Evaluation of Air Quality Models with Near-Road Monitoring Data
Task 4: Data Exploration
Texas A&M Transportation InstituteTexas A&M Transportation Institute
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Task 4: Data ExplorationObjective Near-road monitors are installed primarily close to major roadways for
monitoring near-road concentration levels Understand conditions when relatively high near-road PM
concentrations have been observed Quantitatively assess the associations between key factors Near-road concentration Traffic Meteorology Background concentration
Focus on year 2016 data
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Near-road Monitoring Sites
AQS Number Site Name Address
PollutantsMonitored
Distanceto Nearest
TrafficLane (m)NOx CO PM2.5
481131067 Dallas LBJ Freeway 8652 LBJ Freeway X 24482011066 Houston SW Freeway 5617 Westward Avenue X 24484531068 Austin North I-35 8912 N IH 35 SVRD SB X 27480291069 San Antonio I-35 35 9904 IH 35 N X 20484391053 Fort Worth California Parkway North 1198 California Parkway North X X X 15
482011052 Houston North Loop 822 North Loop X X x 15
Near-Road Sites in Texas
Houston Ft Worth
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Houston SiteParameters Used for Data Exploration Averaging PeriodPollutants (CAMS 1052)• PM2.5• CO• NO, NO2, NOx
24-hrs (1-in-3 days)HourlyHourly
Ambient Parameters (On-site)• Temperature• Wind direction• Wind speed• Peak wind gust
Hourly
Meteorological Data (Off-site)• Atmospheric Stability Hourly
Traffic Data• Volume• Speed
Hourly
Background Ambient Monitors (PM2.5)• CAMS 1, CAMS 35, CAMS 416, CAMS 40 Hourly
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Min= 0 (ppm)Median= 0.4Mean= 0.45Max= 1.798th perc.=1.1
Min= -0.3 (ppb)Median= 12.9Mean= 19.0Max= 187.198th perc.= 84.9
Min= -2.6 (ppb)Median= 11.2Mean= 13.6Max= 51.598th perc.= 40.44
Min= -4.9 (ppb)Median= 26.6Mean= 32.7Max= 227.298th perc.= 114.2
Min= 1.2 (ug/m3)Median= 9.8 Mean= 10.11Max= 2398th perc.=17.476
Frequency Distribution
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Concentration RosesWind Rose
NO2CO
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Background Concentration
Relation between Near-road and Regional Concentrations Ambient monitors are installed primarily for regulatory compliance
and community-exposure monitoring Regional concentrations are influenced by multiple factors related to
meteorology, industrial sources, and regional transport etc. Literature shows near-road PM2.5 concentration to be dominated by
background regional levels (90-95%) Near‐roadConcentration
Does roadways account for majority of the Incremental Contribution?
Ambient Monitors
* C8 was not considered due to a high number of missing records
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Ambient Monitors
* 107 PM2.5 days in 2016
CAMS1 CAMS35
CAMS403 CAMS416
There is a 1.47 µg/m3 (17%) increment at C1052 compared to ambient monitors
Near-road 24hrs PM2.5 is strongly correlated with background conc.
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Traffic Activity
PM2.5 > 15ug/m3
y = 1E-05x + 0.2309R² = 0.0591
-4
-2
0
2
4
6
8
0 50000 100000 150000 200000 250000
∆C =
C10
52 -
C35
(ug/
m3 )
AADT
PM2.5 (μg/m3) measured at CAMS1052 vs AADT measured close to monitor
Near-road increment ∆C (C1052-C35) vs AADT
Near-road and near-road increment 24-hr PM2.5 are not strongly related to AADT
y = 2E-05x + 6.6725R² = 0.0402
0
5
10
15
20
25
0 50000 100000 150000 200000 250000
PM2.
5C
once
ntra
tion
(ug/
m3 )
AADT
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Traffic Activity vs Other Pollutants
Hourly average CO and NO2 is not strongly related to hourly traffic volumes
NO2
CO
NO2CO
*Time series plotted for highest 10 PM2.5 days in 2016
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MeteorologyWind Rose
Meteorological parameters evaluated include wind speed, wind direction, temperature, relative humidity and atmospheric stability
CAMS 1052 ∆C
Dots= Concentrations Levels, Quadrant (0-3600) = Wind Direction, Concentric circles (0-10mph) = Wind Speed
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Traffic Volume and MeteorologyWind Rose
Dots= Concentrations Levels, Quadrant (0-3600) = Wind Direction, Concentric circles (0-250,000) = AADT
Although high conc. values are found along the prevailing wind direction, conc. values are not strongly related to traffic volume
CAMS 1052 ∆C
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Traffic Speed and MeteorologyWind Rose
Dots= Concentrations Levels, Quadrant (0-3600) = Wind Direction, Concentric circles (40-65mph) = Traffic Speed
High conc. values relate to 50-60mph average speed
CAMS 1052 ∆C
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Predictive Tools
Critical Parameters for ∆C corresponds to Wind Direction, Season and Traffic Speed
Back-propagation Neural Network with 10 neurons in hidden layer
Critical Parameters C1052 - Background Conc. and Season ∆C - Wind Direction and Traffic Speed
Important to understand the data to evaluate associations
Decision Tree Artificial Neural Networks
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Study Findings Houston site does not appear to have high pollutant concentration (CO, PM2.5,
NO2) at a frequency sufficient enough to violate the NAAQS.
Near-road PM concentrations vary more by urban-level PM values rather than by AADT
Near-road increment PM2.5 (∆C) found to be influenced by meteorology rather than by AADT
On average, there is a 17% (1.47 µg/m3) increment at near-road site compared to background site
Resolution of PM2.5 measured at 24hrs is a major limitation to explore further associations
Supplemental Material
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CO Concentration Rose CAMS 1052
Wind Rose
Concentration Rose
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Wind Rose NO2 Concentration Rose CAMS 1052
NO2 NO NOx
Concentration Rose
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Land Use
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Land Use
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Traffic Activity: Trucks, FE-AADT
y = 1E-05x + 6.6725R² = 0.0402
0
5
10
15
20
25
0 50000 100000 150000 200000 250000 300000 350000 400000
PM2.
5C
once
ntra
tion
(ug/
m3 )
FE-AADT (Emissions)
PM2.5 > 15ug/m3
PM2.5 > 15ug/m3
Average truck percentage is obtained from STARS counter located close to the monitor
Fleet-Equivalent AADT ((FE-AADT) is a single metric accounting for both traffic volume and fleet mix
y = 0.0003x + 6.6725R² = 0.0402
0
5
10
15
20
25
0 2000 4000 6000 8000 10000 12000 14000 16000
PM2.
5C
once
ntra
tion
(ug/
m3 )
Number of Trucks
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PM2.5 and Traffic Volume
y = 1E-05x + 0.2309R² = 0.0591
-4
-2
0
2
4
6
8
0 50000 100000 150000 200000 250000
∆C =
C10
52 -
C35
(ug/
m3 )
AADT
CAMS 35y = 4E-06x + 0.8274
R² = 0.0091
-6
-4
-2
0
2
4
6
8
0 50000 100000 150000 200000 250000
∆C =
C10
52 -
C1
(ug/
m3 )
AADT
y = 5E-07x + 0.6179R² = 0.0002
-6
-4
-2
0
2
4
6
8
0 50000 100000 150000 200000 250000
∆C =
C10
52 -
C40
3 (u
g/m
3 )
AADT
y = 1E-05x + 0.4004R² = 0.0112
-10
-5
0
5
10
15
20
0 50000 100000 150000 200000 250000
∆C =
C10
52 -
C41
6 (u
g/m
3 )
AADT
CAMS 1
CAMS 403 CAMS 416
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Meteorology (Wind speed and direction)
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Meteorology (Atmospheric Stability)
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Near-road, and Meteorology (WD, Temp)
Dots: Concentrations Levels, Quadrant: Wind Direction, Concentric circles: Temperature
Temperature Distribution
CAMS 1052 ∆C
C1052 ∆C
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Near-road, and Meteorology (WD, Rel Humidity)
Dots: Concentrations Levels, Quadrant: Wind Direction, Concentric circles: Relative Humidity
C1052
Relative Humidity
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All parameters
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Artificial Neural Network (BPNN)
∆C vs all parametersC1052 vs all parameters
• Back-propagation Neural Network with 10 neurons in hidden layer• Weights assigned from input-hidden layer proportional to parameter importance
Parameter ImportanceMonth 5.08
Day 5.73Temperature 6.16
Relative Humidity 8.36Traffic Speed 9.34
Traffic Volume 7.14Pressure 5.53
Wind Direction 9.34Wind Speed 7.87
WindClass 7.73Season 5.09
∆C
Parameter ImportanceMonth 5.59
Day 4.03Temperature 5.66
Relative Humidity 4.04Traffic Speed 2.28
Traffic Volume 3.75Pressure 5.04
Wind Direction 4.25Wind Speed 4.27
WindClass 5.32Season 3.54
CAMS35 6.01
CA
MS1
052
ϴ
x: features, w: weights, ϴ: bias, f: activation function