Aerosol Particle Statistics During Wildfire Events and Their Impact on Air

Home , Aerosol, Detwiler Fire

University of Nevada, Reno

Aerosol particle statistics during wildfire events and their impact on air

quality in Reno, NV

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science

in Atmospheric Science

Grzegorz Swistak

Dr. Andrey Y. Khlystov/Thesis Advisor

August 2019

THE GRADUATE SCHOOL

We recommend that the thesis prepared under our supervision by

Entitled

be accepted in partial fulfillment of the requirements for the degree of

, Advisor

, Committee Member

, Graduate School Representative

David W. Zeh, Ph.D., Dean, Graduate School

ABSTRACT

Ambient aerosol examination is an important task for a number of reasons, including air

pollution, public health impacts, visibility and climate change. While there are numerous studies

relating the direct impact of small airborne particles on the above-mentioned fields, not much information exists on the impact of wildfire on urban aerosol properties. This research focuses on the investigating differences between measured urban aerosol size parameters between wildfire

events and periods unaffected by fire plumes in Reno, NV, as well as what implications these

differences might have on Reno’s environmental conditions and human health.

In order to achieve that, a Scanning Mobility Particle Sizer (SMPS) instrument was

installed inside the Desert Research Institute (DRI) building in Northern Reno, Nevada. The SMPS

operated constantly between July 13th of 2017 and August 13th of 2018. The aerosol data was then

downloaded, converted and analyzed using Python scripts in order to visualize and compare the

variability of the air particle statistics and how it relates to forest fire smoke impacts. Particle number mean volume concentration was on average 13 to 15 times higher during the wildfire events, than that of the non-wildfire baseline events samples.

ACKNOWLEDGEMENTS

Group of people helped me made this research possible, therefore I would like to extend my gratitude to everyone involved.

I want to thank my advisor, Dr. Andrey Khlystov for his patience, assistance, guidance and for providing me with all the supplementary knowledge needed to understand and finish this project.

I would like to thank my committee members, Dr. Vera Samburova and Dr. Patrick Arnott for their mentorship, shared knowledge and assistance with the obstacles I encountered along the way.

I also want to thank my fellow students and colleagues from the Desert Research Institute and from the University of Nevada, Reno for their invaluable assistance, primarily Chiranjivi

Bhattarai, who was crucially involved with the instrumentation preparation, set up and maintenance and Hatef Firouzkouhi for his knowledge and assistance with coding. My sincere thanks and appreciation go to my other fellow students and peers from the Organic Analytical Lab, who were always helpful and offered any assistance possible – Yeongkwon, Irina, Deep, Megan and Kevin.

Lastly, I would like to thank my family, especially my wife Alicja and cousin Beata for their help and support.

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TABLE OF CONTENTS

ABSTRACT i

ACKNOWLEDGEMENTS ii

TABLE OF CONTENTS iii

LIST OF TABLES iv

LIST OF FIGURES v

INTRODUCTION 1

METHODS 4 Measurement equipment and SMPS Principles 4 Data analysis 5 Wildfires and aerosol particle statistics 6

RESULTS 8 Detwiler Fire: July 16th, 2017 – August 24th, 2017 10 October 2017 Northern California wildfires: October 8th, 2017 – October 31st, 2017 14 Ferguson & Carr fires: July 13th, 2018 – August 30th, 2018 18 July 4th, 2018 23

CONCLUSIONS / DISCUSSION 26

REFERENCES 28

LIST OF TABLES

Table 1 Event dates selected for particle statistics analysis, based on high total volume concentration. 7 Table 2 National Ambient Air Quality Standards (as of January 1, 2019). 8 Table 3 Detwiler Fire total number concentration and total volume concentration statistics. 13 Table 4 October 2017 Northern California wildfires total number concentration and total volume concentration statistics. 17 Table 5 Ferguson and Carr wildfires total number concentration and total volume concentration statistics. 21 Table 6 July 4th, 2018 total number concentration and total volume concentration statistics. 24

LIST OF FIGURES

Figure 1 SMPS design schematics. Data from: Wang and Flagan (2002). 5 Figure 2 Monthly Air Quality Index Summary of PM2.5. Source: 2009-18 Washoe County, Nevada Air Quality Trends Report. Air Quality Management Division. 9 Figure 3 Monthly Air Quality Index Summary of PM2.5. Source: 2008-17 Washoe County, Nevada Air Quality Trends Report. Air Quality Management Division. 10 Figure 4 EPA’s Air Quality Index map for 07/15/2017, corresponding to the Baseline 1 data from SMPS. 11 Figure 5 EPA’s Air Quality Index map for 07/16/2017, corresponding to the Plume 1 data from SMPS. 11 Figure 6 EPA’s Air Quality Index map for 07/19/2017, corresponding to the Plumes 2 and 3 data from SMPS. 11 Figure 7 Total number concentration and total volume concentration of aerosol particles over time during Detwiler Fire. 12 Figure 8 Aerosol particle size distribution (PSD) for Detwiler Fire. 14 Figure 9 EPA’s Air Quality Index map for 10/11/2017, corresponding to the Plume 1 data from SMPS. 15 Figure 10 EPA’s Air Quality Index map for 10/10/2017, corresponding to the Baseline 1 data from SMPS. 15 Figure 11 EPA’s Air Quality Index map for 10/12/2017, corresponding to the Baseline 2 data from SMPS. 16 Figure 12 Total number concentration and total volume concentration of aerosol particles over time during October 2017 Northern California wildfires. 16 Figure 13 Aerosol particle size distribution (PSD) for October 2017 Northern California wildfires. 18 Figure 14 EPA’s Air Quality Index map for 08/02/2018, corresponding to the Baseline 1 data from SMPS. 19 Figure 15 EPA’s Air Quality Index map for 08/03/2018, corresponding to the Plume 1 data from SMPS. 20 Figure 16 EPA’s Air Quality Index map for 08/04/2018, corresponding to the Plume 2 data from SMPS. 20 Figure 17 Total number concentration and total volume concentration of aerosol particles over time during Ferguson and Carr wildfires. 21 Figure 18 Aerosol particle size distribution (PSD) for Ferguson and Carr wildfires. 22 Figure 19 EPA’s Air Quality Index map for 07/04/2018. 23 Figure 20 Total number concentration and total volume concentration of aerosol particles over time during the 4th of July weekend. 24 Figure 21 Aerosol particle size distribution (PSD) for 4th of July 2018. 25

INTRODUCTION

Daily increases in particulate matter (PM) mass have been researched and established to

have direct impact on increases in morbidity and mortality (L Bell, 2012). Studies clearly showed

that PM is associated with negative human health effects (Bernard, Samet, Grambsch, Ebi, &

Romieu, 2001), which include increased hospital admissions and emergency room visits, respiratory infections, wheezing and exacerbation of asthma, chronic bronchitis and chronic obstructive pulmonary disease (COPD), cardiovascular diseases, decreased lung function and premature mortality (National Center for Environmental Assessment (Research Triangle Park

N.C.), 2009). Certain groups, like children, older adults, people with pre-existing heart or lung

diseases, people with diabetes are most likely to be affected by the increased particulate matter

pollution (Zanobetti, Schwartz, & Gold, 2000). PM exposure can be manifested by temporary

symptoms like eye, nose and throat irritation, shortness of breath, cough, phlegm, chest tightness,

asthma attacks, acute bronchitis, respiratory infections, heart attack or arrythmia (National Center

for Environmental Assessment (Research Triangle Park N.C.), 2009). People with lung diseases

may experience additional symptoms, including trouble breathing, wheezing, chest discomfort and

unusual fatigue (United States. Environmental Protection Agency. Office of Air and Radiation. &

AIRNow Program (U.S.), 2003).

According to the World Health Organization (WHO) the PM2.5 poses greater risk to mortality, mainly as a result of long-term exposure, than the coarse particles in the PM10 group

(World Health Organization, 2013). Long-term exposure to the PM2.5 pollution can lead to reduced

lung function, chronic bronchitis and premature death or reduced life expectancy. Such consistent

exposure is estimated to reduce the life expectancy by approximately 8.6 months on average in the

European region (World Health Organization, 2013). There is a clear association of airborne PM2.5 2

and adverse cardiovascular and respiratory effect on human health (Brook et al., 2010). Even small

3 increases in daily averaged PM2.5 levels, like 10 μg/m can inflate the risk of all-cause mortality

by 0.8% (Janssen, Fischer, Marra, Ameling, & Cassee, 2013).

PM also contributes to haze and visibility deterioration and can harm the environment by

changing the chemical composition of soil (National Center for Environmental Assessment

(Research Triangle Park N.C.), 2009). Air suspended particles are the overwhelming source for

scattering and absorption of light, leading directly to visibility impairment. When it comes to

scattering, leading to the light extinction, larger aerosols scatter more light than smaller particles

of the same composition and similar shape. However the amount of light scattered in relation to

mass is greatest for aerosol particles with size diameters between 0.3 and 1.0 μm (National Center

for Environmental Assessment (Research Triangle Park N.C.), 2009).

It is also important to look at another aspect of aerosols - their particle size distribution

(PSD), which can determine the dynamics, transport and deposition of particulate matter in a researched environment (Mariam et al., 2017). PSD plays important role in the direct health effects, particularly by influencing the deposition and removal of the particles from the lungs.

Particles larger than 10 μm are usually stopped in the nasal region passages, while smaller ones pass through the nasal region and are deposited in the tracheobronchial and pulmonary regions of the lungs. Those particles are generally between 0.01 and 1 μm in size (Peter Gehr, 2009). Once deposited, the clearance of those insoluble particles is conducted via the mucociliary escalator mechanism (Fedorovitch, 2019).

Particulate matter not only varies by size, but also by its origin, which can be biogenic or anthropogenic. Natural sources can include volcano eruptions, accidental forest fires, dust storms, ocean aerosols, plant emissions. Anthropogenic sources of smaller, under 2.5 μm in particle 3

diameter (PM2.5) are primarily fuel combustion, industrial processes and agriculture. Particulate

matter originating with wildfires is going to be the primary focus of this research. Since the

aerosols sampled, measured and analyzed in this research are in the 25.5 - 697.8 nm range (0.0255

– 0.6978 μm) all data is within the size range of PM2.5.

The severity, frequency and spread of the wildfires in the recent years have been increasing

due to the changing climate (Spracklen et al., 2009), which is especially important for western

United States, where they are common. Plumes of wildfire smoke contain large quantities of PM2.5

(Ammann, Blaisdell, & Lipsett, 2001) and while their contribution to the overall amount of PM2.5

during the days with high exposure are not known, forest biomass burning contributes up to 18% of the total PM2.5 emissions within the US (Phuleria, 2005). Previous studies determined an

increase of the total number of particles by 10 times and even up to 20 times, especially for small

particle diameters between 0.10 and 0.14 μm (Alonso-Blanco, Calvo, Fraile, & Castro, 2012).

This study focuses on three time periods between July 2017 and August 2018, during

wildfire events, which significantly affected the air quality and visibility in Reno, Nevada. It also

looks at a specific one-day long event, 4th of July 2018, which characterizes the differences

between increased-cooking-affected air quality in Reno and the impact of wildfires.

Due to the geographical location and its proximity to California, the town of Reno, Nevada

is frequently exposed to wildfire smoke. California is one of the states with the highest activity of

wildfires within the United States (Westerling, Hidalgo, Cayan, & Swetnam, 2006).

METHODS

Measurement equipment and SMPS Principles

The SMPS setup used to collect the aerosol data for this study consisted of Differential

Mobility Analyzer (DMA) from TSI (St Paul, MN, USA), model 3081 and Condensation Particle

Counter (CPC) from TSI, model 3775. An SMPS spectrometer uses the DMA to select a narrow

band of particles, based on their electrical mobility, that are then counted by the CPC. The inverse

relationship between the electrical mobility and size of the particles allows the SMPS instrument

to determine an aerosol size distribution. Butanol was used as a working fluid of the CPC. The

minimum diameter detected by the CPC is 4 nm and the measurements are rendered over a 0 to

107 particles per cubic centimeter concentration range. The particle size range measured by the

SMPS in this study is between 25 nm and 700 nm.

The instruments were installed on the 4th floor of the Desert Research Institute’s tower located on the northwestern part of the complex. The geocoordinates for the location are

39°34'20.7"N 119°48'07.1"W.

The sample was drawn into the SMPS from the outside using a quarter inch OD copper tube, about 2 meters long, using an internal vacuum pump. The SMPS parameters were: the sampling flow rate of 0.3 L min-1 and sheath flow of 3 L min-1. Each sample was collected with

five minute up-scans and 30 second down-scans, for a total of 330 seconds per size distribution measurement. In the CPC, the aerosol passed through the saturator, condenser and optics. A laser-

diode light source and a diode photodetector tare collected the light scattered by the particles. The

instrument is controlled by an internal microprocessor, which is also used for data processing. The

schematics of the measurement instruments is similar to the one presented in Figure 1.

Sheath Flow Pump Polydisperse Voltage Aerosol (known charge) Supply + (10-10kV) + + + Laser + + Photodetector + Diode Charger + +

+ + + + + Cooled Condenser Impactor + + ++ + + 22°C + + + Alcohol + + Reservior ++ + + + + + + Alcohol Soaked Wick + + + Polydisperse + Aerosol (unknown charge) + + + + + + Heated Saturator 39°C

Excess Flow (Large and neutral particles)

Differential Mobility Analyzer Condensation Particle Counter (DMA) (CPC)

Scanning Mobility Particle Sizer (SMPS) Figure 1 SMPS design schematics. Data from: Wang and Flagan (2002).

Data analysis

Several corrections had to be performed after the sample data was imported from the

particle sizer. The SMPS and specifically the DMA component rely on the electrical mobility (Zp) in order to separate the particles in the aerosol sample. Zp is usually calculated by:

= = ( 1 ) 𝑉𝑉 𝑛𝑛𝑝𝑝 𝑒𝑒 𝐶𝐶𝑐𝑐 𝑍𝑍𝑝𝑝 𝐸𝐸 3 𝜋𝜋 𝜂𝜂 𝐷𝐷𝑝𝑝 (Hinds, 2012) where np represents number of charges on the particles, e is the elementary unit of charge, E is the electric field strength, η quantifies the dynamic viscosity of gas, Dp is the particle diameter, Cc stands for Cunningham slip correction and V is the particle velocity. 6

Due to the electrical mobility (Zp) dependence on the viscosity of gas (η) and therefore the

air density and atmospheric pressure, collected data was inverted to account for the elevation

difference between the sea level and that of Reno, NV (approx. 5000 ft). Atmospheric pressure at

approximately 5000 ft should be 85 kPa.

The SMPS data was then combined into a python compiled dataframe, which was later

used for statistical analysis. The main three indexes this study focused on were the total number

concentration (Ntot), the total volume concentration (Vtot) and the mean size of the particles (Dmean).

Wildfires and aerosol particle statistics

After collecting all the sample aerosol data from the SMPS, the total number concentration, total volume concentration and the mean size of aerosol particles were calculated for the whole period of sampling time. Based on the significantly elevated values of Vtot the following dates were chosen for further evaluation: August 31, 2017; October 10-11, 2017; October 15-16, 2017;

December 10-11, 2017; December 21-22, 2017; January 28-29, 2018; March 17-18, 2018; March

21-22, 2018; July 19, 2018; July 30, 2018 and August 3-4, 2018.

Data for each of those dates was then evaluated, based on the seasonality, time of the day, as well as the correlation between the particle concentration statistics and the air quality levels reported by Environmental Protection Agency (EPA) program AirNow and its Air Quality Index

(AQI). The dates were also compared to regional wildfire events occurring within a reasonable physical distance and time frame. The periods of time, which are introduced in Table 1, were then selected as a final set of events for this study and included three sets of wildfires and a case study of the 4th of July, 2018. 7

Table 1 Event dates selected for particle statistics analysis, based on high total volume concentration.

Event Days of the event Days with Reno’s air quality affected

Detwiler Fire 07/16/2017 – 08/24/2017 07/16/2017 – 07/19/2017

October 2017 Northern California wildfires 10/08/2017 – 10/31/2017 10/11/2017 – 10/17/2017

Ferguson and Carr wildfires 07/13/2018 – 08/30/2018 8/3/2018 – 8/4/2018

4th of July 07/04/2018 07/04/2018 – 07/05/2018

Each of the selected wildfire events was selected based on a number of smoke plumes present at on how the particle distribution is affected the most by the plumes passing by.

RESULTS

Based on the correlation between the total volume concentration statistics and air quality data, this research focuses on 4 case studies described in this chapter. The direct comparison between the selected baseline data and event data during each of the incidents allowed to determine the impact of wildfire plumes on the atmospheric air quality, leading to potential short- and long- term health impacts on local population.

The Clean Air Act administered by the Environmental Protection Agency (EPA), as a federally enforced law, establishes seven primary air pollutants and sets National Ambient Air

Quality Standards (NAAQS) for each of them. Pollutants include ozone, particulate matter

(separately for particles smaller than 2.5 micrometers and smaller than 10 micrometers), carbon monoxide, nitrogen dioxide, sulfur dioxide and lead. This study relates its results and data to the particulate matter, with sizes smaller than 2.5 micrometer (PM2.5). The 24h NAQQS for PM2.5 are presented below in Table 2.

Table 2 National Ambient Air Quality Standards (as of January 1, 2019).

Pollutant Primary Standard Level Secondary Standard Level

PM2.5 35 μg/m3 35 μg/m3

Primary Standard refers to public health, especially that of sensitive populations, such as people with asthma, heart or respiratory system diseases, children and seniors. Secondary standards set limits to protect public welfare, including protections against decreased visibility, damage to animals, crops, and buildings.

Washoe County Air Quality Trends reports determined that in July and August of 2018 there were a total of 7 days with the Air Quality Index (AQI) being Unhealthy for Sensitive Groups. 9

The dates coincide with the Ferguson and Carr wildfires smoke reaching Reno and its surroundings

(Figure 2).

Figure 2 Monthly Air Quality Index Summary of PM2.5. Source: 2009-18 Washoe County, Nevada Air Quality Trends Report. Air Quality Management Division. Similarly, for 2017 the only day with exceedance was July 19th, which coincides with the

Detwiler Fire period of time (Figure 3). 10

Figure 3 Monthly Air Quality Index Summary of PM2.5. Source: 2008-17 Washoe County, Nevada Air Quality Trends Report. Air Quality Management Division.

Detwiler Fire: July 16th, 2017 – August 24th, 2017

Detwiler fire was first reported in the afternoon of July 16th, 2017 near Lake McClure

located in the Sierra Nevada foothills in California. The burned area reached nearly 82,000 acres.

Full containment was achieved on August 24th, 2017. The days when most significant plumes

impacted Reno, NV were between July 16th and July 19th, 2017. The data from EPA’s AirNow

program presents those two days with abnormally high PM AQIs with values ranging from

moderate to unhealthy, while data from July 15th shows relatively clean air (Figure 4, Figure 5 and Figure 6). 11

Figure 4 EPA’s Air Quality Index map for 07/15/2017, corresponding to the Baseline 1 data from SMPS.

Figure 5 EPA’s Air Quality Index map for 07/16/2017, corresponding to the Plume 1 data from SMPS.

Figure 6 EPA’s Air Quality Index map for 07/19/2017, corresponding to the Plumes 2 and 3 data from SMPS. 12

Based on the SMPS data and the calculated Ntot and Vtot values there were three major plumes and three baseline periods selected for particle statistics visualization, as presented in

Figure 7.

Figure 7 Total number concentration and total volume concentration of aerosol particles over time during Detwiler Fire.

The following Table 3 presents the mean values of Ntot and Vtot as well as their corresponding standard deviations (SD):

Table 3 Detwiler Fire total number concentration and total volume concentration statistics.

Event Dates Mean Ntot Ntot SD Mean Vtot Vtot SD

(#/cm3) (#/cm3) (μm3/cm3) (μm3/cm3)

Baseline 1 (B1) 7/15 10:00 - 7/16 06:00 890 342 2.1 0.9

Plume 1 (P1) 7/16 10:00 - 7/16 12:00 3512 795 27.4 7.6

Baseline 2 (B2) 7/16 14:00 - 7/17 07:00 709 394 0.9 0.4

Plume 2 (P2) 7/19 00:30 - 7/19 12:00 2871 821 25.7 7.3

Plume 3 (P3) 7/19 17:00 - 7/19 19:00 1743 512 25.5 10.2

Baseline 3 (B3) 7/21 00:00 - 7/24 06:00 1093 444 2.8 0.9

Based on those results it can be determined that the Ntot has much higher relative variability

than Vtot, which can be attributed to numerous reasons, including morning traffic patterns and

boundary layer variability. However, it is clear that the fire plume events have much bigger impact

on the total volume concentration than on the total number concentration. While Ntot increases by

roughly a factor of 4, at the same time Vtot increases by a factor of 9, 13 or even 30. This assessment is also confirmed when looking at the aerosol particle size distributions (PSD) corresponding to the above time frames, as illustrated by Figure 5. 14

Figure 8 Aerosol particle size distribution (PSD) for Detwiler Fire. Plume 1, which is dominating the distribution in terms of particle number has its mode

being 9000 cm-3 at around 230 nanometers in diameter, while the distribution for the baseline time

frames does not exceed 1500 cm-3 for the whole diameter range, with the modes being between

40 and 150 nm. Similarly, the other 2 plumes are dominated by larger particles, however they have

two smaller modes which can be distinguished within them. Thus, plumes from the same wildfire

can have slightly different size distributions, probably depending on the type of fire (flaming vs smoldering) and/or the degree of smoke plume dilution.

October 2017 Northern California wildfires: October 8th, 2017 – October 31st, 2017

In October of 2017 in Northern California a series of approximately 250 wildfires occurred,

the biggest and largest of which were spreading across Napa and Sonoma Counties, CA. The total 15 affected area was estimated to be around 245,000 acres. Several fires started on October 8th, 2017 in multiple locations, during a very dry season and extremely high-speed winds called Diablo winds, which in some areas reached 70 miles per hour. The day when most significant plume impacted Reno, NV was October 11th, 2017. According to the data from EPA’s AirNow, the day when air quality was primarily affected was also October 11th. The PM AQI values were ranging from moderate to borderline unhealthy that day (Figure 9). Figure 10 and Figure 11 represent the day before the main plume reached Reno, as well as the day after, respectively. Air quality was good on both of those days according to the AirNow data.

Figure 9 EPA’s Air Quality Index map for 10/11/2017, corresponding to the Plume 1 data from SMPS.

Figure 10 EPA’s Air Quality Index map for 10/10/2017, corresponding to the Baseline 1 data from SMPS. 16

Figure 11 EPA’s Air Quality Index map for 10/12/2017, corresponding to the Baseline 2 data from SMPS.

Based on the SMPS data and the calculated Ntot and Vtot values there was one major plume

and two baseline periods selected for particle statistics visualization, as presented in Figure 12.

Figure 12 Total number concentration and total volume concentration of aerosol particles over time during October 2017 Northern California wildfires.

The following Table 4 itemizes the mean values of Ntot and Vtot as well as their

corresponding standard deviations (SD): 17

Table 4 October 2017 Northern California wildfires total number concentration and total volume concentration statistics.

Event Dates Mean Ntot Ntot SD Mean Vtot Vtot SD

(#/cm3) (#/cm3) (μm3/cm3) (μm3/cm3)

Baseline 1 (B1) 10/10 12:00 – 10/11 10:00 791 269 2.0 0.5

Plume 1 (P1) 10/11 13:00 – 10/11 19:00 1081 134 14.7 2.2

Baseline 2 (B2) 10/11 20:00 – 10/12 21:00 886 498 1.1 0.6

Similarly, to the previously described Detwiler Fire event, based on the above listed results it can be determined that the Ntot has much higher relative variability than Vtot. It is again clear that the plume event has much bigger impact on the total volume concentration than on the total number concentration. While Ntot increased by roughly a factor of 1.5, during the wildfire events, at the same time Vtot increased by a factor of 7, or even 13. This assessment is also confirmed when looking at the aerosol particle size distributions (PSD) corresponding to the above time frames, as illustrated by Figure 13. 18

Figure 13 Aerosol particle size distribution (PSD) for October 2017 Northern California wildfires. Plume 1 is very similar to the main one from the previous case of Detwiler fire, where the

particle number is dominated by the larger, accumulation mode particles. However, during October

2017 Northern California wildfires the mode of PSD of Plume 1 has its maximum at around 2100

cm-3, as compared to the nearly 9000 cm-3 in the previous case. The PSDs for the baselines are fairly consistent with each other and dominated by small particles, but in smaller numbers than their corresponding plumes.

Ferguson & Carr fires: July 13th, 2018 – August 30th, 2018

Ferguson Fire originated in the evening of July 13th, 2018 in the Sierra National Forest in

California in a steep, rocky terrain with very little road access. The event impacted local tourism

and recreation within the Sierra National Forest, as well as the Yosemite National park, burning a 19

total of nearly 100,000 acres (Incident Information System, 2018). Full containment was achieved

on August 19th, 2018. The Carr Fire was first reported on the afternoon of July 23rd, 2018 in

Whiskeytown–Shasta–Trinity National Recreation Area, in Shasta County, California. The burn area reached nearly 230,000 acres and the fire lasted until it was fully contained on August 30th,

2018. The days during which the Reno area was primarily affected by the mixed smoke plumes

originating from both of the above-mentioned wildfires were August 3rd and 4th, 2018. The baseline

for this case study was selected between August 1st and 3rd, with relatively clean air on August 2nd,

2018, as visible on below on Figure 14. According to the data from EPA’s AirNow the PM AQI

values for the following two days were ranging from moderate to unhealthy, with the worst

conditions on August 4th (Figure 15 and Figure 16).

Figure 14 EPA’s Air Quality Index map for 08/02/2018, corresponding to the Baseline 1 data from SMPS. 20

Figure 15 EPA’s Air Quality Index map for 08/03/2018, corresponding to the Plume 1 data from SMPS.

Figure 16 EPA’s Air Quality Index map for 08/04/2018, corresponding to the Plume 2 data from SMPS.

Based on the SMPS data and the calculated Ntot and Vtot values there were two major plumes and one baseline period selected for particle statistics visualization, as presented in Figure

17. 21

Figure 17 Total number concentration and total volume concentration of aerosol particles over time during Ferguson and Carr wildfires.

The following Table 5 details the mean values of Ntot and Vtot as well as their corresponding standard deviations (SD):

Table 5 Ferguson and Carr wildfires total number concentration and total volume concentration statistics.

Event Dates Mean Ntot Ntot SD Mean Vtot Vtot SD

(#/cm3) (#/cm3) (μm3/cm3) (μm3/cm3)

Baseline 1 (B1) 8/1 11:00 – 8/3 12:00 1045 517 4.3 2.0

Plume 1 (P1) 8/3 16:00 – 8/3 21:00 1939 191 26.7 5.1

Plume 2 (P2) 8/4 10:00 – 8/4 13:00 4047 753 56.0 11.6

As it was with the previously described events, based on the above listed results, it can be

determined that the Ntot has yet again much higher relative variability than Vtot. It is also clear that

the plume events have much bigger impact on the total volume concentration than on the total 22

number concentration. While Ntot increases by roughly a factor of 2, up to 4, during the wildfire

events, at the same time Vtot increases by a factor of 6, or even 13. This assessment is also

confirmed when looking at the aerosol particle size distributions (PSD) corresponding to the above

time frames, as illustrated by Figure 18.

Figure 18 Aerosol particle size distribution (PSD) for Ferguson and Carr wildfires. It can again be seen that the impact of wildfire smoke is primarily in terms of a major

number increase of the larger particles, in this case with the mode of over 10,000 cm-3 at diameters

around 280 nanometers (for Plume 2). Plume 1 contained less than half of the particles of the previously mentioned one, but the mode is within the same range of diameters.

July 4th, 2018

Final event this study looks at, as a comparison to the effects of wildfire smoke, is the potential impact of increased outdoor cooking activities on the air quality in Reno, NV. In terms of AQI determined by the EPA’s AirNow, the PM index was slightly elevated for July 4th (Figure

19), especially as compared to July 3rd and July 5th. It can be determined that all major cities had moderate air quality, while remote areas were relatively clean.

Figure 19 EPA’s Air Quality Index map for 07/04/2018.

As with the wildfire cases, based on the SMPS data mean Ntot and Vtot values were calculated, focusing on July 3rd, 2018 between 14:00 and 22:00 local time and July 4th, 2018 for the same hours. 24

Figure 20 Total number concentration and total volume concentration of aerosol particles over time during the 4th of July weekend.

The following Table 6 details the mean values of Ntot and Vtot as well as their corresponding standard deviations (SD):

Table 6 July 4th, 2018 total number concentration and total volume concentration statistics.

Event Dates Mean Ntot Ntot SD Mean Vtot Vtot SD

(#/cm3) (#/cm3) (μm3/cm3) (μm3/cm3)

Baseline 1 (B1) 7/3 14:00 - 7/3 22:00 1163 406 3.2 0.7

Event 1 (E1) 7/4 14:00 - 7/4 22:00 1469 456 3.0 0.7

As it was with the previously described events, based on the above listed results, it can be

determined that the Ntot has yet again higher relative variability than Vtot, however the differences

are far less significant. The difference between the increased outdoor cooking event impact and 25

wildfire smoke impact is also noticeable. While Ntot increases are very slight during the event as compared to the baseline, the Vtot stays at nearly the same level. The aerosol particle size distributions (PSD) corresponding to the above time frames are illustrated in Figure 21. There is a clear increase in the particle number for the increased outdoor cooking activity event, as well as a slight shift to a larger particle diameter, consistent with cooking and barbecue smoke.

Figure 21 Aerosol particle size distribution (PSD) for 4th of July 2018.

CONCLUSIONS / DISCUSSION

SMPS aerosol particle measurements, including number and volume concentrations, as well as size distributions, which are a very important component of air quality data allowing scientists to determine impact of various forest fire events on human health, visibility and climate, were measured for a period of over one year in Reno, Nevada. The main result of this study is the fact that wildfire events and smoke plumes travelling hundreds of miles, for hours or days, can have a significant impact on the PSD in urban areas, leading to potentially harmful effects on population. Forest fire plumes have a clearly different size distribution than the unaffected air, with most of the plumes described in this study being characterized by a significantly larger size mode, of diameters between 200 and 250 nanometers, than their respective baseline measurements. The differences between clean air and fire event conditions could have significant implications for the aerosol particles deposition in human respiratory system, as particle size deposition depends directly on particle diameter.

The number of events studied suggest that the plumes originating from wildfires differ also not only from their respective baseline, but also between each other. Some plumes have a dominating large diameter mode, between 200 and 250 nanometers, but some also contain a small particle size mode, around 75 and 100 nanometers. Those differences and relationships need to be investigated further.

The lack of chemical composition data was found to be the one of the most challenging parts of the study. Without this knowledge it is hard to attribute specific plume to forest fire with reasonable certainty. It is also challenging to determine the main fuel type for each fire and plume, which could help to explain how that affects the particle size distribution. 27

Another obstacle to fully understand the wildfire smoke impacts are their short-lived

characteristics, as well as their local variability. Some of the plumes are only present in their

unchanged morphology for minutes or hours, therefore use of 24-hour filter measurements may not be sufficient. Future studies should take that into account and implement usage of instruments allowing for continuous sampling and monitoring.

Different approach, which could be found beneficial in distinguishing the wildfire aerosol containing plumes would be to include biomass burning chamber experiments. This comparison may allow to have a better understanding of size distribution dependency on the fuel type, source and time of combustion. Similarly, a comparison of distant and local wildfire events could demonstrate the differences in particle size distributions as a result of aging and dilution of the plumes.

Finally, the other factors, which should be considered in future studies are the impact of local meteorology, boundary layer properties, as well as the influence of local sources and traffic patterns on the lifetime and properties of researched wildfire plumes.

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