Impact/Purpose:

The objective of this task is to improve the ability to model emissions from selected environmentally-dependent sources, test the performance of the models, incorporate them into a larger emission-modeling framework, and evaluate the effect of the emission models in support of improving the performance of CMAQ at all spatial and temporal scales. Evaluation will be with respect to previous CMAQ modeling results and ambient concentration data. In addition, the task will provide ADP and GIS contractor support for the generation and application of emission data in support of CMAQ development and evaluation as well as emission research.

Description:

Emission data are principal drivers for the Community Multiscale Air Quality (CMAQ) modeling system. Estimation of emission data is also subject to a large degree of uncertainty related to limited knowledge of sources, processes, chemistry, location, and temporal variability. The draft NARSTO Emission Inventory Assessment affirms the large degree of uncertainty in emission inventories, particularly for precursors of airborne fine particulate matter and for sources of organic and elemental carbon and ammonia. Most human-caused emission data from human-controlled sources are estimated and reported by the states to EPA for inclusion in the National Emission Inventory (NEI). Emissions from electric generating utilities are one exception, as there are hourly data from continuous emission monitors (CEM). Emissions that are subject to variation with environmental processes, especially meteorology, may be modeled on a hourly basis rather than being taken from the NEI. They are then combined with CEM and temporally allocated emissions for other sources from the NEI and provided to CMAQ. Currently, the modeled sources are from mobile sources. and biogenic emissions. Emission modeling research seeks to improve modeling for these sources and to develop and test emission models from additional important environmental process-influenced sources. This task will develop and improve the ability to model emissions more accurately, and therefore will improve CMAQ air quality model products at scales ranging from regional to the urban fine-scale interaface with human interaction. Specifically, the task includes the development and implementation of emission models for wild fires, fugitive dust, sea salt, and biogenic emissions. These sources emit ozone precursors (volatile organic compounds and nitrogen oxides), particulate matter, and some air toxins. These sources are also all among those highlighted by the draft NARSTO Emission Inventory Assessment as key areas of significant uncertainty.

Record Details:

Record Type:PROJECT

Start Date:10/01/2004

Projected Completion Date:09/01/2008

Record ID: 113789

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Project Information:

Progress :Previous work on this task element was accomplished under Task 3869, Emissions and Dry Deposition and Task 15171, Atmospheric Model Development. This task is divided into two work areas: (1) emission modeling research and (2) emission modeling support.

Emission Modeling Research

During FY05, several emission advances were intituted: (1) The BlueSky emission model (BlueSky-EM) for wildland fires was adapted and tested for use in the SMOKE emission model. The OAQPS plans to use the plume rise portion of the algorithm in creating a new 2002 emission modeling "platform". (2) The Biogenic Emission Inventory System (BEIS) was updated from Version 3.12 to version 3.13. Improvements include treatment of visible solar radiation, and reduction of the emission factors for spruce and fir to current literature values. BEIS 3.13 performance relative to measurments is improved, and the production of isoprene emissions is generally reduced. (3) A Spatial Allocator tool for the geographic gridding or re-gridding of emission modeling and related geographic data (in conjunction with task 20477) was completed. (4) Work also continued on the modeling of sea salt from surf zones and chlorine emissions. It is anticipated that sea salt emissions will be computed within CMAQ by the end of FY06.

During FY04, work began on adapting the BlueSky wildland fire emission algorithms for use with SMOKE in support of CMAQ. This included development of a new plume rise algorithm specifically for wild fires. Funding from OAQPS (from the Emission Improvement Inventory Program (EIIP)) was used to complete a report recommending an algorithm for emissions of ammonia from agricultural fertilizers applications on soil and ammonia emission factors for natural landscapes.

During FY03, EIIP funding from OAQPS was used to generate improvements to several emission algorithms and to the underlying geographical data. Collaborating with OAQPS and with in-kind support provided by a NOAA scientist, we implemented a prototype episodic blown dust (particulate matter) algorithm in the CMAQ system. GIS-based data sets of several important spatial surrogates were created, including unpaved roads, agricultural tilling practices, and updated vegetative coverage. Ammonia emission estimates were evaluated using an inverse modeling technique. Work continued on an episodic wildfire model, sea salt, and biogenic emissions. Several emission datasets were generated using SMOKE for testing new versions of CMAQ.

During FY02, we supported several enhancements to CMAQ's emissions modeling processor (SMOKE), including a new biogenic emissions processor (BEIS3). We also created the third generation of the Biogenic Emissions Landuse Database (BELD3). Substantial progress was made on new emission algorithms for fugitive dust, wildfires, and sea salt.

Emission Modeling Support

Throughout FY05, modeled emission data sets were created in support of the development and evaluation of CMAQ Version 4.5, which was released at the end of the year. This included both seasonal and an annual emission data set for 2001.



Relevance :Products completed under this task will substantially improve the spatial and temporal accuracy of emission modeling data, the chemical representation of significant environmental process-dependent emission sources, improve software tools needed in emission modeling, modeling information. The improved emission data will in turn help improve the performance of CMAQ performance in addressing GPRA Goal 1 (Clean Air), Objective 6.2, Long-term goals PM-6, PM-8, and OZ-2

Project IDs:

ID Code :20664

Project type :OMIS