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

9

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

17

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.