Guidance for the performance evaluation of three-dimensional air quality modeling systems for particulate matter and visibility

Guidance for the performance evaluation of three-dimensional air quality modeling systems for particulate matter and visibility is presented. Four levels are considered: operational, diagnostic, mechanistic, and probabilistic evaluations. First, a comprehensive model evaluation should be conducted in at least two distinct geographical locations and for several meteorological episodes. Next, streamlined evaluations can be conducted for other similar applications if the comprehensive evaluation is deemed satisfactory. In all cases, the operational evaluation alone is insufficient, and some diagnostic evaluation must always be carried out. Recommendations are provided for designing field measurement programs that can provide the data needed for such model performance evaluations.     

Space-time analysis of the Air Quality Model Evaluation International Initiative (AQMEII) Phase 1 air quality simulations

This study presents an evaluation of summertime ozone concentrations over North America (NA) and Europe (EU) using the database generated from Phase 1 of the Air Quality Model Evaluation International Initiative (AQMEII). The analysis focuses on identifying temporal and spatial features that can be used to stratify operational model evaluation metrics and to test the extent to which the various modeling systems can replicate the features seen in the observations. Using a synoptic map typing approach, it is demonstrated that model performance varies with meteorological conditions associated with specific synoptic-scale flow patterns over both eastern NA and EU. For example, the root mean square error of simulated daily maximum 8-hr ozone was twice as high when cloud fractions were high compared with when cloud fractions were low over eastern NA. Furthermore, results show that over both NA and EU the regional models participating in AQMEII were able to better reproduce the observed variance in ambient ozone levels than the global model used to specify chemical boundary conditions, although the variance simulated by almost all regional models is still less that the observed variance on all spatiotemporal scales. In addition, all modeling systems showed poor correlations with observed fluctuations on the intraday time scale over both NA and EU. Furthermore, a methodology is introduced to distinguish between locally influenced and regionally representative sites for the purpose of model evaluation. Results reveal that all models have worse model performance at locally influenced sites. Overall, the analyses presented in this paper show how observed temporal and spatial information can be used to stratify operational model performance statistics and to test the modeling systems’ ability to replicate observed temporal and spatial features, especially at scales the modeling systems are designed to capture.

Implications: The analyses presented in this paper demonstrate how observed temporal and spatial information can be used to stratify operational model performance and to test the modeling systems’ ability to replicate observed temporal and spatial features. Decisions for the improvement of regional air quality models should be based on the information derived from only regionally representative sites.     

Evaluation of a CMAQ simulation at high resolution over the UK for the calendar year 2003

A comprehensive ‘operational’ evaluation of the performance of the Community Multiscale Air Quality (CMAQ) modelling system version 4.6 was conducted in support of pollution assessment in the UK for the calendar year 2003. The model was run on multiple grids using one-way nests down to a horizontal resolution as fine as 5 km over the whole of the UK. The model performance was evaluated for pollutants with standards and limit values (e.g. O₃, PM₁₀) and species contributing to acidic and nitrogenous deposition (e.g. NH₃, SO₄^2–, NO₃^–, NH₄⁺) against data from operational national monitoring networks. The key performance characteristics of the modelling system were found to be variable according to acceptance criteria and to depend on the type (e.g. urban, rural) and location of the sites, as well as on the time of the year. As regards the techniques that were used for ‘operational’ evaluation, performance generally complied with expected levels and ranged from good (e.g. O₃, SO₄^2–) to moderate (e.g. PM₁₀, NO₃^–). At a few sites low correlations and large standard deviations for some species (e.g. SO₂) suggest that these sites are subject to local factors (e.g. topography, emission sources) that are not well described in the model. Overall, the model tends to over predict O₃ and under predict aerosol species (except SO₄^2–). Discrepancies between predicted and observed concentrations may be due to a variety of intertwined factors, which include inaccuracies in meteorological predictions, chemical boundary conditions, temporal variability in emissions, and uncertainties in the treatment of gas and aerosol chemistry. Further work is thus required to investigate the respective contributions of such factors on the predicted concentrations.     

A suggested method for dispersion model evaluation

Too often operational atmospheric dispersion models are evaluated in their ability to replicate short-term concentration maxima, when in fact a valid model evaluation procedure would evaluate a model's ability to replicate ensemble-average patterns in hourly concentration values. A valid model evaluation includes two basic tasks: In Step 1 we should analyze the observations to provide average patterns for comparison with modeled patterns, and in Step 2 we should account for the uncertainties inherent in Step 1 so we can tell whether differences seen in a comparison of performance of several models are statistically significant. Using comparisons of model simulation results from AERMOD and ISCST3 with tracer concentration values collected during the EPRI Kincaid experiment, a candidate model evaluation procedure is demonstrated that assesses whether a model has the correct total mass at the receptor level (crosswind integrated concentration values) and whether a model is correctly spreading the mass laterally (lateral dispersion), and assesses the uncertainty in characterizing the transport. The use of the BOOT software (preferably using the ASTM D 6589 resampling procedure) is suggested to provide an objective assessment of whether differences in model performance between models are significant.

Implications:

Regulatory agencies can choose to treat modeling results as “pseudo-monitors,” but air quality models actually only predict what they are constructed to predict, which certainly does not include the stochastic variations that result in observed short-term maxima (e.g., arc-maxima). Models predict the average concentration pattern of a collection of hours having very similar dispersive conditions. An easy-to-implement evaluation procedure is presented that challenges a model to properly estimate ensemble average concentration values, reveals where to look in a model to remove bias, and provides statistical tests to assess the significance of skill differences seen between competing models.     

A Qualitative Approach to Evaluating the Anticipated Reliability of a Photochemical Air Quality Simulation Model for a Selected Application

ABSTRACT

Photochemical air quality simulation models are now used widely in evaluating the merits of alternative emissions control strategies on spatial scales from metropolitan to sub-continental. Greatly varying levels of resources have been available to support modeling, from relatively comprehensive databases and evaluation of performance to a paucity of aerometric data for developing model inputs. Where data are sparse, many alternative outcomes are consistent with the knowledge at hand. Where performance evaluation is inadequately supported, the probability of error may be high. In each instance, uncertainties may be large when compared with the signal of interest, and thus confidence in the reliability of the model as an estimator of future air quality may come into question.

This paper proposes a qualitative procedure for assessing whether a particular application of a modeling system is likely to be potentially unreliable, suggesting that either (1) modification and further evaluation is needed, if supportable, prior to adoption for regulatory application; or (2) the model should not be used if improvement is not supportable. The procedure is proposed for use by policy-makers, staffs of public agencies, air quality managers, environmental staffs of industrial organizations, and other interested parties. The proposed use of the procedure is (1) to assess, a priori, whether a proposed application is likely to be judged questionable or unacceptably uncertain in outcome; and (2) to provide, a posteriori, a basis for judging quickly the likely quality of model performance. The procedure is presented with tropospheric ozone as the pollutant of concern. With adjustments, however, the procedure should be applicable for particu-late matter and other pollutants of interest.     

The Plume Volume Molar Ratio Method for Determining NO2/NOx Ratios in Modeling—Part II: Evaluation Studies

ABSTRACT

A previous paper¹ discusses the methodology for a new method for deriving the nitrogen dioxide/nitrogen oxide (NO₂/NO_x) ratio in plumes that originally are composed mainly of (NO_x). It is called the Plume Volume Molar Ratio Method (PVMRM). This paper documents its performance against six different data sets. These performance evaluations show that the PVMRM can realistically predict the NO₂ fraction at close-in receptors yet still provide conservative estimates so that the air quality standards can be protected.     

High Volume Sampling:Evaluation of an Inverted Sampler for Ambient TSP Measurements

Recent advances in the development of receptor-oriented source apportionment techniques (models) have provided a new approach to evaluating the performance of particulate dispersion models. Rather than limiting performance evaluations to comparisons of particulate mass, receptor model estimates of source impacts can be used to open new opportunities for in-depth analysis of dispersion model performance. Recent experiences in the joint application of receptor and dispersion models have proven valuable in developing increased confidence in source impact projections used for control strategy development. Airshed studies that have followed this approach have identified major errors in emission inventory data bases and provided technical support for modeling assumptions.

This paper focuses on the joint application of dispersion and receptor models to particulate source impact analysis and dispersion model performance and evaluation. The limitations and advantages of each form of modeling are reviewed and case studies are examined. The paper is offered to provide several new perspectives into the model evaluation process in the hope that they may prove useful to those that manage our nation’s air resources.     

Current Status of Air Quality Models for Particulate Matter

ABSTRACT

The current status of the mathematical modeling of atmospheric particulate matter (PM) is reviewed in this paper. Simulating PM requires treating various processes, including the formation of condensable species, the gas/ particle partitioning of condensable compounds, and in some cases, the evolution of the particle size distribution. The algorithms available to simulate these processes are reviewed and discussed. Eleven 3-dimensional (3-D) Eulerian air quality models for PM are reviewed in terms of their formulation and past applications. Results of past performance evaluations of 3-D Eulerian PM models are presented. Currently, 24-hr average PM_2.5 concentrations appear to be predicted within 50% for urban-scale domains. However, there are compensating errors among individual particulate species. The lowest errors tend to be associated with SO₄ ^2-, while NO₃ ^-, black carbon (BC), and organic carbon (OC) typically show larger errors due to uncertainties in emissions inventories and the prediction of the secondary OC fraction. Further improvements and performance evaluations are recommended.     

Rethinking the assessment of photochemical modelin systems in air quality planning applications

The U.S. Environmental Protection Agency provides guidelines for demonstrating that future 8-hr ozone (O3) design values will be at or below the National Ambient Air Quality Standards on the basis of the application of photochemical modeling systems to simulate the effect of emission reductions. These guidelines also require assessment of the model simulation against observations. In this study, we examined the link between the simulated relative responses to emission reductions and model performance as measured by operational evaluation metrics, a part of the model evaluation required by the guidance, which often is the cornerstone of model evaluation in many practical applications. To this end, summertime O3 concentrations were simulated with two modeling systems for both 2002 and 2009 emission conditions. One of these two modeling systems was applied with two different parameterizations for vertical mixing. Comparison of the simulated base-case 8-hr daily maximum O3 concentrations showed marked model-to-model differences of up to 20 ppb, resulting in significant differences in operational model performance measures. In contrast, only relatively minor differences were detected in the relative response of O3 concentrations to emission reductions, resulting in differences of a few ppb or less in estimated future year design values. These findings imply that operational model evaluation metrics provide little insight into the reliability of the actual model application in the regulatory setting (i.e., the estimation of relative changes). In agreement with the guidance, it is argued that more emphasis should be placed on the diagnostic evaluation of O3-precursor relationships and on the development and application of dynamic and retrospective evaluation approaches in which the response of the model to changes in meteorology and emissions is compared with observed changes. As an example, simulated relative O3 changes between 1995 and 2007 are compared against observed changes. It is suggested that such retrospective studies can serve as the starting point for targeted diagnostic studies in which individual aspects of the modeling system are evaluated and refined to improve the characterization of observed changes.     

Air Quality Model Evaluation and Uncertainty

The many advances made in air quality model evaluation procedures during the past ten years are discussed and some components of model uncertainty presented. Simplified statistical procedures for operational model evaluation are suggested. The fundamental model performance measures are the mean bias, the mean square error, and the correlation. The bootstrap resampling technique is used to estimate confidence limits on the performance measures, In order to determine if a model agrees satisfactorily with data or if one model is significantly different from another model. Applications to two tracer experiments are described.

It is emphasized that review and evaluation of the scientific components of models are often of greater Importance than the strictly statistical evaluation. A necessary condition for acceptance Of a model should be that it is scientifically correct. It Is shown that even in research-grade tracer experiments, data Input errors can cause errors In hourly-average model predictions of point concentrations almost as large as the predictions themselves. The turbulent or stochastic component of model uncertainty has a similar magnitude. These components of the uncertainty decrease as averaging time increases.     

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.     

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.     

Recommendations on statistics and benchmarks to assess photochemical model performance

Photochemical grid models are addressing an increasing variety of air quality related issues, yet procedures and metrics used to evaluate their performance remain inconsistent. This impacts the ability to place results in quantitative context relative to other models and applications, and to inform the user and affected community of model uncertainties and weaknesses. More consistent evaluations can serve to drive improvements in the modeling process as major weaknesses are identified and addressed. The large number of North American photochemical modeling studies published in the peer-reviewed literature over the past decade affords a rich data set from which to update previously established quantitative performance “benchmarks” for ozone and particulate matter (PM) concentrations. Here we exploit this information to develop new ozone and PM benchmarks (goals and criteria) for three well-established statistical metrics over spatial scales ranging from urban to regional and over temporal scales ranging from episodic to seasonal. We also recommend additional evaluation procedures, statistical metrics, and graphical methods for good practice. While we primarily address modeling and regulatory settings in the United States, these recommendations are relevant to any such applications of state-of-the-science photochemical models. Our primary objective is to promote quantitatively consistent evaluations across different applications, scales, models, model inputs, and configurations. The purpose of benchmarks is to understand how good or poor the results are relative to historical model applications of similar nature and to guide model performance improvements prior to using results for policy assessments. To that end, it also remains critical to evaluate all aspects of the model via diagnostic and dynamic methods. A second objective is to establish a means to assess model performance changes in the future. Statistical metrics and benchmarks need to be revisited periodically as model performance and the characteristics of air quality change in the future.

Implications: We address inconsistent procedures and metrics used to evaluate photochemical model performance, recommend a specific set of statistical metrics, and develop updated quantitative performance benchmarks for those metrics. We promote quantitatively consistent evaluations across different applications, scales, models, inputs, and configurations, thereby (1) improving the user’s ability to quantitatively place results in context and guide model improvements, and (2) better informing users, regulators, and stakeholders of model uncertainties and weaknesses prior to using results for policy assessments. While we primarily address U.S. modeling and regulatory settings, these recommendations are relevant to any such applications of state-of-the-science photochemical models.     

Investigation of real-life operating patterns of wood-burning appliances using stack temperature data

ABSTRACT

A study was undertaken to identify patterns of consumer use of outdoor wood boilers or outdoor wood furnaces (technically referred to as outdoor wood-fired hydronic heaters (OWHHs)) and indoor wood stoves (IWSs) to inform the development of performance testing protocols that reflect real-life operating conditions. These devices are manually fed, and their usage protocols are a function of a number of variables, including user habits, household characteristics, and environmental factors. In this study, researchers logged the stack wall temperatures of 4 OWHH and 20 IWS units in the states of New York and Washington over two heating seasons. Stack wall temperature is an indicator of changes in combustion modes. Two algorithms were developed to identify usage modes and cold and warm start refueling events from the stack wall temperature time series. A linear correlation analysis was conducted to evaluate the effect of heat demand on usage patterns. The results and methods presented here will inform the cataloging of typical operational patterns of OWHHs and IWSs as a step in the development of performance testing procedures that represent actual in-home usage patterns.

Implications: Current US regulatory programs for residential wood heating use a certification program to assess emissions and efficiency performance. Testing under this program uses a test that burns 100% of a single, standardized wood fuel charge to assess performance at different steady-state load conditions. This study assessed in-field operational patterns to determine if the current certification approach accurately characterized typical homeowner use patterns. The data from this study can be used to inform revisions to testing methods to increase certification test comparability between lab and field performance.     

Diagnostics for Determining Influential Species in the Chemical Mass Balance Receptor Model

ABSTRACT

The chemical mass balance (CMB) model can be applied to estimate the amount of airborne particulate matter (PM) coming from various sources given the ambient chemical composition of the particles measured at the receptor and the chemical composition of the source emissions. Of considerable practical importance is the identification of those chemical species that have a large effect on either the source contributions or errors estimated by the CMB model. This paper details a study of a number of influential diagnostics for application of the CMB software. Some of the diagnostics studied are standard regression diagnostics based on single-row deletion diagnostics. A number of new diagnostics were developed specifically for the CMB application, based on the pseudo-inverse of the source composition matrix and called nondeletion diagnostics to distinguish them from the standard deletion diagnostics. Simulated data sets were generated to compare the diagnostics and their response to controlled amounts of random error.

A particular diagnostic called a modified pseudoinverse matrix (MPIN), developed for this study, was found to be the best choice for CMB model application. The MPIN diagnostic contains virtually all the information present in both deletion and nondeletion diagnostics. Since the MPIN diagnostic requires only the source profiles, it can be used to identify influential species in advance without sampling the ambient data and to improve CMB results through possible remedial actions for the influential species. Specific recommendations are given for interpretation and use of the MPIN diagnostic with the CMB model software. Elements with normalized MPIN absolute values of 1 to 0.5 are associated with influential elements. Noninfluential elements have normalized MPIN absolute values of 0.3 or less. Elements with absolute values between 0.3 and 0.5 are ambiguous but should generally be considered noninfluential.     

A statistical model for predicting PM2.5 for the western United States

ABSTRACT

A new statistical model for predicting daily ground level fine scale particulate matter (PM_2.5) concentrations at monitoring sites in the western United States was developed and tested operationally during the 2016 and 2017 wildfire seasons. The model is site-specific, using a multiple linear regression schema that relies on the previous day’s PM_2.5 value, along with fire and smoke related variables from satellite observations. Fire variables include fire radiative power (FRP) and the National Fire Danger Rating System Energy Release Component index. Smoke variables, in addition to ground monitored PM_2.5, include aerosol optical depth (AOD) and smoke plume perimeters from the National Oceanic and Atmospheric Administration’s Hazard Mapping System. The overall statistical model was inspired by a similar system developed for British Columbia (BC) by the BC Center for Disease Control, but it has been heavily modified and adapted to work in the United States. On average, our statistical model was able to explain 78% of the variance in daily ground level PM_2.5. A novel method for implementation of this model as an operational forecast system was also developed and was tested and used during the 2016 and 2017 wildfire seasons. This method focused on producing a continuously-updating prediction that incorporated the latest information available throughout the day, including both updated remote sensing data and real-time PM_2.5 observations. The diurnal pattern of performance of this model shows that even a few hours of data early in the morning can substantially improve model performance.

Implications: Wildfire smoke events produce significant air quality impacts across the western United States each year impacting millions. We present and evaluate a statistical model for making updating predictions of fine particulate (PM_2.5) levels during smoke events. These predictions run hourly and are being used by smoke incident specialists assigned to wildfire operations, and may be of interest to public health officials, air quality regulators, and the public. Predictions based on this model will be available on the web for the 2019 western U.S. wildfire season this summer.     

Sensitivity analyses of factors influencing CMAQ performance for fine particulate nitrate

Improvement of air quality models is required so that they can be utilized to design effective control strategies for fine particulate matter (PM_2.5). The Community Multiscale Air Quality modeling system was applied to the Greater Tokyo Area of Japan in winter 2010 and summer 2011. The model results were compared with observed concentrations of PM_2.5 sulfate (SO₄^2-), nitrate (NO₃^?) and ammonium, and gaseous nitric acid (HNO₃) and ammonia (NH₃). The model approximately reproduced PM_2.5 SO₄^2? concentration, but clearly overestimated PM_2.5 NO₃^? concentration, which was attributed to overestimation of production of ammonium nitrate (NH₄NO₃). This study conducted sensitivity analyses of factors associated with the model performance for PM_2.5 NO₃^? concentration, including temperature and relative humidity, emission of nitrogen oxides, seasonal variation of NH₃ emission, HNO₃ and NH₃ dry deposition velocities, and heterogeneous reaction probability of dinitrogen pentoxide. Change in NH₃ emission directly affected NH₃ concentration, and substantially affected NH₄NO₃ concentration. Higher dry deposition velocities of HNO₃ and NH₃ led to substantial reductions of concentrations of the gaseous species and NH₄NO₃. Because uncertainties in NH₃ emission and dry deposition processes are probably large, these processes may be key factors for improvement of the model performance for PM_2.5 NO₃^?.

Implications: The Community Multiscale Air Quality modeling system clearly overestimated the concentration of fine particulate nitrate in the Greater Tokyo Area of Japan, which was attributed to overestimation of production of ammonium nitrate. Sensitivity analyses were conducted for factors associated with the model performance for nitrate. Ammonia emission and dry deposition of nitric acid and ammonia may be key factors for improvement of the model performance.     

Design and Performance Characterization Strategy Using Modeling for Biofiltration Control of Odorous Hydrogen Sulfide

Abstract

Biofilter, dynamic modeling software characterizing contaminant removal via biofiltration, was used in the preliminary design of a biofilter to treat odorous hydrogen sulfide (H₂S). Steady-state model simulations were run to generate performance plots for various influent concentrations, loadings, residence times, media sizes, and temperatures. Although elimination capacity and removal efficiency frequently are used to characterize biofilter performance, effluent concentration can be used to characterize performance when treating to a target effluent concentration. Model simulations illustrate that, at a given temperature, a biofilter cannot reduce H₂S emissions below a minimum value, no matter how large the biofilter or how long the residence time. However, a higher biofilter temperature results in lower effluent H₂S concentrations. Because dynamic model simulations show that shock loading can significantly increase the effluent concentration above values predicted by the steady-state model simulations, it is recommended that, to consistently meet treatment objectives, dynamic feed conditions should be considered. This study illustrates that modeling can serve as a valuable tool in the design and performance optimization of biofilters.     

Laboratory Investigation of a New Trommel Model

Abstract

Rotary screens, or trommels, are an important unit operation in material and fuel processing. A computer model has been developed based upon fundamental mechanics. The coefficients and variables employed in the model thus have real physical meaning; adjusting them based upon laboratory data allows the model user to draw conclusions about the behavior of the trommel that can be applied to design and operational changes. The laboratory testing program was specifically designed to test the model described in this paper.

The model proved able to track the laboratory data. Important phenomena that were validated or revealed included the significance of particle layering, changes in bed sliding with rotational velocity, and the pre-eminence of residence time.