1 Recent Advances in the Modeling of Airborne Substances George Pouliot Shan He Tom Pierce.

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Presentation transcript:

1 Recent Advances in the Modeling of Airborne Substances George Pouliot Shan He Tom Pierce

2 Introduction In support of air quality modeling, the Atmospheric Modeling Division is seeking to improve emission estimates by building emission models that account for meteorological conditions

3 Improvements to Emission Models in Three Areas Biogenic Emissions Inventory System (BEIS) Mobile Source Emissions Modeling in an Air Quality Forecast System Fugitive Dust Emissions for Unpaved Roads

4 Status on BEIS3  BEIS introduced in 1988 to estimate VOC emissions from vegetation and NO emissions from soils.  BEIS3.09 is the default version in SMOKE km vegetation database by tree species Emission factors for isoprene, terpenes, OVOCs & NO NO soil emissions dependent on temperature only Only species modulated by solar radiation is isoprene Supports CBIV, RADM2, and SAPRC99 mechanisms

5 BEIS 3.10 A research version for CMAQ Includes a 1-km vegetation database that resolves forest canopy coverage by tree species Emission factors for 34 chemicals, including 14 monoterpenes and methanol MBO, methanol, isoprene modulated by solar radiation a soil NO algorithm dependent on soil moisture, crop canopy coverage, and fertilizer application support for CBIV, RADM2, and SAPRAC99 mechanisms.

6 BEIS 3.11 Revises the soil NO algorithm to better distinguish between agricultural and non-agricultural land, and to limit adjustments from temperature, precipitation, fertilizer application, and crop canopy to the growing season and to areas of agriculture. Leaf shading algorithm is added for estimating methanol emissions from non-forested areas.

7 BEIS 3.12 Update to BEIS3.11 Revises Soil NO algorithm for last half of growing season. Reduces the impact of fertilizer application during the latter part of growing season. Available soon on at

8 Comparison of BEIS 3.09 & 3.12 Annual simulation for km continental domain

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11 Domain total (1000 metric tons/yr) CompoundBEIS3.09BEIS3.12% change NO % Total VOC50,32048,365-4% Isoprene22,141 0%

12 A National Air Quality Forecast System is being developed by EPA and NWS Initial Operating Capability for Summer of 2003 Northeastern U.S domain Twice daily forecasts:12Z (48 hr) & 6Z (30 hr) ozone (O 3 ) Mobile Source Emissions Modeling for Air Quality Forecasting

13 Mobile Source Emissions Modeling for Air Quality Forecasting Requirements: Post-processing of meteorological data, emission processing, and the air-quality model simulation must be completed in less than 5.5 hours. Emission processing needs to be complete in less than 15 minutes. Mobile source processing with Mobile5b requires more than an hour. Mobile source processing must be faster.

14 Mobile Source Emissions Modeling for Air Quality Forecasting 1.Separate temperature dependence from MOBILE5B 2.Run Mobile5B with a constant temporal profile 3.Compute coefficients for each species using results from (2) and temperature data for a representative time period 4.Run Mobile5B with a constant temperature 5.Combine the operational temperature data, results from (3) and (4) in a simple loop to calculate the mobile source emissions

15 Mobile Source Emissions modeling for Air Quality Forecasting Nonlinear Least-Squares Method can be applied to the results from Mobile5B to approximate the temperature relationship with a polynomial function This method of estimating mobile emissions is very fast

16 Results from Summer 2003 July 2003 Compare retrospective MOBILE5B with real time mobile source emission calculation using the nonlinear least squares technique Domain wide for NO, VOC, CO New York State for NO, VOC, CO

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23 Summary of Domain Total Results PollutantReal Time AQF system Mobile 5B% differe nce % all emissi ons NOx (tons/dy) 9,3639, %30% VOC (1000 mol C/dy) 339,096347, %11% CO (tons/dy) 54,21955, %56%

24 Fugitive Dust Emissions from Unpaved Roads (Current Method) Does not account for transportable fraction near the source regions Uses road mileage from FHWA Uses rainfall data from a single location in each state to account for rainfall effects Uses AP42 emission factors

25 Fugitive Dust Emissions from Unpaved Roads (Proposed) Use the TIGER road mileage data and grid to the county level. Model the moisture content of the road surface using modeled solar radiation, dew point, wind speed and rainfall data for each grid cell (note: this is an extension of AP-42’s documentation). Incorporate the transport factor developed by Shan He for windblown dust

26 Conclusions BEIS3 tested for an annual simulation. Latest version is now 3.12 An efficient method to estimate emissions for an air quality forecast system has been used for summer 2003 A module in SMOKE to estimate emissions from unpaved roads is being built and tested.