Thesis Powerpoint

Influential Parameters on Ultrafine Particles in Proximity to Open Air Restaurant Patios

Alex LeeSupervisor: Dr. Marianne Hatzopoulou

Department of Civil Engineering and Applied MechanicsFall 2015

MAIN OBJECTIVES Explore influential factors contributing to ultrafine particle (UFP) variability in

the presence of open air restaurant patios

Establishing a linear mixed model to make determinations on greatest effects

Higher than average concentrations at measured sites

Meteorological and traffic effects: most important predictors

MAIN HYPOTHESES

MOTIVATION

TABLE OF CONTENTSI. CONTEXT

I. Near-Road Air Pollution & HealthII. Ultrafine ParticlesIII. Measurement MethodologyIV. Statistical Methodology

II. DATA COLLECTION CAMPAIGNI. Site SelectionII. EquipmentIII. Protocol

III. DATA PROCESSINGI. UFP & Traffic DataII. Meteorological DataIII. Land Use Data

IV. STATISTICAL ANALYSIS : METHODOLOGY

V. RESULTSI. Descriptive StatisticsII. Bivariate AnalysesIII. Modelling ResultsIV. Summary

VI. DISCUSSIONVII. CONCLUSION

CONTEXT: Near-Road Air Pollution & Health in Urban Areas In urban areas, motor vehicle exhaust a

main contributor to air pollution

Diesel vehicles contribute to [UFP] disproportionate to their contribution to overall traffic count

Land use processes : residential/commercial heating

Street geometries

Health Effects: Increased risk of respiratory and cardiovascular effects

CONTEXTI. Air

Pollution & Health

II. UFPIII. MeasuringIV. LMM

DATA COLLECTION CAMPAIGN

DATA PROCESSING

STATISTICAL ANALYSIS : METHODOLOGY

RESULTS

DISCUSSION

CONCLUSION

CONTEXT: Ultrafine Particles A subset of fine particulate matter (equal to or less than 2.5 µm

in aerodynamic diameter) Defined as particles equal to or less than 0.1 µm in

diameter Typically composed of carbon-based material with inorganic ions Nucleated UFP particles COAGULATE or CONDENSE or

EVAPORATE

CONTEXTI. Air

Pollution & Health



DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION

CONTEXTI. Air

Pollution & Health



DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION

CONTEXT: Measurement Methodology Condensation Particle Counter

Measures particles ranging from 0.01 µm to >1.0 µm

User friendliness and convenience

Programmable data logging

CONTEXTI. Air

Pollution & Health



DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION

CONTEXTI. Air

Pollution & Health



DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION

CONTEXT: Statistical Methodology What is a Linear Mixed-

Effects Model? An “extension” of a

general linear model

“Mixed”: contains both FIXED and RANDOM elements

Model quality assessed using Akaike’s Information Criteria (AIC) reading

“Smaller-is-better” terms

CONTEXTI. Air

Pollution & Health



DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION

CONTEXT: Statistical Methodology Why Linear Mixed-Effects Models?

Study of repeated measures (within-subject correlated data) Allows for more accurate interpretations of relationships

CONTEXTI. Air

Pollution & Health



DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION

DATA COLLECTION CAMPAIGNI. Site Selection Identifying areas of interest

Gathering of postal codes

Buffer creation to assess land use composition

Ensure site walkability: checking neighbourhood Walk Score ratings

CONTEXT


I. Site Selection

II. Equipment

III. Protocol

DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION

CONTEXT


I. Site Selection

II. Equipment

III. Protocol

DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION

CONTEXT


I. Site Selection

II. Equipment

III. Protocol

DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION

SITE #1 (Plateau-Mont-Royal borough)DATA COLLECTION CAMPAIGN

CONTEXT


I. Site Selection

II. Equipment

III. Protocol

DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION

DATA COLLECTION CAMPAIGNSITE #2 (Outremont borough)

CONTEXT


I. Site Selection

II. Equipment

III. Protocol

DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION

DATA COLLECTION CAMPAIGNSITE #3 (Ville-Marie [downtown] borough)

CONTEXT


I. Site Selection

II. Equipment

III. Protocol

DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION


CONTEXT


I. Site Selection

II. Equipment

III. Protocol

DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION

DATA COLLECTION CAMPAIGNSITE #5 (Plateau-Mont-Royal borough)

CONTEXT


I. Site Selection

II. Equipment

III. Protocol

DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION


CONTEXT


I. Site Selection

II. Equipment

III. Protocol

DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION


CONTEXT


I. Site Selection

II. Equipment

III. Protocol

DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION

DATA COLLECTION CAMPAIGNSITE #8 (Southwest borough)

DATA COLLECTION CAMPAIGNII. Equipment

Condensation Particle Counter (CPC)

GoPro Video Camera Recorder

CONTEXT


I. Site Selection

II. Equipment

III. Protocol

DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION

DATA COLLECTION CAMPAIGNIII. Protocol 8 study sites (4 visits to each site;

each visit unique in type of day and time of day)

2 hours of data collection per visit Data Collected UFP number concentrations Traffic counts Meteorological Data (from weather

stations)Equipment Position Approximately 1 m above ground Near-roadway, in proximity to patio

CONTEXT


I. Site Selection

II. Equipment

III. Protocol

DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION

DATA COLLECTION CAMPAIGNIII. ProtocolCAMPAIGN STIPULATIONS No site visited twice on the same collection day. No site visited twice during the same day of a collection

week. No measurements conducted on Fridays. Discard data in the event of inclement weather.

CAMPAIGN DURATION

20 days (10 weekdays + 10 weekends) spanning 8 weeks

CONTEXT


I. Site Selection

II. Equipment

III. Protocol

DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION

DATA PROCESSINGUFP DATA & TRAFFIC DATAData entries for each visit divided into 15-minute intervals (8 entries/visit)

* 15-minute averages for logged per-minute UFP data* Manual counts of traffic for matching 15-minute intervals

CONTEXT


DATA PROCESSING

I. UFP & Traffic

II. MeteoIII. Land Use


RESULTS

DISCUSSION

CONCLUSION

DATA PROCESSINGMETEOROLOGICAL DATA- Meteorological data from 2 fixed monitoring stations: Trudeau International Airport and MacTavish Automated Weather Station

Temperaturer = 0.959

Relative Humidityr = 0.901

Wind Speed r = 0.731

CONTEXT


DATA PROCESSING

I. UFP & Traffic



RESULTS

DISCUSSION

CONCLUSION

DATA PROCESSINGMETEOROLOGICAL DATA

FMS Temperature RH Wind SpeedMacTavish -.212 -.134 .119

Dorval (Airport) -.205 -.237 .012

Data Comparison between FMS (Pearson Correlations with ln(UFP))

Dorval (Airport) meteorological data retained for analysis.

Orthogonality Index = sin (θw – θs)

where θw represents the angle at which the wind intersects with the street

θs represents the angle of the street relative to true north(in clockwise direction)

CONTEXT


DATA PROCESSING

I. UFP & Traffic



RESULTS

DISCUSSION

CONCLUSION

DATA PROCESSINGLAND USE DATA - GIS processing:Land use types (100m buffers)Road infrastructureVegetation index Pollution levels--------------------------Land use (entropy) index where a value of 1 indicates complete land use homogeneity

CONTEXT


DATA PROCESSING

I. UFP & Traffic



RESULTS

DISCUSSION

CONCLUSION

STATISTICAL ANALYSIS : METHODOLOGY Linear Mixed Model with Random Intercept

Dependent Variable: Natural logarithm of mean UFP concentrations

Independent Variables:Variable selection based on univariate analysisAvoid collinearity between variablesAdd/create variables to decrease Akaike’s Information Criteria (AIC)

CONTEXT


DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION

RESULTSDESCRIPTIVE STATISTICS

Variable Units Mean Std. Dev Min Max

UFP #/cm3 37946.98 15482.48 8944.5 91694.1

lnUFP - 10.46 0.42 9.1 11.4

CONTEXT


DATA PROCESSING


RESULTSI. Descriptive

StatisticsII. Bivariate

Analyses III. Modelling

ResultsIV. Summary

DISCUSSION

CONCLUSION

RESULTSUFP Concentration by LocationCONTEXT


DATA PROCESSING





ResultsIV. Summary

DISCUSSION

CONCLUSION

RESULTSBIVARIATE ANALYSIS

Temperature vs Mean UFP Concentration

Pearson Correlation: -0.175, p = 0.005

CONTEXT


DATA PROCESSING





ResultsIV. Summary

DISCUSSION

CONCLUSION


Relative Humidity vs Mean UFP Concentration


CONTEXT


DATA PROCESSING





ResultsIV. Summary

DISCUSSION

CONCLUSION


Commercial Zoning vs Mean UFP Concentration

Pearson Correlation: 0.235, p = < 0.0005

CONTEXT


DATA PROCESSING





ResultsIV. Summary

DISCUSSION

CONCLUSION


Entropy Index vs Mean UFP Concentration

Pearson Correlation: 0.197, p = 0.002

CONTEXT


DATA PROCESSING





ResultsIV. Summary

DISCUSSION

CONCLUSION


Weekday vs Mean UFP Concentration


Dummy variable: (1) Indicates a weekday

CONTEXT


DATA PROCESSING





ResultsIV. Summary

DISCUSSION

CONCLUSION


Evening Hour vs Mean UFP Concentration

Pearson Correlation: -.140, p = 0.025

Dummy variable: (1) Indicates evening measurements

CONTEXT


DATA PROCESSING





ResultsIV. Summary

DISCUSSION

CONCLUSION

RESULTSLINEAR MIXED MODEL FOR LN(UFP) (AIC = 182.07)

Parameter Units Estimate SE T Sig. 95% CI

Intercept - 11.221 0.231 48.543 <0.0005 10.758 11.683

Weekday = (0) (dummy) 0.209 0.043 4.86 <0.0005 0.124 0.294

Weekday = (1) (dummy) 0 0

EveningHr = (0) (dummy) 0.134 0.046 2.932 0.004 0.044 0.224

EveningHr = (1) (dummy) 0 0

Temperature_Dorval °C -0.037 0.007 -5.370 <0.0005 -0.056 -0.025

RelHum_Dorval % -0.009 0.002 -4.056 <0.0005 -0.014 -0.005

OrthogonalIndex_Dorval - 0.386 0.074 5.202 <0.0005 0.240 0.533

WindSpd_Dorval km/h -0.011 0.005 -2.199 0.059 -0.023 0.001

Entropy - 0.190 0.153 1.237 0.267 -0.196 0.576

Estimates of Covariance ParametersParameter Estimate S.E.Residual 0.102 0.009

Intercept + <0.0005 0.0008WindSpd_Dorval <0.0005 <0.0005

CONTEXT


DATA PROCESSING





ResultsIV. Summary

DISCUSSION

CONCLUSION

CONTRIBUTION TO KNOWLEDGE Highest levels of UFP measured during daytime periods on

the weekend.

Meteorological variables hold inverse relationships with UFP concentrations.

Orthogonal winds favour increased number concentrations.

Traffic variables affected UFP negligibly.

Variables of particular interest, commercial zoning and number of restaurants, hold positive associations with [UFP].

CONTEXT


DATA PROCESSING





ResultsIV. Summary

DISCUSSION

CONCLUSION

DISCUSSIONAccounting for above average UFP concentrations… Consider effect of smoking and restaurant activity on

pollutant levels (neither were included in the study)

Urban heat island effect

Early May start to campaign measurements conducted in lower temperatures

CONTEXT


DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION

DISCUSSIONExplaining counterintuitive traffic results… Must consider background concentrations from nearby

roads

Possible effect of traffic captured within temporal predictors retained in the final model

Potential counting errors

CONTEXT


DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION

CONCLUSIONFuture Work Additional study sites

On-site meteorological data collection

Need to assess more ‘patio’-centric variables (location of exhaust fan, smoking policies)

Comparison with similar studies conducted in other cities

Before-and-after study to evaluate the impact of initiatives aimed to reduce near-road concentrations (i.e. pedestrianization schemes)

CONTEXT


DATA PROCESSING


RESULTS

DISCUSSION

CONCLUSION

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