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Resolving the sources of PM 2.5 in Georgia using emission and receptor based models. Amit Marmur, Di Tian, Byeong-Uk Kim, James Boylan 6 th Annual CMAS Conference, October 1-3, 2007. Overview. PM 2.5 non-attainment areas in Georgia CMAQ based PM 2.5 sensitivity analysis

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Resolving the sources of pm 2 5 in georgia using emission and receptor based models

Resolving the sources of PM2.5 in Georgia using emission and receptor based models

Amit Marmur, Di Tian, Byeong-Uk Kim, James Boylan

6th Annual CMAS Conference, October 1-3, 2007


Overview

  • PM2.5 non-attainment areas in Georgia

  • CMAQ based PM2.5 sensitivity analysis

  • PM2.5 source-apportionment using a receptor based model, Positive Matrix Factorization (PMF)

  • Comparison between PMF and CMAQ source-apportionment results

  • Policy implications


Background: PM2.5 Attainment Status in Georgia

  • Atlanta, Macon, Floyd county, Chattanooga are in non-attainment of the annual PM2.5 NAAQS (15 g/m3):

    • Designation for the new daily standard (35 g/m3) not yet finalized, but no “new” non-attainment areas expected

Annual PM2.5 non-attainment areas


CMAQ based sensitivity analysis

  • In preparation for the PM2.5 SIP, GA-EPD conducted a PM2.5 sensitivity analysis (see 2005 & 2006 CMAS conference presentations)

  • Based on this previous analysis, the following controls were considered:

    • SO2 controls at major power plants

    • Controls of primary carbon emissions (EC/OC)

    • (ammonia)

  • Controls of NOx and anthropogenic VOCs controls had a negligible effect on PM2.5 levels


Receptor vs emissions based models

Chemistry

Source Impacts

Air Quality

Meteorology

Receptor vs. Emissions-Based Models

Emissions Inventory

Source-compositions

Receptor (monitor)

Emissions-based Model (3D Air-quality Model)

Receptor Model

(e.g., PMF, CMB)

(e.g., CMAQ, CAMx)


PM2.5 source-apportionment using receptor models

Purpose: Identify the main contributors (sources) to measured concentrations of pollutants at a receptor site.

Required input: Speciated ambient measurements, along with “knowledge” of “typical” emissions composition

Ci - ambient concentration of specie i (g/m3)

fi,j - fraction of specie i in emissions from source j

Sj - contribution (source-strength) of source j (g/m3)

n – total number of sources

ei – error term to be minimized (to obtain best fit)

Sj are the unknowns (Ci, fi,j, n – required input)


The pmf receptor model
The PMF receptor model

  • Positive Matrix Factorization (PMF) is the most commonly applied factor analytical technique in recent years

    • Enhanced factor analysis, including constraints to prevent negative source contributions

  • Developed by Dr. Pentti Paatero at the University of Helsinki in Finland

  • In 2005, EPA released the EPA-PMF 1.1 model


Why apply PMF in SIP development? Why compare with CMAQ?

  • Identify the major sources affecting monitors at the non-attainment area via ambient data, rather than emissions inventories

  • Evaluate/expand findings from CMAQ sensitivity analysis

    • Often impractical to quantify all sources via a CMAQ-based sensitivity analysis (time/resources)

  • Improve emissions inventories

  • Analyze events leading to high daily PM2.5 concentrations (emissions vs. meteorology)

  • Quantify impacts of local sources (e.g., FS#8 monitor)

  • Evaluate model performance for SOA


PMF-based PM2.5 source-contributions

Based on STN data, 2003-04


What we’ve learnt so far…

  • CMAQ sensitivity modeling suggests control of primary carbon (PC) emissions for PM2.5 SIP

  • Receptor modeling suggests mobile sources and biomass burning as major sources of PC PM2.5

  • Other major components of PM2.5 are:

    • Sulfate: modeling capabilities and control strategies (CAIR) are fairly mature

    • SOA: understanding of formation mechanism and modeling capabilities are not as well developed

      • Most available evidence suggest majority of SOA is of biogenic origin (yields, C14 analyses), though some studies suggest anthropogenic origin (Sullivan & Weber, WSOC studies)


What needs further investigation…

  • How good is our understanding of PM2.5 impacts from mobile-sources, biomass burning?

    • How do CMAQ and PMF compare?

    • How does that affect policy development?

  • Can PMF assist in CMAQ-SOA evaluation?

  • Can PMF assist in understanding CMAQ’s (high) unspecified/crustal concentrations


Modeling methodology

  • CMAQ4.5 w/SOA mods*: annual brute-force runs based on the VISTAS 2002 G2 “actual” inventory (alga12km domain), to quantify the impacts of:

    • Mobile sources (on and off road)

    • Fires (Rx, wild, agricultural, land clearing, residential)

  • PMF analysis for 2002-2005 using speciated PM2.5 data from eight STN sites in Georgia

    • Analyses done for each site separately and using one combined dataset for all sites; fairly similar results

  • Comparison of source/factor contributions for

    • Mobile sources

    • Fires/ biomass burning

    • Crustals/Soil

    • SOA

Tracked directly by CMAQ

* - Morris et al., Atm. Env., 40, 4960-4972, 2006.


CMAQ-based quarterly PM2.5 contributions

Jan-Mar

Apr-Jun

Jul-Sep

Oct-Dec

Mobile sources

Scale of0.0- 4.0

Fires

Crustals

SOA

g/m3


Comparison b/w CMAQ and PMF:Monthly averages, Atlanta STN site

Fires

Mobile sources

Crustals/Soil

SOA


Comparison b/w CMAQ and PMF:Daily contributions, Atlanta STN site

Mobile, R=0.66

Fires, R=0.23

Jan-Mar, R=0.47/0.67Apr-Dec, R=0.19/0.37

SOA, R=0.56

Road, R=0.66Soil, R=-0.11


Correlations between sources and species:Atlanta STN site

Highlighted values: R0.5


Temporal variability in fire emissionsVISTAS G2 actual emissions

3/23/02

EC (g/s)


Temporal variability in fire emissionsVISTAS G2 actual emissions

3/24/02

EC (g/s)


Temporal variability in fire emissionsVISTAS G2 actual emissions

3/25/02

EC (g/s)


Temporal variability in fire emissionsVISTAS G2 actual emissions

3/26/02

EC (g/s)


Contributions from various biomass-burning sources at the Atlanta SEARCH site

*

* PMF analysis by Kim&Hopke, Atm Env 38, 3349-3362, 2004


Contributions from various biomass-burning sources at the Atlanta SEARCH siteUsing EPA 2001 Rx burning emissions, instead of VISTAS 2002 (see Tian et al., CMAS 2006 presentation for details)

*

* PMF analysis by Kim&Hopke, Atm Env 38, 3349-3362, 2004


Contributions from various biomass-burning sources at the Atlanta SEARCH siteUsing EPA 2001 Rx burning emissions, instead of VISTAS 2002 (see Tian et al., CMAS 2006 presentation for details)

*

* Marmur et al., Atm Env 40, 2533-2551, 2006


Summary and future work Atlanta SEARCH site

  • Moderate agreement b/w CMAQ and PMF estimates for mobile sources and SOA PM2.5

  • Poor agreement for biomass-burning

  • CMAQ crustals overestimated, temporal variability suggest resuspended road dust

  • There are “issues” with any modeling approach:

    • PMF

      • Measurement uncertainties and limitations

      • Fixed source compositions

      • Temporal variability over-estimated

      • Point measruement / Impacts of local sources

    • Models-3 (CMAQ)

      • Uncertainties in emission rates

      • Temporal variability in emissions under-represented

      • Meteorology/mixing

      • Volume average


Summary and future work Atlanta SEARCH site

  • Future work

    • PMF

      • Use of organic markers data

      • Markers for SOA (oxidation products)

      • Investigation of spatial representativeness

    • Models-3 (CMAQ)

      • Investigation into Rx burning emissions (and others)

      • Detailed temporal variability in fire emissions

      • Soil dust emissions as a function of wind speed, moisture; increased “near-source” removal of particles

      • Detailed mobile-sources activity

  • Policy implications

    • Regulatory needs precedes scientific understanding


Contact Information Atlanta SEARCH site

Amit Marmur, Ph.D.Georgia Dept. of Natural Resources4244 International Parkway, Suite 120Atlanta, GA [email protected] 404-363-7072


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