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Iden%fica%on 
and 
Characteriza%on 
of 
Pollutant 
Hot 
Spots 
Integra%ng 
Probe 
Vehicle, 
Traffic 
and 
Land 
Use 
Data ...
I. Introduc@on 
& 
Background 
II. Available 
and 
Collected 
Data 
III. Sta@s@cal 
Analysis 
IV. Conclusions 
& 
Future 
...
3 
BACKGROUND 
• Motor 
Vehicle 
Emissions 
– 
CO2, 
CO, 
HC, 
NOX, 
MSATs 
• Fine 
Par@culate 
MaJer 
(PM2.5) 
– 
noncomb...
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 
d...
6 
OBJECTIVES 
• Develop 
an 
efficient 
method 
to 
iden@fy 
hot 
spot 
loca@ons 
o BeJer 
understand 
which 
variables 
...
7 
LITERATURE 
REVIEW 
1) Air 
Quality 
Health 
and 
Environmental 
Concerns 
2) Air 
Quality 
Modeling 
and 
Measurements...
8 
LITERATURE 
REVIEW 
1) Air 
Quality 
Health 
and 
Environmental 
Concerns 
2) Air 
Quality 
Modeling 
and 
Measurements...
9 
LITERATURE 
REVIEW 
1) Air 
Quality 
Health 
and 
Environmental 
Concerns 
2) Air 
Quality 
Modeling 
and 
Measurements...
10 
AVAILABLE 
& 
COLLECTED 
DATA 
Traffic 
Data 
Land 
Use 
Data 
PM2.5 
Concentra%ons 
Probe 
Vehicle 
Data 
Meteorology...
11 
DATA 
COLLECTION 
Probe 
Vehicle 
Background 
Data 
Analysis 
Conclusions
12 
AVAILABLE 
DATA 
Background 
Data 
Analysis 
Conclusions
MOVING 
AVERAGE 
– 
All 
Study 
Hours 
Background 
Data 
Analysis 
Conclusions
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 
...
16 
• Hot 
CONCLUSIONS 
Spot 
Iden%fica%on 
• Consistency, 
magnitude 
and 
distance 
impacted 
• AM 
vs. 
PM: 
analyze 
t...
17 
FUTURE 
RESEARCH 
• Cold 
Spots 
– 
study 
poten4al 
predictors 
• VOC 
– 
perform 
regression 
analysis 
• Predictors...
ACKNOWLEDGEMENTS 
Oregon 
Transporta@on 
Research 
and 
Educa@on 
Consor@um 
(OTREC) 
& 
Na@onal 
Ins@tute 
for 
Transport...
Katherine 
Bell 
Katherine.e.bell@pdx.edu 
Miguel 
Figliozzi 
figliozzi@pdx.edu 
Civil 
and 
Environmental 
Engineering 
P...
20 
Data 
Introduc@on 
Literature 
Review 
Data 
Analysis 
Conclusions 
• Pollutant 
Concentra@on 
– 
PM2.5 
and 
VOC 
• P...
21 
AVAILABLE 
DATA 
• Traffic 
– 
Wavetronix 
& 
SCATS 
• Land 
Use 
– 
PortlandMaps 
& 
RLIS 
• Meteorological 
– 
DEQ 
...
22 
STATISTICAL 
ANALYSIS 
1) Mann-­‐Whitney-­‐Wilcoxon 
Test 
2) Simple 
Regression 
Analysis 
3) Mul@ple 
Regression 
An...
Introduc@on 
Literature 
Review 
Data 
Analysis 
Conclusions 
23 
Time-­‐Space-­‐Air 
Quality 
Diagram
26 
Simple 
Regression 
-­‐ 
Traffic 
Time 
of 
Day 
Mean 
Speed 
Stdev 
Speed 
Stopped 
Time 
Queue 
Length 
Queue 
Adjac...
Temperature 
Wind 
Speed 
Wind 
Direc@on 
Sin 
Wind 
Direc@on 
Cos 
Background 
PM2.5 
Rela@ve 
Humidity 
Time 
of 
Day 
M...
Industrial 
Residen@al 
Commercial 
Frontage 
Profile 
Height 
Building 
Height 
Building 
Footprint 
Distance 
to 
Gas 
S...
29 
Simple 
Regression 
-­‐ 
Traffic 
Time 
of 
Day 
Mean 
Speed 
Stdev 
Speed 
Stopped 
Time 
Queue 
Length 
Queue 
Adjac...
Temperature 
Wind 
Speed 
Wind 
Direc@on 
Sin 
Wind 
Direc@on 
Cos 
Background 
PM2.5 
Rela@ve 
Humidity 
Time 
of 
Day 
M...
Industrial 
Residen@al 
Commercial 
Frontage 
Profile 
Height 
Building 
Height 
Building 
Footprint 
Distance 
to 
Gas 
S...
32 
Mul%ple 
Regression 
-­‐ 
% 
Contribu%on 
to 
Baseline 
All 
Day 
Model 
-­‐ 
Example 
Introduc@on 
Literature 
Review...
• R 
Step() 
func@on 
– 
uses 
AIC 
criteria 
• p-­‐value 
< 
0.05 
• Variance 
infla@on 
factor 
(VIF) 
< 
5 
• Correla@o...
Correla%ons
37 
Simple 
Regression 
Introduc@on 
Literature 
Review 
Data 
Analysis 
Conclusions
38 
Simple 
Regression 
Introduc@on 
Literature 
Review 
Data 
Analysis 
Conclusions
Katherine bell
Katherine bell
Katherine bell
Katherine bell
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Katherine bell

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Katherine bell

  1. 1. 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
  2. 2. I. Introduc@on & Background II. Available and Collected Data III. Sta@s@cal Analysis IV. Conclusions & Future Research 2 OUTLINE
  3. 3. 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. 4. 4 BACKGROUND Background Data Analysis Conclusions
  5. 5. 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. 6. 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. 7. 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. 8. 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 Mobile Data Analysis Conclusions Sta%onary
  9. 9. 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 Mobile Data Analysis Conclusions Sta%onary Tailpipe In Vehicle Outside Vehicle
  10. 10. 10 AVAILABLE & COLLECTED DATA Traffic Data Land Use Data PM2.5 Concentra%ons Probe Vehicle Data Meteorology Background Data Analysis Conclusions
  11. 11. 11 DATA COLLECTION Probe Vehicle Background Data Analysis Conclusions
  12. 12. 12 AVAILABLE DATA Background Data Analysis Conclusions
  13. 13. MOVING AVERAGE – All Study Hours Background Data Analysis Conclusions
  14. 14. MOVING AVERAGE – All Study Hours HOT SPOT FREQUNCY – All Study Hours
  15. 15. 15 Mul%ple Regression – ALL DAY Data • Linear Model: Adjusted R2 = 36% • Log-­‐Linear Model: Adjusted R2 = 52% BASELINE Background Data Analysis Conclusions
  16. 16. 16 • Hot CONCLUSIONS 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 PM2.5 PM2.5
  17. 17. 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
  18. 18. ACKNOWLEDGEMENTS 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)
  19. 19. Katherine Bell Katherine.e.bell@pdx.edu Miguel Figliozzi figliozzi@pdx.edu Civil and Environmental Engineering Portland State University QUESTIONS Related Masters Thesis will be available at hJp://www.its.pdx.edu/ publica@ons.php
  20. 20. 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. 21. 21 AVAILABLE DATA • Traffic – Wavetronix & SCATS • Land Use – PortlandMaps & RLIS • Meteorological – DEQ Air Quality Monitoring Sta4on Background Data Analysis Conclusions
  22. 22. 22 STATISTICAL ANALYSIS 1) Mann-­‐Whitney-­‐Wilcoxon Test 2) Simple Regression Analysis 3) Mul@ple Regression Analysis Skewed distribu@on Background Data Analysis Conclusions
  23. 23. Introduc@on Literature Review Data Analysis Conclusions 23 Time-­‐Space-­‐Air Quality Diagram
  24. 24. 26 Simple Regression -­‐ Traffic Time of Day Mean Speed Stdev Speed Stopped Time Queue Length Queue Adjacent Traffic Volume Distance to Major Intx -­‐40% -­‐30% -­‐20% -­‐10% 0% 10% 20% Introduc@on Literature Review Data Analysis Conclusions # Of High EmiJers R-­‐square and correla%on sign AM Only PM Only AM & PM
  25. 25. Temperature Wind Speed Wind Direc@on Sin Wind Direc@on Cos Background PM2.5 Rela@ve Humidity Time of Day Mean Speed # Of High EmiJers Queue Adjacent -­‐40% -­‐30% -­‐20% -­‐10% 0% 10% 20% 30% R-­‐square and correla%on sign AM Only PM Only AM & PM 27 Simple Regression Meteorology Introduc@on Literature Review Data Analysis Conclusions Traffic
  26. 26. Industrial Residen@al Commercial Frontage Profile Height Building Height Building Footprint Distance to Gas Sta@on (Far Side) Distance to Gas Sta@on Zoning Distance to Drive Through (i.e., McD's) Mostly Flat Mostly Uphill Temperature Mean Speed -­‐40% -­‐30% -­‐20% -­‐10% 0% 10% 20% R-­‐square and correla%on sign AM Only PM Only AM & PM 28 Simple Regression Introduc@on Literature Review Data Analysis Conclusions Buildings
  27. 27. 29 Simple Regression -­‐ Traffic Time of Day Mean Speed Stdev Speed Stopped Time Queue Length Queue Adjacent Traffic Volume Distance to Major Intx -­‐40% -­‐30% -­‐20% -­‐10% 0% 10% 20% Introduc@on Literature Review Data Analysis Conclusions # Of High EmiJers R-­‐square and correla%on sign AM Only PM Only AM & PM
  28. 28. Temperature Wind Speed Wind Direc@on Sin Wind Direc@on Cos Background PM2.5 Rela@ve Humidity Time of Day Mean Speed # Of High EmiJers Queue Adjacent -­‐40% -­‐30% -­‐20% -­‐10% 0% 10% 20% 30% R-­‐square and correla%on sign AM Only PM Only AM & PM 30 Simple Regression Meteorology Introduc@on Literature Review Data Analysis Conclusions Traffic
  29. 29. Industrial Residen@al Commercial Frontage Profile Height Building Height Building Footprint Distance to Gas Sta@on (Far Side) Distance to Gas Sta@on Zoning Distance to Drive Through (i.e., McD's) Mostly Flat Mostly Uphill Temperature Mean Speed -­‐40% -­‐30% -­‐20% -­‐10% 0% 10% 20% R-­‐square and correla%on sign AM Only PM Only AM & PM 31 Simple Regression Introduc@on Literature Review Data Analysis Conclusions Buildings
  30. 30. 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%
  31. 31. • 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
  32. 32. Correla%ons
  33. 33. 37 Simple Regression Introduc@on Literature Review Data Analysis Conclusions
  34. 34. 38 Simple Regression Introduc@on Literature Review Data Analysis Conclusions

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