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By RonaldJ. Dickson and William R. Oliver The attention given to modeling ozone in recent years has led to a greater appreciation of the limitations inherent in the emission inventories used in photochemical models. In previous large-scale modeling studies, emissions files were developed in a form that made it difficult for study participants to explore the effects of uncertainties on the emission inventory. The method used to develop these emission inventories actually is an emissions model, which we define as an integrated collection of calculational procedures that are encoded for
computer-based computation. In addition, concerns have arisen that the current emissions modeling process is fragmented and not well documented. Moreover, the emissions estimates derived by a n emissions model are mostly unverified: that is, we have not commonly compared the emissions estimates (or model outputs) with measured emission values. The verification of emissions estimates is of considerable importance, because control strategy decisions are no better than the quality of the emissions estimates on which they are based. To address these issues, two studies recently have been initiated to
0013-936x/9110925-1533$02.5010 0 1991 American Chemical Society
treat emissions estimation as a rigorous modeling process. An emissions modeling system (EMS) is und e r development for t h e Lake Michigan Ozone Study (LMOS) and for the San Joaquin ValleylAtmospheric Utility Signatures, Predictions, and Experiments Regional Modeling Adaptation Project (SARMAP). Both of these projects are developing new analytical tools to evaluate the formation of ozone and other pollutants in the atmosphere. Although two separate modeling systems will be developed for LMOS and SARMAP, the two stndies are on parallel paths, and we are designing each system with common software. Environ. Sci. Technol.. VoI. 25, No. 9, 1991 1533
emissions modeling system flow diagram
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EMS needs
An emissions model generates pollutant-specific emissions estimates for point and area sources in a specific region. Emissions data are the information sets typically stored in a database and used as input to the model. After manipulation of the data, the model produces emissions estimates with the requisite temporal and spatial resolution. Figure 1 diagrams the processing of emissions data by an EMS to develop emissions estimates. An EMS that is used to develop inputs to a photochemical model should fulfill several minimum requirements. First, it must provide estimates of base-year emission rates by chemical species grouping for all relevant source categories at the level of spatial and temporal resolution required by the photochemical model. This includes both anthropogenic and biogenic sources. The EMS also should provide projections of future-year emissions estimates. Moreover, estimates of uncertainty in emissions rates need to be provided by the EMS. Also, a means for assessing the state of the science embodied in each emissions model is needed. Finally, complete documentation is required for the model us-
ers. These requirements are now being incorporated into the conceptual design of the LMOS and SARMAP EMS.
Desired modeling attributes As developer of the EMS for each study, Radian Corporation, in conjunction with Computer Sciences Corporation (Research Triangle Park, NC) and the Desert Research Institute (Reno, NV), is formulating the system to meet the following specifications: modular; a set of modules will comprise the EMS to allow for easier operation: user-friendly; the system will be menu driven, requiring less training to operate: flexible; straightforward revisions to the system will allow it to be applied in different geographic regions; and easy to update: modifications and updates will be straightforward.
1534 Environ. Sci. Technol.. VoI. 25, NO.9, 1991
Emissions modeling design The EMS we are developing is a group of models executed in a specific sequence. As shown in Figure 1, different models are used in the system to generate the detailed esti-
mates. At this early design stage, the EMS is composed of the following seven models: point source, area source, biogenic hydrocarbon, mobile source, high-resolution projections, and uncertainty. Each of these models in turn is composed of modules that process the data, develop the emissions estimates for specific locations and time periods, and produce speciated results for regional air quality models. The models will use various elements of emissions data to generate a file of gridded, hourly, speciated emissions estimates. At this time, we plan to develop the EMS using several major software packages: SAS, ARCIINFO, C, and X-Window. These packages were chosen by evaluating benefits and costs of different systems. SAS will be used to develop emissions estimates for selected models, to manage data, and to perform statistical analyses. ARCIINFO, a geographic information system, will be used to manage spatially related data a n d to produce graphics. X-Window and C will be used to integrate the SAS and ARCIINFO applications in the EMS. The LMOS and SARMAP studies provide a new framework for formalizing the emissions modeling
process. Although new techniques will he applied in these two studies to develop gridded, hourly emissions estimates, several issues will require attention as the EMS is developed. The first is uncertainty quantification-we need to identify specific uncertainties and develop mechanisms for quantifying their effects on emissions. The second is verification-we need the ability to verify the emissions estimates from the EMS against data and measurements. The third is projections-the EMS needs to project future changes in emissions resulting from land use and other societal changes. The last is biogenic emissions-environmental factors that influence biomass and emission rates (e.g., wind speed) need to be included in the emissions modeling for vegetative emissions. A user-friendly tool The EMS under development to support both the LMOS and SARMAP projects will be a major step in transforming what has been a n emissions inventory process into an emissions modeling process. This new technique will provide the emissions modeler with a powerful. user-friendly tool to generate emissions estimates for different applications. The system's modular design and use of fourth-generation computer languages will make modifications to t h e software straightforward as future advances occur in emissions modeling. This approach will enhance the longterm usefulness of the EMS.
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Environ. Sci. Technol.. VOl. 25, NO. 9. 1991 1535
