evaluation of air quality models with near-road monitoring data · 2019-09-13 · 16 study findings...

Evaluation of Air Quality Models with Near-Road Monitoring Data

Task 4: Data Exploration

Texas A&M Transportation InstituteTexas A&M Transportation Institute

2

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

3

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= 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

evaluation of air quality models with near-road monitoring data · 2019-09-13 · 16 study findings...

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