An Efficient Gaussian-Plume Multiple-Source Air Quality Algorithm

The information presented in this paper is directed to air pollution scientists with an interest in applying air quality simulation models. RAM is the three letter designation for this efficient Gaussian-plume multiple-source air quality algorithm. RAM is a method of estimating short-term dispersion using the Gaussian steady-state model. This algorithm can be used for estimating air quality concentrations of relatively stable pollutants for averaging times from an hour to a day in urban areas from point and area sources. The algorithm is applicable for locations with level or gently rolling terrain where a single wind vector for each hour is a good approximation to the flow over the source area considered. Calculations are performed for each hour. Hourly meteorological data required are wind direction, wind speed, stability class, and mixing height. Emission information required of point sources consists of source coordinates, emission rate, physical height, stack gas volume flow and stack gas temperature. Emission information required of area sources consists of south-west corner coordinates, source area, total area emission rate and effective area source height. Computation time is kept to a minimum by the manner in which concentrations from area sources are estimated using a narrow plume hypothesis and using the area source squares as given rather than breaking down all sources to an area of uniform elements. Options are available to the user to allow use of three different types of receptor locations: 1 ) those whose coordinates are input by the user, 2) those whose coordinates are determined by thé model and are downwind óf significant point and area sources where maxima are likely to occur, and 3) those whose coordinates are determined by the model to give good area coverage of a specific portion of the region. Computation time is also decreased by keeping the number of receptors to a minimum.     

Hazardous Pollutants in Class II Landfills

Class II landfills accept nontoxic municipal trash. Their gaseous emissions were originally assumed to be relatively free of hazardous substances. However, one Class II site in Southern California was found to be emitting enough vinyl chloride to exceed the California Air Quality Standard of 10 ppb for a 24-hour average in surrounding neighborhood. This paper presents a summary of the results of the analysis of landfill gas from over 20 additional Class II landfills. Ambient air surveys were conducted around five of the landfills. About 90 percent of the landfills contained measurable amounts of vinyl chloride and/or benzene. The concentrations exceeded 1 ppm in about half of the sites studied. Vinyl chloride is produced in situ by the action of bacteria on chlorinated solvents, and can be found in landfills that have been closed for over 30 years. The relative amounts of methane and vinyl chloride vary so much within a single landfill that methane measurements cannot be used as a surrogate for vinyl chloride     

Emission,speciation, and evaluation of impacts of non-methane volatile organic compounds from open dump site

Surface emission from Dhapa, the only garbage disposal ground in Kolkata, is a matter of concern to the local environment and also fuels the issues of occupational and environmental health. Surface emission of the Dhapa landfill site was studied using a flux chamber measurement for nonmethane volatile organic compounds (NMVOCs). Eighteen noncarbonyl volatile organic compounds (VOCs) and 14 carbonyl VOCs, including suspected and known carcinogens, were found in appreciable concentrations. The concentrations of the target species in the flux chamber were found to be significantly higher for most of the species in summer than winter. Surface emission rate of landfill gas was estimated by using two different approaches to assess the applicability for an open landfill site. It was found that the emissions predicted using the model Land GEM version 3.02 is one to two orders less than the emission rate calculated from flux chamber measurement for the target species. Tropospheric ozone formation has a serious impact for NMVOC emission. The total ozone-forming potential (OFP) of the Dhapa dumping ground considering all target NMVOCs was estimated to be 4.9E+04 and 1.2E+05 g/day in winter and summer, respectively. Also, it was found that carbonyl VOCs play a more important role than noncarbonyl VOCs for tropospheric ozone formation. Cumulative cancer risk estimated for all the carcinogenic species was found to be 2792 for 1 million population, while the total noncancer hazard index (HI) was estimated to be 246 for the occupational exposure to different compounds from surface emission to the dump-site workers at Dhapa.

Implications:This paper describes the real-time surface emission of NMVOCs from an open municipal solid waste (MSW) dump site studied using a flux chamber. Our study findings indicate that while planning for new landfill site in tropical meteorology, real-time emission data must be considered, rather than relying on modeled data. The formation of tropospheric ozone from emitted NMVOC has also been studied. Our result shows how an open landfill site acts as a source and adds to the tropospheric ozone for the airshed of a metropolitan city.     

Evaluating fugacity models for trace components in landfill gas

A fugacity approach was evaluated to reconcile loadings of vinyl chloride (chloroethene), benzene, 1,3-butadiene and trichloroethylene in waste with concentrations observed in landfill gas monitoring studies. An evaluative environment derived from fictitious but realistic properties such as volume, composition, and temperature, constructed with data from the Brogborough landfill (UK) test cells was used to test a fugacity approach to generating the source term for use in landfill gas risk assessment models (e.g. GasSim). SOILVE, a dynamic Level II model adapted here for landfills, showed greatest utility for benzene and 1,3-butadiene, modelled under anaerobic conditions over a 10 year simulation. Modelled concentrations of these components (95,300 microg m(-3); 43 microg m(-3)) fell within measured ranges observed in gas from landfills (24,300-180,000 microg m(-3); 20-70 microg m(-3)). This study highlights the need (i) for representative and time-referenced biotransformation data; (ii) to evaluate the partitioning characteristics of organic matter within waste systems and (iii) for a better understanding of the role that gas extraction rate (flux) plays in producing trace component concentrations in landfill gas.     

Modelling methane and vinyl chloride in soil surrounding landfills

Sanitary landfilling is used in many countries as a preferred method for disposal of household wastes for reasons of simplicity and economics. Immediately following its deposition within a landfill, most of the organic fraction of waste will begin to undergo degradation through chemical and bacterial action. Landfill gas (LFG) is a product of biodegradation and consists of primarily methane (explosive) and carbon dioxide, with trace amounts of other volatiles that are often toxic gases (for example, vinyl chloride). LFG can migrate through the soil away from the landfill site and appear at the surface away from where it started. Since methane presents a fire or explosive threat, LFG must be controlled to protect property and public safety. To aid this, consideration must be given to models. Therefore, this study was undertaken to develop a simple numerical model by using a finite difference method in order to predict gas migration through the soil surrounding the landfill. The model construction was described as well as the landfill and its surrounding soil. The model was applied to predict methane and vinyl chloride concentrations at different distances from the landfill. Comparison between the predicted and measured values was calculated to evaluate the validity of the model. The agreement between measured and predicted concentrations was found, and this agreement is sufficiently good     

Analytical solutions to compartmental indoor air quality models with application to environmental tobacco smoke concentrations measured in a house

This paper derives the analytical solutions to multi-compartment indoor air quality models for predicting indoor air pollutant concentrations in the home and evaluates the solutions using experimental measurements in the rooms of a single-story residence. The model uses Laplace transform methods to solve the mass balance equations for two interconnected compartments, obtaining analytical solutions that can be applied without a computer. Environmental tobacco smoke (ETS) sources such as the cigarette typically emit pollutants for relatively short times (7-11 min) and are represented mathematically by a "rectangular" source emission time function, or approximated by a short-duration source called an "impulse" time function. Other time-varying indoor sources also can be represented by Laplace transforms. The two-compartment model is more complicated than the single-compartment model and has more parameters, including the cigarette or combustion source emission rate as a function of time, room volumes, compartmental air change rates, and interzonal air flow factors expressed as dimensionless ratios. This paper provides analytical solutions for the impulse, step (Heaviside), and rectangular source emission time functions. It evaluates the indoor model in an unoccupied two-bedroom home using cigars and cigarettes as sources with continuous measurements of carbon monoxide (CO), respirable suspended particles (RSP), and particulate polycyclic aromatic hydrocarbons (PPAH). Fine particle mass concentrations (RSP or PM3.5) are measured using real-time monitors. In our experiments, simultaneous measurements of concentrations at three heights in a bedroom confirm an important assumption of the model-spatial uniformity of mixing. The parameter values of the two-compartment model were obtained using a "grid search" optimization method, and the predicted solutions agreed well with the measured concentration time series in the rooms of the home. The door and window positions in each room had considerable effect on the pollutant concentrations observed in the home. Because of the small volumes and low air change rates of most homes, indoor pollutant concentrations from smoking activity in a home can be very high and can persist at measurable levels indoors for many hours.     

Modeling Short Range Air Dispersion from Area Sources of Non-buoyant Toxics

A model based on K-theory has been developed for describing the short range air dispersion from area sources of non-buoyant toxics. Model parameter estimation is via boundary layer theory. Lateral dispersion by plume meander is considered but ail other sources of horizontal dispersion are neglected. The model can be applied on and near area sources and it can be adapted for predictions of downwind concentrations with a wide variety of meteorological Inputs.

The model has been evaluated by simulating the data obtained during atmospheric tracer studies and by comparison to vinyl chloride concentrations near the BKK landfill in southern California. The model appears to represent a useful and accurate tool for regulatory planning and risk assessment close to area sources of toxics.     

Development of a speciated, hourly, and gridded air pollutants emission modeling system--a case study on the precursors of photochemical smog in the Seoul metropolitan area, Korea

A speciated, hourly, and gridded air pollutants emission modeling system (SHEMS) was developed and applied in predicting hourly nitrogen dioxide (NO2) and ozone (O3) levels in the Seoul Metropolitan Area (SMA). The primary goal of the SHEMS was to produce a systemized emission inventory for air pollutants including ozone precursors for modeling air quality in urban areas. The SHEMS is principally composed of three parts: (1) a pre-processor to process emission factors, activity levels, and spatial and temporal information using a geographical information system; (2) an emission model for each source type; and (3) a post-processor to produce report and input data for air quality models through database modeling. The source categories in SHEMS are point, area, mobile, natural, and other sources such as fugitive emissions. The emission database produced by SHEMS contains 22 inventoried compounds: sulfur dioxide, NO2, carbon monoxide, and 19 speciated volatile organic compounds. To validate SHEMS, the emission data were tested with the Urban Airshed Model to predict NO2 and O3 concentrations in the SMA during selected episode days in 1994. The results turned out to be reliable in describing temporal variation and spatial distribution of those pollutants.     

Validation of Models for Predicting Formaldehyde Concentrations in Residences due to Pressed-Wood Products

This paper describes a laboratory project to assess the accuracy of emission and indoor air quality models to be used in predicting formaldehyde (HCHO) concentrations in residences due to pressed-wood products made with urea-formaldehyde bonding resins. The products tested were partlcleboard underlayment, hardwood- plywood paneling and medium-density fiberboard (mdf). The products were initially characterized in chambers by measuring their formaldehyde surface emission rates over a range of formaldehyde concentrations, air exchange rates and two combinations of temperature and relative humidity (23° C and 5 0% RH; 26°C and 60% RH). They were then installed in a two-room prototype house in three different combinations (underlayment flooring only; underlayment flooring and paneling; and underlayment flooring, paneling, and mdf). The equilibrium formaldehyde concentrations were monitored as a function of air exchange rate. Particleboard underlayment and mdf, but not paneling, behaved as the emission model predicted over a large concentration range, under both sets of temperature and relative humidity. Good agreement was also obtained between measured formaldehyde concentrations and those predicted by a mass-balance indoor air quality model.     

Neuro-fuzzy and neural network systems for air quality control

In order to define efficient air quality plans, Regional Authorities need suitable tools to evaluate both the impact of emission reduction strategies on pollution indexes and the costs of such emission reductions. The air quality control can be formalized as a two-objective nonlinear mathematical problem, integrating source–receptor models and the estimate of emission reduction costs. Both aspects present several complex elements. In particular the source–receptor models cannot be implemented through deterministic modelling systems, that would bring to a computationally unfeasible mathematical problem. In this paper we suggest to identify source–receptor statistical models (neural network and neuro-fuzzy) processing the simulations of a deterministic multi-phase modelling system (GAMES). The methodology has been applied to ozone and PM10 concentrations in Northern Italy. The results show that, despite a large advantage in terms of computational costs, the selected source–receptor models are able to accurately reproduce the simulation of the 3D modelling system.     

Development of outcome-based, multipollutant mobile source indicators

Multipollutant indicators of mobile source impacts are developed from readily available CO, NOx, and elemental carbon (EC) data for use in air quality and epidemiologic analysis. Two types of outcome-based Integrated Mobile Source Indicators (IMSI) are assessed. The first is derived from analysis of emissions of EC, CO, and NOx such that pollutant concentrations are mixed and weighted based on emission ratios for both gasoline and diesel vehicles. The emission-based indicators (IMSI(EB)) capture the impact of mobile sources on air quality estimated from receptor models and their uncertainty is comparable to measurement and source apportionment uncertainties. The IMSI(EB) have larger correlation between two different receptor sites impacted by traffic than single pollutants, suggesting they are better indicators of the local impact ofmobile sources. A sensitivity analysis of fractions of pollutants in a two-pollutant mixture and the inclusion in an epidemiologic model is conducted to develop a second set of indicators based on health outcomes. The health-based indicators (IMSI(HB)) are weighted combinations of CO, NOx, and EC pairs that have the lowest P value in their association with cardiovascular disease emergency department visits, possibly due to their better spatial representativeness. These outcome-based, multipollutant indicators can provide support for the setting of multipollutant air quality standards and other air quality management activities.     

Estimating Emissions from Air Concentration Measurements

A one-year-long experiment in which two different tracers were simultaneously released from two different locations was used to test various hybrid receptor modeling techniques to estimate the tracer emissions using the measured air concentrations and a meteorological model. Air concentrations were measured over an 8-hour averaging time at three sites 14 to 40 km downwind. When the model was used to estimate emissions at only one tracer source, 6 percent of the short-term (8-h) emission estimates were within a factor of 2 of the actual emissions. Temporal averaging of the 8-h data enhanced the precision of the estimate such that after 10 days 42 percent of the estimates were within a factor of 2 and after six months all of them (each source-receptor pair) were within a factor of 2. To test the ability of the model to separate two sources, both tracer sources were combined, and a multiple linear regression technique was used to determine the emissions from each source from a time series of air concentration measurements representing the sum of both tracers. In general, 50 percent of the short-term estimates were within a factor of 10, 25 percent were biased low, and in another 25 percent the regression technique failed. The bias and failures are attributed to low or no correlation between measured air concentrations and model calculated dispersion factors. In the regression method increased temporal averaging did not consistently improve the emission estimate since the ability of the model to distinguish emissions between sources was diminished with increased averaging time. However, including progressively longer time periods (more data) into the regression or spatially averaging the data over all the receptors was found to be the most effective method to improve the estimated emissions. At best about 75 percent of the estimated monthly emission data were within a factor of 10 of the measured values. This suggests that the usefulness of meteorological models and statistical methods to address questions of source attribution requires many data points to reduce the uncertainty in the emission estimates.     

Source apportionment of particulate matter (PM2.5) in an urban area using dispersion,receptor and inverse modelling

Air pollution emission inventories are the basis for air quality assessment and management strategies. The quality of the inventories is of great importance since these data are essential for air pollution impact assessments using dispersion models. In this study, the quality of the emission inventory for fine particulates (PM_2.5) is assessed: first, using the calculated source contributions from a receptor model; second, using source apportionment from a dispersion model; and third, by applying a simple inverse modelling technique which utilises multiple linear regression of the dispersion model source contributions together with the observed PM_2.5 concentrations. For the receptor modelling the chemical composition of PM_2.5 filter samples from a measurement campaign performed between January 2004 and April 2005 are analysed. Positive matrix factorisation is applied as the receptor model to detect and quantify the various source contributions. For the same observational period and site, dispersion model calculations using the Air Quality Management system, AirQUIS, are performed. The results identify significant differences between the dispersion and receptor model source apportionment, particularly for wood burning and traffic induced suspension. For wood burning the receptor model calculations are lower, by a factor of 0.54, but for the traffic induced suspension they are higher, by a factor of 7.1. Inverse modelling, based on regression of the dispersion model source contributions and the PM_2.5 concentrations, indicates similar discrepancies in the emissions inventory. In order to assess if the differences found at the one site are generally applicable throughout Oslo, the individual source category emissions are rescaled according to the receptor modelling results. These adjusted PM_2.5 concentrations are compared with measurements at four independent stations to evaluate the updated inventory. Statistical analysis shows improvement in the estimated concentrations for PM_2.5 at all sites. Similarly, inverse modelling is applied at these independent sites and this confirms the validity of the receptor model results.     

Volatile organic compound concentrations, emission rates, and source apportionment in newly-built apartments at pre-occupancy stage

The present study investigated the indoor concentrations of selected volatile organic compounds (VOCs) and formaldehyde and their indoor emission characteristics in newly-built apartments at the pre-occupancy stage. In total, 107 apartments were surveyed for indoor and outdoor VOC concentrations in two metropolitan cities and one rural area in Korea. A mass balanced model was used to estimate surface area-specific emission rates of individual VOCs and formaldehyde. Seven (benzene, ethyl benzene, toluene, m,p-xylene, o-xylene, n-hexane, and n-heptane) of 40 target compounds were detectable in all indoor air samples, whereas the first five were detected in all outdoor air samples. Formaldehyde was also predominant in the indoor air samples, with a high detection frequency of 96%. The indoor concentrations were significantly higher than the outdoor concentrations for aromatics, alcohols, terpenes, and ketones. However, six halogenated VOCs exhibited similar concentrations for indoor and outdoor air samples, suggesting that they are not major components emitted from building materials. It was also suggested that a certain portion of the apartments surveyed were constructed by not following the Korean Ministry of Environment guidelines for formaldehyde emissions. Toluene exhibited the highest emission rate with a median value of 138 μg m⁻² h⁻¹. The target compounds with median emission rates greater than 20 μg m⁻² h⁻¹ were toluene, 1-propanol, formaldehyde, and 2-butanone. The wood panels/vinyl floor coverings were the largest indoor pollutant source, followed by floorings, wall coverings, adhesives, and paints. The wood panels/vinyl floor coverings contributed nearly three times more to indoor VOC concentrations than paints.     

Development of a Speciated,Hourly, and Gridded Air Pollutants Emission Modeling System—A Case Study on the Precursors of Photochemical Smog in the Seoul Metropolitan Area,Korea

ABSTRACT

A speciated, hourly, and gridded air pollutants emission modeling system (SHEMS) was developed and applied in predicting hourly nitrogen dioxide (NO₂) and ozone (O₃) levels in the Seoul Metropolitan Area (SMA). The primary goal of the SHEMS was to produce a systemized emission inventory for air pollutants including ozone precursors for modeling air quality in urban areas.

The SHEMS is principally composed of three parts: (1) a pre-processor to process emission factors, activity levels, and spatial and temporal information using a geographical information system; (2) an emission model for each source type; and (3) a post-processor to produce report and input data for air quality models through database modeling. The source categories in SHEMS are point, area, mobile, natural, and other sources such as fugitive emissions. The emission database produced by SHEMS contains 22 inventoried compounds: sulfur dioxide, NO₂, carbon monoxide, and 19 speciated volatile organic compounds. To validate SHEMS, the emission data were tested with the Urban Airshed Model to predict NO₂ and O₃ concentrations in the SMA during selected episode days in 1994. The results turned out to be reliable in describing temporal variation and spatial distribution of those pollutants.     

Ambient Air Quality and Automotive Emission Control

This paper is concerned with uncertainties involved in projecting ambient air quality. Ambient air quality was projected by assuming a linear dependence on estimated future emissions. Future automotive emissions were estimated by a method recommended by EPA. Projections were made for the locations reported to have the highest ambient air concentrations of each pollutant; Chicago for carbon monoxide and the California South Coast Air Basin for hydrocarbon and oxidant. The sensitivity of the projections to several input parameters was determined.

The uncertainty in projection of air quality due to the use of a maximum, once-per-year concentration is large. For example, the reduction in total CO emissions in Chicago in 1975, necessary to meet the air quality standard, was as high as 68% or as low as 26%, depending on whether the historic high, 8 hr average concentration of 44 ppm or the 1970 maximum of 21 ppm was used. The effects of uncertainties in growth rates and fraction of emissions attributed to the automobile were also sizeable. Differences in automotive growth rate had a large near-term effect on projected concentrations, while differences in nonautomotive growth rate or fraction of emissions attributed to the automobile had a large long-term effect. The effect of 1975 interim automotive emission standards on projected air quality was negligible when compared with projected air quality based on the previous Federal automotive emission standards for 1975.     

A Comparative Validation of the RAM and PTMTP Models for Short-Term SO2 Concentrations in Two Urban Areas

A comprehensive and comparative model validation of two EPA models for short-term SO₂ concentrations was performed. The two models tested were RAM (Urban version) and PTMTP (Terrain version). Both are multiple source, multiple receptor gaussian plume models, recommended in the EPA Guideline On Air Quality Models. ¹ The principal difference between the two models is in their use of empirical dispersion coefficients. It was because of the potential for markedly different predicted maximum SO₂ concentrations, and the absence of any testing data on the RAM model, that the validation analysis was undertaken. The current study utilized a full year of air quality data from monitoring sites in two Indiana cities, Michigan City and Indianapolis. Cumulative frequency distributions for each site and model were prepared and comparisons made. The results indicate that the RAM (Urban) model was highly inaccurate in predicting maximum short-term SO₂ concentrations. The PTMTP model, although conservative in its estimates, produces results which more closely resemble the distribution of observed SO₂ concentrations. The body of information presented in this paper is directed to environmental scientists responsible for air quality modeling, and to those persons who set policy on the use of models in air quality studies.     

Comparing estimates of fugitive landfill methane emissions using inverse plume modeling obtained with Surface Emission Monitoring (SEM), Drone Emission Monitoring (DEM), and Downwind Plume Emission Monitoring (DWPEM)

ABSTRACT

As part of the global effort to quantify and manage anthropogenic greenhouse gas emissions, there is considerable interest in quantifying methane emissions in municipal solid waste landfills. A variety of analytical and experimental methods are currently in use for this task. In this paper, an optimization-based estimation method is employed to assess fugitive landfill methane emissions. The method combines inverse plume modeling with ambient air methane concentration measurements. Three different measurement approaches are tested and compared. The method is combined with surface emission monitoring (SEM), above ground drone emission monitoring (DEM), and downwind plume emission monitoring (DWPEM). The methodology is first trialed and validated using synthetic datasets in a hand-generated case study. A field study is also presented where SEM, DEM and DWPEM are tested and compared. Methane flux during two-days measurement campaign was estimated to be between 228 and 350 g/s depending on the type of measurements used. Compared to SEM, using unmanned aerial systems (UAS) allows for a rapid and comprehensive coverage of the site. However, as showed through this work, advancement of DEM-based methane sampling is governed by the advances that could be made in UAS-compatible measurement instrumentations. Downwind plume emission monitoring led to a smaller estimated flux compared with SEM and DEM without information about positions of major leak points in the landfill. Even though, the method is simple and rapid for landfill methane screening. Finally, the optimization-based methodology originally developed for SEM, shows promising results when it is combined with the drone-based collected data and downwind concentration measurements. The studied cases also discovered the limitations of the studied sampling strategies which is exploited to identify improvement strategies and recommendations for a more efficient assessment of fugitive landfill methane emissions.

Implications: Fugitive landfill methane emission estimation is tackled in the present study. An optimization-based method combined with inverse plume modeling is employed to treat data from surface emission monitoring, drone-based emission monitoring and downwind plume emission monitoring. The study helped revealing the advantages and the limitations of the studied sampling strategies. Recommendations for an efficient assessment of landfill methane emissions are formulated. The method trialed in this study for fugitive landfill methane emission could also be appropriate for rapid screening of analogous greenhouse gas emission hotspots.     

Emission and fate assessment of methyl tertiary butyl ether in the Boston area airshed using a simple multimedia box model: comparison with urban air measurements

Expected urban air concentrations of the gasoline additive methyl tertiary butyl ether (MTBE) were calculated using volatile emissions estimates and screening transport models, and these predictions were compared with Boston, MA, area urban air measurements. The total volatile flux of MTBE into the Boston primary metropolitan statistical area (PMSA) airshed was calculated based on estimated automobile nontailpipe emissions and the Universal Quasi-Chemical Functional-Group Activity Coefficient computed abundance of MTBE in gasoline vapor. The fate of MTBE in the Boston PMSA was assessed using both the European Union System for the Evaluation of Substances, which is a steady-state multimedia box model, and a simple airshed box model. Both models were parameterized based on the meteorological conditions observed during air sampling in the Boston area. Measured average urban air concentrations of 0.1 and 1 microg/m3 MTBE during February and September of 2000, respectively, were comparable to corresponding model predictions of 0.3 and 1 microg/m3 and could be essentially explained from estimated temperature-dependent volatile emissions rates, observed average wind speed (the airshed flushing rate), and reaction with ambient tropospheric hydroxyl radical (*OH), within model uncertainty. These findings support the proposition that one can estimate gasoline component source fluxes and use simple multimedia models to screen the potential impact of future proposed gasoline additives on urban airsheds.     

Sensitivity Analysis of Dispersion Models for Point and Area Sources

The body of information presented in this paper is directed to air quality managers in industry and government contemplating modeling emissions from complex sources under the bubble concept.

Point and area source algorithms of PAL, RAM, and ISC-ST were analyzed to show the effect of various input assumptions on model output. Several important parameters were varied individually; receptor grid spacing, emission release height, area source size and source type. Each of these parameters was varied over a range of values while all other modeling parameters, both physical and meteorological, were held constant. The outputs of each model are plotted for easy comparison.

Results indicate that it would be inappropriate to make certain assumptions regarding source characteristics without knowing the behavior of each model. The graphs show how the model predictions can vary for different input parameters when applied to point and area sources. The paper presents general rules of thumb for evaluating model results for many applications such as the bubble concept, emissions banking, offsets, and new source reviews. The results serve as a guide in selecting and using models for both point and area sources.