coefficient values on its upper wind conditions is necessary before making an assessment. The statistical verifications showed that adequate nudging coefficient for both wind and temperature data throughout the model had a consistently positive impact on the atmospheric and air quality field. On the case dominated by large-scale circulation, a large nudging coefficient shows a minor improvement in the atmospheric and air quality field. However, when small-scale convection is present, the large nudging coefficient produces consistent improvement in the atmospheric and air quality field.     

A model for the maximum credible hourly impact on any ground receptor from point sources with thermal plume rise

A pollutant dispersion model is developed, allowing fast evaluation of the maximum credible 1-h average concentration on any given ground-level receptor, along with the corresponding critical meteorological conditions (wind speed and stability class) for stacks with buoyant plumes in urban or rural areas. Site-specific meteorological data are not required, as the computed concentrations are maximized against all credible combinations of wind speed, stability class, and mixing height. The analysis is based on the dispersion relations of Pasquill-Gifford and Briggs for rural and urban settings, respectively, the buoyancy induced dispersion correlation of Pasquill, the wind profile exponent values suggested by Irwin, the buoyant plume rise relations of Briggs, as well as the Benkley and Schulman's model for the minimum mixing heights. The model is particularly suited for air pollution management studies, as it allows fast screening of the maximum impact on any selected receptor and evaluation of the ways to have this impact reduced. It is also suited for regulatory purposes, as it can be used to define the minimum stack size requirements for a given source as a function of the exit gas volume and temperature, the pollutant emission rates and their hourly concentration standards, as well as the source location relative to sensitive receptors.     

A Gaussian puff model with optimal interpolation for air pollution modelling assessment

A Gaussian plume model was modified to simulate the dispersion of non-reactive air pollutants under non-homogeneous wind conditions through a multi-puff approach. It was applied to the city of Lisbon and evaluated by comparison with measured sulphur dioxide data, showing a reasonable skill to estimate the transport and dispersion of pollutants under complex wind field and different atmospheric conditions. The modelling results were integrated with observed data, based on correlation functions determined from historical values, to obtain the improved analytical results by using optimal interpolation. A significant improvement over the predictions by the Gaussian puff model alone was achieved.     

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%.     

Simulating industrial emissions using Atmospheric Dispersion Modeling System: Model performance and source emission factors

In this paper, the Gaussian Atmospheric Dispersion Modeling System (ADMS4) was coupled with field observations of surface meteorology and concentrations of several air quality indicators (nitrogen oxides (NO_X), carbon monoxide (CO), fine particulate matter (PM₁₀) and sulfur dioxide (SO₂)) to test the applicability of source emission factors set by the European Environment Agency (EEA) and the United States Environmental Protection Agency (USEPA) at an industrial complex. Best emission factors and data groupings based on receptor location, type of terrain and wind speed, were relied upon to examine model performance using statistical analyses of simulated and observed data. The model performance was deemed satisfactory for several scenarios when receptors were located at downwind sites with index of agreement d values reaching 0.58, fractional bias “FB” and geometric mean bias “MG” values approaching 0 and 1, respectively, and normalized mean square error “NMSE” values as low as 2.17. However, median ratios of predicted to observed concentrations “Cp/Co” at variable downstream distances were 0.01, 0.36, 0.76 and 0.19 for NO_X, CO, PM₁₀ and SO₂, respectively, and the fraction of predictions within a factor of two of observations “FAC2” values were lower than 0.5, indicating that the model could not adequately replicate all observed variations in emittant concentrations. Also, the model was found to be significantly sensitive to the input emission factor bringing into light the deficiency in regulatory compliance modeling which often uses internationally reported emission factors without testing their applicability.

Implications In the absence of site-specific source emission factors, the use of internationally reported emission factors without testing their validity may generate significant errors. Instead, recorded field measurements and meteorological data may be combined with atmospheric transport and dispersion models to better estimate source emissions, particularly in regulatory compliance studies. In this context, lower model performance is expected at higher wind speeds for most indicators such as CO, PM₁₀, and SO₂.     

Experimental Characterization of Atmospheric Diffusion in Complex Terrain with Land-Sea Interactions

The body of information presented in this paper is directed to scientists working in atmospheric dispersion research and model development. Two years of field measurements in the coastal area of Bilbao in northern Spain show that the diffusion behavior in this complex terrain can be classified into several well defined patterns, which correspond to certain meteorological conditions. The approach taken has been the systematic use of SO₂ remote sensors (COSPEC) and ground level monitors in moving platforms which are used to follow and document the flow of the air mass. Results to date show that complex reentry cycles can occur and that synoptically different flows may be indistinguishable by wind sensors at ground level (affected by channeling), and yet result in totally different observed pollution levels by a fixed monitoring network (affected by topographical effects). These results are being used to parameterize the cause-effect relationships and guide the modeling efforts in this area of complex terrain.     

Validation of modeling approach to evaluate congener-specific concentrations of polychlorinated dibenzo-p-dioxins and dibenzofurans in air and soil near a solid waste incinerator.

A concise modeling approach using long-term averaged meteorological data was developed to estimate site-specific concentrations of congeners of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) near a solid waste incinerator. This approach consists of calculation of atmospheric dispersion, dry and wet deposition of gaseous and particle-bound congeners, and non-steady-state concentrations in soil. The predictability of this approach was evaluated by comparison of calculated concentrations of congeners in soil with those measured at eight locations near a municipal solid waste incinerator (MSWI). The variation of these concentrations due to variability of meteorological parameters is small. A considerable number of mean values show good agreement with measured concentrations within a factor of three. The reasonable agreement between calculated and measured concentrations indicates that algorithms for the calculation of vapor-phase deposition and non-steady-state concentrations in soil must be included in the modeling approach for an accurate estimation of the concentrations of congeners of PCDD/Fs emitted from MSWIs to the atmosphere. For a detailed estimation of site-specific concentrations, it is important to specify the bulk density of soil in the evaluated area, together with meteorological parameters.     

The Aerodyne Inverse Modeling System (AIMS): Source estimation applied to the FFT 07 experiment and to simulated mobile sensor data

The Aerodyne Inverse Modeling System was developed to enable location and characterization of hazardous atmospheric releases from dispersion and meteorological data. It combines an automatically-generated tangent-linear of SCIPUFF with a cost function tailored for practical applications and a minimization algorithm that can search for multiple instantaneous or continuous sources without requiring an initial guess. In this work AIMS was applied to estimate the sources in 84 FFT 07 cases that included instantaneous and continuous releases for up to four source locations. FFT 07 was a controlled short-range (～500 m) dispersion test using 100 digiPIDs evenly distributed over an area of 0.5 × 0.5 km. AIMS estimated sources were in average within 90–150 m of the real sources, with the distances from estimated to real source ranging from 0 to 510 m. AIMS performed better estimating the location of instantaneous sources than of continuous ones. It also performed better for single-source situations than for multiple source scenarios and when 16 sensors were used instead of 4. In addition to using stationary sensors, AIMS also has the capability of processing data from mobile sensors. This was applied using model-generated data in an example of a release in a setting similar to an industrial facility.     

Modeling the impacts of traffic emissions on air toxics concentrations near roadways

The dispersion formulation incorporated in the U.S. Environmental Protection Agency's AERMOD regulatory dispersion model is used to estimate the contribution of traffic-generated emissions of select VOCs – benzene, 1,3-butadiene, toluene – to ambient air concentrations at downwind receptors ranging from 10-m to 100-m from the edge of a major highway in Raleigh, North Carolina. The contributions are computed using the following steps: 1) Evaluate dispersion model estimates with 10-min averaged NO data measured at 7 m and 17 m from the edge of the road during a field study conducted in August, 2006; this step determines the uncertainty in model estimates. 2) Use dispersion model estimates and their uncertainties, determined in step 1, to construct pseudo-observations. 3) Fit pseudo-observations to actual observations of VOC concentrations measured during five periods of the field study. This provides estimates of the contributions of traffic emissions to the VOC concentrations at the receptors located from 10 m to 100 m from the road. In addition, it provides estimates of emission factors and background concentrations of the VOCs, which are supported by independent estimates from motor vehicle emissions models and regional air quality measurements. The results presented in the paper demonstrate the suitability of the formulation in AERMOD for estimating concentrations associated with mobile source emissions near roadways. This paper also presents an evaluation of the key emissions and dispersion modeling inputs necessary for conducting assessments of local-scale impacts from traffic emissions.     

Lagrangian dispersion modeling of vehicular emissions from a highway in complex terrain

Transit traffic through the Austrian Alps is of major concern in government policy. Pollutant burdens resulting from such traffic are discussed widely in Austrian politics and have already led to measures to restrict traffic on transit routes. In the course of an environmental assessment study, comprehensive measurements were performed. These included air quality observations using passive samplers, a differential optical absorption spectroscopy system, a mobile and a fixed air quality monitoring station, and meteorological observations. As was evident from several previous studies, dispersion modeling in such areas of complex terrain and, moreover, with frequent calm wind conditions, is difficult to handle. Further, in the case presented here, different pollutant sources had to be treated simultaneously (e.g., road networks, exhaust chimneys from road tunnels, and road tunnel portals). No appropriate system for modeling all these factors has so far appeared in the literature. A prognostic wind field model coupled with a Lagrangian dispersion model is thus presented here and is designed to treat all these factors. A comparison of the modeling system with results from passive samplers and from a fixed air quality monitoring station proved the ability of the model to provide reasonable figures for concentration distributions along the A10.     

Development of the ClearSky smoke dispersion forecast system for agricultural field burning in the Pacific Northwest

The post-harvest burning of agricultural fields is commonly used to dispose of crop residue and provide other desired services such as pest control. Despite careful regulation of burning, smoke plumes from field burning in the Pacific Northwest commonly degrade air quality, particularly for rural populations. In this paper, ClearSky, a numerical smoke dispersion forecast system for agricultural field burning that was developed to support smoke management in the Inland Pacific Northwest, is described. ClearSky began operation during the summer through fall burn season of 2002 and continues to the present. ClearSky utilizes Mesoscale Meteorological Model version 5 (MM5v3) forecasts from the University of Washington, data on agricultural fields, a web-based user interface for defining burn scenarios, the Lagrangian CALPUFF dispersion model and web-served animations of plume forecasts. The ClearSky system employs a unique hybrid source configuration, which treats the flaming portion of a field as a buoyant line source and the smoldering portion of the field as a buoyant area source. Limited field observations show that this hybrid approach yields reasonable plume rise estimates using source parameters derived from recent field burning emission field studies. The performance of this modeling system was evaluated for 2003 by comparing forecast meteorology against meteorological observations, and comparing model-predicted hourly averaged PM_2.5 concentrations against observations. Examples from this evaluation illustrate that while the ClearSky system can accurately predict PM_2.5 surface concentrations due to field burning, the overall model performance depends strongly on meteorological forecast error. Statistical evaluation of the meteorological forecast at seven surface stations indicates a strong relationship between topographical complexity near the station and absolute wind direction error with wind direction errors increasing from approximately 20° for sites in open areas to 70° or more for sites in very complex terrain. The analysis also showed some days with good forecast meteorology with absolute mean error in wind direction less than 30° when ClearSky correctly predicted PM_2.5 surface concentrations at receptors affected by field burns. On several other days with similar levels of wind direction error the model did not predict apparent plume impacts. In most of these cases, there were no reported burns in the vicinity of the monitor and, thus, it appeared that other, non-reported burns were responsible for the apparent plume impact at the monitoring site. These cases do not provide information on the performance of the model, but rather indicate that further work is needed to identify all burns and to improve burn reports in an accurate and timely manner. There were also a number of days with wind direction errors exceeding 70° when the forecast system did not correctly predict plume behavior.     

Validation of modeling approach to evaluate congener-specific concentrations of polychlorinated dibenzo-p-dioxins and dibenzofurans in air and soil near a solid waste incinerator

A concise modeling approach using long-term averaged meteorological data was developed to estimate site-specific concentrations of congeners of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) near a solid waste incinerator. This approach consists of calculation of atmospheric dispersion, dry and wet deposition of gaseous and particle-bound congeners, and non-steady-state concentrations in soil. The predictability of this approach was evaluated by comparison of calculated concentrations of congeners in soil with those measured at eight locations near a municipal solid waste incinerator (MSWI). The variation of these concentrations due to variability of meteorological parameters is small. A considerable number of mean values show good agreement with measured concentrations within a factor of three. The reasonable agreement between calculated and measured concentrations indicates that algorithms for the calculation of vapor-phase deposition and non-steady-state concentrations in soil must be included in the modeling approach for an accurate estimation of the concentrations of congeners of PCDD/Fs emitted from MSWIs to the atmosphere. For a detailed estimation of site-specific concentrations, it is important to specify the bulk density of soil in the evaluated area, together with meteorological parameters.     

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.     

Lagrangian Dispersion Modeling of Vehicular Emissions from a Highway in Complex Terrain

Abstract

Transit traffic through the Austrian Alps is of major concern in government policy. Pollutant burdens resulting from such traffic are discussed widely in Austrian politics and have already led to measures to restrict traffic on transit routes. In the course of an environmental assessment study, comprehensive measurements were performed. These included air quality observations using passive samplers, a differential optical absorption spectroscopy system, a mobile and a fixed air quality monitoring station, and meteorological observations. As was evident from several previous studies, dispersion modeling in such areas of complex terrain and, moreover, with frequent calm wind conditions, is difficult to handle. Further, in the case presented here, different pollutant sources had to be treated simultaneously (e.g., road networks, exhaust chimneys from road tunnels, and road tunnel portals). No appropriate system for modeling all these factors has so far appeared in the literature. A prognostic wind field model coupled with a Lagrangian dispersion model is thus presented here and is designed to treat all these factors. A comparison of the modeling system with results from passive samplers and from a fixed air quality monitoring station proved the ability of the model to provide reasonable figures for concentration distributions along the A10.