Ground-Based Lidar and Air Quality Observations on Grouse Mountain, British Columbia During the Summer of 2018

Ground-based LiDAR and air quality observations on Grouse Mountain, British Columbia during the summer of 2018

Carrington Pomeroy

B.Sc., Carleton University, 2017

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

Master of Science in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Geography)

THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)

October 2019 ©Carrington Pomeroy, 2019 The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled:

Ground Based LiDAR and Air Quality Observations on Grouse Mountain, British Columbia During the Summer of 2018

submitted by: Carrington Pomeroy in partial fulﬁllment of the requirements for the degree of Master of Science in Geography

Examining Committee:

Ian McKendry, Geography Supervisor

Paul Cottle, Geography Supervisory Committee Member

Brett Eaton, Geography Additional Examiner

ii Abstract

Widespread and persistent summer multi-day episodes characterized by dense layers of wildfire smoke emanating from western wildfires have increased in frequency in recent years across western Canada. These events often occur under otherwise clear sky anti-cyclonic weather conditions and have significant impacts on surface temperatures, surface radiation and energy budgets. Here, we present previously undocumented mountain-top, wildfire influenced particulate matter concentrations and compare them to those recorded in the valley. The distribution of particulate matter both temporally and spatially is presented as well. The focus of this observational study is in the vicinity of Grouse Mountain, near Vancouver, British Columbia. Observations are made using a GRIMM 1.108 Dustcheck mini-mass-spectrometer, a Dylos DC1100 Pro air quality monitor, a mini micropulse LiDAR (light detection and ranging) and vertical sounding using mini sondes (WINDSOND). The Hybrid Single Parti- cle Langrangian Integrated Trajectory (HYSPLIT) air pollution modelling software is used to track parcels of wildfire smoke. Results show enhanced mountain-top particulate matter concentrations with many instances displaying higher concentrations on Grouse than in the valley, most commonly under anti cyclonic conditions. Evidence of a mountain boundary layer in the presence of smoke is presented, as well as signs of suppressed convective venting and more stable vertical profiles, likely due to the radiative effects of smoke.

iii Lay Summary

The interaction between wildfire smoke and mountain top environments has been sparsely addressed in the literature. This study examines the effects of wildfire smoke on particulate matter concentrations and distribution on Vancouver’s Grouse Mountain during the summer of 2018. The difference between air quality measurements taken in Vancouver and on top of Grouse during wildfire events is of particular interest. Results show that when wildfire plumes arrive from the northern, mountainous regions, particulate matter concentrations on Grouse increased well before those in Vancouver. The atmosphere is also shown to be more stable in the presence of smoke and mountain flow processes are shown to still function, albeit with less effect than normal. These results begin to fill knowledge gaps in air pollution and weather modelling as well as air quality monitoring and advisory.

iv Preface

The research topic and study design was developed by Carrington Pomeroy and Ian McK- endry. Quality assurance and quality control for LiDAR data was performed by Paul Cottle. Data from Windsond balloon launches was collected by Carrington Pomeroy, Madison Fer- rara and Dylan Weyell. The ﬁnal manuscript was prepared by Carrington Pomeroy with editing by Ian McKendry, Paul Cottle and Allan Betram. Figures 4.5 and 4.6 appear in a paper that has been submitted for publication:

Ferrara, M, Pomeroy, C, McKendry IG, Stull, R and Strawbridge, K. 2019. Suppression of Mountain Convective Boundary Layer ”Handover” Processes by Persistent Wildﬁre Smoke over Southwestern British Columbia

v Contents

Abstract ...... iii

Lay Summary ...... iv

Preface ...... v

Contents ...... vii

List of Tables ...... viii

List of Figures ...... xi

List of Symbols ...... xii

Acknowledgements ...... xiii

1 Introduction ...... 1 1.1 Background ...... 2 1.1.1 Dispersal of Aerosols ...... 2 1.1.2 Known Responses to Aerosols ...... 4 1.1.3 Potential Impacts in Mountainous Regions ...... 5 1.2 Research Objectives and Thesis Structure ...... 8

2 Experimental Methods and Analysis ...... 10 2.1 Description of Study Area ...... 10 2.1.1 Geography of the Lower Fraser Valley ...... 10 2.1.2 Climate of the Lower Fraser Valley ...... 11 2.1.3 Mesoscale Circulations within the Lower Fraser Valley ...... 12 2.2 Instrumentation and Data Analysis ...... 14 2.2.1 GRIMM 1.108 Aerosol Spectrometer ...... 14 2.2.2 MetroVancouver’s Instrument Network ...... 15

vi 2.2.3 Dylos ...... 17 2.2.4 LiDAR ...... 19 2.2.5 Data Analysis Methods ...... 23

3 Non-Smoke Particulate Matter Concentrations and Distribution . . . . . 24

3.1 PM2.5 Spatial Variability ...... 25 3.2 LiDAR Imagery ...... 27

3.3 GRIMM PM2.5 Concentrations ...... 31 3.4 Conclusion ...... 33

4 Smoke Event Particulate Matter Concentrations and Distribution . . . . 35

4.1 PM2.5 Spatial Variability ...... 36 4.2 LiDAR Imagery ...... 38

4.3 GRIMM PM2.5 Concentrations ...... 43 4.4 Case Study on August Fire Event ...... 45 4.5 Conclusions ...... 54

5 Conclusion ...... 56 5.1 Key Findings ...... 56 5.2 Limitations ...... 57 5.3 Future Studies ...... 58

Bibliography ...... 60

vii List of Tables

2.1 Summary of Field Data Collection ...... 23

viii List of Figures

1.1 A Hybrid Single Particle Langrangian Integrated Trajectory (HYSPLIT) model demonstrating short range smoke transport. Adapted from McKendry et al. (2011)...... 3 1.2 A HYSPLIT model demonstrating long range smoke transport. Adapted from Takahama et al. (2011)...... 4

1.3 Daily PM2.5 readings taken at Vancouver International Airport in 2017 (Source: MetroVancouver) ...... 5 1.4 The different mountain airflow processes. E represents entrainment; MV, mountain venting; AV, advective venting; and MCV, mountain-cloud venting. Vectors show directions of airflow while c(z) and (z) represent vertical profiles of pollutant concentration and potential temperature respectively. Adapted from De Wekker and Kossmann (2015) ...... 6 1.5 A visual representation of the different atmospheres that arise in a mountain environment. Adapted from De Wekker and Kossmann (2015) ...... 7

2.1 Map of locations mentioned in text. Source: Google Maps ...... 11 2.2 Depiction of airﬂow and boundary layer processes over urbanized complex coastal terrain. Adapted from (McKendry and Lundgren, 2000) ...... 13 2.3 Pictures of the GRIMM and its location on Grouse Mountain ...... 15 2.4 Linear regression done on the readings taken from the collocated GRIM and MetroVancouver FEM instruments...... 16 2.5 Pictures of the Dylos and the manner in which it was carried by the surveyor. 18 2.6 The path followed during the walking surveys and diﬀerent locations mentioned in the text ...... 19 2.7 The location of the LiDAR with respect to Grouse Mountain...... 21 2.8 An overview of a windsonde launch. Retrieved from www.windsond.com 2019/08/15 ...... 22

3.1 Figure3.1: Relative PM2.5 concentrations observed around Grouse Mountain’s peak area on July 14th, 2018...... 26

ix 3.2 Averaged relative PM2.5 concentrations observed around Grouse Mountain’s peak area for the nine surveys done on clear days during the summer of 2018. 27 3.3 Ground based, upward facing LiDAR measurements taken from near the base of Grouse Mountain, from June 16th to 21st, 2018...... 28 3.4 Ground based, upward facing LiDAR measurements taken from near the base of Grouse Mountain, from July 23rd to 29th, 2018. The arrow in this ﬁgure points to what are potentially remnants of wildﬁre emissions from Siberia and Alaska...... 29 3.5 Incoming radiation, ozone, PM, temperature and windspeed measurements recorded at Mahon Park, North Vancouver during the ”photochemical smog event”...... 30 3.6 300h HYSPLIT backwards dispersion trajectories run from Grouse Mountain July 26th ...... 31

3.7 PM2.5 concentrations observed on Grouse Mountain and at North Vancouver’s Mahon Park on July 17th and 23rd, 2018...... 32

3.8 PM2.5 concentrations observed on Grouse Mountain and at North Vancouver’s Mahon Park on July 28th and 29th, 2018, during the “photochemical smog event”...... 33

4.1 NOAA’s HMS Imagery(left) and MODIS TERRA satellite imagery (right) on August 13th 2018 ...... 35

4.2 Sample survey of relative PM2.5 concentrations observed around Grouse Moun- tain’s peak area in the presence of wildﬁre smoke on August 17th, 2018. . . . 37

4.3 Averaged relative PM2.5 concentrations observed around Grouse Mountain’s peak area for the nine surveys done on smoke days during the summer of 2018. 38 4.4 Ground based, upward facing LiDAR measurements taken from near the base of Grouse Mountain, from August 13th to 19th, 2018. red arrow points to the main plume of wildfire smoke that affected the LFV in 2018. The red dashed line shows the elevation of Grouse Mountain. The white brackets are used to show smaller plumes...... 39 4.5 Average vertical profiles of potential temperature taken from the base of Grouse Mountain. Profiles from clear days are shown in blue while smoke days are shown in black...... 41 4.6 Vertical profiles superimposed on the earlier LiDAR imagery ...... 42

x 4.7 HYSPLIT backwards dispersion trajectories that were run. The origin of the back trajectories is Grouse Mountain and the models were run for 48 hours starting at 12pm on August 13th, 14th and 15th ...... 42 4.8 HYSPLIT backwards dispersion trajectories that were run. The origin of the back trajectories is Grouse Mountain and the models were run for 48 hours starting at 12pm on August 16th, 17th and 18th ...... 43

4.9 PM2.5 concentrations observed on Grouse Mountain and at North Vancouver’s Mahon Park on August 13th during the day, the afternoon of August 15th and August 16th, 2018...... 44 4.10 Aerosol optical depth values measured at Saturna Island, just southwest of Vancouver over the course of August 2018...... 46 4.11 View of Cypress and Grouse Mountain from Jericho Beach on a clear day (left) and August 20th (right)...... 47 4.12 Ground based, upward facing LiDAR measurements taken from near the base of Grouse Mountain, from August 13th to 25th, 2018. The red arrow points to the main plume of wildfire smoke that affected the LFV in 2018. The red dashed line shows the elevation of Grouse Mountain...... 47 4.13 Ozone, PM, incoming radiation, temperature and windspeed measurements recorded at Mahon Park, North Vancouver during 2018’s main fire event. . . 49 4.14 HYSPLIT backwards dispersion trajectories that were run using EDAS 40km meteorological inputs. The origin of the back trajectories is Grouse Mountain and the models were run for 300 hours starting at 12am on August 19th. . . 50 4.15 A map showing the composite mean pressure using isobars along the west coast of North America for the week of the smoke event...... 51 4.16 A map of all active fires in BC on August 18th 2018. Flames represent fires that present a threat to public safety, red dots are fires that started within the past 24h and orange dots are fires that do not represent threats to public safety...... 52

4.17 PM2.5 concentrations observed on Grouse Mountain and at North Vancouver’s Mahon Park on August 18th, August 19th and the morning of August 20th, 2018...... 53

4.18 PM2.5 concentrations observed at North Vancouver’s Mahon Park from August 19th to August 26th ...... 54

xi List of Symbols

Symbol Deﬁnition Units −3 PM2.5 particulate matter with a diameter < 2.5µm µ g m −3 PM10 particulate matter with a diameter < 10µm µ g m S(z, λ) signal received at the detector counts/µ s

E0 energy of laser pulse J A usable area of the receiver km2 z height km counts γ full system efficiency constant −µs∗µJ O(z) overlap correction function - 2 −1 βπ volume backscatter coefficient km sr α extinction coefficient - −1 SB background signal counts µs d(S) deadtime correction factor - a(z) afterpulse correction factor m counts/µs

xii Acknowledgements

This thesis would not have been possible without the support of many people. I would first like to express sincere thanks and gratitude to Ian McKendry, for his unwavering support, patience and guidance throughout my time at UBC. His hands-off approach allowed me to grow as a researcher and his thoughtful, optimistic demeanor meant that no problem seemed too big. I would also like to thank my committee members, Paul Cottle and Allan Bertram for providing support for this project as well as honest and useful advice. As well as Dylan Weyell for the time and effort he brought to this project. This project would not have been possible if not for the generosity of the Weyell family and the management of Grouse Mountain. Having a safe location to store equipment made this project significantly easier. Accordingly, I would like to thank the Weyells, Erik Bowkett and the entire staff of Grouse Mountain for being so accommodating. I would like to extend thanks to the Natural Sciences and Engineering Research Council, UBC Geography and Diana L. Belhouse (by way of the Henry C. Belhouse scholarship) for providing funding this project and many others. Although they may not think they helped; my friends in Vancouver, particularly Stefan and Claire, made this experience unquestionably easier and more enjoyable. Similarly, the support and advice I’ve gotten from Kieran Jones throughout this process and most of my academic career can’t go unmentioned, his ability to listen and talk through things has been invaluable. As always, family has played a large role in this pursuit. Thank you to my David and Diane Bond for their keen interest in my education and their company during holidays out west. Thank you to my parents, John Pomeroy and Lee Anne Johnston, for their unwavering love and support in my pursuits. I cannot thank them enough for the opportunities they have provided me with. Last but not least, I would like to thank my partner, Isabelle MacLean, for her patience, her belief in me and for providing unmatched inspiration.

xiii Chapter 1

Introduction

Over the past few decades, wildfires have been increasing in severity and frequency in western North America due to a gradual change in climate and increased human involvement(McClure and Jaffe, 2018). The gradual change in climate has resulted in longer, hotter and drier summers as well as an expansion of the latitudinal range of the mountain pine beetle, all of which result in an increase in a forest’s susceptibility to fire (Carroll et al., 2004; McClure and Jaffe, 2018). The increase in human activity in forests has also had this effect due to suppression of past fires resulting in larger present fires and increased exposure to fire (Fusco et al., 2018). As this trend is projected to continue (IPCC , 2014), understanding the impacts of wildfires is becoming increasingly important. Along with the undeniable destruction caused by wildfires, the smoke emitted from the burning materials can have important impacts. Air quality downwind of wildfires can quickly deteriorate due to the small solid particles and other compounds that are generated either directly from the source or indirectly through the reaction between sunlight and the emitted organic compounds (Logan et al., 2013). These small solid particles are called aerosols or more specifically, Particulate Matter (PM) and are of particular interest in this study. The smaller size ranges (PM2.5) are capable of increasing risk of respiratory disease, particularly in populations susceptible to cardio-pulmonary ailments, as well as affecting regional climate and biogeochemical processes by scattering and absorbing solar radiation. Wildfire induced changes to air quality have been well documented in different environments, with urban areas being of particular interest. Yet, due to the complicated interactions mountains have with airflow, little is known about the quality of air and the concentrations of aerosols in mountain environments. Similarly, little is known regarding the effects of mountains on the distribution of aerosols in their surrounding regions. In 2018, British Columbia experienced a second consecutive exceptionally active wildfire season that led to several air quality advisory warnings in the City of Vancouver. There were

1 multiple extended periods with recorded PM2.5 values that exceeded the Canadian Ambient Air Quality Standards(CAAQS) of 28 µg/m3. These values, recorded by a network of sensors throughout the Vancouver area, did not, however, account for one significant area. Grouse Mountain, hereafter referred to as ’Grouse’, is one of the most popular summer tourist destinations in Vancouver, British Columbia. Dubbed ”The Peak of Vancouver”, it is located 15 minutes north of Vancouver’s downtown core and is easily accessible for visitors by car or public transit(Grouse Mountain, 2014). It offers restaurants, outdoor activities and, at 1200m above sea level, a beautiful view of the city. Every summer, 150000 people hike up Grouse’s famous ”Grouse Grind” trail (Grouse Mountain, 2018) while others prefer to take the famous ”skyride” cable car. With daily visits approaching the thousands, Grouse was named Vancouver’s top tourist destination in 2014 (Grouse Mountain, 2014). As there is a limited resident population on Grouse, it is understandable that there is no routine fixed monitoring as is the case in the urbanised Fraser Valley. The second half of this chapter will outline literature that collectively suggests that air quality measurements taken at Grouse could be of great interest.

1.1 Background

1.1.1 Dispersal of Aerosols

Surface air quality can be degraded by aerosols both close to and far from the source. The path aerosols take through the atmosphere depends on many variables including local and regional synoptic conditions(McKendry and Lundgren, 2000), topography and global circulation patterns. Additionally, the chemical, optical and physical properties of aerosols can change as they travel(McKendry et al., 2011). Pollutant dispersal across the surface of the Earth has long been a focus of air quality research. Recently, numerous methods to simulate and predict the transport and dispersion of pollutants have been developed(McKendry and Lundgren, 2000). Developments in both ground-based and satellite-borne remote sensing technologies such as LiDAR (light detection and ranging), have led to signiﬁcant advances in the understanding of aerosol distribution. Using these technologies facilitates the tracing of emission transport and dispersion over various scales. (Amiridis et al., 2009; McKendry et al., 2011). Under stable conditions, plumes are conﬁned to a shallow atmospheric boundary layer, leading to enhanced aerosol concentrations in the local region. Figure 1.1, shows a case of medium range smoke transport. Here, smoke traveled to Vancouver from Northern California while being contained to lower elevations by an elevated inversion (McKendry et al., 2011).

2 Figure 1.1: A Hybrid Single Particle Langrangian Integrated Trajectory (HYSPLIT) model demonstrating short range smoke transport. Adapted from McKendry et al. (2011).

When in proximity to mountains or under unstable conditions, aerosols can be lifted to higher elevations. Once in the free troposphere, large-scale circulation systems can transport them hundreds to thousands of kilometers from the source and impact air quality in these downwind regions (Figure 1.2)(Kaulfus et al., 2017). For example, in 2012 Teakles et al. (2017) and Cottle et al. (2014) documented the transport of smoke from Siberian wildfires to the Pacific Northwest region. Similarly Takahama et al. (2011) observed transpacific transport of smoke from a volcanic eruption in Russia.

3 Figure 1.2: A HYSPLIT model demonstrating long range smoke transport. Adapted from Takahama et al. (2011).

1.1.2 Known Responses to Aerosols

Upon arrival in different ecological settings, aerosols can have various impacts both directly and indirectly, depending on the nature o f the environment. The radiative forcing impacts of aerosols, both direct and indirect, demonstrate significant variability in space and time. Often, when particles are in sufficient concentrations in the atmosphere, the earth absorbs less radiation and the surface experiences cooling (Amiridis et al., 2012). This was observed in 2010 during a wildfire near Boulder, Colorado where the ground beneath the plume was cooled by 2-5oC(Liu et al., 2014). Similarly, Feingold et al. (2005) found that aerosols in a daytime convective boundary layer would warm the top of smoke/aerosol layers and affect cloud formation. In this case, the warming due to the smoke absorbing solar radiation, coupled with the simultaneous cooling of the ground and the lower atmosphere, would make the atmosphere more stable and, therefore, suppress cloud development. With less cloud formation, there is less water input from precipitation, leading to potential drought like conditions(Tosca et al., 2010). The net change in air temperature depends on the magnitude of the absorption and the resulting change in sensible heat flux on the ground surface(Liu et al., 2014). Additionally, a

4 Figure 1.3: Daily PM2.5 readings taken at Vancouver International Airport in 2017 (Source: MetroVancouver) change in atmospheric thermal structure due to the direct radiative forcing of particles will further change regional circulation(Booth et al., 2012). PM can be broken down into two diﬀerent categories based on size: particles with a diameter less than 10 micrometers (PM10) and particles with a diameter less than 2.5 micrometers

(PM2.5). PM2.5 is of primary concern as particles with a diameter of 5 micrometers or less can penetrate the lower respiratory tract and irritate the alveoli and bronchioles(Fowler, 2003). Symptoms of over-inhalation of PM include respiratory problems such as shortness of breath, coughing, wheezing, respiratory tract inﬂammation and discomfort when breath- ing(Fowler, 2003). The CAAQS of 28 µgm−3 has been eclipsed regularly in the presence of smoke. (Figure 1.3) shows PM2.5 readings taken at Vancouver International Airport during 3 a wildﬁre event in September 2017. This period had a max 24h PM2.5 reading of 32.4 ug/m .

1.1.3 Potential Impacts in Mountainous Regions

Almost half of the Earth’s land surface is covered by complex, uneven terrain(De Wekker and Kossmann, 2015). Due to the variety in terrain shape, interactions with airﬂow are quite

5 complex. Similarly, due to the size of features and the complexity of the earth’s systems, their eﬀects can be felt in far away places (De Wekker and Kossmann, 2015).

Mountain Flow Processes

There are multiple different processes by which mountains can affect the dispersal of aerosols and they can be separated into active and passive effects (Kossmann and Sturman, 2003; Teixeira et al., 2016). Active effects include thermally driven wind systems such as slope flows and mountain venting that are generated by horizontal temperature and pressure differences(Zardi and Whiteman, 2013). Slope flows are caused by daily radiative warming of mountain slopes. A warm slope surface generates winds in the upslope direction. When aerosols are present in the system, this process forces them up to higher elevations(De Wekker and Kossmann, 2015). This also results in a weak return flow at higher elevations in order to maintain continuity, resulting in polluted air being injected horizontally into elevated inversion layers. Mountain venting, also called the chimney effect, occurs when strong slope flows and updrafts are forced through an overhead inversion. When this occurs, pollutants can be injected into the free troposphere and possibly form elevated pollutant layers (Figure 1.4) (McKendry and Lundgren, 2000; De Wekker and Kossmann, 2015).

Figure 1.4: The different mountain airflow processes. E represents entrainment; MV, mountain venting; AV, advective venting; and MCV, mountain-cloud venting. Vectors show directions of airflow while c(z) and (z) represent vertical profiles of pollutant concentration and potential temperature respectively. Adapted from De Wekker and Kossmann (2015)

6 Passive effects involve momentum exchange between the surface and the atmosphere, and occur when a flow is modified by the presence of mountains(Richner and Hächler, 2013). Examples include flow blocking, flow channeling, and lee waves. However, these processes are not the sole determinant of how mountains affect airflow. Considerable day- to-day variability has been found in the trends of aerosol and meteorological variables at the Whistler Observatory (seen below in Figure 2.1), reflecting the impact of frequently changing synoptic-scale weather conditions(Gallagher et al., 2011, 2012).

Mountainous Boundary Layer Eﬀects

Planetary Boundary Layer (PBL) heights and the related vertical transport and mixing processes have recently gained attention for their role in explaining uncertainties in estimating air pollution and greenhouse gas budgets. The PBL is usually deﬁned as the atmospheric layer that interacts directly with the Earth’s surface. Over mountainous terrain, the atmospheric structure becomes much more complicated(De Wekker and Kossmann, 2015). There are four generally acknowledged separate atmospheres that pertain to mountain environments, the free, mountain, valley and slope atmospheres. (Figure 1.5).

Figure 1.5: A visual representation of the diﬀerent atmospheres that arise in a mountain environment. Adapted from De Wekker and Kossmann (2015)

Relevant to pollutants, light synoptic-scale winds allow thermal flows to drive the vertical transport of PBL air to the mountaintop level(Gallagher et al., 2011), elevating pollutants to a layer near the mountain peak(Henne et al., 2005). Understanding of such elevated layers of pollution is vital as they represent a potential sink for pollutants from the PBL, thereby effectively ventilating the PBL and influencing tropospheric chemistry. These layers also

7 have the potential to be mixed down to ground and contribute to pollutant concentrations that inﬂuence human and vegetation health(McKendry and Lundgren, 2000).

Aerosol Distribution

Owing to the complex interactions mountains have with airflow, they could potentially have a large impact on how aerosols are distributed. Similarly, aerosols could govern how mountain flow processes proceed. For example, as mountain environments are exposed to wildfire smoke, both mechanical and thermotopographic processes could be affected. An inversion caused by elevated aerosols could prevent most of the air from being injected into the free troposphere and cause it to be recirculated downwards. Similarly, reduced radiation received at the surface would slow down slope flow processes. This could potentially change how mountain circulations evolve during smoke events.

Urban Mountaintop Chemistry

Other areas of interest include mountaintop level aerosol concentrations, speciﬁcally on well traveled mountains such as Grouse. Of particular interest is mountaintop PM2.5 concentrations during wildﬁre events; McKendry et al. (2011) measured CO and O3 mountaintop values at the Whistler Atmospheric Chemistry station during the 2008 smoke events but so

far, PM2.5 measurements are lacking. Considering there was a noticeable increase in both 3 CO and O , it is likely that PM2.5 levels would increase as well.

1.2 Research Objectives and Thesis Structure

Based on the existing literature, there is a high likelihood that, due to thermo-topographic processes during wildfire events, air quality in affected mountain areas will differ greatly from those at lower elevation valley locations. Consequently, over the course of this dissertation I aim to answer the following questions:

• How does air quality on Grouse Mountain compare to other areas around Vancouver, and how does this change during a wildﬁre event? Does Grouse Mountain experience high concentrations of PM during smoke events and are these concentrations high enough to deleteriously impact human health?

• How do Vancouver’s local mountains aﬀect local and regional aerosol distribution? Do the mountains aid in the removal of PM from the valley? Does this process change under smoke conditions?

8 This study will generate new information on the concentrations of aerosols on an urban mountaintop environment, specifically during a wildfire event. This is relevant to towns situated in mountains near urban centres such as Park City, Utah, and Lake Tahoe, Califor- nia. Similarly, the results will have implications for mountains that act as summer tourist destinations such as Whistler-Blackcomb; Yosemite, California; Mt Hood, Oregon and; of course, Grouse Mountain. These results will also advance knowledge on the effects of mountains adjacent to urbanised areas on aerosol distribution. This will help improve accuracy when modeling aerosol distribution from sources such as forest fires, factories or dust events. This has important implications for public health analysts and weather predictions. With extreme events, such as the 2010 dust event and 2017 and 2018 smoke events, occurring more frequently, data derived from these events will be extremely useful when predicting dust or smoke distribution within a mountain PBL. The following chapters are designed around these two questions. Chapter 2 presents the study location and its associated climate and geography. The methods of data collection and analysis are also presented, including the methods used for instrument calibration. Chapter 3 concentrates on the background air quality found on Grouse and how it com- pares to Vancouver proper. The spatial variability of aerosols in non-smoke conditions is presented, sources and sinks are proposed, and concentrations are compared to an instrument network in lower elevation Vancouver. Chapter 4 addresses the changes that occur at Grouse and corresponding nearby areas under the influence of wildfire emissions. The topics covered in Chapter 3 are addressed again and a case study examining synoptic conditions during these events is presented. Chapter 5 provides a summary of the findings of the preceding two chapters, as well as placing the work within a real-world context and suggesting possible future avenues of research.

9 Chapter 2

Experimental Methods and Analysis

2.1 Description of Study Area

2.1.1 Geography of the Lower Fraser Valley

The Lower Fraser Valley (LFV) in Southwestern British Columbia, includes Vancouver, one of Canada’s largest cities, and a variety of complex terrain (Figure 2.1). This landscape includes features such as coastlines, agricultural land, mountains, and urban centres. The North Shore Mountains, including Grouse Mountain, Cypress Mountain, and Mount Sey- mour, form the northern boundary of the valley while the North Cascade Mountains comprise the barrier to the south. The city of Vancouver is situated on the western edge of the valley and is adjacent to the Strait of Georgia, a large body of water separating Vancouver’s Lower Mainland and Vancouver Island. To the east, the valley is dominated by agricultural land and smaller urban centres.

10 Figure 2.1: Map of locations mentioned in text. Source: Google Maps

2.1.2 Climate of the Lower Fraser Valley

The climate of the region is primarily controlled by seasonal shifts in the position and strength of the jet stream. In late fall and winter, a strong westerly jet stream results in the frequent passage of frontal systems over the region that often bring rain to lower elevations and snow to the mountains (Oke and Hay, 1994). The presence of the ocean to the west also has a large impact on the LFV’s climate. This large body of water moderates local temperatures and acts as the source of the majority of the precipitation that falls in this region (Mass, 2009). This temperature moderation results in very mild winters with temperatures rarely falling below 0oC. However, cold temperatures do occur when cold continental air is pushed over the Rocky Mountains (to the North-East) and undercuts a marine air-mass (Mass, 2009). Most of the annual precipitation falls during winter months, in part deﬁning the LFV as a Mediterranean climate.

11 In the summer (the present study period), the northward migration of the jet stream results in persistent anti-cyclonic conditions in this area. An upper-level ridge is common over the LFV during summer months and leads to stable, fair weather conditions near the surface. Summers are typically dry in Southwestern British Columbia compared to winters with only about 10% of annual precipitation occurring between June - August (EnvironmentCanada). Weather observations taken at the Vancouver International Airport (YVR) between 1981 - 2010 show an average temperature of 18 oC for July and August and 14.9oC for September. The station at YVR is about 2 km from the coast and a few meters above sea-level and is therefore aﬀected greatly by the ocean temperature moderation. Consequently, areas further inland from the coast experience a slightly higher average temperature during summers and high elevation areas experience slightly lower average temperatures.

2.1.3 Mesoscale Circulations within the Lower Fraser Valley

Mesoscale air circulation patterns are commonly the result of pressure gradients caused by differential heating of a landscape and often arise in areas of complex terrain. In Vancouver, the ocean and mountains result in the formation of two different mesoscale circulations, a land/sea breeze and mountain-valley breezes. They are ubiquitous throughout the summer and can play a significant role in the distribution of pollutants when present (McKendry and Lundgren, 2000). Land/sea breeze circulations are common in the presence of a coastal environment. Wa- ter’s relatively large thermal inertia, compared to land, results in the land heating much more rapidly than the ocean during the day. When larger scale flow is relatively weak, a horizontal pressure gradient can form perpendicular to the coast during the day, bringing marine air from the cold ocean to the warm valley (Figure 2.2 number 2). These processes become particularly pronounced during Vancouver’s summers due to the light winds and solar forcing that arise from the common, clear anticyclonic conditions. Sea-breezes of around 3m/s in this region have been observed extending to Abbottsford about 60% of the time when the breeze is present (Oke and Hay, 1994). At night, the direction of the pressure gradient switches due to the land cooling much more rapidly than the ocean. This results in a near-surface wind flowing from the valley to the coast (Figure 2.2 number 1). This breeze is normally weaker than the sea-breeze, with an average of 2 m/s (Oke and Hay, 1994). Local scale pressure gradients are also responsible for the formation of mountain-valley breezes. As sloped terrain is heated during the day, the near-surface air is heated faster than the adjacent air. Again, this results in a horizontal pressure gradient that pushes wind toward the slope, where it is then angled upwards by the physical presence of the slope. This

12 flow is referred to as upslope or ”anabatic” wind and as discussed in the previous chapter, is the dominant driver of mountain venting(Figure 2.2 number 5). Moreover, to compensate for this upslope flow, air over the valley subsides and brings air from higher elevations towards the surface (Rampanelli and Zardi, 2004), potentially lowering pollutants into the valley (Figure 2.2 number 6). During the night, the direction of the pressure gradient switches resulting in ‘katabatic’ or down-slope flow, reversing the circulation (Figure 2.2 number 1).

Figure 2.2: Depiction of airﬂow and boundary layer processes over urbanized complex coastal terrain. Adapted from (McKendry and Lundgren, 2000)

13 2.2 Instrumentation and Data Analysis

To examine aerosol concentrations and distributions at Grouse’s peak area, several instruments were deployed. As this was the ﬁrst study of this nature to be done at Grouse, solely the size and concentrations of particles were observed, not the composition. A GRIMM 1.108 Aerosol Spectrometer (https://www.wmo-gaw-wcc-aerosol-physics.org/ﬁles/opc-grimm-model– 1.108-and-1.109.pdf) was used to observe mountain top concentrations and a Dylos DC 1700 Laser Particle Counter (http://www.dylosproducts.com/dc1700.html) was used to conduct walking surveys to examine aerosol spatial variability on the summit. This section will provide a more detailed description of the instruments used along with the data analysis methods used in subsequent chapters.

2.2.1 GRIMM 1.108 Aerosol Spectrometer

The GRIMM 1.108 Aerosol Spectrometer (Figure 2.3 ) is a portable (24 x 12 x 6cm) mass spectrometer. It is capable of measuring particles in the range of 0.3 to 20 micrometers in diameter and can provide PM data in either particle counts (particles per volume of air) or mass concentrations (mass per meter cubed of air). Further, the mass concentrations can be

output as either ”environmental” which outputs PM10, PM2.5 and PM1 or as 16 particle sizes within the range mentioned above. For this study, we chose to use the latter as it provided the opportunity to examine potential sources of PM. Measurements can be obtained every six seconds when connected to a computer via an RS-232C cable interface, or once every minute when run on its own. The GRIMM was not permanently connected to the computer for this field study. The GRIMM instrument samples at a constant 1.21 l/min rate using an isokinetic pump. Particles then enter the sample cell that contains a laser diode beam and a photo diode detector. The entering particles subsequently disrupt and scatter the path of the laser beam at angles proportional to their size. The photo diode detector recognizes these aberrations and determines particle size using the magnitude of their scattering angle, and sends a signal to a pulse height analyser. There, the mass concentration is estimated using a fixed density estimate, the fixed pump volume and the particle size. The GRIMM was placed in a location out of reach of foot traffic. Similarly, it was housed in a Stevenson Screen to avoid solar radiation and wind altering the sample rate and to protect it from the elements. The screen’s angled slats minimize the interior windspeed while still permitting airflow, and the roof and white color eliminate the possibility of airflow induced by a forced temperature gradient.

14 Figure 2.3: Pictures of the GRIMM and its location on Grouse Mountain

Calibration

Preceding this study, the GRIMM was collocated with Metro Vancouver’s sensors that were

upgraded in 2013 to meet the U.S. Environmental Protection Agency’s PM2.5 Federal Equiva- lent Method (FEM) (Metro Vancouver, 2010) at Vancouver Airport for three days. Through- out the summer and following the study, the GRIMM was brought down to run next to Metro Vancouver’s FEM sensor for 30 to 60 minutes to ensure that the GRIMM was still reporting accurately. A linear regression was performed on over 200 observations taken using the two instruments when collocated, the resulting R2 value was 0.9917 (Figure 2.4)

2.2.2 MetroVancouver’s Instrument Network

The LFV Air Quality Monitoring Network includes 29 air quality monitoring stations located from Horseshoe Bay in West Vancouver to Hope.(Metro Vancouver, 2010) Air quality and weather data from all but one station are collected automatically on a continuous basis, transmitted to Metro Vancouver’s Head Oﬃce in Burnaby, and stored in an electronic database. The station used most commonly in this study is found at Mahon Park in North Vancouver and is located approximately 7km south of Grouse’s summit (Figure 2.1)and thus provides reliable information about the The federal CAAQS 24-hour PM2.5 standard of 28 µg/m3 was implemented in 2015. British Columbia’s objective is 25 µg/m3 (Metro Vancou- ver, 2010). A standard or objective is achieved if ambient concentrations are at or lower than the stated objective concentrations(Metro Vancouver, 2010).

15 Figure 2.4: Linear regression done on the readings taken from the collocated GRIM and MetroVancouver FEM instruments.

16 2.2.3 Dylos

A low-cost Dylos DC1700 Pro air quality monitor was used to determine aerosol distribution on the summit area. Although there are studies (Semple et al., 2015; Steinle et al., 2015; Manikonda et al., 2016)) describing the use of the Dylos as an eﬀective instrument for measuring PM2.5, this portion of the study focused on relative diﬀerences/magnitudes in aerosol concentrations and not exact values, calibration of the Dylos with MetroVancouver’s FEM instruments was not undertaken. The Dylos is a small, very portable laser particle counter that contains a small, very quiet fan that channels air through the measurement chamber. Like the Grimm, measurements are taken using a laser diode and a photo diode detector. Unlike the Grimm, it only logs particles in two size classes: 0.5-2.5 µm ”small” and 2.5 µm ”large”. In this study, these particle counts are then corrected using the conversion developed by Steinle et al. (2015) seen in eq 2.1. In their study, which examined calibration methods for the Dylos and the viability of using it as low-cost PM2.5 sensor, determined that this linear equation was found to result in the best calibration in an urban outdoor environment.

−5 PM2.5 = 4.75 + 2.8 × 10 × Dylos Small count (2.1)

On a full battery charge, the Dylos runs for approximately 6 hours. The built-in memory can store approximately one week of data when sampling continuously. This means that the Dylos can be used for multiple walking surveys before being brought to a PC where the data can then be downloaded as a text ﬁle for further analysis. This transfer requires a 9pin serial cable or USB-to-COM adapter. To carry the Dylos and to ensure consistent sampling, it was placed in the outer pocket of a small hiking backpack and secured with zip-ties, similar to the method used in Steinle et al. (2015). Since the the Dylos is not water-proof, yet needs to be exposed to ambient air, it could only be deployed in dry conditions. The chosen survey path (see Figure 2.6) accounted for all the main features of Grouse’s summit. Beginning at the exit of Grouse’s gondola, it passes the main lodge and follows the route suggested for Grouse Mountain tourists. The densely developed main area, the open plain, the bear habitat and the peak area/chairlift are all accounted for. The loop was approximately 1.5 kilometers and was retraced in each instance to increase accuracy.

17 Figure 2.5: Pictures of the Dylos and the manner in which it was carried by the surveyor.

Garmin

A Global Positioning System (GPS) receiver was used in combination with the Dylos to relate observed particle concentrations to time and location. The Garmin Edge 500 GPS was selected for this study because of its small form factor (4 × 7 × 2 cm), low weight (60g) and the ease of data transfer from the device to the computer in usable formats. The Garmin recorded date, time, altitude, longitude and latitude approximately every 2 seconds, depending on signal quality. The path followed during mobile surveys using the Dylos monitor is shown in Figure 2.6. Each loop was performed mid morning on non-rainy days and took approximately 25 minutes. This path followed the peak’s walking trail, passing all of the area’s main attractions. Upon completion of a loop, a subsequent loop was performed in reverse to reduce the risk of contamination by external factors as well as to exclude the potential for temporal changes in concentration during the survey. Areas that were left out of the survey path included the mountain bike trails and rental facility as well as the Grouse Mountain staﬀ oﬃce.

18 Figure 2.6: The path followed during the walking surveys and diﬀerent locations mentioned in the text

2.2.4 LiDAR

Remote sensing of aerosols is often used to monitor the location, transport and elevation of aerosol layers. McKendry et al. (2010),McKendry et al. (2011), Cottle et al. (2014) and Teakles et al. (2017) have used various forms of LiDAR to study wildﬁre plumes in British Columbia. Similarly, Akaoka et al. (2017) used LiDAR to examine emissions of coal carrying trains in South Delta, BC and Cottle et al. (2013) used it to document dust transport from Asia. For this project, a mini micro-pulse LIDAR (mMPL) was set up two kilometers from the base of Grouse Mountain (Figure 2.6) for the duration of the summer and was used to examine the vertical structure of pollutants above and around Grouse Mountain.

19 The mMPL is a cost-effective and portable alternative to most LiDAR systems. It continuously emits thousands of low-energy pulses at 532 nm and integrates them into a single profile. Each pulse has an energy of 4µJ which is several orders of magnitude less than larger, non-portable systems. To make up for the weaker signal, the mMPL has a wider beam diameter of 7.62 cm and a much higher pulse frequency of 4 KHz. Owing to this, the system is rated eye-safe at 3.5 m and is therefore less restricted by safety regulations. The mMPL is also housed in a steel, air-conditioned enclosure that allows it to be run in unfavorable conditions. Due to incomplete overlap of the emitted beam and the receiver’s field of view, the first 150 m above the LiDAR cannot be profiled. However, the mMPL can obtain a profile the following 12 km overhead. The time of integration can be set anywhere between the range 1 s - 60 min and the vertical resolution can be set to 5, 15, 30, or 75 m. For this study, an averaging time of 5 min and vertical resolution of 30 m was chosen. The main output of the mMPL in this study is Normalized Relative Backscatter (NRB). NRB is a range corrected product that is derived from the standard LiDAR equation (2.2).

Z z A 0 0 S(z, λ) = Eo(λ) 2 γO(z)βπ(z, λ) exp −2 α (z , λ) dz + SB (2.2) z 0

Where S(z, λ) is the signal received at the detector (counts/µs), E0 is the energy of laser pulse (J), A is usable area of the receiver (km2), z is height (km), γ is the full system counts efficiency constant ( −µs∗µJ ). O(z) is the incomplete overlap correction function (unitless), 2 −1 βπ(z, λ) is volume backscatter coefficient (km sr ), α is the extinction coefficient [unitless], −1 and SB is the background signal (counts µs ). The signal in eq 2.2 is then corrected in eq 2.3 to arrive at NRB.

S(z) ∗ d(S) − a(z) − S (z) NRB(z) = B ∗ z2 (2.3) E0 ∗ O(z) Where d(S) represents the deadtime correction factor and a(z) is the afterpulse correction factor.

20 Figure 2.7: The location of the LiDAR with respect to Grouse Mountain.

Dispersion Modelling

In the interest of determining the source of the aerosols aﬀecting Grouse, trajectory modeling was utilized. Lagrangian particle dispersion models are often used to track emissions or determine their sources (Figure 1.2). The National Oceanic and Atmospheric Association (NOAA)’s Hybrid Single Particle Langrangian Integrated Trajectory (HYSPLIT) model has been used to great eﬀect in western North America (McKendry et al., 2011) and was used in this study to determine the origin of air parcels on Grouse for the entire summer. The high resolution EDAS 40km meteorology dataset was used to create back trajectories starting from Grouse Peak at 0, 100 and 500m above ground level.

Windsonds

Windsonds (http://windsond.com/) consist of a miniature host of sensors that are deployed from the ground upwards through the atmosphere by way of a helium ﬁlled balloon. The Windsond measures air temperature, relative humidity, pressure, wind speed, wind direction, as well as altitude and GPS location.

21 Figure 2.8: An overview of a windsonde launch. Retrieved from www.windsond.com 2019/08/15

This allows the user to create an atmospheric profile of the variables of interest. The maximum altitude the Windsond can reach is dependent on the initial volume of the balloon as well as air pressure at upper-levels. In this case, the sensor was placed in a 12 g Styrofoam cup and attached to a 30-litre helium balloon. The Windsond collects data at three-second intervals and generally has an ascent rate between one to two meters per second, resulting in an average vertical resolution of about three to four meters. In this study, the ascent rate was much slower, likely due to insufficient inflation of the balloons. These balloons reached at least two km above ground level (AGL) for this study. The temperature sensor has an accuracy of 0.3°C.

22 2.2.5 Data Analysis Methods

This thesis is an observational study using data from in-situ measurements. Spatial and temporal variations in PM concentrations on Grouse Mountain are analyzed to better understand the variations in PM levels in this area. The focus of this study is to understand how PM concentrations on Grouse compare to those in Vancouver proper, especially during wildﬁre smoke events. Therefore, the following chapters will compare the diﬀerences between values observed by Metro Vancouver’s air quality sensor network and those on top of Grouse during both clear and smoke conditions. Further, the recorded concentrations will be compared to the National and Provincial Air Quality Standards.

Table 2.1: Summary of Field Data Collection

Clear Days Smoke Days Dylos Surveys 9 7 Grimm Deployments (days) 13 6 Windsond Flights (days) 13 20 Lidar Deployment June 26th - September 7th

23 Chapter 3

Non-Smoke Particulate Matter Concentrations and Distribution

In this chapter, spatial and temporal variations in PM on Grouse Mountain under non-smoke conditions during the summer of 2018 are investigated. For the purposes of this analysis, any day that NOAA’s satellite observation based Hazard Mapping System (HMS) Fire and Smoke Product imagery (https://www.ospo.noaa.gov/Products/land/hms.html) displayed a smoke plume that covered the LFV constituted a smoke day and all others were considered ”non-smoke” days. Accordingly, even though the last week of July registered very high PM values, those days were considered non-smoke days because there were no plumes detected by satellite imagery in the Vancouver area. Analysis is performed using data collected from the GRIMM, Dylos and LiDAR on non-smoke days. Observations are compared to the those registered at Metro Vancouver’s Mahon Park station and then averaged over a 24h period when possible, to be compared to the CAAQS. In terms of concentrations, based on existing literature (Monn et al., 1997),we expect to see relatively low concentrations at Grouse’s peak under non-smoke conditions with very little spatial and temporal variability. Although Grouse’s helicopter will affect the spatial variation on some surveys, it is expected that these effects will be too rare to impact the overall study. However, the chairlift that takes visitors to Grouse’s actual peak may well have lower concentrations due to its separation from dust kicked up on the established walking paths. As Grouse’s peak is situated above the Lower Fraser Valley’s (including Vancouver) boundary layer, pollutants from the city and valley are not expected to mix high enough to influence concentrations. However, mountain venting processes such as those described in the introduction, could lead to enhanced PM values around the summit (De Wekker and Kossmann, 2015). This is particularly possible during the cloudless summer days that are

24 typical in Vancouver’s climate. With enhanced heating of mountain slopes, the potential for mountain venting is increased. On a smaller temporal and spatial scale, potential sources of PM such as the main lodge and the helicopter pad, exist at the peak and could lead to spikes in concentration.

3.1 PM2.5 Spatial Variability

Example results from these loops are contained in Figure 3.1. Each circle represents a moment when both the GPS and the Dylos recorded a measurement simultaneously. The size and color of each circle represent the relative magnitude of the value recorded. As mentioned earlier, we cannot be completely conﬁdent in the absolute reading of the Dylos but we will use them to approximate relative concentration magnitudes and we will be comparing readings to each other to get an idea of spatial variability. Immediately noticeable amongst these ﬁgures are small spikes in the otherwise consistent concentration values. These spikes appear near the main lodge and bear refuge in most (but not all) of the survey results. These areas are arguably the busiest areas of the summit and thus, these readings are consistent with expectations.

25 Figure 3.1: Figure3.1: Relative PM2.5 concentrations observed around Grouse Mountain’s peak area on July 14th, 2018.

Figure 3.2 is a hexbox summary plot of the nine clear day surveys done during the summer of 2018. Each hexagon in this plot takes the average of every measurement found within its range. Since the path was retraced in every survey, each hexagon should be the average of approximately 18 measurements. Due to variation in the GPS’s recording frequency, this number could vary. The resulting figure illustrates the average of all the concentration paths. It is shown here that there is a consistent difference between the bear refuge area and the rest of the mountain path. The rest of the peak area demonstrates consistently lower concentrations, especially in the area accessed by the peak chairlift. These local maxima are likely due to dust kicked up by local foot traffic. However, in the subsequent sections, we show that although the bear refuge and main lodge show high concentrations relative to the rest of the peak area, the concentrations are still not high enough to be of concern.

26 Over the nine surveys, the standard deviation was 4.1 µgm−3 (90% of the mean of 4.7) and the max recorded was 16 µgm−3. Since the surveys were taken at the same time every day under similar synoptic conditions, there was likely little effect due to differences in boundary layer growth. The observed mean was very similar to the average reported by the Grimm for the entire summer and was lower than the 7.4 µgm−3reported by MetroVancouver at Mahon Park. Interestingly, the maximum value was higher than the 10.4 µgm−3 maximum observed at Mahon Park. Since the highest value was recorded near the helicopter pad, this was likely observed during a helicopter takeoff.

Figure 3.2: Averaged relative PM2.5 concentrations observed around Grouse Mountain’s peak area for the nine surveys done on clear days during the summer of 2018.

3.2 LiDAR Imagery

Given that aerosols emanating from the surface are normally contained within the boundary layer, and that LiDAR backscatter values give a relative representation of aerosol concentrations, the LiDAR backscatter values can be used here to approximate boundary layer growth and height (Hennemuth and Lammert, 2006; Manninen et al., 2018; De Arruda Moreira et al., 2019).

27 Similarly, due to the LiDAR’s proximity to the base of Grouse, the overlying boundary layer is affected by Grouse’s elevation and is therefore likely much higher than that observed over the Lower Fraser Valley. Because of this, it is possible to examine the extent to which aerosols are mixed as far up as Grouse’s peak elevation. Examples of the resulting imagery are found in Figure 3.3 and Figure 3.4. These figures display the change in aerosol backscatter in the atmosphere directly above the LiDAR over time. As the LiDAR was set up below our area of interest, there is a red line accompanying the graph that represents Grouse peak’s elevation. In Figure 3.3, the first three days were clear and demonstrated consistent diurnal boundary layer growth to above 3km (shown by the height of the green columns).

Figure 3.3: Ground based, upward facing LiDAR measurements taken from near the base of Grouse Mountain, from June 16th to 21st, 2018.

The aerosol backscatter values in Figure 3.3 show evidence of consistent diurnal convective boundary layer growth under clear sky, anti-cyclonic conditions. This is shown by the relative absence of aerosol backscatter at 500-2000m of elevation during the night and an increase in concentrations at those heights during the day. Interestingly, on June 19th, it looks as though there are two layers that have formed, one that extends to 1200m in height (A) and one that extends to almost 4000m in height (B). The former is likely evidence of the classic convective boundary layer found over ﬂat ground (Stull, 1988) and the latter could potentially be the mountain boundary layer that was mentioned earlier and ﬁrst proposed by De Wekker and Kossmann (2015).

28 Most importantly, Figure 3.3 show that the convective boundary layer grows to over 1000m, or at least there is evidence of increased aerosol concentrations at that height, demonstrating that aerosols generated in Vancouver proper are capable of being mixed upwards to Grouse’s peak elevation. Under prolonged hot, anti-cyclonic conditions, with very little nighttime cooling, as seen July 25-30 2018 (Figure3.4) the concentrations at 1000m persist for the entire duration, with no nighttime decline. This is likely due to the prolonged warming, but also due to the low winds associated with high pressure conditions. Figure 3.5 displays the abnormally high daytime and nighttime temperatures of that week, along with the relatively low wind speeds. Similarly, the smooth sinusoidal incoming radiation values are indicative of a prolonged high pressure system with little to no clouds. With little wind to expel anthropogenic aerosols, they accumulate in the surrounding area. Owing to this, under these conditions, above average aerosol concentrations are observed at Grouse’s peak (Figure 3.8) and even up to 2000m. It is important to note, however, that under these conditions, air quality is still worse at the surface (shown by the yellow coloring).

Figure 3.4: Ground based, upward facing LiDAR measurements taken from near the base of Grouse Mountain, from July 23rd to 29th, 2018. The arrow in this ﬁgure points to what are potentially remnants of wildﬁre emissions from Siberia and Alaska.

29 Figure 3.5: Incoming radiation, ozone, PM, temperature and windspeed measurements recorded at Mahon Park, North Vancouver during the ”photochemical smog event”.

30 Of note, the high concentrations observed in this weeklong event were reported by CBC (2018) to have been affected by a local bog fire and remnants of wildfire emissions from Alaska and Siberia, the latter explaining the long descending line of aerosols shown by the arrow in Figure 3.4. Figure 3.6illustrates a Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model run for those days, showing the origins and paths of different air parcels that reached Grouse during this period. This model was run using GDAS 0.5 degree meteorological inputs. The origin of the back trajectories is Grouse Mountain and the models were run for 300 hours. Back-trajectories were calculated to determine where air parcels at 1000m, 3500m and 4000m above sea level originated. These heights where chosen to correspond with the top of Grouse, the lower extent of the aerosols seen descending towards the LiDAR on July 26th in Figure 3.4 and the upper extent of the same mass of aerosols, respectively. The two higher trajectories are shown to have passed through Russia and all paths are shown passing through Alaska, further substantiating the wildfire provenance of the event.

Figure 3.6: 300h HYSPLIT backwards dispersion trajectories run from Grouse Mountain July 26th

3.3 GRIMM PM2.5 Concentrations

Figure 3.7 shows the minute averaged concentrations recorded by the GRIMM on Grouse Mountain on two ”non-smoke” days. To reiterate, as seen in Figure 2.2the observations recorded by the GRIMM were found to be nearly identical to those recorded by MetroVan- couver’s FEM instruments when co-located. Also included on the ﬁgures are a line indicating the minute averaged concentrations taken simultaneously by MetroVancouver’s instruments. Again, it is important to reiterate, that due to concerns regarding the eﬀect of moisture on

31 the GRIMM’s internal mechanisms, this instrument was only used on low humidity days.

Figure 3.7: PM2.5 concentrations observed on Grouse Mountain and at North Vancouver’s Mahon Park on July 17th and 23rd, 2018.

On clear days, the concentrations recorded by the GRIMM were always lower than those recorded at Mahon Park. Under these conditions, the average observed hour-long PM2.5 concentration was 4.8µgm−3 with a maximum of 7.4 µgm−3 whereas the average seen at Mahon Park was 7.6µgm−3 with a max of 12.3µgm−3. The many small spikes seen in the recordings taken at Grouse are potentially due to the helicopter that would take oﬀ and land a few hundred meters from the GRIMM instrument. Neither location registered an hour-long period where the average measurement was close to the one hour CAAQS of 28µgm−3 under clear conditions. Further, likely due to the decline of the boundary layer and the lack of tourist activity, values recorded at Grouse tend to drop at night and rise gradually throughout the day. This agrees with the conclusions drawn from the LiDAR plots in the previous section. The average ”non-smoke” day minute concentration from 6pm to 6am was found to be 3.25µgm−3while the average between 6am and 6pm was 6.41µgm−3. In the urban area, at Mahon Park, there was an increase during the day as well, but it was much less pronounced. It changed from an average of 6.9µgm−3 to 8.3µgm−3 during the day. Not included in the results above was the ”photochemical smog event” that occurred under the persistent high-pressure system from July 25-30. This event provided the only instance where PM concentrations were higher on Grouse during ”non-smoke” conditions.

32 Figure 3.8: PM2.5 concentrations observed on Grouse Mountain and at North Vancouver’s Mahon Park on July 28th and 29th, 2018, during the “photochemical smog event”..

Over this period, the recorded PM2.5 concentrations at each location were substantially closer relative to a normal clear day. This can be see in Figure 3.8and in the recorded average minute concentrations for this period. Measurements on Grouse showed an average of 13.4µgm−3 and Mahon Park reported an average of 14.1µgm−3 over this event.

Similarly, as opposed to the other clear days, PM2.5 concentrations revealed very little diurnal variability during this event. The nighttime averages at Mahon Park and Grouse peak were 13.9µgm−3 and 13.2 µgm−3 respectively while the daytime averages were 14.3µgm−3 and 13.6 µgm−3 respectively. These observations align well with the LiDAR imagery seen in the previous section that shows consistent aerosol distribution with height during this event. This in turn, strengthens the case for the prolonged high-pressure system and associated high nighttime temperatures leading to a persistent convective boundary layer even through the night. However, even in this week of poor air quality, neither location recorded an hour that −3 averaged PM2.5 over 28 µgm . It should be noted that, Mahon Park came close with a maximum hour long average of 22.1 µgm−3 while Grouse peak had a max of 19.6 µgm−3.

3.4 Conclusion

In this chapter, the spatial and temporal variations in PM on Grouse Mountain under non- smoke conditions were analyzed. Using the GRIMM, Dylos and GPS it was shown that under normal, clear sky conditions, Grouse’s peak area has very little spatial and temporal variability in PM concentrations. This was further corroborated using ground based LiDAR observations that show very little variation in PM concentrations at Grouse’s elevation on clear days. As predicted, the concentrations observed on these days were very low, were

33 lower than those recorded in the valley at Mahon Park and show no risk of being harmful to visitor’s health. During 2018’s photochemical smog event, however, the prolonged high-pressure conditions caused PM to build up in the lower atmosphere (a ”smog” event). This resulted in an increase in PM levels and a decrease in the difference between concentration levels on Grouse and those in the valley. Again, the LiDAR imagery reinforced this point by displaying higher, more uniform PM concentrations with height in the lower atmosphere. Finally, variations in boundary layer growth around Grouse were also observed. Under clear sky conditions, evidence of the mountain boundary layer proposed by De Wekker and Kossmann (2015) was observed and is shown in Figure 3.4. This is an interesting finding in terms of the meteorology of the area and further research could be very beneficial. Boundary layer depth plays a key role in weather modelling and air pollution movement.

34 Chapter 4

Smoke Event Particulate Matter Concentrations and Distribution

This chapter will investigate the eﬀect wildﬁre smoke has on PM concentration and distribution around Grouse Mountain. NOAA’s HMS imagery was used again here to determine whether smoke was present in the LFV. All days that displayed smoke in the LFV were considered ”smoke days”. Figure 4.1 shows an example of what constitutes a smoke day in this study. In the image on the left, obtained from NOAA’s HMS Imagery, the LFV (circled in blue) is covered in smoke. On the right, NOAA’s MODIS TERRA satellite shows a plume of smoke covering the LFV

Figure 4.1: NOAA’s HMS Imagery(left) and MODIS TERRA satellite imagery (right) on August 13th 2018

35 This analysis is performed using the data collected from the GRIMM, Dylos and LiDAR on smoke days. Data was collected using the GRIMM and Dylos on seven different occasions while the LiDAR was run for the entire duration of the fire season. Unfortunately, towards the end of the summer’s main fire event, the GRIMM filter became clogged with PM, and the subsequent measurements were unusable. The usable observations are compared to those registered at Metro Vancouver’s Mahon Park station and then averaged over a 24h period, when possible, to compare to the CAAQS. Due to the limited number of studies of this nature, information regarding wildfire smoke PM concentrations around mountains is limited. However, with an understanding of general mountain flow processes, and results from similar studies over flat terrain, general effects can be proposed. In studies by (Peshev et al., 2017) and (Ansmann et al., 2018), it is shown that, over flat ground, the most concentrated portion of a smoke plume is often found toward the top of the boundary layer and far from the surface. In Chapter 1, basic mountain flow processes are outlined and show that mountains facilitate the transport of air parcels from lower elevations to higher ones. It is possible that PM concentrations at the top of Grouse Mountain will be higher than those in the LFV. If Grouse is contained solely within a mountain boundary layer, the concentrations will not be substantially higher, but if the peak is affected by the LFV’s boundary layer, concentrations will likely be much higher.

4.1 PM2.5 Spatial Variability

The path followed during these surveys is the same as that is shown in Figure 3.1. Figure 4.2 shows a sample survey taken under smoke conditions, on August 17th, when smoke from central British Columbia was starting to arrive in the LFV. Relative to Figure 3.2, the concentrations recorded in the presence of smoke are larger in magnitude but follow a similar spatial distribution. The highest recorded values on this day are located around the bear refuge and the area’s main attractions. The diﬀerence in values between the smoke and non smoke days will be covered later in this chapter.

36 Figure 4.2: Sample survey of relative PM2.5 concentrations observed around Grouse Moun- tain’s peak area in the presence of wildﬁre smoke on August 17th, 2018.

Figure 4.3 is a hexbox summary plot of the seven smoke day surveys done during the summer of 2018. Each hexagon in this plot takes the average of every measurement found within its range. Since the path was retraced in every survey, each hexagon represents the average of approximately 14 measurements. The resulting figure illustrates the average of all the concentration paths. The main difference between the figure produced in the presence of smoke and the one produced in clear conditions is the magnitude of the observations. The maximum value reported in one of the hexagons in absence of smoke was over seven whereas in the presence of smoke, the highest value was over 80 µgm−3. Later in this chapter, the absolute values observed on Grouse Mountain will be reported and discussed.

37 Because of the difference in magnitude of the values in Figure 4.2 and Figure 3.3, this distribution appears to be more uniform. However, the magnitude of the relative difference between locations is similar to those observed on clear days with a standard deviation of 28 µgm−3 (90% of the mean). Again, the observations in the area at the top of the peak chairlift (the northernmost point) are still lower, meaning that even in the presence of wildfire smoke, separation from human activities is still an important factor or that the smoke is being contained in this area for another reason. As the surveys were taken at the same time every day under similar synoptic conditions, there was likely little effect due to differences in boundary layer growth.

Figure 4.3: Averaged relative PM2.5 concentrations observed around Grouse Mountain’s peak area for the nine surveys done on smoke days during the summer of 2018.

4.2 LiDAR Imagery

As in the previous chapter, LiDAR imagery is used to approximate aerosol concentrations in the atmosphere. In the case of wildﬁre emissions, the vertical extent to which the smoke is spread and how that changes over time is of interest, as are the concentrations and how they compare to the smoke free days. An example of the resulting imagery is found in Figure 4.4. Figure 4.4 displays the week of August 13th 2018. This week began with light smoke in

38 the Valley from earlier ﬁres. Smoke persisted for most of the week (shown by the consistent colors in the imagery) before the large smoke plume arrived in the Valley (shown by the arrow). This smoke plume resulted in the LFV having some of the worst air quality in the world over the weekend of the 19th and the following days. Aersol backscatter values in Figure 4.4 show very little evidence of diurnal variability. When compared to (Figure 3.3), the backscatter values remain very consistent over time and do not drop over night. The vertical extent of the aerosols did not seem to change over the week, with high concentrations not extending past 2000m. For most of the week, highest backscatter (and likely highest concentrations) appeared to be below the elevation of Grouse’s peak (shown by the red dashed line) with the brighter green colors contained mostly between that height and the surface. However, towards the end of the 18th and early on the 19th the values at 1000m were much higher than those on the surface. This will be discussed in more detail later.

Figure 4.4: Ground based, upward facing LiDAR measurements taken from near the base of Grouse Mountain, from August 13th to 19th, 2018. red arrow points to the main plume of wildﬁre smoke that aﬀected the LFV in 2018. The red dashed line shows the elevation of Grouse Mountain. The white brackets are used to show smaller plumes.

As for clear days (Chapter 3), there is a deﬁned boundary layer limit at around 1000m of elevation. It is shown by the green colored portion of the plot that is contained below the 1000m line. There is also evidence of a second boundary layer that extends to around 2000m and is shown by the blue colored portion of the plot. This second boundary layer is likely more evidence of the mountain boundary layer as described by De Wekker and Kossmann (2015). This is especially interesting because this boundary layer has been previously undocumented in the presence of smoke. This could simply be an artifact of smoke’s vertical variability in the atmosphere or that mountain ﬂow processes continue to function, at least in part, in the

39 presence of wildfire smoke. The latter implies that the reduced radiative impact of smoke would not completely shut down active flow processes. A better understanding of this is required as both phenomena would affect how smoke dispersion and weather, in general, are modeled. Contrary to what was seen in the photochemical smog event, with smoke in the atmosphere, there was little to no enhanced boundary layer growth. Throughout this event, the lower boundary layer extends close to 1000m, which is similar to what is seen in Figure 3.3, suggesting that, in this particular case, the reduced radiative input at the surface resulted in less convective turbulence and therefore less boundary layer growth (Talukdar et al., 2019). Potential temperature and its lapse rate is often used to determine the stability of the lower atmosphere. Figure 4.5 displays the average of vertical profiles of potential temperature taken from the base of Grouse Mountain. Profiles from 13 sondes launched on clear days are shown in blue while the 20 sondes launched on smoke days are shown in black. This shows that on smoke days potential temperature on average increases more with height than on clear days (this is indicative of a more stable environment). These conditions are much less suited to boundary layer growth and are likely to reduce the venting effects of the mountains and lead to a build up of particles in the atmosphere.

40 Figure 4.5: Average vertical proﬁles of potential temperature taken from the base of Grouse Mountain. Proﬁles from clear days are shown in blue while smoke days are shown in black.

Figure 4.6 is another LiDAR plot to help illustrate this phenomenon. This shows vertical proﬁles taken during smoke days in 2018 superimposed on the contemporaneous LiDAR imagery. Here, sudden changes in potential temperature at or near the top of the smoke plumes are evidence of very stable, capping inversions. These inversions occur at or within 200m of Grouse’s peak height (shown by the red line), meaning that they would likely suppress venting mechanisms normally associated with the mountain. Figure 4.7 and Figure 4.8 depict HYSPLIT trajectories that were run using EDAS 40km meteorological inputs. The origin of the back trajectories is Grouse Mountain and the models were run for 48 hours starting at 12pm on August 13th , 14th and 15th (Figure 4.7) and 16th, 17th and 18th (Figure 4.8). Notably, the days that appear to have smoke at Grouse’s elevation in Figure 4.4 (Aug 13, 14 and 16) have HYSPLIT trajectories pass through the northern mountains, whereas the days where there is no smoke at Grouse’s elevation, have HYSPLIT trajectories that come oﬀ the water.

41 Figure 4.6: Vertical proﬁles superimposed on the earlier LiDAR imagery

Figure 4.7: HYSPLIT backwards dispersion trajectories that were run. The origin of the back trajectories is Grouse Mountain and the models were run for 48 hours starting at 12pm on August 13th, 14th and 15th

42 Figure 4.8: HYSPLIT backwards dispersion trajectories that were run. The origin of the back trajectories is Grouse Mountain and the models were run for 48 hours starting at 12pm on August 16th, 17th and 18th

4.3 GRIMM PM2.5 Concentrations

Figure 4.9 shows a subset of the minute averaged concentrations recorded by the GRIMM on Grouse Mountain on smoke days. Also included on each ﬁgure is a line indicating the concentrations taken simultaneously by MetroVancouver’s FEM instruments.

43 Figure 4.9: PM2.5 concentrations observed on Grouse Mountain and at North Vancouver’s Mahon Park on August 13th during the day, the afternoon of August 15th and August 16th, 2018.

In the presence of smoke, PM2.5 concentrations recorded by the GRIMM were often higher than those recorded at Mahon Park. In Figure 4.9, the HYSPLIT model runs show that these smoke plumes passed from north or northeast of the city before arriving at Grouse Mountain. It is likely then, that these plumes were contained in the mountain boundary layer before arriving in the LFV and subsequently entrained downwards into the valley’s boundary layer. This would result in the GRIMM recording the higher concentrations first at Grouse’s peak, and then Mahon Park recording these observations later on once the smoke arrived lower in the valley. This is supported by the LiDAR imagery that shows, for these days, smoke plumes that begin above the altitude of Grouse Mountain when the concentrations are higher at that elevation and are then shown to be affecting lower elevations when the concentrations are similar between the two elevations.(shown by the white brackets on Figure 4.4. As this happens, since the stability has likely increased (Wang and Christopher, 2006) and active flow processes have been reduced (Calvo et al., 2010), less venting of smoke is likely, meaning that the air quality will worsen near the surface rather than what happened during the photochemical smog event.

44 Correspondingly, on the 16th, when the GRIMM and Mahon Park’s readings lined up, the air that was shown to have arrived at Grouse that day did not come from the North or Northeast. Rather, it came from the Southwest and therefore was likely not carrying smoke or the smoke had already descended into the valley boundary layer. This implies that, depending on the synoptic conditions, air quality on Grouse will worsen before the air quality in the LFV. Under these conditions, the average observed hour-long −3 −3 PM2.5 concentration was 56.3µgm with a maximum of 180.2µgm , whereas the average seen at Mahon Park was 32.7µgm−3 with a max of 114. µgm−3. Both locations registered multiple 24-hour long periods that exceeded the one-hour CAAQS of 28µgm−3. Similar to observations during the photochemical smog event, lack of diurnal variability in the boundary layer resulted in the concentrations recorded at Grouse remaining constant throughout the day and night. This agrees with the conclusions drawn from the LiDAR plots in the previous section. The average ”non-smoke” day minute concentration from 6pm to 6am was found to be 58.1µg/m3 while the average between 6am and 6pm was 54.5µgm−3. In the urban area, at Mahon Park, observations on these days also exhibited very little diurnal variability. Concentrations changed from an average of 30.9µgm−3 at night to 34.5µgm−3 during the day.

4.4 Case Study on August Fire Event

Between August 13th and August 25th, wildfire emissions were prevalent in Vancouver and the LFV. Aerosol Optical Depth (AOD) values, which are a measure of light attenuation through the atmosphere and under clear skies, average around 0.1 (Mckendry, 2018) but during the smoke event, they were regularly over two and surpassed four on many occasions. Figure 4.10 shows the AOD values recorded just southwest of Vancouver for the month of August 2018. The first ten days of the month recorded normal AOD values for this area while August 11th to 25th display values consistently over 1. The different colored lines depict the different aerosol optical depths for each wavelength. i.e AOD 500 is the aerosol optical depth of light with a wavelength of 500nm.

45 Figure 4.10: Aerosol optical depth values measured at Saturna Island, just southwest of Vancouver over the course of August 2018.

46 Figure 4.11: View of Cypress and Grouse Mountain from Jericho Beach on a clear day (left) and August 20th (right).

LiDAR imagery in Figure 4.12 displays the vertical extent of aerosols over the course of this wildﬁre smoke event. As mentioned earlier, the red arrow points to the main plume of smoke that aﬀected Vancouver. This plume is also the most interesting from this study’s perspective because it very clearly begins above the height of Grouse mountain and the, from the perspective of the Lidar, descends downwards over the course of two days. It is also possible that this was due to the inherent vertical variability of air masses. This observation has served to inform the basis of this case study designed to look at all possible observations to better understand what happened.

Figure 4.12: Ground based, upward facing LiDAR measurements taken from near the base of Grouse Mountain, from August 13th to 25th, 2018. The red arrow points to the main plume of wildﬁre smoke that aﬀected the LFV in 2018. The red dashed line shows the elevation of Grouse Mountain.

47 Figure 4.13 displays ozone, PM, windspeed, incoming radiation and temperature over the course of this event. When compared to readings from the photochemical smog event in Figure 3.6 the relative difference in PM and Ozone is substantial. In the presence of wildfire smoke, the average PM reading was 48.3µgm−3 and the average Ozone reading was 15.3 ppb. While during the photochemical smog event, the average PM reading was 11.4µgm−3 and the average Ozone reading was 25.6ppb. Also of note are the abnormally low surface temperature and incoming radiation values, especially when compared to Figure 3.6. This meant that the LFV was not receiving as much incoming radiation as normal, nor as much as was forecast. Therefore, most thermotopographic processes were likely not functioning as they normally do (Mckendry, 2018). Conceivably, this could have affected mountain flow processes as radiation plays a key role in the active processes. This will be touched on later.

48 Figure 4.13: Ozone, PM, incoming radiation, temperature and windspeed measurements recorded at Mahon Park, North Vancouver during 2018’s main ﬁre event.

Figure 4.14 depicts a HYSPLIT dispersion model run for those days, demonstrating the origins and paths of diﬀerent air parcels that reached Grouse during this period. The dispersion model was run backwards to see where air parcels at 500m, 1000m and 2500m originated. These heights were chosen to correspond with midway up Grouse, and the lower and higher extents of aerosols seen descending towards the LiDAR on August 18th in Figure 4.11, respectively. The largest plume, whose path is best shown by the green line in Figure 4.14, is shown to be part of an air parcel that passed east over the mountains to the north of Vancouver towards interior BC where most of the summer’s ﬁres occurred (shown in Figure 4.16).

49 Following this, the parcel turned west and approached Vancouver from the northeast via the Coast Mountains. This meant that the parcel needed to be similar to or above the height of the mountains when it arrived at Grouse, which is what is shown to have happened in Figure 4.11.

Figure 4.14: HYSPLIT backwards dispersion trajectories that were run using EDAS 40km meteorological inputs. The origin of the back trajectories is Grouse Mountain and the models were run for 300 hours starting at 12am on August 19th.

50 Figure 4.15: A map showing the composite mean pressure using isobars along the west coast of North America for the week of the smoke event.

51 Figure 4.16: A map of all active fires in BC on August 18th 2018. Flames represent fires that present a threat to public safety, red dots are fires that started within the past 24h and orange dots are fires that do not represent threats to public safety.

Average PM2.5 concentration observed at Vancouver International Airport for this period was 42µgm−3, which included seven out of the 12 days registering averages over the CAAQS. This includes the 20th, 21st and 22nd, all of which recorded 24h averages of over 89µgm−3. These enhanced concentrations were not limited to the Valley; readings on Grouse Mountain were as high, if not higher than those recorded in Vancouver, as seen in Figure 4.17.

52 On the morning of August 18th, PM2.5 levels on Grouse Mountain were similar to those found in Mahon Park. However, towards the end of the day, concentrations began to increase rapidly as the smoke plume, show by the red arrow in Figure 4.11, drifted in. At Mahon Park, observed PM2.5 concentrations remained consistent with what was seen earlier in the day as the LFV had not yet begun to be exposed to this plume. This reﬂects what is seen in Figure 4.4 where, on August 18th, the plume existed at or above 1000m and did not touch lower elevations. The following Figure 4.17 shows how concentrations at Mahon Park changed over the course of the following week in response to this smoke plume. Unfortunately, on the morning of August 19th, the increased concentrations clogged the GRIMM’s ﬁlter and measurements were no longer reliable.

Figure 4.17: PM2.5 concentrations observed on Grouse Mountain and at North Vancouver’s Mahon Park on August 18th, August 19th and the morning of August 20th, 2018.

Figure 4.18 depicts the course of PM2.5 concentrations in the week following the large smoke plume’s arrival in the LFV. Concentrations spiked towards the end of August 19th, and held at levels above 100µgm−3 for most of the day. This is important because this spike occurred half a day after the major spike observed on Grouse Mountain. This reﬂects what is shown above in Figure 4.11 where the major smoke plume (shown by the red arrow) resides at an altitude close to that of Grouse’s peak on August 18th/early 19th before smoke moves into the valley throughout the 19th and early on the 20th. This further substantiates the idea that, depending on the synoptic conditions, for a short period of time, Grouse’s peak will see much higher PM concentrations than the valley below. Similarly, it can serve as a warning or an indicator of imminent PM levels in the LFV.

53 Figure 4.18: PM2.5 concentrations observed at North Vancouver’s Mahon Park from August 19th to August 26th

4.5 Conclusions

In this chapter, the spatial and temporal variations in PM on Grouse Mountain in the presence of smoke were analyzed. Using the GRIMM, Dylos and GPS it was shown that in the presence of smoke Grouse’s peak area still has very little spatial variability in PM concentrations, similar to what was shown under clear sky conditions. Temporal variations in concentrations were more prominent in the presence of smoke. Concentrations observed using the LiDAR, the GRIMM and the Dylos varied considerably between days with smoke present. However, contrary to what was seen in under clear sky conditions, there was very little diurnal ﬂuctuation in concentrations. Instead of rising over the course of the day and declining overnight, concentrations would regularly experience a rapid increase and then a very slow decline. At times during this event, concentrations on Grouse Mountain surpassed 100 µgm−3 and there were four 24h periods where the average concentration was over the CAAQS and thus very harmful to human health. Considering the demand of most activities on Grouse Mountain, the staﬀ may consider closing on days with forecasted poor air quality. It may be wise to install their own air quality sensors for the safety of both the visitors and themselves.

54 More interesting was that Grouse often registered high values well before the LFV did. Specifically, when smoke arrived from the north, northeast or northwest, concentrations on Grouse would spike around half a day earlier than the concentrations in the valley. Following this, the smoke would descend into the valley and concentrations would become more uniform with height. This was corroborated using the LiDAR imagery. Knowing this, further research into this matter may be of interest for Grouse’s staff. If the early, elevated concentrations continue to be a regular occurrence, it would be in their and their visitor’s best interest for them to monitor and track the progress of BC wildfire plumes. Radiosonde vertical profiles taken during this event demonstrated greater atmospheric stability within smoke layers (during otherwise clear sky conditions) when compared to normal clear sky conditions. This increase in stability could conceivably suppress the area’s regular mountain venting mechanisms and is potentially responsible in part for the less defined mountain boundary layer in Figure 4.11. This relationship has not been well studied yet, and is an area that requires more research. With reduced venting mechanisms that are not accounted for in models, air quality in the LFV is likely to be worse than predicted in the presence of smoke. Finally, similar to under non-smoke conditions, evidence of the mountain boundary layer proposed by (De Wekker and Kossmann, 2015) was observed and is shown in Figure 4.11. This is interesting in the context of the LFV’s meteorology and because boundary layer development around mountains in the presence of smoke has been very sparsely studied. Further research on this topic, with aid from more radiosonde flights, could advance understanding of these processes in the interest of improving weather modelling as well as the accuracy of air quality advisories.

55 Chapter 5

Conclusion

5.1 Key Findings

The main objective of this project was to improve understanding of the spatial and temporal variations in PM concentrations on Grouse Mountain in the presence and absence of wildfire emissions. The effects of wildfire emissions on air quality was of primary interest, but their interactions with mountain flow processes were also examined. This chapter will summarize the outcomes of this project and provide suggestions for future research. At Grouse’s peak area, the walking air quality surveys demonstrated very little spatial variability in PM. On both clear and smoke days, PM concentrations were mostly uniform across the summit. As seen in Figure 3.3 and Figure 4.2, there was some variation along the chosen path with the area near the bear habitat having slightly higher readings. On clear days, over nine surveys, the spatial standard deviation was 4µgm−3 (90% of the mean) and the max recorded was four times that (16µgm−3). On smoke days, the absolute values were much higher (max > 100µgm−3) but the percent difference across the summit was similar with a standard deviation of 28µgm−3 (90% of the mean). In contrast, the temporal variation of PM on Grouse Mountain was noteworthy, both in the presence of smoke and not. PM levels exhibited both diurnal and longer-term variability, depending on different factors. The discrepancy between levels observed on Grouse and lower in the valley at Mahon Park also varied substantially over the summer. Under non-smoke conditions, the diurnal variation in the boundary layer and anthropogenic activity drove the temporal variation in PM concentrations. The average of levels observed from 6pm to 6am was found to be 3.25µgm−3 while the average between 6am and 6pm was 6.41µgm−3. Evidence of the effect of the boundary layer is shown in Figure 4.4. There, aerosol concentrations are shown to rise throughout the atmosphere with time. The photochemical smog event that occurred during the week of July 23rd brought a

56 complete change in the temporal variability of PM. Instead of exhibiting a diurnal pattern, concentrations remained relatively constant throughout every 24-hour period. The nighttime average at Grouse peak was 13.2µgm−3 while the daytime average was 13.6µgm−3. This too, is shown by LiDAR imagery in Figure 3.4 where concentrations at Grouse peak’s elevation remain relatively similar over time. Overall, in the presence of smoke, the average observed hour-long PM concentration on Grouse Peak was 56.3µgm−3 with a maximum of 180.2µgm−3, higher than the average seen at Mahon Park which was 32.7µgm−3 with a max of 114.3µgm−3, demonstrating the need for long term monitoring at Grouse Mountain. In the presence of smoke, diurnal variations were subdued in comparison to non-smoke conditions, but longer-term variations were enhanced. As seen in Figure 4.4, concentrations at Grouse’s elevation did not diminish overnight. However, PM levels varied significantly over the course of the week shown in Figure 4.4 and Figure 4.11. Using the LiDAR plots, the GRIMM observations, a composite map and the HYSPLIT model runs, it was shown that these variations depend on the synoptic conditions and time since first exposure. Many of the major plumes that affected Grouse arrived via the North, Northwest or Northeast (Figure 4.8 and Figure 4.9) in the presence of anti cyclonic conditions (Figure 4.15. In these cases, air quality on Grouse degraded much earlier than the air quality in the city. Later, the smoke would entrain downwards into the valley and air quality values would on Grouse and in the valley would become more uniform. Finally, local circulation processes were also affected by the presence of smoke. In Figure 4.5, the average potential temperature lapse rate in the presence of smoke is shown to be much lower than the corresponding non smoke lapse rate; indicating a more stable atmosphere. This, coupled with less solar radiative input at the surface likely led to reduced mountain venting processes and accordingly, a less defined mountain boundary layer in Figure 4.4 (smoke) when compared to Figure 3.4 (no smoke). The reduced venting processes would also likely reduce cloud formation and aid in the persitance of smoke in the valley, creating a feedback loop that would be extremely important to consider in weather forecasting in the presence of smoke.

5.2 Limitations

As an observational study, this project does come with limitations. Although it forms the basis for other, more thorough studies, it is diﬃcult to draw concrete conclusions from it, owing to the lack of statistical rigour. Many more events would need to be sampled to draw deﬁnitive conclusions. The intermittent nature of the GRIMM observations at Grouse

57 Mountain and its failure halfway through the largest smoke event also limited the results of this study. The installation of a more permanent, reliable instrument would have greatly increased the validity of the outcomes. A lack of a full calibration for the Dylos instrument also limited the potential use of the results as having more certainty in the absolute results would have allowed more concrete conclusions to be drawn about the difference between areas on the mountain. Another option would have been to pass the Dylos by the GRIMM multiple times during the Dylos surveys. Lastly, based on the results, this study would have benefitted from the use of a scanning Lidar instead of, or as well as, the stationary Lidar. Point measurements are more of use over flat ground where there are less variables affecting the distribution of aerosols and horizontal movement is less important (Feingold et al., 2005; Stone et al., 2008). Another option would have been to have more Lidar instruments with at least another to the north of Grouse and one on top of Grouse as well as increased use of radiosondes for a more robust measurement of boundary layer depth.

5.3 Future Studies

As wildfire events are limited to a short period of time each year, these results would benefit from further similar studies in the future. Ideally, a long-term air quality monitoring station would be set up on Grouse Mountain to advance knowledge in this area and give staff at Grouse Mountain means to make informed air quality related decisions. A quantification of photochemical smog concentrations on Grouse Mountain in the presence and absence of smoke would complement this study as well. In increased concentrations, photochemical smog can be detrimental to human health (Fowler, 2003) and is one of the main pollutants associated with wildfire smoke (McKendry and Lundgren, 2000). In chapter 3 it is explained that Vancouver underwent an ”photochemical smog event” in late July and the effects of this event on mountaintop concentrations could have been documented in a similar manner to how PM was addressed in this study. Similarly, another long period of LiDAR measurements and Windsond flights would also be beneficial in the interest of understanding the proposed boundary layer dynamics and mountain flow processes in the presence of smoke. A comparison of these results with weather models that predict boundary layer height such as the WRF model would also be useful in the interest of validating and improving these models. Incorporating realistic smoke plumes into weather models such as WRF to compare and contrast to these events would also be of use to better understand these processes. Finally, similar studies in other locations would be useful. Given the recent propensity

58 for wildfires in California and the abundance of mountains in the north end of the state, it would be an ideal location, as would most locations in the Pacific Northwest. However, it would also be useful to examine mountainous locations that are less influenced by the presence of a coastline.

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