Science, Faculty of
Earth, Ocean and Atmospheric Sciences, Department of
DSpace
UBCV
Akaoka, Kevin J.
Master of Science - MSc
University of British Columbia
The transport of coal by train through residential neighbourhoods in Metro Vancouver, British Columbia, Canada is a growing concern for many residents living near the railway. This study aimed to identify and quantify any potential particulate matter (PM) increase caused by the presence of rail traffic adjacent to John Oliver Park in Delta, BC. Field work was carried out during August and September 2014, using a GRIMM optical particle counter that measured PM concentration at various size ranges. A select number of passing trains were confirmed visually, while the majority of passages were identified with audio data recorded by a microphone. A horizontally operating mini-micropulse lidar system was also set up at the park on three individual days to make intensive backscatter measurements. Wind data were recorded by collocated instruments maintained by Metro Vancouver. Finally, the Corporation of Delta had a dustfall measurement campaign during the same time period. Trains carrying coal are associated with a 5.28, 4.11, and 2.55 µg/m³ average increase in concentration over a 15 minute period, compared to control conditions for PM₃, PM₁₀, and PM₂₀, respectively. These increases are all statistically significant at α=0.01. PM concentrations during train passages of all types were not found to be significantly different from PM concentrations during control conditions. The presence of coal dust particles at the site was confirmed by the dustfall measurements carried out by the Corporation of Delta. Lidar backscatter imagery provided individual snapshots of train passages. However, it is clear that not every train passage causes an increase in PM concentration, and the effect appears to be highly dependent on wind direction and local meteorology.
Impact of Coal-Carrying Trains on Particulate MatterConcentrations in the Lower Mainland region of BritishColumbia, CanadabyKevin J. AkaokaB.Sc. (Hons.), The University of Western Ontario, 2012a thesis submitted in partial fulfillmentof the requirements for the degree ofMaster of ScienceinThe Faculty of Graduate and Postdoctoral Studies(Atmospheric Science)The University of British Columbia(Vancouver)October 2015© Kevin J. Akaoka, 2015AbstractThe transport of coal by train through residential neighbourhoods in Metro Vancou-ver, British Columbia, Canada is a growing concern for many residents living nearthe railway. This study aimed to identify and quantify any potential particulatematter (PM) increase caused by the presence of rail trac adjacent to John OliverPark in Delta, BC.Field work was carried out during August and September 2014, using a GRIMMoptical particle counter that measured PM concentration at various size ranges. Aselect number of passing trains were confirmed visually, while the majority of pas-sages were identified with audio data recorded by a microphone. A horizontallyoperating mini-micropulse lidar system was also set up at the park on three individ-ual days to make intensive backscatter measurements. Wind data were recorded bycollocated instruments maintained by Metro Vancouver. Finally, the Corporationof Delta had a dustfall measurement campaign during the same time period.Trains carrying coal are associated with a 5.28, 4.11, and 2.55 µg/m3 averageincrease in concentration over a 15 minute period, compared to control conditions forPM3, PM10, and PM20, respectively. These increases are all statistically significantat ↵ = 0.01. PM concentrations during train passages of all types were not foundto be significantly di↵erent from PM concentrations during control conditions. Thepresence of coal dust particles at the site was confirmed by the dustfall measurementscarried out by the Corporation of Delta.Lidar backscatter imagery provided individual snapshots of train passages. How-ever, it is clear that not every train passage causes an increase in PM concentration,and the e↵ect appears to be highly dependent on wind direction and local meteo-rology.iiPrefaceThis thesis consists of original, unpublished, and independent work completed bythe author. Input on research design and analysis was provided by Dr. Ian McK-endry. All wind data and some PM concentration data used in Chapters 3 and 4were provided by Metro Vancouver’s Air Quality group. The dustfall observationsin Section 3.4 were provided by the Corporation of Delta’s Climate Action & En-vironment department. The lidar backscatter images in Chapter 4 were created byPaul Cottle.iiiTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiiAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.1 Health E↵ects . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.2 Trains as a PM Source . . . . . . . . . . . . . . . . . . . . . . 21.2.3 Coal Dust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2.4 Zone of Influence of a Linear Source . . . . . . . . . . . . . . 51.3 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Study Area and Methodology . . . . . . . . . . . . . . . . . . . . . 72.1 Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.1.1 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.1.2 Instrumentation . . . . . . . . . . . . . . . . . . . . . . . . . 112.1.3 Data Validation . . . . . . . . . . . . . . . . . . . . . . . . . . 132.1.4 Train Passages . . . . . . . . . . . . . . . . . . . . . . . . . . 16iv3 Influence of Train Passages on PM Concentrations . . . . . . . . . 193.1 Background Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193.1.1 Diurnal Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . 193.1.2 Weekly Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . 213.1.3 Wind Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233.2 Train Trac . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263.3 Particulate Matter Concentration . . . . . . . . . . . . . . . . . . . . 283.3.1 E↵ect of Passing Trains (All Types) . . . . . . . . . . . . . . 283.3.2 E↵ect of Passing Trains with Wind Direction . . . . . . . . . 303.3.3 E↵ect of Passing Coal Trains . . . . . . . . . . . . . . . . . . 333.3.4 E↵ect of Passing Coal Trains with Wind Direction . . . . . . 433.4 Dustfall Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . 463.5 Spatial Decay of PM Signal . . . . . . . . . . . . . . . . . . . . . . . 483.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513.6.1 PM Enhancement Due to Trains (All Types) . . . . . . . . . 513.6.2 PM Enhancement Due to Coal Trains . . . . . . . . . . . . . 523.6.3 Zone of Influence . . . . . . . . . . . . . . . . . . . . . . . . . 534 Case Studies Using Mini-MPL Imagery . . . . . . . . . . . . . . . 554.1 Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554.1.1 August 14, 2014 . . . . . . . . . . . . . . . . . . . . . . . . . 554.1.2 August 26, 2014 . . . . . . . . . . . . . . . . . . . . . . . . . 614.1.3 September 4, 2014 . . . . . . . . . . . . . . . . . . . . . . . . 654.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 705 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 725.1 Key Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 725.1.1 Study Limitations & Future Research . . . . . . . . . . . . . 74Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76vList of TablesTable 2.1 Summary of data sources. . . . . . . . . . . . . . . . . . . . . . . 9Table 2.2 Description of periods of unreliable particle concentration datarecorded by the GRIMM OPC. . . . . . . . . . . . . . . . . . . . 14Table 2.3 Comparison of GRIMM and E-Sampler Measurements for PM10concentration. 1 minute averaging time. . . . . . . . . . . . . . . 18Table 3.1 Comparison of wind data distribution from 2014-08-12 to 2014-09-21 between daytime and nighttime. . . . . . . . . . . . . . . . . . 23Table 3.2 Basic statistics of PM concentration during train passages ver-sus control data, from 2014-08-12 to 2014-09-21. Combination ofGRIMM and E-Sampler data. . . . . . . . . . . . . . . . . . . . . 28Table 3.3 Welch t-test of di↵erences of means with log transformed data (p= passage, c = control). ↵ = 0.01, . . . . . . . . . . . . . . . . . 30Table 3.4 Basic statistics of PM concentration during train passages versuscontrol data, segmented by wind direction, from 2014-08-12 to2014-09-21. Combination of GRIMM and E-Sampler data. . . . . 31Table 3.5 Basic statistics of PM concentrations for stratified data, from 2014-08-12 to 2014-09-21. Combination of GRIMM and E-Sampler data. 37Table 3.6 Summary of parametric hypothesis testing for log-transformed PM3data. ↵ = 0.01, ↵0= 0.01/3 = 0.00333 . . . . . . . . . . . . . . . 41Table 3.7 Summary of parametric hypothesis testing for log-transformed PM10data. ↵ = 0.01, ↵0= 0.01/3 = 0.00333 . . . . . . . . . . . . . . . 41Table 3.8 Summary of parametric hypothesis testing for log-transformed PM20data. ↵ = 0.01, ↵0= 0.01/3 = 0.00333 . . . . . . . . . . . . . . . 42viTable 3.9 Basic statistics of PM10 concentration data, stratified by wind,from 2014-08-12 to 2014-09-21. Combination of GRIMM and E-Sampler data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44Table 3.10 Summary of parametric hypothesis testing for PM10 data, strati-fied by wind. ↵ = 0.01 . . . . . . . . . . . . . . . . . . . . . . . . 45Table 3.11 Summary of Games-Howell post-hoc analysis for PM10 data, strat-ified by wind. Subscripts: D = Downwind, U = Upwind. ↵0=0.01/15 = 0.00067 . . . . . . . . . . . . . . . . . . . . . . . . . . . 45Table 3.12 Corporation of Delta dustfall measurement results for 2014. . . . 47Table 3.13 Corporation of Delta dustfall size distribution data for 2014. . . . 47Table 3.14 Basic statistics of PM concentrations at two distances, stratifiedby train type. E-Sampler data. . . . . . . . . . . . . . . . . . . . . 50Table 4.1 Summary of PM concentration data during train passages on Au-gust 14, 2014, recorded by GRIMM OPC. . . . . . . . . . . . . . 60Table 4.2 Summary of PM concentration data during train passages on Septem-ber 4, 2014, recorded by GRIMM OPC. . . . . . . . . . . . . . . . 69viiList of FiguresFigure 2.1 Map of Lower Mainland region of British Columbia, with relevantlocations highlighted. Taken from Apple Maps Version 2.0. Mapdata from TomTom and others. Satellite data from DigitalGlobe. 8Figure 2.2 Map of John Oliver Park with instrument locations, taken fromApple Maps Version 2.0. Map data from TomTom and others.Satellite data from DigitalGlobe. . . . . . . . . . . . . . . . . . . 10Figure 2.3 Image of equipment set up location B. GRIMM OPC and MetroVancouver E-Sampler and wind instrumentation shown. . . . . . 12Figure 2.4 Image of mini-MPL set up at location C. . . . . . . . . . . . . . 13Figure 2.5 Comparison of UBC and MV data, 1 minute averaging time. . . 15Figure 2.6 Scatterplot of UBC and MV data, 1 minute averaging time. . . . 16Figure 2.7 Comparison of UBC and MV data, 30 minute averaging time. . . 16Figure 2.8 Scatterplot of UBC and MV data, 30 minute averaging time. . . 17Figure 2.9 Comparison of UBC and MV data, 30 minute averaging time,99th percentile cuto↵. . . . . . . . . . . . . . . . . . . . . . . . . 17Figure 3.1 Trac counts on Highway 99 near John Oliver Park for September2014, sorted by hour. . . . . . . . . . . . . . . . . . . . . . . . . . 20Figure 3.2 Mean PM concentrations recorded at John Oliver Park from 2014-08-14 to 2014-09-21, sorted by hour. Combination of GRIMM andE-Sampler data. . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Figure 3.3 Trac counts on Highway 99 near John Oliver Park for September2014, sorted by day. . . . . . . . . . . . . . . . . . . . . . . . . . 22Figure 3.4 Mean PM concentrations recorded at John Oliver Park from 2014-08-14 to 2014-09-21, sorted by day. Combination of GRIMM andE-Sampler data. . . . . . . . . . . . . . . . . . . . . . . . . . . . 22viiiFigure 3.5 Summary of wind data from 2014-08-12 to 2014-09-21 for daytimehours (07:00 - 21:00) at John Oliver Park. Recorded by MetroVancouver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24Figure 3.6 Summary of wind data from 2014-08-12 to 2014-09-21 for night-time hours (21:00 - 07:00) at John Oliver Park. Recorded byMetro Vancouver . . . . . . . . . . . . . . . . . . . . . . . . . . . 25Figure 3.7 Train passages recorded at John Oliver Park from 2014-08-12 to2014-09-21, sorted by hour. Combination of visual observationsand audio recordings. . . . . . . . . . . . . . . . . . . . . . . . . 26Figure 3.8 Train passages recorded at John Oliver Park from 2014-08-12 to2014-09-21, sorted by day. Combination of visual observationsand audio recordings. . . . . . . . . . . . . . . . . . . . . . . . . 27Figure 3.9 Comparison of PM concentration during train passages versuscontrol, from 2014-08-12 to 2014-09-21. Combination of GRIMMand E-Sampler data. . . . . . . . . . . . . . . . . . . . . . . . . . 29Figure 3.10 Comparison of PM concentration during train passage versus con-trol, stratified by wind direction, from 2014-08-12 to 2014-09-21.Combination of GRIMM and E-Sampler data. . . . . . . . . . . 32Figure 3.11 Boxplots of each PM3 group used in ANOVA testing. (D) de-notes downwind conditions, while (U) denotes upwind conditions.Combination of data from GRIMM OPC and E-Sampler. . . . . 34Figure 3.12 Boxplots of each PM10 group used in ANOVA testing. (D) de-notes downwind conditions, while (U) denotes upwind conditions.Combination of data from GRIMM OPC and E-Sampler. . . . . 35Figure 3.13 Boxplots of each PM20 group used in ANOVA testing. (D) de-notes downwind conditions, while (U) denotes upwind conditions.Combination of data from GRIMM OPC and E-Sampler. . . . . 36Figure 3.14 ANOVA diagnostic tests for PM3 data. . . . . . . . . . . . . . . 38Figure 3.15 ANOVA diagnostic tests for PM10 data. . . . . . . . . . . . . . . 39Figure 3.16 ANOVA diagnostic tests for PM20 data. . . . . . . . . . . . . . . 40Figure 3.17 Diagnostic tests for ANOVA testing, stratified by wind. . . . . . 44Figure 3.18 Comparison of PM10 measurements at two distances, stratifiedby wind direction. E-Sampler data. . . . . . . . . . . . . . . . . . 49Figure 3.19 Comparison of PM10 measurements at two distances, stratifiedby train type. E-Sampler data. . . . . . . . . . . . . . . . . . . . 49ixFigure 4.1 Individual train passages on August 14, 2014. . . . . . . . . . . . 57Figure 4.2 Lidar imagery and PM concentration for August 14, 2014. PMdata recorded by GRIMM OPC. . . . . . . . . . . . . . . . . . . 58Figure 4.3 Wind direction and speed, and wind component plots for August14, 2014. Recorded by Metro Vancouver. . . . . . . . . . . . . . 59Figure 4.4 Individual train passages on August 26, 2014. . . . . . . . . . . . 62Figure 4.5 Lidar imagery and PM concentration for August 26, 2014, recordedby E-Sampler. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63Figure 4.6 Wind direction and speed, and wind component plots for August26, 2014. Recorded by Metro Vancouver. . . . . . . . . . . . . . 64Figure 4.7 Individual train passages on September 4, 2014. . . . . . . . . . . 66Figure 4.8 Lidar imagery and PM concentration for September 4, 2014, recordedby GRIMM OPC. . . . . . . . . . . . . . . . . . . . . . . . . . . 67Figure 4.9 Wind direction and speed, and wind component plots for Septem-ber 4, 2014. Recorded by Metro Vancouver. . . . . . . . . . . . . 68xAcknowledgmentsFirst and foremost, I would like to sincerely thank my mentor and thesis supervisor,Dr. Ian McKendry. This research project would not have been possible withouthis constant guidance, encouragement, and assistance. Moreover, I would like tothank the entire Department of Geography at UBC for adopting me and providingme with fantastic support for the entire duration of my graduate program.Research funding was provided by Natural Sciences and Engineering ResearchCouncil (NSERC) operating grants to Dr. Ian McKendry. The author was fi-nancially supported by a scholarship from the NSERC Collaborative Research andTraining Experience program in Atmospheric Aerosols (CREATE-AAP), in con-junction with top-up funds from the Department of Earth, Ocean, and AtmosphericSciences at UBC.There were numerous collaborators that were crucial in helping complete thisresearch. I would like to personally thank: Dr. Julie Saxton and Ken Reid atMetro Vancouver, Stephanie Smith at Communities and Coal, Paul Cottle in theDepartment of Geography at UBC, Dr. Dan Ja↵e at the University of Washington,and my field assistants Rachel Cli↵, Meng Si, and Mark Richardson.I am incredibly fortunate to have had a fantastic support system during my grad-uate studies. My colleagues and peers have inspired me countless times with theirbrilliance and dedication to their work. My friends and family, here in Vancouverand back home in Toronto have constantly supported me and made my transitioninto a new city and province so much easier. Finally, I would like to specially thankMikayla Uber for her constant support, encouragement, and always being there forme.xiChapter 1Introduction1.1 BackgroundExposure to particulate matter (PM) is a growing concern for residents of urbanenvironments across the world. PM sources can be conceptualized as point sources(such as individual buildings), or they can be considered as 1-dimensional linesources. The most significant line sources for PM typically correspond to trans-portation corridors. There are numerous examples of studies focusing on PM con-centrations along roadways, as well as the health e↵ects one would experience livingclose to a roadway. However, little attention has been given to railways, where trainsgenerate significant amounts of PM including diesel particulate matter (DPM) (Ab-basi et al., 2013). DPM is characterized by fine and ultrafine particles of greatestconcern to human health (Peters et al., 1997). Trains also contribute non-exhaustemissions related to the wearing down of train components, such as brake pads andwheels (Abbasi et al., 2013).Even less work has been done regarding the generation of coal dust from freighttrains. As trains with more than 100 open coal cars travel along railways to theirdestination, some of the coal dust may be lost into the air, a↵ecting the local PMconcentration (Ja↵e et al., 2014). These particles are typically larger than DPM,allowing for their detection using size-segregated measuring techniques (Ja↵e et al.,2014). The research described here is focused on the collection and analysis of PMconcentrations measured adjacent to a railway running through the Lower Mainlandregion of British Columbia, Canada.This railway is of particular interest, as previous work (Ja↵e et al., 2014) has1studied emissions from coal-carrying trains at a di↵erent location along the sameroute, in the U.S. state of Washington. There is also concern from a public healthperspective, as sections of the railway pass through densely populated neighbour-hoods in White Rock, and Surrey, BC. Moreover, the future possibility of increasedcoal train trac through the region suggests a need for research into the air qualityimpacts of coal trains.1.2 Literature Review1.2.1 Health E↵ectsHealth impacts related to PM exposure have been well documented, with a generalconsensus in the air pollution epidemiology literature that both acute and ambi-ent exposure to elevated PM concentrations results in detrimental health e↵ects(Bascom et al., 1996; Dockery and Pope, 1994). These e↵ects involve strain onthe respiratory system, which over time may lead to the development of chronicrespiratory diseases and increased mortality rates (Dockery and Pope, 1994). Thedamage su↵ered by the respiratory system is related to the size of the particles, withthe smallest particles causing the highest level of respiratory distress (Peters et al.,1997). These particles in the fine mode of PM are typically referred to as PM2.5,meaning all particles with diameters less than 2.5 µm. The second major categoryof particles is termed PM10, or the coarse mode. In the context of the respira-tory system, coarse particles typically impact and deposit in the upper respiratorytract (Heyder, 2004). Fine particles, in contrast, are able to travel further throughthe respiratory system, depositing in the lower respiratory tract, the portion of thesystem that includes the alveoli (Heyder, 2004). Given the sensitive nature of thealveoli, inflammation in this part of the system can lead to the most serious healthe↵ects (Peters et al., 1997).1.2.2 Trains as a PM SourceFor those living in urban environments, ambient PM exposure is typically associ-ated with the transportation sector (Hoek et al., 2002). DPM has been the focus ofseveral studies (e.g. Galvis et al. (2013); Liukonen et al. (2002); Zhu et al. (2002))examining human exposure to PM caused by major transportation corridors (such2as freeways). In addition to motor vehicles, significant transportation-related emis-sions are from rail vehicles. Studies involving PM emissions from rail trac dividethe emissions into two broad source categories: exhaust and non-exhaust. “Non-exhaust” refers to sources such as the wearing down of rails, brake pads, and othersimilar railway components (Abbasi et al., 2013). Gehrig et al. (2007) concluded thatPM10 concentrations at three Swiss railway sites with both freight and passengertrac were indeed greater than those representative of a “background site”. Furtheranalysis indicated that the majority of the PM10 increase could be attributed to ironparticles (Gehrig et al., 2007). Since trains in Switzerland are almost exclusivelyelectric, only non-exhaust emissions were able to account for this increase. (Gehriget al., 2007).Exhaust emissions, specifically DPM, are of concern to human health due to thetypically small size range of the particles generated. Liukonen et al. (2002) examinedDPM by analyzing concentrations of elemental and organic carbon. The findingsshow a broad range of measured concentrations, likely dependent on several factors.Overall, the authors conclude that due to numerous confounding sources, elementalcarbon concentrations are the best proxy for measuring DPM. Moreover, the expo-sure of train crews to DPM is ”comparable to background urban exposures”, witha range of <1 to 45 µg/m3 being recorded.1.2.3 Coal DustOne aspect of this project’s primary focus is on a specific non-exhaust emissionsource: coal dust from open train cars. Coal dust generation has been studied inboth controlled conditions (e.g. Ferreira and Vaz (2004)), and, in recent years, realworld conditions (e.g. Higginbotham et al. (2013) and Ja↵e et al. (2014)). Ferreiraand Vaz (2004) examined coal dust release from a scale model in a wind tunnelexperiment. While the results of this study should not be expected to scale well tothe real world, the authors demonstrated significant loss of mass at an undisturbedflow velocity of 13.4 m/s (Ferreira and Vaz, 2004). Interestingly, at a lower flowvelocity (10.1 m/s), the coal particles were not lost from the scale model train cars,suggesting the presence of a threshold velocity required to initiate coal dust loss(Ferreira and Vaz, 2004).Two studies conducted in 2013 directly examined PM increases related to thegeneration of coal dust and DPM from coal trains: Higginbotham et al. (2013) and3Ja↵e et al. (2014). The former monitored a railway in Newcastle, Australia overthree days, observing PM increases associated with the passage of 73 coal trains.Instruments were set up approximately 10 metres from the tracks, and measurementswere segregated based on seven unique “signatures” representing variations in factorssuch as wind speed/direction and train speed (Higginbotham et al., 2013). Allsignatures showed a significant increase of PM10 concentration. The signaturesfrom passing coal trains were also compared to a signature from a passing freighttrain carrying grain. This signature showed a smaller increase in PM concentrationcompared to the coal trains, suggesting that the coal train signature was due to coaldust deposition, rather than DPM (Higginbotham et al., 2013). Somewhat counter-intuitively, it appeared that unloaded coal trains exhibited a much larger signaturethan loaded trains (Higginbotham et al., 2013). However, the authors suggestedthat this could be the e↵ect of train speed, as unloaded trains typically passed thesite at higher speeds than loaded trains (Higginbotham et al., 2013). Moreover, sizesegegrated data (TSP, PM10, PM2.5, and PM1) showed that the majority of theconcentration increases were due to coarse mode particles, once again pointing tothe e↵ect of coal dust particles (Higginbotham et al., 2013).Ja↵e et al. (2014) conducted their study in and around Seattle, Washingtonwith similar methodology to Higginbotham et al. (2013), while also including dataon CO2 concentration. This study collected PM concentration data at 10 secondintervals using a DustTrak DRX Aerosol Monitor approximately 25 metres from therailway, detecting PM concentrations at the same size fractions as Higginbothamet al. (2013). The DustTrak DRX is a photometer, which operates similarly to theoptical particle counter proposed for use in this project. The key conclusion fromthis paper is that on average, PM2.5 concentrations increased by 6.8 µg/m3 withthe passing of a coal train (Ja↵e et al., 2014). The data also showed that coal trainscaused an increase in the concentrations of larger particles, which is theorized to befrom aerosolized coal dust (Ja↵e et al., 2014).Another consideration discussed in Ja↵e et al. (2014) and Higginbotham et al.(2013) is the e↵ect of meteorology on PM measurements. Of greatest importanceis the relationship between recorded PM concentrations and wind direction. If theinstruments located at a study site are not downwind of the railway, measurementswill underestimate any impact caused by passing trains. Ja↵e et al. (2014) alleviatedthis problem by only analyzing events with a PM1-CO2 correlation coecient ofgreater than 0.5, or an increase in CO2 of more than 3 ppmv (parts per million of4volume). Additionally, the passing of a train travelling at high speed will disturb theair flow around it, further complicating these e↵ects (Higginbotham et al., 2013).1.2.4 Zone of Influence of a Linear SourceThe health impacts of a given PM source are highly dependent on the zone ofinfluence of the source. Much work has been done on determining the relationshipbetween measured pollutant concentrations, and distance from a source. Karneret al. (2010) provides an excellent summary on the various studies attempting toidentify a zone of influence for various pollutants, and the results show large amountsof variation. While each individual source is unique, di↵erences are also caused bythe pollutant being studied (Karner et al., 2010). For linear PM sources, the zoneof influence is somewhat unclear. Some results show a minor or gradual decreasein concentration with distance, but there are other instances of PM concentrationnot having any relationship with distance (Karner et al., 2010; Roorda-Knape et al.,1998). For situations with a gradual decrease in PM concentration over distance, thebackground concentration was reached after 176 m, giving an idea of the potentialsize of a zone of influence (Karner et al., 2010).1.3 Research ObjectivesThis study aims to build on previous work on emissions from coal trains by focusingon a few key areas of interest. As this research has implications in a variety offields (i.e. meteorology, air pollution, and public health), the associated researchobjectives reflect this. They are as follows:1. Investigate the impacts of coal-carrying trains on PM concentrations within50 metres of the tracks. Specific goals include quantification of the magnitude,frequency, and duration of PM impacts. In addition, impacts of coal-carryingtrains will be compared with other train types.2. Utilize size-segregated PM measurements to determine the size distribution ofPM emissions from coal trains.3. Investigate the “zone of influence” of train PM sources by using concentrationsmeasured at two distances from the track, and by using a novel horizontaldeployment of a micropulse lidar system.54. Explore the relationship between meteorological conditions and observed PMconcentrations.These research questions will be explored using data from a variety of sources.Chapter 3 focuses on a broad statistical analysis of observed PM concentration data,stratified by train type, as well as wind direction. The results from this analysis arethen contrasted in Chapter 4 with a case study analysis of three intensive observationdays, including data from a micropulse lidar system.6Chapter 2Study Area and Methodology2.1 Study AreaThe Lower Mainland is an area with 2.5 million residents centred on the FraserRiver (Statistics Canada, 2014). The city of Vancouver serves as the region’s largestcity. The Lower Mainland’s coastal location makes it home to several importantports, all of which are overseen by Port Metro Vancouver, a federal agency. PortMetro Vancouver is Canada’s largest port by tonnage, and the 4th largest in NorthAmerica (Port Metro Vancouver, 2014). Westshore Terminals, located in Delta,BC, is Canada’s largest coal exporting facility, exporting both metallurgical (usedfor the production of steel) and thermal (used for power generation) coal frommines in Alberta, British Columbia, Montana, and Wyoming (Westshore TerminalsLtd, 2014). Coal arrives at Westshore Terminals via trains operated by CanadianNational Railway (CN), Canadian Pacific Railway (CP), and the BNSF Railway(Cascadia Discovery Institute, 2011).BNSF trains entering Canada from the United States operate on a railway run-ning adjacent to the Peace Arch border crossing in White Rock, BC. From here, theline follows the shore of Boundary Bay passing directly through downtown WhiteRock and Crescent Beach, a community northwest of White Rock. The railway thencrosses both the Nicomekl and Serpentine Rivers as well as BC Highway 99, beforearriving at Colebrook junction. At this junction, BNSF trains join westbound CPand CN trac, with all trac travelling through Delta and finally terminating atWestshore Terminals (Cascadia Discovery Institute, 2011).The bulk of observations made for this study were taken at John Oliver Park, a7public park and sports facility in the southeastern corner of Delta, BC. The railwaybeing studied passes directly by the southern edge of the park. This section oftrack is located west of Colebrook junction, and therefore, includes train tracoperated by all three of CN, CP, and BNSF. Rail trac passing by John OliverPark consists of trains carrying either mixed-freight, or coal, allowing for a directcomparison between these two types of trac. Figure 2.1 shows an overview of theLower Mainland with all of these locations labelled.Figure 2.1: Map of Lower Mainland region of British Columbia, with relevantlocations highlighted. Taken from Apple Maps Version 2.0. Map datafrom TomTom and others. Satellite data from DigitalGlobe.In addition, there is a major freeway approximately 200 metres south of wherethe GRIMM was located. This freeway, British Columbia Highway 99 is a ma-jor north-south route connecting the communities of Richmond, Delta, and WhiteRock with both the United States border, and Vancouver. Trac patterns followa strongly commuter-influenced pattern: northbound trac peaks in the morningrush hour, while southbound trac peaks in the afternoon rush hour. The freewaytherefore serves as a significant line source that runs parallel to the study railway.82.1.1 Data SourcesThe data collected at John Oliver Park can be broadly divided into three groups:wind data, PM data, and lidar data. In addition, some data were obtained viaa collaboration with the Corporation of Delta, and Metro Vancouver, who weresimultaneously conducting a study regarding PM impacts of coal trains. Detailedinformation on each of the data sources is shown in Table 2.1.The various instruments used for this study were setup at three locations withinthe park (Figure 2.2). Location A is closest to the study railway, located approx-imately 15 m to the north. Location B is situated slightly further away, at 50 mfrom the study railway. Finally, Location C, where the lidar was utilized, was 400m north of the study railway.Table 2.1: Summary of data sources.Operator Location Instrument Variables Measured Dates MeasuredUBC B GRIMM OPC PM concentration12/08/2014 -21/09/2014UBC CSigmaSpace Backscatter and 12/08/2014 -Mini-MPL depolarization ratios 21/09/2014MetroAE-Sampler PM concentration 12/08/2014 -Vancouver Anemometer Wind speed/direction 21/09/2014MetroBE-Sampler PM concentration 12/08/2014 -Vancouver Anemometer Wind speed/direction 21/09/2014CorporationA Dustfall gauge PM mass04/01/2014 -of Delta 04/30/20149Figure 2.2: Map of John Oliver Park with instrument locations, taken fromApple Maps Version 2.0. Map data from TomTom and others. Satellitedata from DigitalGlobe.102.1.2 InstrumentationPM concentration data were collected by UBC using a GRIMM 1.108 optical particlecounter, and by Metro Vancouver using a Met One Instruments E-Sampler. TheGRIMM 1.108 is a scattering-based instrument that measures particle counts at16 di↵erent size bins, ranging from 0.23 to 20 µm. These particle counts can beused to indirectly calculate particle mass, and thus, particle concentrations. Thedetermination of particle concentrations, is dependent on a gravimetric correctionfactor, which in turn is dependent on “particle density, shape, and refractive index”(GRIMM Aerosol Technik GmbH & Co. KG, 2010). A given site can be assumed tohave a uniform particle density associated with its emissions (Maletto et al., 2003),meaning one calibration factor should be suitable for all measurements made at thesame location.The GRIMM OPC was housed in a Stevenson screen to protect it from precipi-tation and other external disturbances (Figure 2.3). The sample air was drawn intothe instrument through a Teflon tube that was fed into the Stevenson screen. Theusage of the GRIMM in this set up may a↵ect the measurements in several di↵erentways. Firstly, any bends in the Teflon tubing may result in impaction of particles ononto the sides of the tube (von der Weiden et al., 2009). The length of the tube mayalso prevent some particles larger than 0.5µm from reaching the instrument due tosedimentation processes within the tube (von der Weiden et al., 2009). Finally, asthe GRIMM is a scattering-based instrument, the determination of particle size fromscattering measurements is dependent on certain optical variables (GRIMM AerosolTechnik GmbH & Co. KG, 2010). The GRIMM was calibrated with dolomite dust,a material with di↵erent optical properties than coal dust, for example. Peters et al.(2006) compared the performance of the GRIMM to an aerodynamic particle sizer(APS), an instrument that is not dependent on optical properties. Both instrumentsmeasured a sample consisting of Arizona road dust, and the study concluded thatthe GRIMM “provided similar results to those from the APS” (Peters et al., 2006).The temporal resolution of measurements made using the GRIMM was initially setat 1 minute from 12/08/2014 to 26/08/2014. The remainder of the GRIMM mea-surements (from 26/08/2014 to 21/09/2014) were recorded at a 6 second resolution.Lidar data were obtained using a SigmaSpace mini-micropulse lidar (mini-MPL),a relatively small, eye-safe remote sensing instrument capable of determining spa-tially and temporally averaged backscatter and depolarization ratios. The averagingtime and distance was set to 30 seconds and 15 metres, respectively. The lidar oper-11Figure 2.3: Image of equipment set up location B. GRIMM OPC and MetroVancouver E-Sampler and wind instrumentation shown.ated with a laser energy of 3-4 µJ at 2500 hz, with a wavelength of 532 nm, appearingas green light. The instrument was mounted horizontally, and aimed at a heading ofapproximately 190, which intersected the study railway at a nearly perpendicularangle. In addition, the mini-MPL requires at least 150 metres of distance beforeproducing reliable data. Due to this, the mini-MPL was set up approximately 400metres from the study railway (Figure 2.4).The E-Sampler instrument used by Metro Vancouver uses a scattering-basedapproach similar to the GRIMM OPC, while also including gravimetric analysis todetermine PM concentration. Data collected by Metro Vancouver includes concen-trations of PM10 and PM2.5. Two E-Samplers were employed by Metro Vancouver,yielding data at both 15 and 50 m from the study railway. In addition, wind di-rection and speed were recorded by Metro Vancouver using anemometers collocatedwith the E-Samplers. All data collected by Metro Vancouver was recorded at atemporal resolution of 1 minute.The Corporation of Delta’s dustfall measurements used a standardized “test12Figure 2.4: Image of mini-MPL set up at location C.method for collection and measurement of dustfall” outlined by the standards orga-nization ASTM International (Corporation of Delta, 2015).2.1.3 Data ValidationThe primary method of validating the GRIMM data was by comparison to datacollected by Metro Vancouver. This data from the E-Sampler that was collocatedwith the GRIMM OPC was used to determine if the GRIMM was properly recordingPM concentrations. The first comparison between data sets was done at a resolutionof 1 minute. As the E-Sampler recorded PM10 concentrations, these values had to becalculated using the GRIMM data. The GRIMM reports concentrations in terms ofconcentrations of particles larger than each size cut. Thus, in order to estimate PM10concentrations, the concentration for particles larger than 10.0 µm was subtractedfrom the concentration for particles larger than 0.23 µm (the smallest size cut).13Next, all values were averaged to a 1 minute resolution, to match the E-Samplerdata.At some periods during the study, the GRIMM may have been impacted byadverse weather conditions that included significant rainfall, leading to moistureaccumulating within the intake tube. As such, there are several instances of theGRIMM recording concentrations that are highly suspect and unreasonable giventhe nature of the study site. These periods are characterized by sustained concen-tration values larger than 150 µg/m3, or simply, concentration values of 0 µg/m3.Any unreliable data that was qualitatively identified as such was therefore removedfrom the data set. A list of these periods is shown in Table 2.2.Table 2.2: Description of periods of unreliable particle concentration datarecorded by the GRIMM OPC.Dates/Times2014-08-13 09:26 - 10:002014-08-14 02:29 - 03:042014-08-14 05:27 - 05:422014-08-24 23:49 - 2014-09-02 23:592014-09-11 23:18 - 23:202014-09-14 16:55 - 18:502014-09-15 19:45 - 20:052014-09-19 23:20 - 2014-09-21 07:32This reduced data set was then directly compared to the E-Sampler data. Asseen in Figure 2.5, the GRIMM data appears to have more variability, with moreextreme values than the E-Sampler data. Nevertheless, the large-scale trends overthe span of several days do seem to match between the data sets. The scatterplotin Figure 2.6 confirms this, as the GRIMM data has numerous high concentrationvalues associated with low values recorded by the E-Sampler. Overall, there is someweak correlation between the data sets (r = 0.177). One method used to filter outsome of the extreme values recorded by the instruments was to use a 99th percentilecuto↵ to remove the top percent of values from the data. This results in a strongercorrelation coecient of 0.353.Given the nature of the study location, and the di↵erence in instruments, thedata sets were also compared after being averaged to 30 minute time steps. This14strategy once again serves to filter out extreme variability, at the expense of filteringout any short-term spikes in PM10 concentration, such as those potentially associ-ated with train passages. Nevertheless, this strategy was useful in comparing datasets and validating the GRIMM data.At a 30 minute resolution, there is a much stronger relationship between the twodata sets. Visually, it can be seen that concentration variations on a roughly diurnalscale are captured by both instruments in a very similar way (Figure 2.7). It isimportant to note that the GRIMM appears to record lower concentrations than theE-Sampler, with the exception of various periodic spikes (Figure 2.7). The potentialsources of particle loss caused by the Teflon tubing used by the GRIMM (discussedin Section 2.1.2) may help explain the lower concentration values. The scatterplot inFigure 2.8 shows a much tighter distribution, and a moderate correlation coecientof 0.552.If both of these strategies (using a 99th percentile cut-o↵, and averaging to 30minutes) are employed, the agreement between the two data sets is quite strong.Figure 2.9 shows that both diurnal-scale variations and the short-term spikes co-incide as well. The correlation coecient in this instance is once again higher, at0.646.020406080100Date1 minute PM10Concentration (µg/m3 )Aug 12 Aug 19 Aug 26 Sep 02 Sep 09 Sep 16UBCMetro VancouverFigure 2.5: Comparison of UBC and MV data, 1 minute averaging time.Overall, the evidence presented here suggests that the GRIMM data is reliableenough for further analysis, given its satisfactory agreement with a collocated instru-ment with validated data. 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●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●● ●●●●●●●●●●●●●●●●●● ● ●● ●●●●●●●●●●● ●●●●●●●● ●●●●●● ●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●● ●●● ●● ●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●● ●●●●● ●● ●●●●●●●●●● ● ●● ●● ●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●● ●●●●●●●●●●●●●●0 50 100 150 200050100150200UBC Data 1 minute PM10 Concentration (µg/m3)Metro Van 1 minute PM10 Concentration (µg/m3 )Figure 2.6: Scatterplot of UBC and MV data, 1 minute averaging time.020406080100Date30 minute PM10Concentration (µg/m3 )Aug 12 Aug 19 Aug 26 Sep 02 Sep 09 Sep 16UBCMetro VancouverFigure 2.7: Comparison of UBC and MV data, 30 minute averaging time.mass concentration is a reasonable assumption for the purposes of this study.2.1.4 Train PassagesIn order to determine whether an enhancement of PM concentration is present dur-ing a train passage, the time scale must be first determined. Each train passagemust be associated with a certain amount of time where PM measurements would16●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●● ●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●● ●●● ●●●●●●●●●●● ●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●● ●●●●● ● ● ●●●● ●●●●●●●●●●●●●● ●●● ●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●● ● ●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●● ●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●● ●●●●●●●● ●●●●●●● ●●●●●●●●●●●●●●●●●● ●●●●● ●●●●●●●●●●●● ●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●● ●●● ● ●●●●● ●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●● ● ●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●● ●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●0 20 40 60 80 100020406080100UBC Data 30 minute PM10 Concentration (µg/m3)Metro Van 30 minute PM10 Concentration (µg/m3 )Figure 2.8: Scatterplot of UBC and MV data, 30 minute averaging time.01020304050Date30 minute PM10Concentration (µg/m3 )Aug 12 Aug 19 Aug 26 Sep 02 Sep 09 Sep 16UBCMetro VancouverFigure 2.9: Comparison of UBC and MV data, 30 minute averaging time,99th percentile cuto↵.supposedly be a↵ected by the train passage. This period of time is not static and isdependent on a wide range of factors, including meteorological conditions, instru-mentation, train speed, etc. Previous studies ((Ja↵e et al., 2014), (Higginbothamet al., 2013)) have detected increases of pollutant concentrations that appear to laston the order of minutes. In these studies, any purported e↵ect by a passing train17Table 2.3: Comparison of GRIMM and E-Sampler Measurements for PM10concentration. 1 minute averaging time.Instrument n Mean Median Standard DeviationGRIMM 42433 15.80 µg/m3 13.61 µg/m3 15.61 µg/m3E-Sampler 57851 13.93 µg/m3 11.60 µg/m3 9.72 µg/m3was seen within 10 minutes of the passage of a train.Due to this, the analysis in this study will use 10 minutes as an approximationfor the time period of influence of a passing train. Based on the train audio datacollected, as well as visual confirmation, trains at the study site took approximately4-5 minutes to completely pass. There were some observed instances of just individ-ual locomotives (without any cars attached) passing by the site, but these were notconsidered as train passages. There was consistency across all trains with regardsto speed and time taken to completely pass the site. In order to account for anydesynchronization of measurements between the GRIMM and the microphone, ithas been assumed that the time period of influence for each individual train pas-sage lasted 14 minutes. This 14 minute period consists of the train passage itself (4minutes), and two 5-minute bu↵ers before and after the train passage.The PM concentrations of this 14 minute period of influence can then be com-pared to control data, representing the background PM concentrations. Since thereare numerous confounding factors (meteorology, vehicular trac, etc.), the controldata was defined as a 10 minute period before and after the 14 minute period ofinfluence associated with each train passage. This was done to try and minimizethe e↵ects of changing meteorology or vehicular trac, due to the variable nature ofthese factors on an hourly scale (Section 3.1). Furthermore, if the control data fora certain train passage happened to coincide with the period of influence of anothertrain passage, the overlapping data was not included in the control data set.18Chapter 3Influence of Train Passages onPM ConcentrationsThis chapter aims to identify the e↵ect that passing trains have on observed PM con-centrations. This analysis will be conducted by first examining background trendsfor the purpose of isolating typical patterns in the PM data in order to more accu-rately discriminate the e↵ect of passing trains. The main statistical analyses used inthis chapter are two Analysis of Variance (ANOVA) procedures: one with the datastratified by train type, and the other with the data stratified by train type andwind direction. The results of these analyses are then compared to in situ dustfallmeasurements conducted by the Corporation of Delta. Finally, the PM concentra-tion data recorded by Metro Vancouver at two di↵erent distances from the railwayis analyzed to isolate any evident spatial trends.3.1 Background Trends3.1.1 Diurnal ScaleOutdoor PM concentrations exhibit a typical diurnal pattern as a result of changesin both meteorology and source patterns. In order to examine these diurnal patterns,all of the PM data was aggregated and then isolated by hour of the day. For thisdiurnal scale analysis, the influence of passing trains is likely obscured by the morepersistent trends of vehicular trac on BC Highway 99 to the south of the park.The patterns of vehicular trac on Highway 99 can be analyzed using trac19data obtained from the BC Ministry of Transportation. Based on these data, itappears that trac on the highway is in agreement with expectations, with overallvolumes reaching a maximum during rush hour periods. However, since observedconcentrations are also a↵ected by local meteorological conditions, the observeddata may not necessarily mirror the trac patterns. For example, while vehiculartrac (and therefore, emissions) reach a local maximum during the afternoon rushhour, this corresponds with the time of day with the highest amount of atmosphericmixing, serving to dilute pollutants and reduce observed PM concentrations.The mean trac counts and mean PM concentrations sorted by hour of the dayare plotted in Figures 3.1 and 3.2, respectively. From these figures, it appears thatthe distribution of trac on Highway 99 is bimodal in nature (as expected). ThePM concentrations, however, seem to follow a diurnal pattern with relatively highconcentrations overnight, steadily decreasing into the afternoon, and then increas-ing during the evening. This distribution is consistent with the diurnal pattern ofdispersion through the atmosphere (McKendry, 2000). It is likely that despite theheavier trac during the afternoon rush hour, the daytime heating of the surfaceincreases mixing, thereby o↵setting the increase in emissions.0 2 4 6 8 10 12 14 16 18 20 22HourCount0500100015002000250030003500NorthboundSouthboundFigure 3.1: Trac counts on Highway 99 near John Oliver Park for September2014, sorted by hour.200 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23HourConcentration (µg/m3 )0510152025PM3PM10PM20Figure 3.2: Mean PM concentrations recorded at John Oliver Park from 2014-08-14 to 2014-09-21, sorted by hour. Combination of GRIMM and E-Sampler data.3.1.2 Weekly ScaleIf the same trac and PM concentration data is sorted by day of the week, pat-terns on a weekly scale can be identified. As expected, trac counts appear to beslightly lower on weekends compared to weekdays (Figure 3.3). Interestingly, PMconcentrations (Figure 3.4) do not exhibit the same pattern. Concentrations appearto be highest on Thursdays, with the lowest concentration days being Monday andFriday. It does not appear that weekends are associated with the lowest mean PMconcentrations.21Sun Mon Tue Wed Thu Fri SatDayCount01000020000300004000050000NorthboundSouthboundFigure 3.3: Trac counts on Highway 99 near John Oliver Park for September2014, sorted by day.Sun Mon Tue Wed Thu Fri SatDayConcentration (µg/m3 )05101520PM3PM10PM20Figure 3.4: Mean PM concentrations recorded at John Oliver Park from 2014-08-14 to 2014-09-21, sorted by day. Combination of GRIMM and E-Sampler data.223.1.3 Wind DataJohn Oliver Park is situated to the north of Boundary Bay, a large body of waterthat is a major factor controlling the flow regimes of this site. These flow regimesexhibit a strong diurnal cycle, likely driven by a sea breeze-land breeze circulationpattern (Steyn et al., 1997). The daytime wind patterns are dominated by southerlyflow coming o↵ of the water, across Highway 99 and the railway, and towards theinstruments set up as part of this study (Figure 3.5). Wind speeds are generallylight (93.7 % of the time the wind speed is lower than 2 m/s), and higher windspeeds are associated with wind directions from the west. The nighttime regime, incontrast, features a dominantly easterly flow (Figure 3.6), with overall lower windspeeds (98.6 % of wind speeds are lower than 2 m/s). The proportion of data ineach wind speed and direction bin is compared between day and night in Table 3.1.Table 3.1: Comparison of wind data distribution from 2014-08-12 to 2014-09-21 between daytime and nighttime.Day 45 90 135 180 225 270 315 3600-1 m/s (%) 1.62 3.52 8.13 24.00 6.57 5.18 2.50 1.321-2 m/s (%) 0.35 1.78 1.62 20.22 6.95 6.99 2.66 0.302-3 m/s (%) 0.17 0.46 0.07 0.05 0.65 2.68 1.86 0.033-4 m/s (%) 0.02 0.02 0.00 0.00 0.05 0.11 0.12 0.004-5 m/s (%) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Night 45 90 135 180 225 270 315 3600-1 m/s (%) 7.23 27.79 18.37 10.61 4.66 7.51 6.85 6.351-2 m/s (%) 1.05 5.31 0.65 0.84 0.63 0.16 0.08 0.102-3 m/s (%) 0.65 0.48 0.00 0.00 0.05 0.01 0.00 0.003-4 m/s (%) 0.13 0.07 0.00 0.00 0.00 0.00 0.00 0.004-5 m/s (%) 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.0023Frequency of counts by wind direction (%)WSNE5%10%15%20%25%30%35%40%45%50%55%mean = 1.06calm = 0%0 to 1 1 to 2 2 to 3 3 to 4 4 to 5(m s−1) Figure 3.5: Summary of wind data from 2014-08-12 to 2014-09-21 for day-time hours (07:00 - 21:00) at John Oliver Park. Recorded by MetroVancouver24Frequency of counts by wind direction (%)WSNE5%10%15%20%25%30%35%40%mean = 0.546calm = 0%0 to 1 1 to 2 2 to 3 3 to 4 4 to 5(m s−1) Figure 3.6: Summary of wind data from 2014-08-12 to 2014-09-21 for night-time hours (21:00 - 07:00) at John Oliver Park. Recorded by MetroVancouver253.2 Train TracOver the course of the study period, a combination of direct and indirect methodsrecorded the passage of 408 individual trains by the study site. Of this total, 42trains were directly visually identified. The remainder of train passages were indi-rectly recorded using audio data. There does not appear to be any significant trendwhen the observed trains are sorted by hour (Figure 3.7). It appears that trainpassages occur at all times during the day. There is potentially a lower amount oftrac during rush hour times in the morning and evening, but more specific datais likely needed to confirm this. When the data is stratified by day of the week, nosignificant trend is evident (Figure 3.8). The data displayed shows a higher numberof trains being recorded on Thursdays, Fridays, and Saturdays, and a much lowertrain frequency being seen on Mondays. Once again however, train scheduling isdependent on numerous factors that have not been explored here, and more datais likely needed to confirm these trends. Nevertheless, based on these observations,the train trac at John Oliver Park appears to be somewhat random in nature.0 2 4 6 8 10 12 14 16 18 20 22HourCount0510152025CoalFreightUnknownFigure 3.7: Train passages recorded at John Oliver Park from 2014-08-12 to2014-09-21, sorted by hour. Combination of visual observations andaudio recordings.26Sun Mon Tue Wed Thu Fri SatDay of WeekCount010203040506070CoalFreightUnknownFigure 3.8: Train passages recorded at John Oliver Park from 2014-08-12 to2014-09-21, sorted by day. Combination of visual observations and au-dio recordings.273.3 Particulate Matter Concentration3.3.1 E↵ect of Passing Trains (All Types)Detecting any enhancement of PM concentration during a train passage is simplya matter of combining the PM concentration and train passage data. Using themethodology outlined in section 2.1.4, the PM concentrations associated with pass-ing trains of all types can be broadly compared against the control data. In order todetect di↵erences across particle sizes, this comparison was done for each of PM3,PM10, and PM20. Figure 3.9 shows a comparison between the distributions in box-plot form, while Table 3.2 lists basic statistics for each group.Table 3.2: Basic statistics of PM concentration during train passages versuscontrol data, from 2014-08-12 to 2014-09-21. Combination of GRIMMand E-Sampler data.Group n Mean (µg/m3) Median (µg/m3) Std. Dev. (µg/m3)PM3 Passage 4048 10.38 8.53 8.35PM3 Control 4095 10.17 8.48 9.01PM10 Passage 5217 13.16 9.66 16.00PM10 Control 5322 12.68 9.56 16.32PM20 Passage 4048 15.53 11.73 20.88PM20 Control 4095 15.30 11.68 28.12On first glance, the two distributions appear quite similar across all size ranges.If the means for each distribution are compared using a t-test (with Welch correctionfor unequal variances), the means for all size ranges are found to be not significantlydi↵erent from one another at ↵ = 0.01 (Table 3.3). A significance level of 0.01was chosen to use in this analysis to reduce the chance of a Type I error, giventhe numerous confounding factors that could a↵ect the measurements. Note thatfor the purposes of these tests, the data was base 10 log transformed to create adistribution closer to normality.28●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●During Train Passage Control01020304050PM3 Train Passage ComparisonConcentration (µg/m3 )●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●During Train Passage Control01020304050PM10 Train Passage ComparisonConcentration (µg/m3 )●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●During Train Passage Control01020304050PM20 Train Passage ComparisonConcentration (µg/m3 )Figure 3.9: Comparison of PM concentration during train passages versuscontrol, from 2014-08-12 to 2014-09-21. Combination of GRIMM andE-Sampler data.29Table 3.3: Welch t-test of di↵erences of means with log transformed data (p= passage, c = control). ↵ = 0.01,Comparison Hypothesis Test Test Statistic p-valuePM3 µp 6= µc 1.469 0.142PM10 µp 6= µc 1.007 0.314PM20 µp 6= µc 1.339 0.1813.3.2 E↵ect of Passing Trains with Wind DirectionAs the wind direction strongly influences observed PM concentrations, we can fur-ther analyze the data by taking wind direction into account. The approach usedhere broadly categorizes wind direction into one of two groups: directions when theinstruments are downwind of the railway, and directions when the instruments areupwind of the railway. The specific criteria used here were defined by using theaxis of the railway. The railway runs approximately 74 - 254. Thus, any headingsbetween 74 to 254 represent the wind blowing from the south, across the railway,and towards the instruments set up at the park. Any headings outside of that rangewould represent the wind blowing from the north, making the instruments upwind ofthe railway, and therefore unlikely to be influenced by the highway or railway. Theconvention used in this report will be from the frame of reference of the instrumentsbeing either upwind or downwind with respect to the highway/railway.Figure 3.10 shows, once again, minimal di↵erences between passage and controlgroups. However, the e↵ect of wind direction on observed concentrations is quiteapparent, with downwind conditions resulting in higher mean concentrations thanupwind conditions. Moreover, it appears that the enhancement of mean PM con-centration due to downwind conditions gets weaker with larger size ranges. For thecontrol data, downwind conditions result in a mean enhancement of 2.35, 1.57, and1.70 µg/m3 for PM3, PM10, and PM20, respectively. Another trend that appears inthe data is the di↵erence in variability across both size ranges and wind directions.The standard deviation appears to increase with both increasing particle size anddownwind conditions (Table 3.4). A larger amount of variability in the data wouldseem to suggest stronger periodic spikes in concentration. The fact that this is asso-ciated with downwind conditions makes sense, since PM blowing from the highwayand railway may cause more variability in the recorded data. In contrast, as there30are few strong PM sources at John Oliver Park north of the instrument site, upwindconditions should produce less variability in the data.Table 3.4: Basic statistics of PM concentration during train passages versuscontrol data, segmented by wind direction, from 2014-08-12 to 2014-09-21. Combination of GRIMM and E-Sampler data.Group n Mean (g/m3) Median (g/m3) Std. Dev. (g/m3)PM3 Passage (Downwind) 2731 10.94 9.33 8.32PM3 Control (Downwind) 3523 10.91 9.34 9.78PM3 Passage (Upwind) 1317 9.21 6.55 8.30PM3 Control (Upwind) 1691 8.56 6.66 6.82PM10 Passage (Downwind) 3632 13.41 10.14 16.06PM10 Control (Downwind) 4751 13.14 10.32 18.28PM10 Passage (Upwind) 1585 12.59 8.52 15.87PM10 Control (Upwind) 2043 11.57 8.45 10.02PM20 Passage (Downwind) 2731 15.86 12.23 21.83PM20 Control (Downwind) 3523 15.83 12.09 31.38PM20 Passage (Upwind) 1317 14.86 10.20 18.75PM20 Control (Upwind) 1691 14.13 10.28 19.2531●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Passage (Downwind) Passage (Upwind) Control (Downwind) Control (Upwind)01020304050PM3 Train Passage ComparisonConcentration (µg/m3 )●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Passage (Downwind) Passage (Upwind) Control (Downwind) Control (Upwind)01020304050PM10 Train Passage ComparisonConcentration (µg/m3 )●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Passage (Downwind) Passage (Upwind) Control (Downwind) Control (Upwind)01020304050PM20 Train Passage ComparisonConcentration (µg/m3 )Figure 3.10: Comparison of PM concentration during train passage versuscontrol, stratified by wind direction, from 2014-08-12 to 2014-09-21.Combination of GRIMM and E-Sampler data.323.3.3 E↵ect of Passing Coal TrainsIsolating the e↵ect of passing coal trains required comparing PM data to the timesof confirmed coal train passages. At certain times, the GRIMM data was found tobe unreliable (Section 2.1.3), and given the relatively small number of confirmedcoal train passages, the data collected by Metro Vancouver was combined with theGRIMM data for this analysis. While the Metro Vancouver data was recorded atthe same location at John Oliver Park ( 50 m north of the study railway), the E-Sampler instrument used only recorded PM10 concentrations. Thus, the PM10 dataincludes all 16 visually confirmed coal train passages (out of 42 visually confirmedpassages, and 408 total passages), while the PM3 and PM20 data only includes sevenvisually confirmed coal train passages (out of 27 visually confirmed passages, and307 total passages).Each data set was stratified into three groups: coal train passages, all trainpassages, and control. The method used to identify membership to these groups isthe same as described in Section 2.1.4. It is also important to note that the groupsfor coal train passages and all train passages are not mutually exclusive; coal trainpassages are included in the “all trains” group. However, both of these groups aremutually exclusive with the control group. Figures 3.11-3.13 show the distributionsof these groups, while Table 3.5 summarizes the basic statistics for each distribution.As mentioned in Section 3.3.1, the PM data being used here is not normallydistributed. As such, a base 10 log transformation was applied to the data tomore closely approximate a normal distribution. Before performing an ANOVA tocompare the three groups of data, diagnostic tests were used to ensure that ANOVAwas appropriate for this data. The diagnostic tests for each of PM3, PM10, and PM20are shown in Figures 3.14-3.16, respectively.The normal Q-Q plots show that the data is somewhat normally distributed.However, the assumption of normality can be met by assuming that the central limittheorem holds, given the large sample sizes being used. However, the assumption ofhomoscedasticity of residuals is more problematic. The residuals vs. fitted plots inFigures 3.14-3.16 show unequal variance across the fitted values. Moreover, usingLevene’s Test for homogeneity of variance yields a significant p-value at ↵ = 0.01for all PM ranges. This results in rejecting the null hypothesis, suggesting thatthe data is heteroscedastic. Nevertheless, the one-way ANOVA procedure can stillbe performed while including a Welch correction for the heteroscedasticity. All sizeranges yield a significant p-value at ↵ = 0.01, suggesting that the groups (coal trains,33●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Coal Trains All Trains Control0102030405060PM3 Coal Train Passage ComparisonConcentration (µg/m3 )●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●0102030405060Concentration (µg/m3 )Coal Trains (D)Coal Trains (U)All Trains (D)All Trains (U)Control (D)Control (U)Figure 3.11: Boxplots of each PM3 group used in ANOVA testing. (D)denotes downwind conditions, while (U) denotes upwind conditions.Combination of data from GRIMM OPC and E-Sampler.34●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Coal Trains All Trains Control0102030405060PM10 Coal Train Passage ComparisonConcentration (µg/m3 )●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●0102030405060Concentration (µg/m3 )Coal Trains (D)Coal Trains (U)All Trains (D)All Trains (U)Control (D)Control (U)Figure 3.12: Boxplots of each PM10 group used in ANOVA testing. (D)denotes downwind conditions, while (U) denotes upwind conditions.Combination of data from GRIMM OPC and E-Sampler.35●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Coal Trains All Trains Control0102030405060PM20 Coal Train Passage ComparisonConcentration (µg/m3 )●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●0102030405060Concentration (µg/m3 )Coal Trains (D)Coal Trains (U)All Trains (D)All Trains (U)Control (D)Control (U)Figure 3.13: Boxplots of each PM20 group used in ANOVA testing. (D)denotes downwind conditions, while (U) denotes upwind conditions.Combination of data from GRIMM OPC and E-Sampler.36Table 3.5: Basic statistics of PM concentrations for stratified data, from 2014-08-12 to 2014-09-21. Combination of GRIMM and E-Sampler data.Group n Mean (g/m3) Median (g/m3) Std. Dev. (g/m3)PM3 Coal Trains 78 15.09 12.24 10.75PM3 All Trains 4048 10.38 8.53 8.35PM3 Control 5214 9.81 8.13 8.09PM10 Coal Trains 200 16.44 11.00 10.26PM10 All Trains 5217 13.16 9.66 16.00PM10 Control 6794 12.33 9.41 14.87PM20 Coal Trains 78 17.53 14.12 12.68PM20 All Trains 4048 15.53 11.73 20.88PM20 Control 5214 14.98 11.49 38.38all trains, and control) do not belong to the same population (Table 3.6-3.7).Post-hoc tests (Tables 3.6-3.7) can be used to detect the pairs of groups that arecausing the overall significant result for the three size ranges. Due to the unequalvariances and sample sizes, the Games-Howell procedure was used for pairwise com-parisons. Since there are three pairs of groups, the p-values from these tests arecompared against an ↵0value of 0.0033 (↵/3). The results of the post-hoc testsdi↵er between the size ranges. For PM3, all pairs of groups were found to be sig-nificantly di↵erent from one another. For PM10, the coal train group was foundto be significantly di↵erent from both the all trains group and the control group,but no significant di↵erence was found between the all trains group and the controlgroup. The PM20 post-hoc tests produced interesting results. Despite the overallsignificant result for the ANOVA test, the post-hoc tests did not find any significantpairwise di↵erences. This suggests that any overall di↵erences between groups maybe fairly weak.370.90 0.95 1.00 1.05−1.5−0.50.51.5Fitted valuesResiduals●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Residuals vs Fitted897428692690●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●−4 −2 0 2 4−4−20246Theoretical QuantilesStandardized residualsNormal Q−Q8974286926900.90 0.95 1.00 1.050.00.51.01.52.0Fitted valuesStandardized residuals●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Scale−Location8974286926900.000 0.004 0.008 0.012−40246LeverageStandardized residuals●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Cook's distanceResiduals vs Leverage34353Figure 3.14: ANOVA diagnostic tests for PM3 data.381.00 1.04 1.08 1.12−1.5−0.50.51.5Fitted valuesResiduals●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Residuals vs Fitted897428698975●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●−4 −2 0 2 4−40246Theoretical QuantilesStandardized residualsNormal Q−Q8974286989751.00 1.04 1.08 1.120.01.02.0Fitted valuesStandardized residuals●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Scale−Location8974286989750.000 0.002 0.004−402468LeverageStandardized residuals●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Cook's distanceResiduals vs Leverage343335Figure 3.15: ANOVA diagnostic tests for PM10 data.391.06 1.08 1.10 1.12−1012Fitted valuesResiduals●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Residuals vs Fitted7930114778974●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●−4 −2 0 2 4−402468Theoretical QuantilesStandardized residualsNormal Q−Q7930114778971.06 1.08 1.10 1.120.01.02.0Fitted valuesStandardized residuals●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Scale−Location79301147789740.000 0.004 0.008 0.012−402468LeverageStandardized residuals●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Cook's distanceResiduals vs Leverage342233Figure 3.16: ANOVA diagnostic tests for PM20 data.40Table 3.6: Summary of parametric hypothesis testing for log-transformedPM3 data. ↵ = 0.01, ↵0= 0.01/3 = 0.00333Test Hypotheses p-value Reject H0?Shapiro-WilkH0: The data is normal < 0.0001 YesH1: The data is not normalLevene’s TestH0: Variances equal across groups < 0.0001 YesH1: Variances not equal across groupsOne-Way ANOVAH0: µcoal = µall = µcontrol 0.0022 YesH1: µcoal 6= µall 6= µcontrolGames-Howell Post-HocH0: µcoal = µall 0.002 YesH1: µcoal 6= µallH0: µcoal = µcontrol 0.0002 YesH1: µcoal 6= µcontrolH0: µall = µcontrol 0.001 YesH1: µall 6= µcontrolTable 3.7: Summary of parametric hypothesis testing for log-transformedPM10 data. ↵ = 0.01, ↵0= 0.01/3 = 0.00333Test Hypotheses p-value Reject H0?Shapiro-WilkH0: The data is normal < 0.0001 YesH1: The data is not normalLevene’s TestH0: Variances equal across groups < 0.0001 YesH1: Variances not equal across groupsOne-Way ANOVAH0: µcoal = µall = µcontrol < 0.0001 YesH1: µcoal 6= µall 6= µcontrolGames-Howell Post-HocH0: µcoal = µall < 0.0001 YesH1: µcoal 6= µallH0: µcoal = µcontrol < 0.0001 YesH1: µcoal 6= µcontrolH0: µall = µcontrol 0.0248 NoH1: µall 6= µcontrol41Table 3.8: Summary of parametric hypothesis testing for log-transformedPM20 data. ↵ = 0.01, ↵0= 0.01/3 = 0.00333Test Hypotheses p-value Reject H0?Shapiro-WilkH0: The data is normal < 0.0001 YesH1: The data is not normalLevene’s TestH0: Variances equal across groups < 0.0001 YesH1: Variances not equal across groupsOne-Way ANOVAH0: µcoal = µall = µcontrol 0.001 YesH1: µcoal 6= µall 6= µcontrolGames-Howell Post-HocH0: µcoal = µall 0.26 NoH1: µcoal 6= µallH0: µcoal = µcontrol 0.087 NoH1: µcoal 6= µcontrolH0: µall = µcontrol 0.0034 NoH1: µall 6= µcontrol423.3.4 E↵ect of Passing Coal Trains with Wind DirectionUsing the same methodology as in 3.3.2, the data can be further stratified by winddirection (the instruments being downwind or upwind of the railway). Given therelative lack of data for PM3 and PM20 that coincides with coal train passages, thisanalysis will only be performed on the PM10 data. The distributions of these sixgroups are shown in Figure 3.12. From the boxplots, it appears that the down-wind coal train group is associated with higher PM10 concentrations than the othergroups. Moreover, the e↵ect of wind direction is also visible here, with downwindconditions resulting in higher concentrations than upwind conditions. Interestingly,the group with the smallest median value is the upwind coal train group. However,this could be explained by a lack of data (only 4 train passages satisfy this criterion).The basic statistics of each group is summarized in Table 3.9.Similar to Section 3.3.3, the diagnostic plots in Figure 3.17 show that the dataexhibits both non-normality and heteroscedasticity to a certain degree. The Shapiro-Wilk test suggests that the data is significantly di↵erent from normally distributed,while Levene’s Test suggests that the data is not homoscedastic. Therefore, theOne-Way ANOVA with a Welch correction was used to test for di↵erences betweeneach group, assuming that normality holds due to the central limit theorem. Thesignificant result in the ANOVA analysis leads to post-hoc testing using the Games-Howell procedure. The results of the post-hoc testing are summarized in Table 3.11(a significance level of ↵0= 0.01/15 = 0.00067 has been assumed).The results of the post-hoc test show that the only pairs of groups that do nothave significantly di↵erent means are: Coal (Upwind) and Control (Upwind); AllTrains (Downwind) and Control (Downwind); and All Trains (Upwind) and Control(Upwind). The overall trend in significance in Table 3.11 suggest that both winddirection and coal train passages are causing the overall significant result. This isin agreement with the previous analyses conducted in this chapter.43Table 3.9: Basic statistics of PM10 concentration data, stratified by wind,from 2014-08-12 to 2014-09-21. Combination of GRIMM and E-Samplerdata.Group n Mean (g/m3) Median (g/m3) Std. Dev. (g/m3)Coal Trains (Downwind) 161 18.85 16.20 10.05Coal Trains (Upwind) 39 6.52 6.61 0.88All Trains (Downwind) 3632 13.41 10.14 16.06All Trains (Upwind) 1585 12.59 8.52 15.87Control (Downwind) 4751 12.85 10.19 16.55Control (Upwind) 2043 11.12 8.36 9.800.8 0.9 1.0 1.1 1.2−1.5−0.50.51.5Fitted valuesResiduals●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Residuals vs Fitted798221507983●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●−4 −2 0 2 4−40246Theoretical QuantilesStandardized residualsNormal Q−Q7982215079830.8 0.9 1.0 1.1 1.20.01.02.0Fitted valuesStandardized residuals●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Scale−Location7982215079830.000 0.010 0.020−402468LeverageStandardized residuals●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Cook's distanceResiduals vs Leverage34478233Figure 3.17: Diagnostic tests for ANOVA testing, stratified by wind.44Table 3.10: Summary of parametric hypothesis testing for PM10 data, strat-ified by wind. ↵ = 0.01Test Hypotheses p-value ResultShapiro-WilkH0: The data is normal < 0.0001 Reject H0H1: The data is not normalLevene’s TestH0: Variances equal across groups < 0.0001 Reject H0H1: Variances not equal across groupsOne-Way ANOVAH0: All groups have equal means < 0.0001 Reject H0H1: At least one group’s mean is not equalTable 3.11: Summary of Games-Howell post-hoc analysis for PM10 data,stratified by wind. Subscripts: D = Downwind, U = Upwind. ↵0=0.01/15 = 0.00067H0 H1 p-value Reject H0?µcoalD = µcoalU µcoalD 6= µcoalU < 0.0001 YesµcoalD = µallD µcoalD 6= µallD < 0.0001 YesµcoalD = µallU µcoalD 6= µallU < 0.0001 YesµcoalD = µcontrolD µcoalD 6= µcontrolD < 0.0001 YesµcoalD = µcontrolU µcoalD 6= µcontrolU < 0.0001 YesµcoalU = µallD µcoalU 6= µallD < 0.0001 YesµcoalU = µallU µcoalU 6= µallU < 0.0001 YesµcoalU = µcontrolD µcoalU 6= µcontrolD < 0.0001 YesµcoalU = µcontrolU µcoalU 6= µcontrolU < 0.0001 NoµallD = µallU µallD 6= µallU < 0.0001 YesµallD = µcontrolD µallD 6= µcontrolD 0.273 NoµallD = µcontrolU µallD 6= µcontrolU < 0.0001 YesµallU = µcontrolD µallU 6= µcontrolD < 0.0001 YesµallU = µcontrolU µallU 6= µcontrolU 0.609 NoµcontrolD = µcontrolU µcontrolD 6= µcontrolU < 0.0001 Yes453.4 Dustfall MeasurementsThe final part of the results from this study includes passive sampling undertakenby the Corporation of Delta. The sampling campaign took place over the courseof one year, with analysis being done every three months. Each analysis was ona sample collected over the course of one month (April 2014, July 2014, October2014, and January 2015). The aim of this project was to determine the proportionof the sample that was coal dust. The following section will focus on the July 2014analysis, given the similar timeframe to the other data collected at John Oliver Park.A summary of the results from July 2014 is provided in Table 3.12.The results for April and July 2014 clearly show the presence of coal dust atthe John Oliver Park site. Moreover, out of five locations examined by this study,the only two locations with large proportions (> 10%) of coal particles present weredirectly adjacent to the railway. This clearly suggests a relationship between therailway in question and the generation of coal dust. The current BC Air Qualityguidelines for dustfall in a non-residential area is 2.9 mg/dm2/day. Based on theanalysis for July 2014, the John Oliver Park site exceeded this guideline by 1.81mg/dm2/day. Due to the high proportion of coal particles in the sample, it ap-pears that the exceedance of the guideline is due to the coal dust found at the site.Interestingly, it appears that the amount of dustfall drastically decreases into theautumn and winter months. Given the dependency of measured PM on meteorolog-ical conditions, it is possible that this change is due to a change in day-to-day localmeteorology. Indeed, the report prepared by the Corporation of Delta points to asuspected regional change in operational or meteorological conditions as the causeof the decrease in dustfall.The size distribution of the dustfall samples was also determined as part ofthis study (Table 3.13). For all the sampling periods, it appears that the massdistribution is overwhelmingly dominated by particles larger than 10 microns indiameter. Given that the large proportion of the sample being coal particles, thissuggests that the coal dust exists primarily in the coarse mode. As such, it isexpected that any coal PM enhancement caused by a passing train would result inan increase at larger size ranges rather than smaller sizes.46Table 3.12: Corporation of Delta dustfall measurement results for 2014.Month Total Dustfall (mg/dm2) Daily Dustfall (mg/dm2/day) Coal % Daily Coal Dustfall (mg/dm2/day)April 2014 206.60 6.89 90 6.20July 2014 141.43 4.71 80 3.77October 2014 19.79 0.66 75 0.49January 2015 21.45 0.71 15 0.11Table 3.13: Corporation of Delta dustfall size distribution data for 2014.Month Total Dustfall (mg) > 1 mm (mg) > 10µm (mg) Between 3 and 10 µm (mg) Between 1 and 3 µm (mg)April 2014 374.7 0 371.0 3.3 0.4July 2014 256.5 0 254.3 1.6 0.6October 2014 35.9 0 35.5 0.3 0.1January 2015 38.9 0 38.2 0.4 0.3473.5 Spatial Decay of PM SignalIn order to attempt to isolate the e↵ect of distance from the railway on PM concen-tration, the means for PM10 concentration were calculated for both sites. The 15 msite had a mean of 15.7 µg/m3, compared to a mean of 13.9 µg/m3 at the 50 m site.Furthermore, the mean di↵erence in concentration between the two sites was 2.04µg/m3. The variability of the measurements can be examined by calculating thestandard deviation for each site. Once again, the 15 m site has a higher standarddeviation (11.2 µg/m3) than the 50 m site (9.72 µg/m3), suggesting that the 15 mdata includes more instances of extreme concentrations. This lends support to theidea that acute, short-term PM events such as a train passage or heavy trac maya↵ect the closer site more strongly. While this appears to be a small di↵erence,these values were calculated using six weeks worth of data at a 1 minute resolution(nearly 60 000 data points).When the PM data is stratified by wind direction, the 15 m site has a meanconcentration of 16.21 µg/m3 when the instruments are downwind of the highwayand railway, compared to 14.52 µg/m3 during upwind conditions (Figure 3.18). The50 m site exhibits a similar trend (14.09 µg/m3 for the downwind situation, 13.53µg/m3 for the upwind situation). Since the primary sources of PM10 in the area arethe railway and highway, the impact of these sources would naturally decrease withdistance, including any di↵erences caused by wind direction. The variability of theupwind group at 15 m is also higher than that of the downwind group at 15 m (11.5µg/m3 vs. 10.4 µg/m3). The di↵erences between the two sites also exhibit somedependency on wind direction. Downwind conditions result in a larger di↵erencebetween observations (2.41 µg/m3) than upwind conditions (1.50 µg/m3).The PM data at the two sites can be stratified by train type (Figure 3.19), onceagain showing the elevated PM concentrations associated with coal trains comparedto all trains, or control data. In addition, Figure 3.19 also shows the decrease inconcentrations between the two distances. If the basic statistics for each group areexamined (Table 3.14), the 50 m site has lower mean concentrations for all groupscompared to the 15 m site. The magnitude of this decrease is approximately 1.0-2.0µg/m3 over the 35 m change in distance, and appears to be fairly consistent in termsof magnitude over the three groups. The change in variability also exhibits similarconsistency across groups, including the control data.48●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Downwind (15 m) Downwind (50 m) Upwind (15 m) Upwind (50 m)Wind Direction and Measurement LocationPM10 Concentration (µg/m3 )0255075100125150Figure 3.18: Comparison of PM10 measurements at two distances, stratifiedby wind direction. E-Sampler data.●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●0102030405060Concentration (µg/m3 )Coal Trains (10 m)Coal Trains (50 m)All Trains (10 m)All Trains (50 m)Control (10 m)Control (50 m)Figure 3.19: Comparison of PM10 measurements at two distances, stratifiedby train type. E-Sampler data.49Table 3.14: Basic statistics of PM concentrations at two distances, stratifiedby train type. E-Sampler data.Group Mean (g/m3) Median (g/m3) Standard Deviation (g/m3)Coal Trains15 m 18.32 11.6 11.250 m 17.00 15.4 8.88% Change -7.24 +32.8 -20.7All Trains15 m 13.83 11.4 10.1750 m 12.07 10.4 7.75% Change -12.8 -8.78 -23.8Control15 m 13.78 11.4 9.9950 m 11.81 9.9 7.30% Change -14.3 -13.2 -27.0503.6 Discussion3.6.1 PM Enhancement Due to Trains (All Types)When examining the overall impact that passing trains have on local PM concen-tration, there does not appear to be any significant relationship. It can be arguedthat this is in general agreement with studies such as Gehrig et al. (2007), thathave found “small, however, measureable increases in ambient PM10 concentrationsin the immediate vicinity of the tracks”. The same study determined an “railway-induced” enhancement of PM10 concentration of between 1.4-2.0 µg/m3. Accordingto Table 3.2, the raw enhancement of PM concentration due to train passages iseven smaller than these values, ranging from 0.26-0.44 µg/m3 across the three sizeranges analyzed in this study.Interestingly, Gehrig et al. (2007)’s study took place in Switzerland, where vir-tually all train trac is electric, suggesting that all PM emissions due to trains arenon-exhaust in nature. However, there is some evidence of sharp increases in PMconcentration during certain train passages in this study (discussed more in Chapter4). Therefore, the overall lack of a large enhancement in PM concentration due totrain passages in this study then suggests several hypotheses that will be discussedhere. The potential PM enhancement due to trains may be masked by:1. The proximity of a major highway to the study site, and its assumed temporalvariability in emissions2. Di↵ering wind speed and directions that may serve to underestimate any po-tential enhancement3. The zone of influence of the railway being relatively smallFor this study, any emissions from the highway were considered part of the“background” PM present at the site. As the magnitude of emissions from thehighway is ostensibly variable (due to varying trac counts as per Figure 3.1), thebackground PM concentration will be a↵ected by this variability (in addition todiurnal variability driven by meteorology). Therefore, any potential enhancementdue to a passing train may be masked by variations in highway trac at that specifictime.The local wind meteorology at this site, and the contrast between daytime andnightime conditions (Table 3.1) may also underestimate the e↵ect of a passing train.51As train passages are relatively short-lived, any potential PM enhancement is highlydependent on the wind speed and wind direction at that exact time. Moreover, dueto the prevailing wind directions after sunset, any enhancement caused by a passingtrain at night are less likely to be directly observed by the instruments (as in Section4.1.3). Nevertheless, when the data is stratified by wind direction (Figure 3.10),there still do not appear to be significant enhancements caused by passing trains.The background concentration of this site (including the e↵ects of the highway)is however still elevated when compared to PM concentration data recorded byMetro Vancouver at a site not adjacent to a highway. The mean 24-hour PM2.5concentration at the Tsawwassen, BC (approximately 15 km southwest of JohnOliver Park) for the same study period was recorded to be 5.7 µg/m3. In comparison,the mean 24-hour PM3 concentration at John Oliver Park during the study periodwas 11.97 µg/m3. Due to the lack of a statistically significant PM increase due topassing trains, it appears that the di↵erence between these two sites is primarilydriven by the proximity of the highway.3.6.2 PM Enhancement Due to Coal TrainsWhen a distinction is made between the type of train passing the site, coal trains areassociated with a statistically significant increase in PM3 and PM10 concentration(Tables 3.6-3.7). However, it must be noted that for PM3 and PM20, only sevencoal trains were used in these analyses compared to 16 in the PM10 analysis dueto unreliable data. Despite the overall significant PM increase associated with coaltrains, the results in Chapter 4 show that not all coal trains exhibit an obvious PMconcentration increase.Interestingly, the increase in PM concentration associated with a passing coaltrain di↵ers between size ranges. The mean PM3 concentration during coal trainpassages is 53.8% higher than the control group, compared to a 33.3% increase inPM10 concentration, and a 17.0% increase in PM20 concentration. It should also benoted that the median concentration during coal train passages increased across allof PM3, PM10, and PM20 (50.6%, 16.9%, and 22.9%, respectively). If we considerPM20 to represent total suspended particulates (TSP), the proportion that each sizerange represents can be calculated. For the control data, PM3 represents 65.5% ofTSP, compared to 86.1% for the coal train data. PM10 represents 82.3% and 93.8%of TSP for control and coal train data, respectively. The larger increase in proportionof TSP for PM3 seems to suggest that the PM enhancement associated with coal52trains is driven by smaller particles. The insignificant results in the PM20 post-hoctests also support this conclusion (Table 3.8). This appears to be in contrast with thefindings of Ja↵e et al. (2014), which showed that the strongest PM concentrationincreases occurrred in larger size ranges. There is also strong evidence from theCorporation of Delta’s dustfall measurements to support the claim that these largerparticles are indeed coal particles (Table 3.13). One possible explanation for this isthat Ja↵e et al. (2014) had equipment set up closer to the railway than this study.Due to the larger distance involved, it is possible that more of the larger particleswere able to settle out before reaching the instrumentation at John Oliver Park.This hypothesis will be explored in Section 3.6.3.Another study on coal trains conducted in Australia (Higginbotham et al., 2013)had data showing extreme increases in PM concentration with coal train passages(up to a 13 fold increase in some cases). Nevertheless, variability was found in themeasurements from the study, as 20% of passing coal trains did not produce any“discernible signature” (Higginbotham et al., 2013). The average PM concentrationincrease for the Australian study were found to be around 25 µg/m3, much largerthan the increases recorded at John Oliver Park. It is possible that the coal-carryingtrains in Australia have di↵erent regulations regarding chemical treatments to reducedust, but details on these regulations are unknown.The results in Table 3.11 also show that there is an enhancement of PM10 con-centration associated with coal trains during both downwind and upwind conditions.It is interesting that upwind conditions still result in an enhancement that is statisti-cally significant, given the strong dependency of PM concentration on wind directionthat has been discussed.3.6.3 Zone of InfluenceDetermining a zone of influence for the highway and railway at John Oliver Parkis quite challenging due to the numerous potential confounding factors. Previousstudies such as Roorda-Knape et al. (1998) have examined how various pollutantsare related to distance from a linear source such as a highway. Roorda-Knape et al.(1998) show evidence to suggest that concentrations of pollutants such as blacksmoke and NO2 decrease with distance from a highway, but the results for PM2.5and PM10 are less clear. In addition, the importance of wind direction and thecurvilinear relationship of pollutant concentration with distance was highlighted(Roorda-Knape et al., 1998).53In 2010, Karner et al. (2010) presented results that summarized 41 di↵erentstudies looking at a variety of pollutants. Once again, the results for PM2.5 andPM10 show evidence of a very gradual decay over distance, and even no trend at allin some cases (Karner et al., 2010). For the data showing gradual decay of PM10,concentrations were found to reach background levels after 176 m (Karner et al.,2010). This would seem to suggest that the zone of influence for the linear sourcesat John Oliver Park extends past the 50 m site.The key di↵erence with this study compared to previous work on spatial relation-ships of pollutants is the burst-like nature of passing trains. While PM concentrationtrends from a continuously emitting line source such as a highway appear to grad-ually rise and fall, the enhancement that can be seen with an individual passingtrain can be very spiky in nature. The variability in PM concentration associatedwith these bursts of emission appears to sharply decrease over a short distance, asevidenced by the decrease of standard deviation with distance for coal trains and alltrains groups (Table 3.14). During times without these bursts (the control data),variability also sharply decreases in the same manner with distance. Based on thedata from this study, as well as previous work, the presence of the railway appearsto have some measurable e↵ect on PM concentration at distances over 50 m, but theextreme values caused by train passages are unlikely to be seen at further distances.54Chapter 4Case Studies Using Mini-MPLImageryIn this chapter, three intensive observation days are presented in order to:1. Demonstrate the novel deployment of a mini micropulse lidar system for in-vestigation of plumes from linear sources2. Show detailed, high temporal resolution “signatures” of trains passages incases where the train characteristics are well defined.As such, this chapter provides a natural extension to the broad statistical anal-ysis presented in Chapter 3, and illustrates the short time scales and day-to-dayvariability associated with the train passages.4.1 Case Studies4.1.1 August 14, 2014The Mini-MPL was deployed from 11:03 until 20:36 local time on this day. Duringthis time, six trains passed by the site. Three of these trains were coal trains, oneof which was a loaded train traveling westbound.From the PM data recorded by the GRIMM (Figure 4.2b), there appear to bestrong increases in particulate matter associated with 4 of the 6 train passages acrossall size intervals. Interestingly, two of these trains were mixed freight trains, while55the other two were coal trains. If the wind plots for the same time period are exam-ined (Figure 4.3), there does not appear to be any readily apparent trend explainingwhy only some of the trains were associated with an obvious increase in PM concen-tration. The lidar imagery from the same day (Figure 4.2a) shows the backscatterratio through a horizontal cross-section oriented approximately southeast. Fromthe imagery, there appears to be a consistent band of elevated backscatter that ispersistent through the day, located at approximately 600 m. This band representsBC Highway 99, with the emissions from vehicular trac increasing the amount ofbackscatter recorded by the MPL. The instrument also happened to be positionedat the height of the passing train cars, resulting in a blockage of the laser beamduring most of the train passages. This is represented on the output as black bandsthat last for the approximate amount of time of a train passage (5˜-6 minutes).During the train passages beginning at 14:52, 15:03, 17:30, 17:54, and 18:04(Trains 1-5) there are short-term instances of elevated backscatter at distances closeto the MPL. However, this elevated backscatter is not present during the trainpassage at 19:29. If the wind plot (Figure 4.3a) is examined once again, during thedaytime the wind was coming from a generally southerly direction. Towards thelater parts of the evening and sunset, the direction shifts more towards a south-easterly direction. This is confirmed by the along-railway component changing to anegative value around this time (Figure 4.3b). This could be a potential explanationas to why Train 6 at 19:29 is not associated with an increase in PM concentrationfrom the GRIMM or MPL data.Out of these six train passages, four of them are associated with a visible increasein PM concentration from the GRIMM data, within 5 minutes of the train passage(Figure 4.1). Interestingly, both freight trains (Trains 2 and 4) and coal trains(Trains 3 and 5) are associated with PM concentration increases. A summary ofthe PM data during all six passages is provided in Table 4.1. The most strikingtrend apparent here is that major increases in PM concentration occur within the10-20 µm range, with the exception of Train 5. Train 5 is the only instance of astrong increase in concentration across all size ranges. Moreover, there does notappear to be a discernable di↵erence on this day in terms of the e↵ects of coal trainsversus freight trains. Overall, either type of train may be associated with majorincreases of PM concentration, or little to no increases. Out of these six trains,Train 5 appears to be the most unique, with a strong PM concentration increaseacross all size ranges.560102030405060Time (PDT)Concentration (µg/m3 )14:50 14:55 15:00 15:05 15:10 15:15Size RangePM3PM10PM20Train DirectionEastboundWestboundTrain CargoCoalFreight0102030405060Time (PDT)Concentration (µg/m3 )17:20 17:25 17:30 17:35 17:40Size RangePM3PM10PM20Train DirectionEastboundWestboundTrain CargoCoalFreight0102030405060Time (PDT)Concentration (µg/m3 )17:50 17:55 18:00 18:05 18:10Size RangePM3PM10PM20Train DirectionEastboundWestboundTrain CargoCoalFreight0102030405060Time (PDT)Concentration (µg/m3 )19:20 19:25 19:30 19:35 19:40Size RangePM3PM10PM20Train DirectionEastboundWestboundTrain CargoCoalFreightTrain 2Train 1Train 3Train 5Train 4Train 6Figure 4.1: Individual train passages on August 14, 2014.57(a) Backscatter ratio020406080Time (PDT)Concentration (µg/m3)12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00Size RangePM3PM10PM20Wind DirectionUpwindDownwindTrain DirectionEastboundWestboundTrain CargoCoalFreight(b) PM concentration dataFigure 4.2: Lidar imagery and PM concentration for August 14, 2014. PM data recorded by GRIMM OPC.5801234Time (PDT)09018027036012:00 14:00 16:00 18:00 20:00Wind speed (m/s)Wind direction (°)Wind SpeedWind DirectionUpwindDownwind(a) Wind speed and direction−2−1012Time (PDT)Wind Component (m/s)11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00Wind ComponentCross−RailwayAlong−RailwayTrain DirectionEastboundWestboundTrain CargoCoalFreight(b) Wind componentsFigure 4.3: Wind direction and speed, and wind component plots for August 14, 2014. Recorded by Metro Van-couver.59Table 4.1: Summary of PM concentration data during train passages on Au-gust 14, 2014, recorded by GRIMM OPC.Size Range Minimum (g/m3) Maximum (g/m3) % IncreaseTrain 1 - Coal - 14:52 PDTPM3 17.9 22.1 23.5PM10 19.2 23.8 24.0PM20 19.2 23.8 24.0Train 2 - Freight - 15:03 PDTPM3 15.7 21.4 36.3PM10 17.0 24.7 45.3PM20 17.1 39.9 133.3Train 3 - Coal - 17:30 PDTPM3 20.0 26.3 31.5PM10 21.2 30.4 43.4PM20 21.2 52.5 147.6Train 4 - Freight - 17:54 PDTPM3 22.1 28.9 30.8PM10 24.2 34.3 41.7PM20 24.2 46.8 93.4Train 5 - Coal - 18:04 PDTPM3 25.3 46.3 83.0PM10 28.7 54.8 90.9PM20 28.7 54.8 90.9Train 6 - Freight - 19:29 PDTPM3 13.8 16.5 19.6PM10 14.9 18.7 25.5PM20 14.9 20.8 40.0604.1.2 August 26, 2014The Mini-MPL was deployed from 08:50 until 17:30 PDT on this day. During thistime, eight trains passed by the site. Four of these trains were coal trains, threewere freight trains, and one was unidentified. The PM concentration data recordedby the GRIMM on this day was found to be unreliable, and due to this MetroVancouver’s data were used in the following analysis. The data recorded on this dayrepresents very di↵erent conditions than August 14th. Firstly, only one of the trainpassages (occurring at approximately 16:00) appears to be directly associated withan increase in PM concentration. Moreover, this train passage is a freight train,rather than a coal train. There does appear to be a spike in PM concentrationshortly before the coal train passage at 16:47 (Train 7), but it is unclear whetherthis spike is due to the train passages. Another spike in concentration occurs inthe morning, around 9:40 AM, which appears to be well separated from any trainpassages. In general, the background trend in PM concentration seems to agreewith the results discussed in Section 3.1.1, where concentration decreases duringthe afternoon after the morning rush hour, and slowly rises to coincide with theafternoon rush hour.If the lidar imagery is examined (Figure 4.5a), there is once again a sharp contrastto the imagery taken on August 14. Firstly, there appears to be a second persistentband of elevated backscatter ratio occurring at approximately 1600 m. Since lidarreturns at this distance begin to become unreliable due to the optical thicknessof the layer, it is unclear what is causing this feature. During the morning hours,backscatter appears to be elevated, being in agreement with the higher observed PMconcentrations. During the afternoon, the MPL records a markedly clearer cross-section, again agreeing with the PM data that shows relatively lower concentrationsthat are consistent and steady. The large increase in PM concentration associatedwith the train passage at 16:00 (Figure 4.4) is also visible in the MPL data as avisible plume that appears to move towards the MPL.610102030405060Time (PDT)Concentration (µg/m3 )08:45 08:50 08:55 09:00 09:05Size RangePM10Train DirectionEastboundWestboundTrain CargoCoalFreight0102030405060Time (PDT)Concentration (µg/m3 )10:25 10:30 10:35 10:40 10:45Size RangePM10Train DirectionEastboundWestboundTrain CargoCoalFreight0102030405060Time (PDT)Concentration (µg/m3 )11:10 11:15 11:20 11:25 11:30Size RangePM10Train DirectionEastboundWestboundTrain CargoCoalFreight0102030405060Time (PDT)Concentration (µg/m3 )13:35 13:40 13:45 13:50 13:55Size RangePM10Train DirectionEastboundWestboundTrain CargoCoalFreight0102030405060Time (PDT)Concentration (µg/m3 )15:40 15:45 15:50 15:55 16:00 16:05 16:10Size RangePM10Train DirectionEastboundWestboundTrain CargoCoalFreight0102030405060Time (PDT)Concentration (µg/m3 )16:40 16:45 16:50 16:55 17:00 17:05Size RangePM10Train DirectionEastboundWestboundTrain CargoCoalFreightTrain 8Train 7Train 6Train 5Train 4Train 3Train 2Train 1Figure 4.4: Individual train passages on August 26, 2014.62(a) Backscatter ratio020406080Time (PDT)Concentration (µg/m3)09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00Size RangePM3PM10PM20Wind DirectionUpwindDownwindTrain DirectionEastboundWestboundTrain CargoCoalFreight(b) PM concentration dataFigure 4.5: Lidar imagery and PM concentration for August 26, 2014, recorded by E-Sampler.6301234Time (PDT)09018027036009:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00Wind speed (m/s)Wind direction (°)Wind SpeedWind DirectionUpwindDownwind(a) Wind speed and direction−2−1012Time (PDT)Wind Component (m/s)09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00Wind ComponentCross−RailwayAlong−RailwayTrain DirectionEastboundWestboundTrain CargoCoalFreight(b) Wind componentsFigure 4.6: Wind direction and speed, and wind component plots for August 26, 2014. Recorded by Metro Van-couver.644.1.3 September 4, 2014The Mini-MPL was deployed from 13:04 until 21:39 PDT on this day. During thistime, six trains passed by the site. Three of these trains were coal trains, one ofwhich was a loaded train traveling westbound. The wind conditions on this dayare quite di↵erent than the previous two examples that have been discussed. Firstand foremost, the wind is predominantly northwesterly for most of the day (Figure4.6a), a sharp contrast to the southerly conditions that dominate the other two daysdiscussed. As such, the cross-railway wind component is negative for the majorityof the day (until a reversal at sunset), signifying that the wind is blowing away fromthe instruments (Figure 4.9b). Due to this, observed PM concentrations shouldbe lower than when the cross-railway wind component is positive, since the majorsource of PM (the highway) would be generating a plume that moves away from theinstruments. The PM data in Figure 4.8b reflects this, with lower concentrationscompared to August 14 and 26, and very few instances of short-term spikes inconcentration. The results from this day lend support to the dominating influenceof wind direction on PM concentration.In comparison to August 14 and 26, the MPL data also appears to show arelatively clean atmosphere through the day. There does appear to be some elevatedbackscatter at a distance slightly beyond the highway that is persistent through mostof the day. However, it does not appear that this feature is associated with any roador rail trac. The only instance of a PM increase caused by a train passage beingpicked up by the MPL is at 20:41 (Train 6). This freight train passage coincideswith the MPL imagery showing a plume being generated. This plume, emanatingat the time and distance associated with the train passage appears to move awayfrom the MPL, which is consistent with the wind data at that time (Figure 4.9).The GRIMM data during each train passage (Figure 4.7) confirms that no trainpassage is associated with an enhancement of PM, except for Train 6. Interestingly,there appear to be two peaks in concentration, one just before the train passes, andone approximately 5 minutes after the passage. If the lidar imagery is examinedonce again during that time (Figure 4.8a), there appear to be strong sources ofPM that are at distances other than the railway or highway. It is unknown whatthese sources could be, but given the change in wind direction, the GRIMM may berecording the e↵ect of these sources at that time.650102030405060Time (PDT)Concentration (µg/m3 )13:55 14:00 14:05 14:10 14:15Size RangePM3PM10PM20Train DirectionEastboundWestboundTrain CargoCoalFreight0102030405060Time (PDT)Concentration (µg/m3 )15:50 15:55 16:00 16:05 16:10 16:15 16:20Size RangePM3PM10PM20Train DirectionEastboundWestboundTrain CargoCoalFreight0102030405060Time (PDT)Concentration (µg/m3 )16:50 16:55 17:00 17:05 17:10 17:15 17:20 17:25 17:30Size RangePM3PM10PM20Train DirectionEastboundWestboundTrain CargoCoalFreight0102030405060Time (PDT)Concentration (µg/m3 )20:30 20:35 20:40 20:45 20:50Size RangePM3PM10PM20Train DirectionEastboundWestboundTrain CargoCoalFreight0102030405060Time (PDT)Concentration (µg/m3 )22:15 22:20 22:25 22:30 22:35Size RangePM3PM10PM20Train DirectionEastboundWestboundTrain CargoCoalFreightTrain 1Train 2 Train 3Train 4 Train 5Train 6Train 7Figure 4.7: Individual train passages on September 4, 2014.66(a) Backscatter ratio020406080Time (PDT)Concentration (µg/m3)14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00Size RangePM3PM10PM20Wind DirectionUpwindDownwindTrain DirectionEastboundWestboundTrain CargoCoalFreight(b) PM concentration dataFigure 4.8: Lidar imagery and PM concentration for September 4, 2014, recorded by GRIMM OPC.6701234Time (PDT)09018027036014:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00Wind speed (m/s)Wind direction (°)Wind SpeedWind DirectionUpwindDownwind(a) Wind speed and direction−2−1012Time (PDT)Wind Component (m/s)13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00Wind ComponentCross−RailwayAlong−RailwayTrain DirectionEastboundWestboundTrain CargoCoalFreight(b) Wind componentsFigure 4.9: Wind direction and speed, and wind component plots for September 4, 2014. Recorded by MetroVancouver.68Table 4.2: Summary of PM concentration data during train passages onSeptember 4, 2014, recorded by GRIMM OPC.Size Range Minimum (g/m3) Maximum (g/m3) % IncreaseTrain 1 - Freight - 14:05 PDTPM10 8.24 11.2 35.3Train 2 - Coal - 15:58 PDTPM10 6.72 8.70 29.5Train 3 - Coal - 16:13 PDTPM10 5.58 7.78 39.4Train 4 - Freight - 16:56 PDTPM10 4.85 7.20 48.5Train 5 - Coal - 17:19 PDTPM10 4.20 7.16 70.5Train 6 - Freight - 20:41 PDTPM10 16.8 58.5 248.2Train 7 - Unknown - 22:22 PDTPM10 16.5 30.6 85.5694.2 DiscussionExamining a series of individual train passages helps to determine the range ofpossible outcomes associated with a passing train. There are certainly instances ofsharp increases in PM concentration with a passing coal train, but there are alsoincreases associated with passing freight trains. From the data presented here, it isclear that the passage of a train is not guaranteed to result in an increase of PMconcentration. However, when a PM increase occurs, it does tend to be across bothfine and coarse modes. A potential explanation for this relates back to the threepotential sources of PM associated with a train passage. According to previouswork (Abbasi et al., 2013; Ja↵e et al., 2014), PM can be generated by a passingtrain through various processes:• the diesel locomotive itself• particles formed from the wearing down of rails and other materials• particles on the ground introduced into the air due to the turbulence causedby the passing train• in the case of coal-carrying trains, coal dust lost into the air from the opencarsIt is dicult to discriminate between these sources without chemical analysis(which was not performed as part of this study), but there are various indicatorsthat suggest what processes are at work. Size segregated measurements can helpdiscern between the various sources. In previous studies, 90% of DPM was shown tobe smaller than 1.0 µm in diameter (Liukonen et al., 2002), suggesting that a strongincrease in concentration at larger size ranges would not be due to DPM. Since theresults show an increase across all size ranges, it appears that more than just DPMis contributing to the PM enhancement during train passages. Moreover, the resultsof the dustfall study showing the presence of coal particles in the collected samplesshows the influence of coal-carrying trains as a PM source. The PM enhancementduring some freight train passages serves as evidence of the non-exhaust sources(wearing down of materials, and ground particles) also being present. As somefreight trains are associated with an increase in PM across all size ranges, the coarsemode particles would likely be generated from a combination of these two sources(Abbasi et al., 2013). As the largest concentration increase during a coal train70passage occurs with PM3 (Section 3.6.2), it would appear that the instruments setup at 50 m were most a↵ected by the smaller particles associated with DPM, ratherthan the other non-exhaust sources.There is some evidence that wind direction is the most important factor in de-termining if an enhancement will be seen at a certain location beside a linear source.The data for September 4, 2014 shows that none of the train passages during theday are associated with an increase in PM. The wind direction at this time showeda negative cross-railway component, indicating upwind conditions. However, the re-versal of wind direction at night appears to drive the increase in PM concentrationassociated with the train passage at 20:41 PDT. The wind speed during all of thedays examined in this chapter was relatively low (typically between 1-2 m/s), sug-gesting a small degree of mixing of pollutants in the atmosphere. Due to this, plumemovement may not be as pronounced as in other situations. In complex coastal ter-rain, any e↵ect of pollutant sources will be strongly dependent on phenomena suchas land/sea breeze circulations.The mini-MPL served as a useful tool in examining plume movement, as well asconfirming some of the OPC data. The plumes that are visible in the lidar imagerydemonstrate the various sources that are present at the site, indicating its utility inthe qualitative examination of a site’s sources and sinks. However, given the shortrange of the mini-MPL when operated horizontally, there are certain drawbacksassociated with its usage.Moreover, the analysis of this data from a case study perspective illustrates thetrends and patterns that are not identified by general statistical tests. The resultsof these case studies show that the issue of PM enhancement is quite complex, andrequires large amounts of data and a coordinated data collection plan.71Chapter 5Conclusions5.1 Key FindingsThe statistical analyses performed in Chapter 3 yielded several key conclusions:• The increase in PM concentration associated with a train passage of any typeis modest and highly variable (average increases of 0.21, 0.44, and 0.23 µg/m3were found for PM3, PM10, and PM20, respectively)• Observed PM concentration is highly dependent on wind direction. Downwindconditions are associated with higher PM concentrations than upwind condi-tions. For control condtions, the mean enhancement due to wind direction is2.35, 1.61, and 1.70 µg/m3 for PM3, PM10, and PM20, respectively• Coal trains were associated with a statistically significant (↵ = 0.01) PMconcentration increase of 53.8%, 33.3%, and 17.0% for PM3, PM10, and PM20,respectively, at a distance of 50 metres from the tracks• PM3 represented 65.5% of total suspended particulates (TSP) during controlconditions, compared to 86.1% during coal train passages. PM10 represented82.3% of TSP during control conditions, and 93.8% of TSP during coal trainpassages• Train passages have an identifiable e↵ect on PM concentrations at both the15 m and 50 m sites72• The variability of PM concentrations during train passages sharply decreaseswith distanceWhen the lidar data was analyzed in conjunction with high temporal resolu-tion PM concentration and wind data, the results expand on the conclusions fromChapter 4. The high resolution data shows:• A PM concentration enhancement was not present for all observed train pas-sages• When a PM concentration enhancement was observed, it tended to be presentacross all measured size ranges• There is evidence of PM concentration enhancements being present with bothfreight and coal train passages• In some instances, plumes generated from a train passage could be seen onthe backscatter output• Wind direction strongly influences observed PM concentrations, as evidencedby the results from September 4, 2014 (Section 4.1.3)• There is evidence to suggest the presence of both exhaust (DPM), and non-exhaust sources (wearing down of railway materials, ground-level particlesbeing disturbed by turbulence, and coal dust from coal-carrying trains)Overall, the usage of a variety of data sources in addition to wind data is crucialin determining the presence of a PM enhancement e↵ect due to passing trains.Statistical analysis of a suciently large data set is useful in quantifying any e↵ectsfrom passing trains, but a more in-depth look at individual train passages providesconclusions that are helpful from a more practical standpoint. Due to the short-term nature of a train passage, it is unlikely that the enhancement e↵ect due topassing trains would cause a violation of current PM standards, as these standardsare measured on much longer time scales.735.1.1 Study Limitations & Future ResearchThe results from this study present numerous interesting possibilities for futurework. Firstly, the reliability problems surrounding some of the data recorded by theGRIMM may be resolved with a setup less sensitive to adverse weather conditions.Furthermore, due to the sharp decrease in variability between the 15 m and 50 msites (Section 3.5), it is likely that placing the GRIMM closer to the railway wouldresult in a higher sensitivity to any e↵ect caused by the railway.The results from Section 3.5 demonstrate an opportunity for more research tobe conducted on the spatial decay of PM generated by a railway. The locationused in this study was not optimal for investigating this, due to the influence ofthe highway nearby. More PM measurements at several distances from a railwaywould be helpful in characterizing the relationship between PM concentration anddistance.In addition, studies of this nature at a di↵erent site would be quite useful inexamining how local meteorology processes govern any e↵ects caused by a railway.Understanding the e↵ects of local meteorology would serve to better predict theimpact that rail trac would have on any residents who happen to live in closeproximity. The results from the Corporation of Delta dustfall study show that theamount of observed coal dust di↵ers based on the time of year. More research oncoal dust during the winter would be useful in exploring this phenomenon undera wider range of meteorological conditions. There has been previous evidence ofcertain coal trains that emit very large amounts of coal dust (Ja↵e et al., 2014), butthere is little knowledge on how to predict these events. Furthermore, the detectionof strong PM concentration enhancements caused by coal trains may be due to theexperimental set up used in this study. Particle loss within the Teflon tubing ofthe GRIMM caused by sedimentation and impaction may have underestimated PMconcentrations, especially at larger particle sizes.The limitations presented by the small sample size of the train passage datapresented in this study could be rectified by a more robust methodology of trainidentification. Potential ways to achieve this include motion-activated cameras thatcan operate in night-time conditions, similar to those used in Ja↵e et al. (2014). Inaddition, instrumentation that can measure a wider variety of pollutants such asCO2 can serve to supplement the analysis of PM concentration. The nature of coaldust generation from trains is also somewhat unclear without more information onthe chemical surfactants used by train companies to prevent coal dust from leaving74the cars. 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Thesis/Dissertation
eng
Atmospheric Science
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivs 2.5 Canada
Graduate
Impact of coal-carrying trains on particulate matter concentrations in the Lower Mainland region of British Columbia, Canada
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