resolving the sources of pm 2 5 in georgia using emission and receptor based models n.
Download
Skip this Video
Download Presentation
Resolving the sources of PM 2.5 in Georgia using emission and receptor based models

Loading in 2 Seconds...

play fullscreen
1 / 26

Resolving the sources of PM 2.5 in Georgia using emission and receptor based models - PowerPoint PPT Presentation


  • 89 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Resolving the sources of PM 2.5 in Georgia using emission and receptor based models' - aelwen


Download Now An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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

slide2

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
slide3

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

slide4

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)

slide6

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
slide8

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
slide9

PMF-based PM2.5 source-contributions

Based on STN data, 2003-04

slide10

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)
slide11

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
slide12

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.

slide13

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

slide14

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

Fires

Mobile sources

Crustals/Soil

SOA

slide15

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

slide21

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

*

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

slide22

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

slide23

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

slide24

Summary and future work

  • 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
slide25

Summary and future work

  • 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
slide26

Contact Information

Amit Marmur, Ph.D.Georgia Dept. of Natural Resources4244 International Parkway, Suite 120Atlanta, GA 30354amit_marmur@dnr.state.ga.us 404-363-7072

ad