1 Recent Advances in the Modeling of Airborne Substances George Pouliot Shan He Tom Pierce.
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Transcript of 1 Recent Advances in the Modeling of Airborne Substances George Pouliot Shan He Tom Pierce.
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 2.0• 1-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 www.epa.gov/asmd/biogen.html
8
Comparison of BEIS 3.09 & 3.12
• Annual simulation for 2001• 36 km continental domain
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NO SOIL Emissions
0
500
1000
1500
2000
2500
3000
3500
4000
Jan-01 Feb-01 Mar-01 Apr-01 May-01 Jun-01 Jul-01 Aug-01 Sep-01 Oct-01 Nov-01 Dec-01
Date
NO
to
ns/
day
B309 B312
10
Total VOC Beis309 vs Beis312 with isoprene
0
50000
100000
150000
200000
250000
300000
350000
400000
450000
Jan-01 Feb-01 Mar-01 Apr-01 May-01 Jun-01 Jul-01 Aug-01 Sep-01 Oct-01 Nov-01 Dec-01
Date
VO
C m
etr
ic t
on
sC
/da
y
B309 B312 Isoprene
11
Domain total (1000 metric tons/yr)
Compound BEIS3.09 BEIS3.12 % change
NO 467 609 +30%
Total VOC 50,320 48,365 -4%
Isoprene 22,141 22,141 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 (O3)
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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Mobile Source NO Domain Total Emissions
0
1000
2000
3000
4000
5000
6000
7000
7/6/03 7/7/03 7/8/03 7/9/03 7/10/03 7/11/03 7/12/03 7/13/03 7/14/03 7/15/03 7/16/03 7/17/03
Date
Da
ily T
ota
ls [
ton
s/d
ay]
NO Mobile5B NO real time
18
Mobile Source VOC Domain Total Emissions
0
50,000,000
100,000,000
150,000,000
200,000,000
250,000,000
300,000,000
350,000,000
400,000,000
7/6/03 7/7/03 7/8/03 7/9/03 7/10/03 7/11/03 7/12/03 7/13/03 7/14/03 7/15/03 7/16/03 7/17/03
Date
Da
ily T
ota
ls [
mo
les
C /d
ay]
VOC MOBILE5B VOC real time
19
Mobile Source CO Domain Total Emissions
0
10000
20000
30000
40000
50000
60000
70000
7/6/03 7/7/03 7/8/03 7/9/03 7/10/03 7/11/03 7/12/03 7/13/03 7/14/03 7/15/03 7/16/03 7/17/03
Date
Dai
ly E
mis
sio
n T
ota
ls
[to
ns/
day
]
CO Mobile5B CO real time
20
Mobile Source NO Emissions (New York only)
0
100
200
300
400
500
600
7/6/03 7/7/03 7/8/03 7/9/03 7/10/03 7/11/03 7/12/03 7/13/03 7/14/03 7/15/03 7/16/03 7/17/03
Date
Da
ily T
ota
ls [
ton
s/d
ay]
NO Mobile5B NO real time
21
Mobile Source VOC Emissions (New York only)
0
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
7/6/03 7/7/03 7/8/03 7/9/03 7/10/03 7/11/03 7/12/03 7/13/03 7/14/03 7/15/03 7/16/03 7/17/03
Date
Dai
ly T
ota
ls [
mo
lesC
/day
]
VOC M5B VOC real time
22
Mobile Source CO Emissions (New York only)
0
1000
2000
3000
4000
5000
6000
7/6/03 7/7/03 7/8/03 7/9/03 7/10/03 7/11/03 7/12/03 7/13/03 7/14/03 7/15/03 7/16/03 7/17/03
Date
Da
ily E
mis
sio
n T
ota
ls [
ton
s/d
ay]
CO Mobile5B CO real time
23
Summary of Domain Total Results
Pollutant Real Time AQF system
Mobile 5B % difference
% all emissions
NOx (tons/dy)
9,363 9,333 +0.3% 30%
VOC
(1000 mol C/dy)
339,096 347,048 -2.3% 11%
CO
(tons/dy)
54,219 55,379 -2.0% 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.