Comparison Of Onsite And Nws Meteorology Data Sets Based On Varying Nearby La...BREEZE Software
A comparison of meteorological parameters influencing AERMOD-predicted concentrations between a meteorological dataset using only NWS data and one incorporating onsite wind speed and direction data is presented in this paper.
Delineation of Mahanadi River Basin by Using GIS and ArcSWATinventionjournals
Precipitation is the significant segment of hydrologic cycle and this essential wellspring of overflow. In hydrological models precipitation as information, release is mimicked at the outlet of a watershed. Exactness of release re-enactment relies on drainage zone of the watershed. Therefore in the present work Mahanadi river basin lying within Odisha (drainage area approximately 65000 sq. km.) has been delineated in to five subbasins based on the five CWC operated discharge sites in Odisha. In the present work Arc-Swat has been used to delineate the watershed with the help of the (digital elevation model) DEM. At last as indicated by area of release locales, the aggregate study range was isolated into five sub-basins in particular Kesinga, Kantamal, Salebhata, Sundergarh and Tikarpada. It was observed that number of sub-watersheds into which the study area is being depicted relies on number of outlets and density of drainage. For a specific number of outlets, the thick is the density of drainage the more is the quantity of sub-watershed and the other way around.
Sensitivity of AERMOD to Meteorological Data Sets Based on Varying Surface Ro...BREEZE Software
The purpose of this study was to demonstrate the sensitivity of the AERMOD3 Model in modeling identical sources with meteorological data sets derived using both airport and industrial site land use characteristics.
Comparison Of Onsite And Nws Meteorology Data Sets Based On Varying Nearby La...BREEZE Software
A comparison of meteorological parameters influencing AERMOD-predicted concentrations between a meteorological dataset using only NWS data and one incorporating onsite wind speed and direction data is presented in this paper.
Delineation of Mahanadi River Basin by Using GIS and ArcSWATinventionjournals
Precipitation is the significant segment of hydrologic cycle and this essential wellspring of overflow. In hydrological models precipitation as information, release is mimicked at the outlet of a watershed. Exactness of release re-enactment relies on drainage zone of the watershed. Therefore in the present work Mahanadi river basin lying within Odisha (drainage area approximately 65000 sq. km.) has been delineated in to five subbasins based on the five CWC operated discharge sites in Odisha. In the present work Arc-Swat has been used to delineate the watershed with the help of the (digital elevation model) DEM. At last as indicated by area of release locales, the aggregate study range was isolated into five sub-basins in particular Kesinga, Kantamal, Salebhata, Sundergarh and Tikarpada. It was observed that number of sub-watersheds into which the study area is being depicted relies on number of outlets and density of drainage. For a specific number of outlets, the thick is the density of drainage the more is the quantity of sub-watershed and the other way around.
Sensitivity of AERMOD to Meteorological Data Sets Based on Varying Surface Ro...BREEZE Software
The purpose of this study was to demonstrate the sensitivity of the AERMOD3 Model in modeling identical sources with meteorological data sets derived using both airport and industrial site land use characteristics.
การนำเสนอบทความวิชาการในการประชุมวิชาการ 15th GMSARN International Conference 2020 on “Sustainable Energy, Environment and Climate Change Transitions in GMS” 21-22 December 2020, Krungsri River Hotel, Phra Nakhon Si Ayutthaya, Thailand. ในรูปแบบออนไลน์
หัวข้อ Integration of Future Meteorological Drought Hazard Assessment for Agriculture Area in Upper Ping River Basin, Thailand
This paper compares AERMOD with CALINE3-based models (CALINE4) and RLINE (Snyder and Heist, 2013) using a field study conducted in downtown Los Angeles in 2008. The evaluation supports the proposed replacement when AERMOD is executed with onsite meteorological data.
The PuffR R Package for Conducting Air Quality Dispersion AnalysesRichard Iannone
PuffR is all about helping you conduct dispersion modelling using the CALPUFF modelling system. It is a software package currently being developed using the R statistical programming language. Dispersion modelling is a great tool for understanding how pollutants disperse from sources to receptors, and, how these dispersed pollutants affect populations’ exposure. The presentation goes over basic concepts in air dispersion modelling using CALPUFF, the goals of the project are outlined, the PuffR workflow is described, and a project roadmap is provided.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Numerical tools dedicated to wind engineering MeteodynStephane Meteodyn
This paper presents a global methodology to compute wind flow in complex urban areas in order to assess wind pedestrian comfort, wind energy, wind safety or natural
ventilation potential. The numerical tool presented here is composed of a CFD software suite covering both regional scale (20 km) and urban scale (1km), and able to model the wind in any complex terrains and in large urban environments. Examples are presented in the paper in order to show the advantages of the methodology for urban designers...
Amy Stidworthy - Optimising local air quality models with sensor data - DMUG17IES / IAQM
An unapologetically technical conference, DMUG remains the key annual event for experts in this field. Unmissable speakers will be examining topical issues in emissions, exposure and dispersion modelling.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Attempt To Use Interpolation to Predict Rainfall Intensities tor Crash Ana...IJMERJOURNAL
ABSTRACT: This study uses different interpolation techniques to predict rainfall intensity at locationsthat are not directly located near a rainfall gauges. The goal of being able to interpolate the rainfall intensity is to study its impact on traffic crashes. To perform the study, a collection of rainfall gauges in Alabama were used as subject locations where rainfall intensity was predicted from surrounding gauges, while also providing validation data to compare the predictions. Essentially, the actual rainfall intensities at existing gauges were interpolated using nearby gauges and the results were analyzed.The interpolation techniques used in the study included proximal, averaging and a distance weighted average. The results of the study indicated that none of the interpolation methodologies were sufficient to accurately predict the rainfall intensity values any significant distance from the actual gauges.
Determination of homogenous regions in the Tensift basin (Morocco).IJERA Editor
The aim of this study is to determine homogenous region in the Tensift basin within which the hydrological behavior is similar. In order to do this we used two methods: The Principal components analysis on the monthly precipitation registered at the 23 rainfall stations. This resulted in setting apart 4 groups of stations. The second method is analysis of land use map, geological map, pedagogical map, vegetation map and slope map of the studied area. This method allowed us to delineate 4 homogenous areas. The two methods yielded complementary results and the superposition of groups and regions obtained allowed us to retain 4 homogenous regions corresponding to 3 groups of stations.
El 29 de febrero y el 1 de marzo de 2016, la Fundación Ramón Areces analizó la relación entre 'Big Data y el cambio climático' en unas jornadas. ¿Puede el Big Data ayudar a reducir el cambio climático? ¿Cómo contribuirá ese análisis masivo de datos a prevenir y gestionar catástrofes naturales? Son solo algunas de las preguntas a las que intentarán responder los ponentes. Las ciencias vinculadas al clima tienen en el Big Data una herramienta muy prometedora para afrontar diferentes fenómenos asociados al cambio climático.
การนำเสนอบทความวิชาการในการประชุมวิชาการ 15th GMSARN International Conference 2020 on “Sustainable Energy, Environment and Climate Change Transitions in GMS” 21-22 December 2020, Krungsri River Hotel, Phra Nakhon Si Ayutthaya, Thailand. ในรูปแบบออนไลน์
หัวข้อ Integration of Future Meteorological Drought Hazard Assessment for Agriculture Area in Upper Ping River Basin, Thailand
This paper compares AERMOD with CALINE3-based models (CALINE4) and RLINE (Snyder and Heist, 2013) using a field study conducted in downtown Los Angeles in 2008. The evaluation supports the proposed replacement when AERMOD is executed with onsite meteorological data.
The PuffR R Package for Conducting Air Quality Dispersion AnalysesRichard Iannone
PuffR is all about helping you conduct dispersion modelling using the CALPUFF modelling system. It is a software package currently being developed using the R statistical programming language. Dispersion modelling is a great tool for understanding how pollutants disperse from sources to receptors, and, how these dispersed pollutants affect populations’ exposure. The presentation goes over basic concepts in air dispersion modelling using CALPUFF, the goals of the project are outlined, the PuffR workflow is described, and a project roadmap is provided.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Numerical tools dedicated to wind engineering MeteodynStephane Meteodyn
This paper presents a global methodology to compute wind flow in complex urban areas in order to assess wind pedestrian comfort, wind energy, wind safety or natural
ventilation potential. The numerical tool presented here is composed of a CFD software suite covering both regional scale (20 km) and urban scale (1km), and able to model the wind in any complex terrains and in large urban environments. Examples are presented in the paper in order to show the advantages of the methodology for urban designers...
Amy Stidworthy - Optimising local air quality models with sensor data - DMUG17IES / IAQM
An unapologetically technical conference, DMUG remains the key annual event for experts in this field. Unmissable speakers will be examining topical issues in emissions, exposure and dispersion modelling.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Attempt To Use Interpolation to Predict Rainfall Intensities tor Crash Ana...IJMERJOURNAL
ABSTRACT: This study uses different interpolation techniques to predict rainfall intensity at locationsthat are not directly located near a rainfall gauges. The goal of being able to interpolate the rainfall intensity is to study its impact on traffic crashes. To perform the study, a collection of rainfall gauges in Alabama were used as subject locations where rainfall intensity was predicted from surrounding gauges, while also providing validation data to compare the predictions. Essentially, the actual rainfall intensities at existing gauges were interpolated using nearby gauges and the results were analyzed.The interpolation techniques used in the study included proximal, averaging and a distance weighted average. The results of the study indicated that none of the interpolation methodologies were sufficient to accurately predict the rainfall intensity values any significant distance from the actual gauges.
Determination of homogenous regions in the Tensift basin (Morocco).IJERA Editor
The aim of this study is to determine homogenous region in the Tensift basin within which the hydrological behavior is similar. In order to do this we used two methods: The Principal components analysis on the monthly precipitation registered at the 23 rainfall stations. This resulted in setting apart 4 groups of stations. The second method is analysis of land use map, geological map, pedagogical map, vegetation map and slope map of the studied area. This method allowed us to delineate 4 homogenous areas. The two methods yielded complementary results and the superposition of groups and regions obtained allowed us to retain 4 homogenous regions corresponding to 3 groups of stations.
El 29 de febrero y el 1 de marzo de 2016, la Fundación Ramón Areces analizó la relación entre 'Big Data y el cambio climático' en unas jornadas. ¿Puede el Big Data ayudar a reducir el cambio climático? ¿Cómo contribuirá ese análisis masivo de datos a prevenir y gestionar catástrofes naturales? Son solo algunas de las preguntas a las que intentarán responder los ponentes. Las ciencias vinculadas al clima tienen en el Big Data una herramienta muy prometedora para afrontar diferentes fenómenos asociados al cambio climático.
Example to help in PPT preparation for dissertation defense in medicine. This thesis was in PCOS and infertility management. Fresh versus frozen thawed ET in PCOS
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Thesis defense presentation of Justin Phillips (SDSU). "The Role of Relatedness and Autonomy in Motivation of Youth Physical Activity: A Self-Determination Perspective."
Practical guidance on how to present data using PowerPoint. This presentation covers best practices taught in management consultancies and visual cognition. Based on a lecture given at Tsinghua University, Beijing in December 2011.
If you have feedback or suggestions (especially specific examples of great or terrible slides you think could be included in a future version), please email professionalenquiries@gmail.com or leave comments below.
Landfill Compliance Monitoring: Achieving Long Term EfficiencyHydroTerra Pty Ltd
Richard Campbell presentation from the 2017 Institute of Public Works Engineering Australasia (IPWEA) leadership workshop. Richard covers the changing face of landfill environmental compliance reporitng through automated monitoring technology.
Gathering of air pollution data in real time and
storing them in a database for further use
using them for real time alerting system
would be the key step in developing an Air Quality Management (AQM) system
Easy to create a bouquet of services will be a primary need of specific areas management agencies and their funding bodies (Municipalities, Regional and Central Government)
1. Influential Parameters on
Ultrafine Particles in Proximity to
Open Air Restaurant Patios
Alex Lee
Supervisor: Dr. Marianne Hatzopoulou
Department of Civil Engineering and Applied Mechanics
Fall 2015
2. 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
4. TABLE OF CONTENTS
I. CONTEXT
I. Near-Road Air Pollution & Health
II. Ultrafine Particles
III. Measurement Methodology
IV. Statistical Methodology
II. DATA COLLECTION CAMPAIGN
I. Site Selection
II. Equipment
III. Protocol
III. DATA PROCESSING
I. UFP & Traffic Data
II. Meteorological Data
III. Land Use Data
IV. STATISTICAL ANALYSIS :
METHODOLOGY
V. RESULTS
I. Descriptive Statistics
II. Bivariate Analyses
III. Modelling Results
IV. Summary
VI. DISCUSSION
VII. CONCLUSION
5. 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
CONTEXT
I. Air
Pollution &
Health
II. UFP
III. Measuring
IV. LMM
DATA COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
6. 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
CONTEXT
I. Air
Pollution &
Health
II. UFP
III. Measuring
IV. LMM
DATA COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
7. CONTEXT
I. Air
Pollution &
Health
II. UFP
III. Measuring
IV. LMM
DATA COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
8. CONTEXT: Measurement Methodology
Condensation Particle Counter
Measures particles ranging from 0.01 µm
to >1.0 µm
User friendliness and convenience
Programmable data logging
CONTEXT
I. Air
Pollution &
Health
II. UFP
III. Measuring
IV. LMM
DATA COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
9. CONTEXT
I. Air
Pollution &
Health
II. UFP
III. Measuring
IV. LMM
DATA COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
10. 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
CONTEXT
I. Air
Pollution &
Health
II. UFP
III. Measuring
IV. LMM
DATA COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
11. CONTEXT: Statistical Methodology
Why Linear Mixed-Effects Models?
Study of repeated measures (within-subject correlated data)
Allows for more accurate interpretations of relationships
CONTEXT
I. Air
Pollution &
Health
II. UFP
III. Measuring
IV. LMM
DATA COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
12. DATA COLLECTION CAMPAIGN
I. 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
DATA COLLECTION
CAMPAIGN
I. Site
Selection
II. Equipment
III. Protocol
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
14. CONTEXT
DATA COLLECTION
CAMPAIGN
I. Site
Selection
II. Equipment
III. Protocol
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
SITE #1 (Plateau-Mont-Royal borough)
DATA COLLECTION CAMPAIGN
16. CONTEXT
DATA COLLECTION
CAMPAIGN
I. Site
Selection
II. Equipment
III. Protocol
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
DATA COLLECTION CAMPAIGN
SITE #3 (Ville-Marie [downtown] borough)
17. CONTEXT
DATA COLLECTION
CAMPAIGN
I. Site
Selection
II. Equipment
III. Protocol
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
DATA COLLECTION CAMPAIGN
SITE #4 (Ville-Marie [downtown] borough)
18. CONTEXT
DATA COLLECTION
CAMPAIGN
I. Site
Selection
II. Equipment
III. Protocol
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
DATA COLLECTION CAMPAIGN
SITE #5 (Plateau-Mont-Royal borough)
19. CONTEXT
DATA COLLECTION
CAMPAIGN
I. Site
Selection
II. Equipment
III. Protocol
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
DATA COLLECTION CAMPAIGN
SITE #6 (Ville-Marie [downtown] borough)
20. CONTEXT
DATA COLLECTION
CAMPAIGN
I. Site
Selection
II. Equipment
III. Protocol
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
DATA COLLECTION CAMPAIGN
SITE #7 (Ville-Marie [downtown] borough)
22. DATA COLLECTION CAMPAIGN
II. Equipment
Condensation
Particle Counter (CPC)
GoPro Video
Camera Recorder
CONTEXT
DATA COLLECTION
CAMPAIGN
I. Site
Selection
II. Equipment
III. Protocol
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
23. DATA COLLECTION CAMPAIGN
III. 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
DATA COLLECTION
CAMPAIGN
I. Site
Selection
II. Equipment
III. Protocol
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
24. DATA COLLECTION CAMPAIGN
III. Protocol
CAMPAIGN 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
DATA COLLECTION
CAMPAIGN
I. Site
Selection
II. Equipment
III. Protocol
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
25. DATA PROCESSING
UFP DATA & TRAFFIC DATA
Data 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 COLLECTION
CAMPAIGN
DATA PROCESSING
I. UFP &
Traffic
II. Meteo
III. Land Use
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
26. DATA PROCESSING
METEOROLOGICAL DATA
- Meteorological data from 2 fixed monitoring stations: Trudeau International
Airport and MacTavish Automated Weather Station
Temperature
r = 0.959
Relative Humidity
r = 0.901
Wind Speed
r = 0.731
CONTEXT
DATA COLLECTION
CAMPAIGN
DATA PROCESSING
I. UFP &
Traffic
II. Meteo
III. Land Use
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
27. DATA PROCESSING
METEOROLOGICAL DATA
FMS Temperature RH Wind Speed
MacTavish -.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 COLLECTION
CAMPAIGN
DATA PROCESSING
I. UFP &
Traffic
II. Meteo
III. Land Use
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
28. DATA PROCESSING
LAND USE DATA
- GIS processing:
Land use types
(100m buffers)
Road infrastructure
Vegetation index
Pollution levels
--------------------------
Land use
(entropy) index
where a value of 1
indicates complete land
use homogeneity
CONTEXT
DATA COLLECTION
CAMPAIGN
DATA PROCESSING
I. UFP &
Traffic
II. Meteo
III. Land Use
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
29. STATISTICAL ANALYSIS : METHODOLOGY
Linear Mixed Model with Random Intercept
Dependent Variable:
Natural logarithm of mean UFP concentrations
Independent Variables:
Variable selection based on univariate analysis
Avoid collinearity between variables
Add/create variables to decrease Akaike’s Information Criteria
(AIC)
CONTEXT
DATA COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
30. RESULTS
DESCRIPTIVE 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 COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
I. Descriptive
Statistics
II. Bivariate
Analyses
III. Modelling
Results
IV. Summary
DISCUSSION
CONCLUSION
31. RESULTS
UFP Concentration by LocationCONTEXT
DATA COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
I. Descriptive
Statistics
II. Bivariate
Analyses
III. Modelling
Results
IV. Summary
DISCUSSION
CONCLUSION
32. RESULTS
BIVARIATE ANALYSIS
Temperature vs Mean
UFP Concentration
Pearson Correlation:
-0.175, p = 0.005
CONTEXT
DATA COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
I. Descriptive
Statistics
II. Bivariate
Analyses
III. Modelling
Results
IV. Summary
DISCUSSION
CONCLUSION
33. RESULTS
BIVARIATE ANALYSIS
Relative Humidity vs
Mean UFP Concentration
Pearson Correlation:
-0.212, p = 0.001
CONTEXT
DATA COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
I. Descriptive
Statistics
II. Bivariate
Analyses
III. Modelling
Results
IV. Summary
DISCUSSION
CONCLUSION
34. RESULTS
BIVARIATE ANALYSIS
Commercial Zoning vs
Mean UFP Concentration
Pearson Correlation:
0.235, p = < 0.0005
CONTEXT
DATA COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
I. Descriptive
Statistics
II. Bivariate
Analyses
III. Modelling
Results
IV. Summary
DISCUSSION
CONCLUSION
35. RESULTS
BIVARIATE ANALYSIS
Entropy Index vs Mean
UFP Concentration
Pearson Correlation:
0.197, p = 0.002
CONTEXT
DATA COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
I. Descriptive
Statistics
II. Bivariate
Analyses
III. Modelling
Results
IV. Summary
DISCUSSION
CONCLUSION
36. RESULTS
BIVARIATE ANALYSIS
Weekday vs Mean UFP
Concentration
Pearson Correlation:
-0.199, p = 0.001
Dummy variable:
(1) Indicates a weekday
CONTEXT
DATA COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
I. Descriptive
Statistics
II. Bivariate
Analyses
III. Modelling
Results
IV. Summary
DISCUSSION
CONCLUSION
37. RESULTS
BIVARIATE ANALYSIS
Evening Hour vs Mean
UFP Concentration
Pearson Correlation:
-.140, p = 0.025
Dummy variable:
(1) Indicates evening
measurements
CONTEXT
DATA COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
I. Descriptive
Statistics
II. Bivariate
Analyses
III. Modelling
Results
IV. Summary
DISCUSSION
CONCLUSION
38. RESULTS
LINEAR 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 Parameters
Parameter Estimate S.E.
Residual 0.102 0.009
Intercept + <0.0005 0.0008
WindSpd_Dorval <0.0005 <0.0005
CONTEXT
DATA COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
I. Descriptive
Statistics
II. Bivariate
Analyses
III. Modelling
Results
IV. Summary
DISCUSSION
CONCLUSION
39. 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 COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
I. Descriptive
Statistics
II. Bivariate
Analyses
III. Modelling
Results
IV. Summary
DISCUSSION
CONCLUSION
40. DISCUSSION
Accounting 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 COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
41. DISCUSSION
Explaining 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 COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
42. CONCLUSION
Future 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 COLLECTION
CAMPAIGN
DATA PROCESSING
STATISTICAL
ANALYSIS :
METHODOLOGY
RESULTS
DISCUSSION
CONCLUSION
While many studies have been done exploring UFPs, very few have focused on UFPs in mixed use environments
Influential factors relating to meteorology, to built environment, and to traffic
Higher than average concentrations : most of the sites located in the downtown core
Define what “average” means use chart on Slide 9
Interdisciplinary nature of the study
Popularity of spaces
Contribution of spaces to city’s cultural identity
The by-product of what is generally accepted as good urban design (when you consider how mixed use environments reduce origin-destination trip length – mixed used neighbourhoods housing employment centres close to retail/commercial spaces) *In many instances, cities identify these growth corridors and strategically invest in transit along these corridors
Important in how this study relates back to our field of transportation engineering
Discuss the pie chart. (from HEI Health Panel)
Mobile sources accounting for over half of overall UFP concentrations (identified using mass emissions data for Los Angeles, 1996)
Residential and commercial heating: this is especially important during the winter months (likewise, cooling during the summer months)
So what happens, for example, in the winter, is that the Earth’s surface receives less warmth from the Sun and warmer air aloft acts as a lid and presses down on the column of colder air closer to the ground, trapping pollutants coming from homes and businesses
Trapping of pollutants on street-level due to configuration of road (i.e. canyon effects)
Increased risk of health effects: symptoms exacerbated among individuals with respiratory illnesses, but also among healthy individuals
Discuss process from nucleation of UFP emission brief lifespan of UFP (a couple of seconds to a couple days) options to either coagulate to form larger particles, gas condensing onto particles, evaporation, or deposition
Generation:
UFPs are formed in the engine during the combustion process as well as during the journey of the exhaust as it moves through the exhaust line and toward the tailpipe, when it is finally released into the atmosphere as nanoparticles
The gradual cooling of the exhaust causes organic compounds and ions to nucleate, forming new particles in the nanometer range as a result
Once these nanoparticles are emitted, several scenarios can occur:
They may coagulate and form larger particles
They may also grow larger in size as new material condenses onto its surface
They may evaporate completely
This partially explains why UFP concentrations are believed to be difficult to capture
So this graph explains the formation of nanoparticles
As you can see, the majority of UFPs are formed during the process where exhaust begins to cool (this stage known here as the nuclei mode), but they contribute almost nothing to the overall mass
This is why many researchers opt to measure PNC as opposed to PMC
Despite the fact that many larger sized particles are measured based on PMC, the previous slide illustrates why it is difficult to measure UFP PMC
These machines are extremely portable and are able to be transported from site to site
This graph is a compilation of UFP studies conducted based on the type of measurement location showing the mean and median of their UFP results
Here we have the median UFP count measured inside a tunnel at 99 000 particles and the median UFP count measured at clean backgrounds at 3 000 particles
As you can see, the greater the air circulation in a particular environment, the lesser the mean/median UFP counts
Focus your attention on the third couple of columns (road-side) median of 34 000 – 35 000 particles
Random effects: Influence concentrations differently at different locations
Difference with a general linear model is that mixed models consider potential correlations between observed measurements
Hierarchy of multiple levels upper level indicates subjects, lower level indicates measurements within subjects
AIC: based on the rule of ‘smaller value is better’
We are able to interpret clearer relationships in the second graph if there exists correlation between the measurements
Very walkable spaces
Lunch and dinner hour
Consistency of results
Whiskers overlap with each other, suggesting that on any given day, we may be able to observe UFP concentrations that are the same across all locations, and this may partially explain some of the final results
Simply looking at general trends
Concentrations are higher and more variable during the weekend
Concentrations are higher during the daytime (which is what we expect as we expect more foot traffic during the daytime)
So we have retained temperature, RH, OI, and wind speed as meteorological predictors, entropy as a land use predictor, and type and time of day as temporal predictors
EXPLAIN WHY WIND SPEED AND ENTROPY ARE INCLUDED IN THE MODEL EVEN THOUGH THEY ARE NOT STATISTICALLY SIGNIFICANT.
Wind Speed: absence of on-site meteorological data collection may play a factor in variable not being statistically significant, and wind speed when added as a random effect lowers the AIC
Entropy: small sample size of 8 sites, 8 sites displaying similar land use compositions (at least based on buffers created)
Possibly more personal freedom allowed during the weekend, leading to possible increased trip generation and attraction to these spaces; greater daytime concentrations may suggest that measurements were conducted during episodes of lower temperatures or in the event of perpendicular winds
Meteorology: these relationships were hypothesized and confirmed
Another hypothesis confirmed is the increase of PNC in the event of orthogonal winds
A key finding is the negligible effect of traffic variables on UFP (given what we know about the generation of UFP in vehicle engines and through exhaust lines, this was surprising)
Finally, another key finding is that commercial zoning as well as the increased land use homogeneity of a certain area increases UFP, and this pertains to this study of looking at mixed use neighbourhoods and patio environments
So how could we possible account for above average UFP concentrations at the selected sites? (Some sites exhibited concentrations as high as 70 000 and 80 000)
Effects of nearby smoking and cooking activities were not captured in this study, and both are known to play a role in increasing concentrations
Possible urban heat island effects relates to the nature of building materials such as concrete which absorbs more warmth from the Sun
As a result, this effect raises demand for electrical energy, which puts more demand on power plants, which in turn emit more pollutants
Early start to the campaign meant measuring in lower temperatures (lower temperatures associated with higher concentrations)
DOES THIS POINT CONTRADICT THE FORMER POINT?
A balance between the lower temperatures and the amount of energy consumed is achieved?
Background streets and roads may possibly contribute to observed measurements (these background sources were not captured in our study)
Time of day and type of day inherently linked with traffic
Potential counting errors that may arise as a result of manual counting
Future improvements of this study may include:
-Additional study sites: In the end, it was difficult to achieve an optimal degree of land use variability between study sites (site surroundings fairly similar among a number of the sites, which does make sense because nobody wants to be eating on a patio located beside an industrial park…but they may exist)
-More patio-related variables: given the importance of cooking fumes and smoking as urban sources of UFP, future studies can propose methodologies to capture these effects as well
-Comparison with other similar studies from other cities: Analyzing other cities where patios are popular and prevalent (particularly European cities, which share some of the ‘cultural flavours’ of Montreal and also because they differ from many NA cities in terms of their land use composition)
-Before and after studies: looking to evaluate the effectiveness of initiatives aimed at reducing near-road concentrations such as pedestrianizing roads during the weekday daytime periods (as we have found in this study as observing the highest UFP concentrations) , policies such as requiring restaurants to implement better exhaust fan technologies or implementing smoking bans
See if time allows to discuss implications of results
