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Transcript of Katherine bell
Iden%fica%on and Characteriza%on of Pollutant Hot Spots
Integra%ng Probe Vehicle, Traffic and Land Use Data
By Katherine E. Bell, P.E. Miguel A Figliozzi, Ph.D.
FRIDAY SEMINAR January 17, 2014
1
I. Introduc@on & Background
II. Available and Collected Data
III. Sta@s@cal Analysis
IV. Conclusions & Future Research
2
OUTLINE
3
BACKGROUND
• Motor Vehicle Emissions – CO2, CO, HC, NOX, MSATs
• Fine Par@culate MaJer (PM2.5) – noncombus4on & combus4on
o Carcinogenic o Heart problems o Respiratory problems
• Vola@le Organic Compounds (VOC) – ozone precursors, carcinogens
• HOT SPOT: Subsec@on of corridor that consistently has an average pollutant concentra@on above the 85th percen@le when compared to all other subsec@ons on the corridor.
Background Data Analysis Conclusions
4
BACKGROUND
Background Data Analysis Conclusions
5
STUDY AREA – SE Powell Boulevard • 4.6 miles – SE 7th Ave to I-‐205
• Mul@-‐modal
• 2-‐lanes each direc@on
• Variety of land uses
Background Data Analysis Conclusions
6
OBJECTIVES
• Develop an efficient method to iden@fy hot spot loca@ons
o BeJer understand which variables are most related to variability in pollutant levels
o BeJer understand the variability of exposure levels along a corridor
• Long-‐term: BeJer inform personal exposure models and
health analyses
Background Data Analysis Conclusions
7
LITERATURE REVIEW
1) Air Quality Health and Environmental Concerns
2) Air Quality Modeling and Measurements
3) Powell Boulevard Research
4) Land Use Regression
Background Data Analysis Conclusions
8
LITERATURE REVIEW
1) Air Quality Health and Environmental Concerns
2) Air Quality Modeling and Measurements
3) Powell Boulevard Research
4) Land Use Regression
Background Data Analysis Conclusions
Sta%onary Mobile
9
LITERATURE REVIEW
1) Air Quality Health and Environmental Concerns
2) Air Quality Modeling and Measurements
3) Powell Boulevard Research
4) Land Use Regression
Background Data Analysis Conclusions
Sta%onary Mobile
Tailpipe In Vehicle Outside Vehicle
10
AVAILABLE & COLLECTED DATA
Traffic Data Land Use Data
PM2.5 Concentra%ons
Probe Vehicle Data
Meteorology
Background Data Analysis Conclusions
11
DATA COLLECTION
Probe Vehicle
Background Data Analysis Conclusions
12
AVAILABLE DATA
Background Data Analysis Conclusions
Background Data Analysis Conclusions
MOVING AVERAGE – All Study Hours
MOVING AVERAGE – All Study Hours
HOT SPOT FREQUNCY – All Study Hours
15
Mul%ple Regression – ALL DAY Data
• Linear Model: Adjusted R2 = 36% • Log-‐Linear Model: Adjusted R2 = 52%
BASELINE
Background Data Analysis Conclusions
16
• Hot Spot Iden%fica%on • Consistency, magnitude and distance impacted
• AM vs. PM: analyze together AND separately
• Sta%s%cal Analysis • Strongest (+) Rela5onships: Rela4ve Humidity, Background PM2.5 Concentra4ons, Presence of “High EmiJers”
• Strongest (-‐) Rela5onships: Temperature, Wind Speed, Traffic Speed
• Land Use variables also have sta4s4cally significant rela4onships with PM2.5 concentra4ons
• Mul4ple Regression models can be adjusted depending on data available
• Mobile Outside Vehicle Measurements + Land Use Regression • Valuable tool to beJer understand rela4onships between hot spot loca4ons and other variables
CONCLUSIONS
PM2.5
PM2.5
17
FUTURE RESEARCH
• Cold Spots – study poten4al predictors • VOC – perform regression analysis
• Predictors of Hot Spot Frequency • Study “outliers” • Other variables
o Construc4on o Underpasses o Vehicle Classifica4ons (more detailed)
Background Data Analysis Conclusions
Oregon Transporta@on Research and Educa@on Consor@um (OTREC) & Na@onal Ins@tute for Transporta@on and Communi@es (NITC)
FHWA Eisenhower Fellowship Program
Thesis CommiJee: Dr. Miguel Figliozzi, Dr. Robert Ber@ni and Dr. Chris Monsere
Alex Bigazzi, Adam Moore (PSU)
ACKNOWLEDGEMENTS
Katherine Bell [email protected]
Miguel Figliozzi
Civil and Environmental Engineering Portland State University
QUESTIONS
Related Masters Thesis will be available at hJp://www.its.pdx.edu/[email protected]
20
Data
Introduc@on Literature Review Data Analysis Conclusions
• Pollutant Concentra@on – PM2.5 and VOC • Probe Vehicle Behavior – Loca4on, Speed, Standard devia4on of speed,
Percent 4me accelera4ng, Stopped 4me
• Traffic – Queue length, Queue adjacent, Volume, Distance to major intersec4on, # of high emiJers
• Meteorological – Wind Speed/Direc4on, Background PM2.5 , Rela4ve humidity, Temperature
• Zoning – Commercial, Residen4al, Industrial, Open-‐space
• Buildings & Businesses – Drive-‐through business (i.e., McDonalds) Gas sta4on, Building height, Building footprints
• Eleva@on Changes – Flat, Uphill, Downhill, High point, Low point
21
AVAILABLE DATA
• Traffic – Wavetronix & SCATS
• Land Use – PortlandMaps & RLIS
• Meteorological – DEQ Air Quality Monitoring Sta4on
Background Data Analysis Conclusions
22
1) Mann-‐Whitney-‐Wilcoxon Test
2) Simple Regression Analysis
3) Mul@ple Regression Analysis
STATISTICAL ANALYSIS
Skewed distribu@on
Background Data Analysis Conclusions
23 Introduc@on Literature Review Data Analysis Conclusions
Time-‐Space-‐Air Quality Diagram
26
Simple Regression -‐ Traffic
Introduc@on Literature Review Data Analysis Conclusions
-‐40% -‐30% -‐20% -‐10% 0% 10% 20%
# Of High EmiJers
Distance to Major Intx
Traffic Volume
Queue Adjacent
Queue Length
Stopped Time
Stdev Speed
Mean Speed
Time of Day
R-‐square and correla%on sign
AM Only
PM Only
AM & PM
-‐40% -‐30% -‐20% -‐10% 0% 10% 20% 30%
Queue Adjacent
# Of High EmiJers
Mean Speed
Time of Day
Rela@ve Humidity
Background PM2.5
Wind Direc@on Cos
Wind Direc@on Sin
Wind Speed
Temperature
R-‐square and correla%on sign
AM Only
PM Only
AM & PM
27
Simple Regression
Introduc@on Literature Review Data Analysis Conclusions
Meteo
rology
Traffi
c
-‐40% -‐30% -‐20% -‐10% 0% 10% 20%
Mean Speed
Temperature
Mostly Uphill
Mostly Flat
Distance to Drive Through (i.e., McD's)
Distance to Gas Sta@on
Distance to Gas Sta@on (Far Side)
Building Footprint
Building Height
Frontage Profile Height
Commercial
Residen@al
Industrial
R-‐square and correla%on sign
AM Only
PM Only
AM & PM
28
Simple Regression
Introduc@on Literature Review Data Analysis Conclusions
Build
ings
Zoning
29
Simple Regression -‐ Traffic
Introduc@on Literature Review Data Analysis Conclusions
-‐40% -‐30% -‐20% -‐10% 0% 10% 20%
# Of High EmiJers
Distance to Major Intx
Traffic Volume
Queue Adjacent
Queue Length
Stopped Time
Stdev Speed
Mean Speed
Time of Day
R-‐square and correla%on sign
AM Only
PM Only
AM & PM
-‐40% -‐30% -‐20% -‐10% 0% 10% 20% 30%
Queue Adjacent
# Of High EmiJers
Mean Speed
Time of Day
Rela@ve Humidity
Background PM2.5
Wind Direc@on Cos
Wind Direc@on Sin
Wind Speed
Temperature
R-‐square and correla%on sign
AM Only
PM Only
AM & PM
30
Simple Regression
Introduc@on Literature Review Data Analysis Conclusions
Meteo
rology
Traffi
c
-‐40% -‐30% -‐20% -‐10% 0% 10% 20%
Mean Speed
Temperature
Mostly Uphill
Mostly Flat
Distance to Drive Through (i.e., McD's)
Distance to Gas Sta@on
Distance to Gas Sta@on (Far Side)
Building Footprint
Building Height
Frontage Profile Height
Commercial
Residen@al
Industrial
R-‐square and correla%on sign
AM Only
PM Only
AM & PM
31
Simple Regression
Introduc@on Literature Review Data Analysis Conclusions
Build
ings
Zoning
32
Mul%ple Regression -‐ % Contribu%on to Baseline All Day Model -‐ Example
Introduc@on Literature Review Data Analysis Conclusions
Baseline +7.2%
+6.5%
+3.5% -‐4.6% -‐3.1%
• R Step() func@on – uses AIC criteria
• p-‐value < 0.05 • Variance infla@on factor (VIF) < 5
• Correla@ons • Log-‐linear Models
33
Mul%ple Regression
Introduc@on Literature Review Data Analysis Conclusions
Correla%ons
37
Simple Regression
Introduc@on Literature Review Data Analysis Conclusions
38
Simple Regression
Introduc@on Literature Review Data Analysis Conclusions