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Cross-border Transport of Fine Particulate Matter into Texas from Agricultural Burning
Presented at the2004 Models-3 Conference
Chapel Hill, NCOctober 18-20, 2004
Myoungwoo Kim,
Ieesuck Jung, and
Kuruvilla John
Department of Environmental and Civil Engineering
Texas A&M University – Kingsville
Kingsville, Texas 78363
Introduction • Corpus Christi, Texas is a major petrochemical center and its port is
the fifth busiest one in the United States in terms of annual tonnage. • Corpus Christi with a population of about 300,000 is the sixth largest
Consolidated Metropolitan Statistical Area (CMSA) in Texas.• It is currently in attainment of the NAAQS for all criteria pollutants.• However, the monitored air quality trend indicates that the city could
potentially be in danger of violating the annual average NAAQS for fine particulate matter in the near future.
• PM2.5 level of Corpus Christi area is influenced by atypical episodes affected by the long-range transport of continental haze and smoke events attributed to agricultural burns in Texas and beyond.
• Recent air pollution episodes such as the May 2-19, 2003 smoke event and the Sep 11-14, 2002 continental haze events significantly contributed to elevated levels of PM2.5.
• In this study, potential source contribution function (PSCF) and the UNMIX receptor model were used to obtain source apportionment details and to identify source-receptor relationship affecting the Corpus Christi urban area.
Annual PM2.5 levels in Corpus Christi, Texas
0.00
5.00
10.00
15.00
20.00
25.00
30.00
Date
PM
Fil
ter
Mass C
on
(m g/m
3 )
The annual average concentration of the fine particles measured using TEOMs operated by TCEQ showed an increase of 10.6% between 2001 and 2002. The increase between 2002 and 2003 was approx. 17.3 %.
02468
1012
PM2.5 TEO M annual average (ug/m3)
2001 2002 2003
Year
PM2.5 Monitoring sites in South Texas
Sampling and Data
The study period: 2001-2003. Continuous PM2.5 data from TEOM located at the
CAMS04 site in Corpus Christi. FRM filter mass and fifty-three speciation elements
measured at CAMS199 by the Texas Commission on Environmental Quality (TCEQ) - once every six days.
The chemical speciation analysis: gravimetric, energy dispersive XRF, ion chromatography and thermal-optical methods.
As, Br, Cr, Cu, Fe, Pb, Mn, Mo, Ni, Sn, V, Si, S, Ta, K, K+, NH4
+, Na, Na+, EC, Non Volatile Nitrates (NvN),
OCX (Non Organic Carbonate Carbon) and OC.
South Texas PM2.5 level during smoke event
Corpus Christi
Laredo Mission Brownsville
1-hr max PM2.5 (mg/m3) 64 83 105 80
Source: TCEQ
Time Series Analysis
Pollution Rose for CAMS040
30
60
90
120
150
180
210
240
270
300
330
0
4
8
12
16
0
4
8
12
16
May 2-19, 2003
PM
2.5 C
on
ce
ntr
ati
on
(mg
/m3)
Corpus Christi CAMS04
Windspeed(mph)
Morning(6-10 AM) Afternoon(1-5 PM)
5
10
15
20
25
30
35
40
45
50
110
0
30
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120
150
180
210
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270
300
330
0
4
8
12
16
0
4
8
12
16
May 2-19, 2003
PM
2.5 C
on
ce
ntr
ati
on
(mg
/m3)
Mission CAMS43
Windspeed(mph)
Morning(6-10 AM) Afternoon(1-5 PM)
5
10
15
20
25
30
35
40
45
50
110
0
30
60
90
120
150
180
210
240
270
300
330
0
4
8
12
16
0
4
8
12
16
May 2-19, 2003
PM
2.5 C
on
ce
ntr
ati
on
(mg
/m3)
Brownsville, CAMS80
Windspeed(mph)
Morning(6-10 AM) Afternoon(1-5 PM)
5
10
15
20
25
30
35
40
45
50
110
DATA ANALYSIS METHODOLOGIES
Backward Trajectory Calculations
• NOAA’s Air Resources Laboratory developed and used HYSPLIT4 for air parcel trajectory computations.
• HYSPLIT4 trajectory model was used to effectively integrate winds in the transport layer over time, distance, and source regions.
• 2-day back trajectories were drawn using wind fields from datasets of:
- Eta Data Assimilation System (EDAS): 2003 - Model start height: 500 m, middle of mixed layer - Model start times: UTC hour of observed PM2.5 concentrations• The region covered by the trajectories was divided into
1583 grid cells of 11 latitude and longitude
Potential Source Contribution Function (PSCF)
• Hopke et al. developed and used the PSCF for air pollution source apportionment and source-receptor relationship studies.
• If a backward trajectory endpoint lies in the ijth cell, the air parcel assumes to collect PM emitted in the cell and transports along the trajectory to the monitoring site.
• PSCFij is the conditional probability that an air parcel that passed through the ijth cell has a high concentration upon arrival at the monitoring site
ijij
ij
mPSCF =
n
nij : total number of end points that fall in the ijth cell
mij : number of end points that exceeded the threshold criterion
(in this study, average concentration of PM was used for the threshold criterion)
Downweight Functions• Small values of nij produce high PSCF values with
high uncertainties.
• To minimize the artifacts, PSCF values were downweighted with weight function (W) when nij was less than the average nij.
)( ijnW
ijn43
435 ijn
52 ijn
2ijn
Average nij 43.2 1.0
Maximum nij 329 0.7
Standard Deviation
48.7
0.5
0.2
Potential Source Contribution Function (PSCF)
• PSCF describes the spatial distribution of probable geographical source locations.
• Grid cells which have high PSCF values are the potential source area whose emissions can contribute to the levels observed at the receptor (monitoring) site.
• For secondary pollutants, the high PSCF area may also include areas where secondary formation is enhanced.
Source Apportionment (UNMIX)
• R. C. Henry developed the multivariate receptor model, UNMIX, for source apportionment studies.
• The UNMIX model was applied to a set of air quality compositional data to identify the number, composition, and contribution of the various sources of air pollution.
• The data used in this study included concentrations of As, Fe, Si, S, K, NH4
+, Na+, OC, EC, Non Volatile Nitrates (NvN), OCX (Non Organic Carbonate Carbon), and SO4
• UNMIX was then applied to a set of PM2.5 and species data with and without data collected during the smoke events to identify unique source types associated with this event.
SUMMARY RESULTS
16
20
24
28
-110 -105 -100 -95 -90
48-hours Backward Trajectory Calculations
Corpus Christi
Analysis of the 2003 Agricultural Burning Season
May 2-19, 2003: Every spring, at the end of the tropical dry season, agricultural burning and wildfires produce large amounts of smoke in southeastern Mexico and Central America. The fires usually begin in March and by late May or early June the smoke production diminishes as the rainy season begins. The peak of the burning is usually in late April and early May. Persistent southeasterly winds carry the resulting smoke to Texas, most frequently in April and May.
Corpus Christi
MODIS source: University of Wisconsin (SSEC) and Texas Commission on Environmental Quality
Three-dimensional transport of PM2.5
• PSCF analysis was also applied to altitude and longitude to evaluate three-dimensional transport of the smoke event.
• Starting height of backward trajectory was 500m. • It was noted that air parcels arriving in the Corpus Christi area at the
500m level were transported from the source region and mixed within the PBL of approximately 1 km.
Corpus Christi
Source Apportionment (UNMIX)
Six-sources
sulfate44%
vehicles19%
soil11%
nitrate6%
seaspray10%
K10%
Species Source#1 Source#2 Source#3 Source#4 Source#5 Source#6
Total Mass 1.04441 0.56008 0.96753 0.97753 1.75667 4.16212As -0.00002 0.00012 0.00008 0.00002 0.00035 0Fe 0.052 -0.0008 -0.0044 0.00282 0.00778 0.00115Si 0.20325 -0.0047 -0.0061 0.0054 0.00086 0.00363S 0.02435 0.03672 0.206 -0.0677 0.06736 0.15983K 0.01573 0.00182 -0.0003 0.04502 -0.0006 0.00229
NH3 0.00459 0.20093 0.06021 -0.0792 0.07416 0.16061Na+ 0.02327 -0.0096 0.14937 0.0841 -0.0147 0.00186OC 0.08324 0.08326 0.22478 0.40437 0.55809 0.19364EC -0.011 0.01841 0.0323 0.00779 0.1284 0.01989
NvN 0.00519 0.41144 0.04622 0.00885 -0.0048 -0.0041OCX 0.02883 0.00225 0.18611 0.16274 0.20363 0.07204SO4 0.08194 0.11353 0.62681 -0.1563 0.15688 0.49786Na 0.0015 -0.0119 0.23667 0.01001 -0.004 -0.001
Source Identification Soil Nitrate Sea sprayPotassiumVehicles Sulfate
The six source model output revealed that crustal soil, nitrates, salt from sea spray, potassium, vehicular sources and sulfates were the major factors contributing to the total ambient PM2.5 mass in the
Corpus Christi urban airshed.
• The UNMIX model was run with and without the smoke episode data and the primary difference was noted in the apportionment of potassium (K) in the six-source model. K is a strong indicator of burning sources and is linked to agricultural burns in the region.
• Sulfate indicating industries accounted for 44% of the total variance.
Analysis of the Sept. 11-14, 2002 Haze Event
Sept. 11-14, 2002: Increasing levels of haze and high ozone began arriving in East and Central Texas. The haze and air pollution had accumulated for several days in a stagnant air mass centered near the junction of the lower Ohio River Valley and the middle Mississippi River Valley. A weak cool front then pushed the hazy air mass south-southwest into South Texas.
Corpus Christi
MODIS source: University of Wisconsin (SSEC) and Texas Commission on Environmental Quality
Summary Results • The PM2.5 level is influenced by long-range transport of
haze and smoke during episodes such as May 2-19, 2003. These events significantly elevated the annual PM2.5 levels observed in the Corpus Christi airshed.
• PSCF and UNMIX receptor models were applied to understand the source-receptor relationship and identify various source types affecting the urban area.
• PSCF analysis identified southern Mexico and Central America as possible source areas affecting the Corpus Christi area during the May 2-19, 2003 smoke event.
• UNMIX receptor modeling suggested that agricultural burning as one of the primary sources of PM during the smoke episode.
Acknowledgements
The authors are grateful to Texas Commission on Environmental Quality (TCEQ) – Austin Office for providing access to the air quality data and to the National Oceanic and Atmospheric Administration’s (NOAA) Air Resources Laboratory for the use of the HYSPLIT4 Model.
References• Henry, R.C., Lewis, C.W., Hopke, P.K., Williamson, H.J., Atmospheric
Environment 1984 18, 1507–1515.• Gordon, G.E., Environmental Science and Technology 1988 22, 1132–
1142 • Philip K. Hopke J. Chemometrics 2003, 17, 255-265.• Lin, C. J.; Cheng, M. D.; Schroeder, W.H. Atm. Env. 2001, 35, 1141-
1154.• Pollissar, A.V.; Hopke, P.K.; Paatero, P.; Kaufmann, Y.J.; Hall, D.K.;
Bodhaine, B.A.; Dutton, E.G.; Harris, J.M. Atm. Env. 1999, 33, 2441-2458.
• Draxler, R.R.; Hess, G.D. Description of the HYSPLIT4 Modeling System, NOAA Tech Memo: 1997; ERL ARL-224.
• Lucey, D.; Hakjiiski, L.; Hopke, P.K.; Scudlark, J.R.; Church, T. Atm. Env. 2001, 35, 3979-3986.
• Zeng, Y.; Hopke, P.K. Atm. Env. 1989, 23, 1499-1509.
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