Title: PM%202.5%20Source%20Apportionment%20and%20Control%20Strategy
1PM 2.5 Source Apportionment and Control Strategy
- Sun-Kyoung (Helena) Park
- Environmental Engineering, Georgia Institute of
Technology
2Content
- PM 2.5 in Atlanta
- Source Apportionment Receptor Modeling
- Chemical Mass Balance (CMB)
- Principal Component Analysis (PCA)
- Control Strategy
- Conclusion
3PM 2.5 in Atlanta
- PM 2.5 Particles of size less than 2.5 mm
4Content
- PM 2.5 in Atlanta
- Source Apportionment Receptor Modeling
- Chemical Mass Balance (CMB)
- Principal Component Analysis (PCA)
- Control Strategy
- Conclusion
5Source Apportionment - Receptor Modeling
Objective
Receptor models use the chemical and physical
characteristics of gases and particles measured
and source and receptor to identify the presence
and to quantify source contributions to receptor
concentration
Methods
Chemical Mass Balance (CMB) receptor model
Each receptor chemical concentration is expressed
as a linear sum of products of source profile and
source contributions
Principal Component Analysis (PCA)
Identify principal components which maximize the
covariance matrix of pollutant dataset
6Content
- PM 2.5 in Atlanta
- Source Apportionment Receptor Modeling
- Chemical Mass Balance (CMB)
- Principal Component Analysis (PCA)
- Control Strategy
- Conclusion
7Chemical Mass Balance (CMB)
A ( x1 0.23x2 0.73x3 30.80x4 19.7x5 )
B ( 15.20 x1 0.36 x2 3.15 x3 4.09 x4
5.20 x5 ) C ( 0.26 x1 0.36 x2
0.21 x3 2.40 x4 52.93 x5 ) 3.36 x1
0.97 x2 1.2 x3 1.29 x4 2.91 x5 Number of
Unknowns 3, Number of the Independent Equations
5 ? A, B and C are fitted using the least square
fitting
8Chemical Mass Balance (CMB)
PM 2.5 in Jefferson Street
9Content
- PM 2.5 in Atlanta
- Source Apportionment Receptor Modeling
- Chemical Mass Balance (CMB)
- Principal Component Analysis (PCA)
- Control Strategy
- Conclusion
10Principal Component Analysis (PCA)
Identify principal components which maximize the
covariance matrix of pollutant dataset
11Content
- PM 2.5 in Atlanta
- Source Apportionment Receptor Modeling
- Chemical Mass Balance (CMB)
- Principal Component Analysis (PCA)
- Control Strategy
- Conclusion
12Control Strategy
Roll-Back Method
E(c) Annual Mean ConcentrationE(c)s Annual
Mean Concentration in the Futurek
growth factorcb Background Concentration
( 3 mg/m3)
 Emission source reduction required for
meeting NAAQS
Jefferson Street Fort McPherson South Dekalb Tucker
Mean PM 2.5 21.1 mg/m3 19.1 mg/m3 16.9 mg/m3 20.2 mg/m3
Based on the mean concentration 33.7 25.4 13.7 30.2
13Content
- PM 2.5 in Atlanta
- Source Apportionment Receptor Modeling
- Chemical Mass Balance (CMB)
- Principal Component Analysis (PCA)
- Control Strategy
- Conclusion
14Conclusion
- Major species of PM 2.5 in Atlanta ?
Sulfate 24 ? Nitrate 5 ? Ammonium 8.5
? Elemental Carbon 7.8 ? Organic Carbon
29.7 - The major sources of PM 2.5 in Jefferson Street
site ? Diesel Engine ? Wood Burning ? Power
Plant - The emission source reductions required ? Fort
McPherson 25.4 ? South Dekalb 13.7 ?
Tucker 30.2 ? Jefferson Street 33.7
- The control strategy should focus on the major
sources of PM 2.5 and consider the sensitivity
between source and the pollutant concentration
15Questions Comments
16Appendix
17Chemical Mass Balance (CMB)
18Chemical Mass Balance (CMB)
- Chemical Mass Balance (CMB) receptor model
- Each receptor chemical concentration is expressed
as a linear sum of products of source profile and
source contributions - Assumption
- Compositions of source emissions do not change.
- All potential sources are identified and
characterized. - The source profiles are linearly independent of
each other - Number of source categories is less than or
equal to the number of species
19Principle Component Analysis (PCA)
Identify principal components which maximize the
covariance matrix of pollutant dataset
20Site Map
- Jefferson Street
- Fort McPherson Army Center
- South Dekalb
- Tucker