Urban vs. Rural Atlanta
An assessment of :
1) PM2.5 composition and trends
2) The Atlanta Urban Heat Island Effect
Outline
• Part I: PM2.5 Compositional analysis and trends
▫
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Background
Assessment
Composition
Spatial and temporal Analysis
Monthly Analysis
Meteorological Correlations
• Part II: Urban Heat Island Index and Effect
▫ Background
▫ Diurnal UHI in urban vs. rural environment
▫ Meteorological Correlations
• Part III: Conclusions
Part I: PM2.5: Why are we so concerned?
• Aerosols
• PM
▫ Environmental Issues
▫ Health Risks
• PM2.5
▫ Humans inhale it
▫ It diffuses
• Primary and secondary origin
▫ Formation poorly understood
PM Assessment
2.5
• Part of Georgia Tech’s ASACA project
• 3 sites
▫ Fire Station 8 (FSE) (urban) (Daily)
▫ South Dekalb (SD) (urban) (Daily)
▫ Yargo (YG) (rural) (1 in 3 days)
• 3 filter types
▫ Quartz (EC/OC analysis)
▫ Nylon (wsioi and ions)
 Na+, NH4+,K+,Ca2+,Cl-,
NO2-, NO3-, SO42- ,
CH3COO-, HCOO-, C2O42-
▫ Teflon (trace metals)
• PCM, IC, TOT, TEOM, aeth.
• Only 2012 data analyzed
Figure 1: Map of the three different sampling sites. A: Fort Yargo
State Park, B: Fire Station Eight, C: South Dekalb site
PM Composition in 2012
2.5
PM2.5 Analysis: Spatial and temporal
12
25
10
20
8
15
6
10
4
5
2
0
0
10
20
FSE_PM2.5
30
SD_PM2.5
40
50
0
0
10
20
YG_PM25.
30
FSE_OC
3
40
SD_OC
50
YG_OC
4.5
4
2.5
3.5
2
3
2.5
1.5
2
1
1.5
1
0.5
0.5
0
0
0
10
20
FSE_EC
30
SD_EC
40
YG_EC
50
0
10
20
FSE_SO4
30
SD_SO4
40
YG_SO4
50
PM2.5 Analysis: Spatial and temporal
1.4
1.4
1.2
1.2
1
1
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0
0
10
20
FSE_NH4_Avg
30
SD_NH4_Avg
40
50
0
YG_NH4_Avg
10
20
FSE_NO3
30
SD_NO3
40
YG_NO3
4.5
4
3.5
3
2.5
R Square
Adjusted R
Square
0.833
Standard Error
0.189
0.826
2
1.5
1
Coefficients Standard Error
0.5
0
0
10
20
FSE_SO4
30
SD_SO4
40
YG_SO4
50
P-value
Intercept
0.408
0.110
0.0011
X Variable 1
2.711
0.242
3.35E-11
50
PM Analysis: Monthly
2.5
Average OC (microg/m3)
9
8
7
6
5
4
3
2
1
0
2.5 0
Average Sulfate (ug/m3)
2012 Monthly OC Average
2
SD
YG
FSE
2
4
6
Month
8
10
SD
YG
1
0
14
FSE
1.5
0.5
12
2012 Monthly Sulfate Average
PM2.5 Analysis: Meteorological
Correlations
40
35
30
25
Avg T (deg C)
20
FSE_PMSpec
15
10
5
0
11/3/2011
-5
12/23/2011
2/11/2012
4/1/2012
5/21/2012
7/10/2012
8/29/2012 10/18/2012
12/7/2012
1/26/2013
70
60
Avg Windspeed
(km/hr)
50
40
FSE_PMSpec
30
20
10
0
11/3/2011
12/23/2011
2/11/2012
4/1/2012
5/21/2012
7/10/2012
8/29/2012
10/18/2012
12/7/2012
1/26/2013
PM2.5: Meteorological Correlations
Avg Temp (deg C) - YG
y = 0.3956x + 14.683
R² = 0.0717
35
30
25
20
Avg Temp (deg C)
15
Linear (Avg Temp
(deg C))
10
5
Regression
Statistics
R Square
Adjusted R
Square
Standard Error
0.0717
0.0643
4.727
Coefficients
Standard Error
P-value
Intercept
6.0758
1.148
5.19E-07
X Variable 1
0.181
0.058
0.00225
0
0
10
20
30
Avg Windspeed (km/hr) - SD
40
35
y = -0.8552x + 12.322
R² = 0.1202
30
25
Avg Windspeed SD
20
15
Regression
Statistics
R Square
Adjusted R
Square
Standard Error
Linear (Avg
Windspeed SD)
10
5
0
0
5
10
15
Intercept
X Variable 1
0.12
0.118
4.785
Coefficients
Standard Error
P-value
12.322
-0.855
0.489
0.121
9.49E-82
8.92E-12
Part II: Urban Heat Island Index and
Effect
• Urban Area Temperature > Rural Area
Temperature
▫ Population density, geography, building structure
and material, vegetation
▫ Urban cities trap radiation near the surface
 Large differences in solar radiation and heat and
water balances
UHII
15
UHI
10
5
0
11/3/2011
-5
12/23/2011
2/11/2012
4/1/2012
5/21/2012
7/10/2012
8/29/2012
10/18/2012
12/7/2012
1/26/2013
Urban Atlanta Albedo
Diurnal UHI in Urban vs Rural Sites
January 16
May 1
February 16
November 7
UHII vs Meteorological Correlations
Rel Windspeed
14
y = -0.14x + 6.0322
R² = 0.1146
12
10
Rel Windspeed
8
6
Linear (Rel
Windspeed)
4
2
0
-10
-2
0
10
20
y = -0.0872x + 3.7069
R² = 0.2378
8
6
2
0
-40
-20
-2
Coefficients
Intercept
X Variable 1
6.032
-0.14
Adjusted R
Square
0.236
Standard Error
1.910
Standard
Error
0.1133
0.0204
P-value
6.95E-174
2.92E-11
Coefficients
Standard Error
P-value
Intercept
3.707
0.2194
1.15E-47
X Variable 1
-0.0872
0.00819
2.93E-23
Rel. Humidity
4
-60
2.059
Regression
Statistics
14
10
0.112
30
Rel. Humidity
12
Regression
Statistics
Adjusted R
Square
Standard
Error
0
20
To sum up…
• PM2.5 is of concern in both urban and rural
Atlanta
▫ Although composition similar, concentrations
need to be monitored
▫ Meteorological factors play a big role
• Atlanta is a hub for UHI
▫ More attention needed in diurnal changed in
meteorological patterns
• A lot can change in a small distance!
Special Thanks to
• 2012 - 2013 ASACA team
▫ Jeremiah Redman
▫ Kyle Digby
▫ Boris Galvis
References
•
Bell, et al. "Spatial and Temporal Variation in PM2.5 Chemical Composition in the United States for
Health Effects Studies." Environmental Health Perspectives: n. pag. Print.
•
Chow, et al. "PM2.5 chemical composition and spatiotemporal variability during the California Regional
PM10/PM2.5 Air Quality Study (CRPAQS)." Journal of Geophysical Research Atmospheres: n. pag.
Print.
•
Clarke, Azadi-Boogar, and Andrews. "Particle size and chemical composition of urban aerosols." Science
of the Total Environment: n. pag. Print.
•
Kim. "Urban Heat Island." International Journal of Remote Sensing 13.12 (1992): n. pag. Print.
•
Myrup. "A Numerical Model of the Urban Heat Island." American Meteorology Society: n. pag. Print.
•
Myrup, Leonard. "A Numerical Model of Urban Heat Island." Journal of Applied Meteorology: n. pag.
Print.
•
The Urban Environment. N.p., n.d. Web. 24 Apr. 2013. <http://www.coa.gov.in/mag/Archi_Apr09Lowres-pdf/20-25-Urban%20heat%20island.pdf>.
•
Weng, Lu, and Schubring. "Estimation of land surface temperature–vegetation abundance relationship
for urban heat island studies." Remote Sensing of Environment 89.4 (2004): n. pag. Print.
Any questions?
Thanks for listening!