Emerging Science
EPA’s ORD Supports
Regional Haze Program
RPO National Technical Meeting
June 9-10, 2005
Denver, CO
Fred Dimmick, Acting Chief
Process Modeling Research Branch
Human Exposure and Atmospheric Sciences Division
Agenda
Quick overview of ORD atmospheric
sciences activities
• Related to regional haze
• Only a portion of research
Introduce “posters” from EPA’s BOSC
review and Science Forum
Take questions now (or afterwards)
Receptor models estimate contribution of different
source types to ambient PM concentrations
Sample Screens from EPA PMF and EPA Unmix
Receptor models estimate contribution of different
source types to ambient PM concentrations
Recent Enhancements
Focused on approaches for guiding decisions in applying models
and interpreting results.
Include development of statistics summarizing uncertainty in
modeled solutions.
Future Directions
Enhancement of receptor models through EPA STAR grants.
•developing the next generation of receptor models
•assessing the accuracy and precision of the existing models.
Enhancements are being folded into a suite of multivariate receptor
models that EPA is freely distributing to the user community.
PM Supersites Program

Sampling and analysis methods to measure the
chemical and physical characteristics of PM and
important precursor species,

Enhanced temporal and compositional
characterization that complements routine ambient
air monitoring networks, and

Insights into policy relevant phenomena that
corroborate current policies, cause rethinking and
modification, and provide direction for future policy
formulation.
Fresno
Los Angeles
Pittsburgh New York
Baltimore
St. Louis
Atlanta
Houston
PM Supersites Program
Policy Relevant Synthesis of Research
-- 17 Science/Policy Relevant Questions - Methods (Qs 1-3)
 Characterization (Qs 4- 8)
 Receptor-Based Models and EmissionsBased Chemical Transport Models (Qs 911)
 Atmospheric Processes (Qs 12 – 15)
 Emissions Estimates (Q 16)
CMAQ
Research Goals
…
Identify PM components that contribute to model performance
strengths and weaknesses for total PM2.5.
Target and diagnose sources of error for PM component
predictions with the highest level of uncertainty
Determine role of input sources of error (e.g., emissions,
meteorology) versus uncertainties in modeled processes
Evaluate model responses and sensitivities to emission
changes.
Secondary Organic Aerosol (SOA)
EXPERIMENTAL ATMOSPHERIC CHAMBER
Objectives
Identify the major SOA precursors
Identify tracer compounds for the major SOA
precursors
Determine reaction mechanisms for SOA formation
Improve treatment of SOA in CMAQ
Use the smog chamber to generate atmospherically
relevant air mixtures for exposure studies
SOA Tracer Compounds from Laboratory and Field Samples
Secondary Organic Aerosol (SOA) - Experimental Chamber
Future Directions
Continue comparing chamber concentrations and compositions of SOA
formed with atmospherically relevant individual and mixtures
hydrocarbons irradiated in the presence of NOx and SO2 with model
results for proposed SOA formation mechanisms.
Identify SOA tracer compounds for sesquiterpenes.
Continue measuring and refining values for l for SOA precursors and
use them to estimate contributions of SOA precursors to ambient PM2.5
concentrations.
Assess whether SOA yields in complex hydrocarbon mixtures are
additive.
Work with modelers to develop the CMAQ version of the PM chemistry
model.
Human Exposure and Atmospheric Sciences Division
Linda Sheldon, Director
Tim Watkins, Deputy Director
Source attribution and apportionment
Shelly Eberly ([email protected])
Gary Norris ([email protected])
PM Supersites and Ambient PM Methods
Paul Solomon
Bob Vanderpool ([email protected])
CMAQ
Alice Gilliland ([email protected])
Atmospheric chamber
Ed Edney and Tad Kleindienst
Fred Dimmick ([email protected])
Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy.