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IMPLEMENTATION OF THE SMOKE EMISSION DATA PROCESSOR AND SMOKE TOOL INPUT DATA PROCESSOR IN MODELS-3
Citation:
Benjey, W G., M. R. Houyoux, AND J. W. Susick. IMPLEMENTATION OF THE SMOKE EMISSION DATA PROCESSOR AND SMOKE TOOL INPUT DATA PROCESSOR IN MODELS-3. Presented at The Emission Inventory Conference, Denver, CO, May 1-4, 2001.
Impact/Purpose:
To improve the accuracy of emissions and dry deposition algorithms in the Agency's regulatory air quality and multimedia simulation models. This effort requires developing process-oriented algorithms, assembling geographical data, evaluating algorithms against field data, and designing and collaborating on field experiments to collect the data needed to test these algorithms.
Description:
The U.S. Environmental Protection Agency has implemented Version 1.3 of SMOKE (Sparse Matrix Object Kernel Emission) processor for preparation of area, mobile, point, and biogenic sources emission data within Version 4.1 of the Models-3 air quality modeling framework. The SMOKE system includes MOBILE 5b to model on-road emissions and the Biogenic Emission Inventory System, Version 2 (BEIS 2) to model biogenic emissions. Although SMOKE may be operated from scripts outside of the Models-3 framework, integration within the system allows automatic registration and tracking of input and output files, as well as close coupling to the Community Multiscale Air Quality (CMAQ) modeling system and visualization tools by using the Net CDF I/O API data format convention. Models-3 also includes the SMOKE Tool to assist in preparation of emission input data for SMOKE. The SMOKE Tool performs basic functions for the user, including 1) import and quality checking of emission inventory and related data, 2) preparation of user-defined model grids and gridding of emission data and surrogates (SMOKE Tool requires a Geographic Information System (GIS), 3) preparation of user-defined "packet" files which instruct SMOKE to apply factors including growth, control, and reactivity analysis factors (individual species may be added or deleted by source and location), and 4) allows the user to edit input files. Manual preparation of input files outside of the system is possible, but increases the probability of human error and substantially increases the time needed for data preparation.