U.S. EPA Office of Research & Development October 16, 2012

Prakash Bhave, Adam Reff, Alexis Zubrow, Venkatesh Rao

U.S. Environmental Protection Agency

CMAS ConferenceChapel Hill, NC

October 15 – 17, 2012

Evaluation of Urban PM2.5 Emission Inventories across the U.S.

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division

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Conclusions: CMAS 2010

• In the past decade, which modeling system refinements contributed most to PM2.5 performance improvement?→Meteorology inputs (2)→Emissions & deposition (4)→Atmospheric chemistry (2)

IMPROVE Observations (1996)

PM2.5 Components (μg m-3)

CM

AQ

v4.

1

NO3SO4

OC

IMPROVE Observations (1996)

PM2.5 Components (μg m-3)

CM

AQ

v4.

1

IMPROVE Observations (1996)

PM2.5 Components (μg m-3)

CM

AQ

v4.

1

NO3SO4

OC

PM2.5 Components (μg m-3)

IMPROVE Observations (2002 – 2006)

CM

AQ

v4.

7

NO3

SO4

OC

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division

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Background & Motivation• U.S. has most detailed national inventory for PM2.5

– Spatial resolution

– Source resolution

– Chemical resolution

• Inventory accuracy

very difficult to check– CTM is often used

– Can we find & fix gross

inventory errors without

running CMAQ? Reference: Reff et al. (ES&T, 2009)

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division

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• Cass & McRae (ES&T, 1983) demonstrated a simple approach for PM2.5 inventory evaluation• Compare emission rates

directly against ambient concentrations• Only works because,

*most trace elements are conserved*

• Results• Ti, Ni emissions too high• Zn too low• Ambient Cu data error

•We applied same method to 2001 NEI in 21 cities…

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division

Secondary Species

Below MDL

Reff et al. (Intl Aerosol Conf. 2006)

Al Ca Fe KSi

Prior Evaluation: 2001 NEI

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division

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Prior Evaluation: 2001 NEIEmissions Allotment of Si

Em

iss

ion

s (t

on

/yea

r)

Dallas Minneapolis St. Louis

Emissions Allotment of Si

Em

iss

ion

s (t

on

/yea

r)

Dallas Minneapolis St. Louis

Factor Dilutionc Atmospheri

ionConcentrat Ambient

• In many cities, we found positive biases in the emissions of– Agricultural soil– Unpaved road dust

Methodological Shortcomings• Limited number of sites (n = 21)• 36 km grid resolution• “old” version of NEI• Only able to identify gross

overestimates• Unable to quantify the emission

errors

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division

Methodology• 2005ak NEI• Mobile emissions from 2005cr, output by MOVES• Spatial allocation: 12km ConUS grid• Temporal allocation: monthly

• 85 source categories with unique PM2.5 speciation profiles

• Aggregate to 159 Core-Based Statistical Areas (CBSA)

Result> 7×104 pairs of diluted emissions & ambient concentrations

• Multiply emissions by month-

& site-specific dilution ratio

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division

Methodology• Apply principles of chemical mass balance (CMB)

correction factor

• Data in each city/month are fit separately

• Key result: source-specific F value for each site & month

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division

MethodologyForce Fij to be positive

Account for measurement

error

Minimize this

Penalize fit for over-correcting the

emissions

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division

Preliminary ResultsF values for Agricultural Burning

100

1

0.01J F M A M J J A S O N D

• PM2.5 from crop burning is biased high by ~10x

• Pouliot, McCarty, et al. have diagnosed the reason for these overestimates

• Revisions will be incorporated into 2008 NEI

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division

Preliminary ResultsF values for Unpaved Road Dust

100

1

0.01J F M A M J J A S O N D

• PM2.5 from unpaved roads is biased high by ~30x

• Is this entirely due to emissions error?• see poster by Appel et al.

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division

Preliminary ResultsF values for Unpaved Road Dust

100

1

0.01J F M A M J J A S O N D

Median of Monthly F values

U.S. EPA Office of Research & Development, Atmospheric Modeling & Analysis Division

Summary•Methodology to quantify source-specific biases in

PM2.5 inventory has been developed

•Preliminary results look quite promising!

• In process of assessing our results for other source

categories