DAQ-2024-011367

Office of Research
Office of Sponsored Programs February 01, 2024
4333 Brooklyn Avenue NE Box 359472 Seattle, WA 98195-9472
206.543.4043 fax [phone] www.washington.edu/research/osp
Chris Pennell
State of Utah
P.O. Box 144820
Salt Lake City, UT 84114-4820
Dear Mr. Pennell:
The University of Washington is pleased to submit this letter in support of the application
entitled, "VOC to NOx relationships and Impacts of Smoke on Ozone in the Wasatch Front."
This application was prepared by Dan Jaffe.
We present this application for your review and request support in the amount of $71,136 for the
period June 01, 2024 through August 30, 2025.
Thank you for your consideration.
Sincerely,
Cindy Shirley
Director, Office of Sponsored Research
University of Washington Bothell
Please reference our #A200043 on all correspondence concerning this application.
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Summary Information Page for:
VOC to NOx relationships and Impacts of Smoke on Ozone in the Wasatch
Front
A proposal to the Utah Division of Air Quality, February 2024

Applicant Information:
Daniel Jaffe, University of Washington (principal investigator)

Principal Investigator Contact Information:
Dr. Dan Jaffe, Professor
University of Washington Bothell
18115 Campus Way NE
Bothell, WA 98011-8246
[e-mail]
425-352-5357

Sponsored Programs Office Information:
University of Washington Bothell
Cindy Shirley, Director UW Bothell Sponsored Programs
Tel: [phone], email: [e-mail]

Funding Requested:
We request $71,138 from the Utah Division of Air Quality to carry out this project.
Project Period:
The project will begin on July 1, 2024 and end on August 30, 2025.
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VOC to NOx relationships and Impacts of Smoke on Ozone in the Wasatch
Front
SCOPE OF WORK
Abstract
Meeting the O3 standard in the Wasatch Front will require emission reductions from local
sources and careful tracking of non-local sources, such as wildfires. To make the best decisions
on emission reductions, it is essential to understand the regional VOC-NOx sensitivity. In this
project, we will use existing and new observations to:
1) Identify smoke days for 2018-2024 and identify O3 exceedance days that are exacerbated
by wildfire smoke; and
2) Examine the formaldehyde (CH2O) to NO2 ratio (FNR) across the region using both in-
situ observations and new satellite data to understand the O3 sensitivity to NOx and
VOCs.
For the first goal, we will use satellite data plus surface PM2.5 to identify smoke days and
understand the frequency and impacts of wildfires on both PM2.5 and O3 for multiple sites in the
Wasatch Front. We will then apply Generalized Additive Modeling to the data for the 2018-
2024 time period to estimate the wildfire contribution to the O3 maximum daily 8-hour average
(MDA8). The GAMs will be applied to 4 or more sites within the Wasatch Front.
For the second goal, we will use existing in-situ formaldehyde and NO2 data from several sites,
new observations data obtained in 2024 and new high-resolution data from the TEMPO satellite
instrument. Combining these will give important insights into the VOC-NOx sensitivity for O3
production across a wide region of the Wasatch Front.
These two goals will provide important policy relevant insights into the processes that control
high O3 days in the Wasatch Front.

Basis and Rationale
Ozone (O3) is a key pollutant that has been shown to cause health problems for
individuals, especially children and vulnerable populations. In urban areas, O3 is formed from
photochemical reactions of NOx and VOCs. The SLC region has been designated as a non-
attainment area for the 2015 8-hour O3 standard by the U.S. EPA. To be in compliance with this
standard, the annual fourth highest maximum daily 8-hour average (MDA8) O3 concentration
averaged over three years must be 70 ppb or less. Figure 1 shows the annual fourth highest
MDA8 for the Hawthorne site in SLC going back to 2006 and PM2.5 on the four highest O3 days
each year. During this time period, there has been little overall change in the fourth highest
value, despite a significant reduction (~30%) in the concentration of NOx over the same time
period (Jaffe et al 2024). While typical urban/industrial emissions produce sufficient NOx and
VOCs to generate O3, wildfire smoke adds to these pollutants, especially VOCs, and is known to
increase O3 concentrations in SLC and other regions (Gong et al., 2017; Jaffe, 2021; Ninneman
and Jaffe 2021).
3

Figure 1. Annual
fourth highest O3
MDA8 value for
Hawthorne site (left
axis) and PM2.5 on
the top four O3 days
each year for
Hawthorne site
(right axis).

Figure 2 shows the number of days with an MDA8 over 70 ppb and the number of those days
that have smoke for the Hawthorne site.
Figure 2. Annual
number of
exceedance days
(MDA8>70 ppb)
and exceedance
days with smoke for
the Hawthorne site
in SLC region,
2006–2022. From
Jaffe et al (2024).

Figures 1 and 2 demonstrate that smoke is very important in some years, less so in others.
For example, 2020 and 2021 had 43 and 63 smoke days at Hawthorne, by our definition (see
below). In 2021, there were 17 O3 exceedance days (MDA8>70 ppb), 14 of which had smoke.
In contrast, 2019 had zero smoky days and fewer exceedance days, but still the fourth highest
MDA8 exceeded 70 ppb. While the SLC area is currently considered attainment for the 2012
PM2.5 standard, the EPA has announced that the current annual NAAQS level will go down to 9
or 10 µg m-3, and thus it will become increasingly important to identify these smoke influenced
days, both for O3 and PM2.5.
4

The Salt Lake regional Smoke, Ozone and Aerosol Study (SAMOZA) was recently
completed by a team of scientists from the University of Washington, the University of Montana
and Utah State University. The project was led by D.Jaffe, who is also the PI of this project.
The goals of SAMOZA were focused on understanding O3 production, for both smoky and non-
smoky days, at urban sites in the Wasatch front. The experimental phase of the project took
place in the summer of 2022. During this time, we made measurements of a suite of VOCs,
including oxygenated VOCs. O3 data were obtained by from the standard UDAQ observations
and also using a novel method that has been shown to have no interferences in smoke influenced
airmasses. Our measurements took place at the Utah Tech Center in Salt Lake City, which is an
existing UDAQ air monitoring site. Using the SAMOZA data and the routine measurements by
UDAQ, we conducted a variety of modeling and analyses. The SAMOZA measurement period
(summer 2022), took place in a year with a relatively lower fourth highest MDA8 O3 value (72
ppb) and a modest number of smoke days (10 days), compared to the past decade (means of 76
ppb and 19 days, respectively). At Hawthorne, there were 4 exceedance days in 2022, two of
which had smoke, while at the UTC site, there were 6 exceedance days in 2022, two of which
had smoke (the same smoke-exceedance days at both sites). Key results from the experiment
include:
1. In 2022, the SLC region had 3–6 days with MDA8 O3 over 70 ppb, depending on the site,
and several of these days were clearly smoke influenced. Both the number of exceedance and
smoke days was lower than the mean for the previous decade.
2. We found no evidence for bias with the standard O3 measurements made by UDAQ using a
Teledyne T400 instrument in smoke at PM2.5 concentrations up to 60 µg m–3.
3. Smoke significantly enhanced the concentration of PM2.5, CO, O3 and nearly all VOCs. NOx
was also enhanced on smoke days, but this was complicated by day of week effects.
4. Formaldehyde (CH2O) and other aldehydes are key O3 precursors. We measured
formaldehyde by two different methods and these showed generally good correlation, but the
Proton Transfer Reaction Mass Spectrometry (PTR-MS) measurements of formaldehyde and
other carbonyls were approximately 50% greater than the 2,4-dinitrophenylhydrazine
(DNPH) measurements on smoky days. The cause for this difference is not yet known.
5. There appear to be primary sources of formaldehyde in the SLC urban region. Identifying
and controlling these sources could lead to significant reductions in O3.
6. Photochemical modeling of O3 production rates at the Utah Tech Center demonstrates a
strong sensitivity to VOC concentrations and less sensitivity to NOx. For non-smoke days,
reductions in VOCs of ~40% would result in significantly reduced O3 production, potentially
meeting the O3 standard. Reductions in NOx of ~60% are needed to get a significant
reduction in O3 production for non-smoke days. The VOCs with the greatest contribution to
O3 production are oxygenated VOCs, especially aldehydes, along with alkenes.
7. We used a machine learning/statistical approach called Generalized Additive Modeling
(GAM) to estimate O3 from a variety of meteorological predictors and evaluate the impact
from smoke on the MDA8. The MDA8 contribution calculated by the GAM approach on
smoky days was similar to the enhancements calculated using the photochemical model.
Analysis of the GAM results show that 23% of the smoke days have model residuals/smoke
contributions that exceed the EPA (2015) criteria for statistical analysis of O3 data, and thus
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this method could be used as support for exceptional event cases for those days. Further work
to improve the GAMs could provide a more accurate estimate of the smoke contributions to
the MDA8.
These conclusions are described in detail in three peer reviewed papers (Lee and Jaffe 2023;
Ninneman et al 2023; Jaffe et al 2024).
1. Lee H. and Jaffe D.A. Impact of wildfire smoke on ozone concentrations using a
Generalized Additive model in Salt Lake City, Utah, USA, 2006–2022, Journal of the Air &
Waste Management Association, DOI: 10.1080/10962247.2023.2291197, 2023.

2. Ninneman M., Lyman S., Hu U., Cope E., Ketcherside D. and Jaffe D. Investigation of
Ozone Formation Chemistry during the Salt Lake Regional Smoke, Ozone, and Aerosol
Study (SAMOZA). ACS Earth and Space Chemistry. 7 (12), 2521-2534 DOI:
10.1021/acsearthspacechem.3c00235, 2023.

3. Jaffe D.A., Ninneman M., Nguyen L., Lee H., Hu L., Ketcherside D., Jin L., Cope
E., Lyman S., Jones C., O’Neil T. and Mansfield M.L. Key results from the salt lake
regional smoke, ozone and aerosol study (SAMOZA), Journal of the Air & Waste
Management Association, DOI: 10.1080/10962247.2024.2301956, 2024.

While SAMOZA found that O3 production at the UTC site was most sensitive to VOC
reductions (Ninneman et al 2023) this may reflect the fact that this is a high NOx site. Table 1
shows mean NO2 concentrations for the summer of 2022 across all sites with data.
Table 1. Mean NO2 concentrations across the Wasatch front for August-September 2022. The
regional mean is calculated from all sites.
Copper
View Erda Haw-
thorne
Herri-
man
Inland
Port
Lake
Park
South
SL
Rose
Park UTC Regional
mean
Mean
(ppb) 9.8 2.7 7.6 3.5 7.1 7.7 15.2 9.9 13.4 8.5
% of
regional
mean
115 32 89 41 82 91 177 116 156 100

Formaldehyde to NO2 ratio (FNR) as an indicator of VOC-NOx sensitivity
Formaldehyde is a key precursor and intermediate in the photochemical production of O3.
Both formaldehyde and NO2 are clearly observed by space based instruments and the FNR from
satellite observations is a good indicator of the VOC vs NOx sensitivity for O3 production
(Martin et al., 2004; Duncan et al 2010; Souri et al 2020; Tao et al 2022; Jin et al 2020; 2023).
While earlier studies suggested a transition between NOx sensitive and VOC sensitive regimes at
an FNR value of 1-2 (Duncan et al 2010), more recent work suggests an FNR value of <3 is
consistent with a VOC sensitive O3 production regime whereas values >3 are consistent with a
NOx sensitive regime (Jin et al 2020; Tao et al 2022).
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The FNR measured at the UTC site in August-September 2022 is shown in Figure 3.
The FNR values are generally below 1, consistent with a VOC sensitive regime. The FNR
values are slightly higher on exceedance days, possibly due to the influence of temperature on
formaldehyde concentrations (Tao et al 2022; Liu et al 2023). Here we show only the non-
smoke days, as smoky days were found to have significantly higher formaldehyde concentrations
(Jaffe et al 2024). Figure 4 shows daily mean formaldehyde concentrations vs daily maximum
temperature measured by the PTR-MS at the UTC site. A robust and significant correlation is
observed. However these observations are from a single location (UTC), while O3 production
depends on the spatial distribution of the VOC-NOx sensitivity as an airmass is transported to
the receptor location. In this project, we will examine both in-situ and satellite data to evaluate
the VOC-NOx sensitivity using the FNR over a wide region of the Wasatch Front.

Figure 3. Formaldehyde to NO2
(FNR) ratio observed on non-smoke
days in August-September 2022.
These data are from the SAMOZA
experiment and were collected at the
UTC site. For the non-smoke days,
there were 3 exceedance days
(MDA8>70 ppb) and 48 non-
exceedance days.

Figure 4. Daily mean formaldehyde
concentrations vs daily maximum
temperature on non-smoke days for
August-September 2022. These
data are from the SAMOZA
experiment and were collected at the
UTC site.

For the FY 2024 "Science for Solutions" program, the Utah Division of Air Quality (UDAQ) has
requested proposals to study O3 and PM2.5 in the Wasatch Front. Specific areas of interest
include:
Summertime Ozone Chemistry and Sources:
i. Oxygenated VOCs
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ii. O3-NOx-VOC sensitivity
iii. Utah-specific validation of remote sensing products
iv. Measurements of “background” ozone and ozone precursors
Our project directly addresses these areas of interest. The specific goals for our project are:
1. Identify smoke days for the 2018-2024 timeframe and identify O3 exceedance days that
are exacerbated by wildfire smoke. This addresses the goals to understand background
sources of O3.
2. Examine the formaldehyde (CH2O) to NO2 ratio (FNR) across the region using both in-
situ observations and new satellite data to understand the O3 sensitivity to NOx and
VOCs. This addresses the first three items listed above.

Technical Approach
Task 1: Identify smoke days for the 2018-2024 timeframe and identify O3 exceedance days
that are exacerbated by wildfire smoke.
We start with this task as its important to identify those days that are influenced by
wildfire smoke before examining the VOC-NOx sensitivity, due to the much higher VOC
loading on smoky days (Jaffe et al 2024). For this, we will combine satellite observations from
the National Oceanic and Atmospheric Administration (NOAA) Hazard Mapping System-Fire
and Smoke Product (HMS-FSP; https://www.ospo.noaa.gov/Products/land/hms.html) with
surface PM2.5 data. The HMS product (Rolf et al 2009) gives overhead smoke plumes with
three qualitative indicators (light, medium and heavy), but while the HMS product is useful to
identify the source and transport of smoke plumes, several studies have found it is a weaker
indicator of surface smoke (e.g. Kaulfus et al 2017; Buysse et al 2019). For this reason, we will
use a combination of the HMS data with surface PM2.5. This is done by first using the HMS
smoke product to identify days with no likely smoke impact from the HMS product (HMS=0).
From this subset of the data, we get the mean and standard deviation of these non-smoke days.
We then define a PM2.5 criteria for each site as:
PM2.5 (smoke criteria) = PM2.5(mean for HMS=0) + 1 standard deviation (HMS=0)
Smoke days are then defined as those having:
HMS=1 (light, medium or heavy) and observed PM2.5 > PM2.5 (smoke criteria)
In other words, “smoke days” are those that have overhead smoke, as indicated by the HMS
product, and elevated surface PM2.5. We will do this analysis for every AQS site in Utah with
>80% PM2.5 data coverage for the period of 2018-2024. We can then quantify the distribution of
observed smoke day PM2.5 for each site by year, month and HMS category (light, medium or
heavy).
To quantify the smoke contribution to the MDA8 we will use a Generalized Additive
Modeling (GAM) to predict O3 for smoky and non-smoky days, similar to the work done for the
SAMOZA project (Lee and Jaffe 2023). GAMs are a type of machine learning that uses
observations to train a dataset to predict a key parameter. Relevant to our application, GAMS are
particularly useful in that they can incorporate linear, non-linear and categorical predictors
(Wood 2017). In this case, the predicted parameter is the O3 MDA8. In previous applications,
8

we have used meteorological variables, such as the daily maximum temperature or geopotential
height, day of week, back-trajectory distance and direction, surface chemical measurements and
satellite observations as predictors. Our group has used GAMs to quantify the additional O3
associated with smoke in numerous urban areas, including SLC (Gong et al., 2017; Jaffe et al.,
2020; Jaffe, 2021). In addition, we have applied this approach in several successful exceptional
event demonstrations to quantify the influence of smoke on the O3 MDA8 (LDEQ, 2018; TCEQ,
2017). Our detailed analysis methodology is described in a number of previous publications (e.g.
Gong et al. 2017; Jaffe 2021; Lee and Jaffe 2023). The GAMs provide an additional tool to
quantify smoke impacts on O3, which can be directly compared to photochemical box modeling
or other approaches. Note that GAMs are simpler to develop and compute than photochemical
models.
Essentially, the GAM predicts the MDA8 O3 on any given day from the known
meteorological factors. The GAMs are computed on the non-smoke day and evaluated against a
validation dataset that was not part of the original training data. The original model is then
applied to the smoke day dataset. In all cases to date, we have found that the residuals from the
training data and validation data are insignificantly different from zero, whereas the residuals
from the smoke data are significantly greater than zero. Figure 5, from the SAMOZA analysis,
shows the residuals for four sites in the SLC region on smoky days.

Figure 5. GAM residuals for smoke days at
four SLC sites: Hawthorne (HW); Bountiful
Viewmont (BV), Herriman (HR) and Erda
(ER). Boxes and whiskers represent the
25th–75th percentiles and 1.5 times
interquartile range (1.5IQR), respectively;
squares indicate means and horizontal lines
within boxes indicate medians; the dashed
red line indicates the 97.5th percentile of
residuals on no-smoke days for all sites
(10.9 ppb). (Lee and Jaffe 2023)

The question arises as to how much of the residual should be “counted” as coming from
the smoke contribution. In a guidance document for statistical methods, the EPA recommends
reducing the interpreted smoke contribution by the 97.5th percentile of all residuals (U.S. EPA
2016). We applied this method to our recent analysis for the Salt Lake City region and based on
this, we now report minimum and maximum smoke contributions to the MDA8 for each smoke
day. We will also examine the outliers from the residual distribution by using the absolute
deviation around the median, as recommended by Leys et al (2013).
Our team has a strong history of using GAMs (Gong et al 2017; McClure et al 2018;
Jaffe 2021; Lee and Jaffe 2023), and some of these results have been incorporated into
exceptional event demonstrations (LCEA 2018; TCEQ 2017). Over this time, we have
improved the modeling approach and quality control. We have also modified how we estimate
9

the smoke contribution to only include those residuals that are considered outliers from the
distribution. GAMs are best conducted on sites with multiple years of data. For this work, we
will conduct GAM analysis for four sites in the Wasatch front: Hawthorne, Erda, Bountiful
Viewmont and Herriman for the 2018-2024 O3 seasons.

Task 2: Examine past and future NOx and VOC data along with TEMPO satellite data to
understand the NOx-VOC sensitivity in the region.
For this task we will examine past NOx and VOC data from three sites Hawthorne,
Bountiful Viewmont (BV) and the Utah Tech Center (SAMOZA). Our focus will be on
formaldehyde (CH2O) which can be used to calculate the formaldehyde to NO2 ratio. We will
focus on non-smoke days, as identified from Task 1. We note there are not a lot of routine VOC
measurements in the Wasatch front region. Table 1 shows formaldehyde data from two sites
with data collected by UDAQ: Bountiful Viewmont (BV) and Hawthorne, along with data
collected during SAMOZA at the UTC site. During SAMOZA formaldehyde was measured by
both by the PTR-MS and the DNPH cartridge method. The UDAQ measurements at BV and
Hawthorne are also made using the DNPH method. The table shows only summer data for each
location.

Table 2. Data for formaldehyde in the Wasatch front region. Only summer data is shown.
Site location Time period Number of
samples
Mean all
data (ppb)
Mean no
smoke
days (ppb)
Mean
smoke
days (ppb)
BV July-Sept
2017-2022
85 3.3 3.2 4.6
Hawthorne July-Sept
2021-2022
116 2.8 2.7 3.6
UTC PTR-MS
(SAMOZA)
August-Sept
2022
continuous 4.3 3.7 7.0
UTC DNPH
(SAMOZA)
August-Sept
2022
176 3.5 3.4 4.6

The four sets of observations give similar for the non-smoke periods. Smoke clearly has greater
concentrations of formaldehyde and the three DNPH methods are fairly consistent, whereas the
PTR-MS method gives higher concentrations during smoke. At present, it is not known if this is
due to an over-estimate by the PTR-MS method or an under-estimate by the DNPH method (S.
Lyman, USU, personal communication). Regardless, it appears that formaldehyde (and other
VOCs not shown) are relatively uniform across the Wasatch front region. In contrast, NO2 data
show larger spatial variations of approximately ± 60% (see Table 1 above). These variations
indicate that the NOx-VOC sensitivity to O3 production may vary strongly as an airmass is
transported through the region.
The TEMPO satellite borne instrument will provide outstanding chemical information
relevant to O3 production at high spatial and temporal resolution (Zoogman et al 2017; Chance et
10

al 2019; Naeger et al 2021). The TEMPO instrument was launched on April 7th, 2023 and after
quality control checks and testing, the first spectral images and NO2 data were obtained on
August 2, 2023 (Smithsonian Institute, 2023). At present, the TEMPO spectrometer is operating
as planned and mission specifications will likely be met. The public data release is scheduled
for April 2024 and data will become routinely available at that time in near-real time. TEMPO
will measure NO2, CH2O and tropospheric O3 at high temporal and spatial resolution. Table 3
shows relevant specifications for TEMPO data for these compounds and Figure 6 shows an
approximation of the TEMPO footprint over the SLC region.

Table 3. Temporal and spatial resolution for NO2, CH2O and O3 from the TEMPO instrument.
Spatial
resolution (km)
Temporal resolution
NO2 2.0×4.75 Hourly
CH2O 2.0×4.75 Hourly
Tropospheric O3 column 8.0×4.75 Hourly
0-2 km O3 column 8.0×4.75 Two hours

Figure 6. Approximate
TEMPO footprint for NO2 and
CH2O over the SLC region,
along with location of UDAQ
NO2 measurement sites. The
TEMPO spatial resolution for
these compounds is 2.0×4.75
km. The grid is slightly offset
from true north due to the
fixed location of the
geostationary satellite and the
scanning geometry.

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Data from the TEMPO instrument will provide important information on the spatial distribution
of the VOC-NOx sensitivity. We can also examine how and where are the highest O3
concentrations in the Wasatch Front using the 0-2 km O3 data. It is possible that the highest
concentrations observed by TEMPO are in locations that are not monitored by UDAQ. We will
compare TEMPO data to surface observations of NO2 and CH2O, along with the FNR.
We note that similar instruments have collected tropospheric NO2 and CH2O, along with
the FNR from the OMI and TROPOMI satellite instruments (Jin et al 2023; Jung et al 2022;
Souri et al 2020; Tao et al 2022). Compared to these earlier results, the TEMPO instrument will
have much greater spatial and temporal resolution, both of which are key for this analysis. The
analysis by Tao et al (2022), for the Northeastern U.S., shows the lowest FNR values near city
centers, due to higher NOx concentrations, but also that the FNR tends to increase on high O3
days, likely due to enhanced formaldehyde with temperature. For this reason, we will analyze
the FNR separately for O3 exceedance days and non-exceedance days. Finally, we will use back
trajectories to identify the most important regions for high O3 days and examine the FNR along
the back-trajectory to identify the VOC-NOx sensitivity for each high O3 day.
TEMPO data will be available via the EPA’s Remote Sensing Information Gateway
(Smithsonian 2023). We will apply recommended quality control flags to the data. As needed,
we will co-average the gridded TEMPO data to reduce random noise. For example, we can
average several hours of data at the photochemical maximum periods (12-2 pm local time)
and/or average data across multiple exceedance days. While the focus of this work is on
controllable non-smoke days, we will also look at the TEMPO data on smoke days. We expect
smoke days to have higher FNR values, consistent with our previous work (Ninneman et al
2023) and other satellite studies (Jin et al 2023).
If for some reason TEMPO data is not available, we will do a similar analysis using
TROPOMI satellite data. While the TROPOMI NO2 and CH2O products have lower spatial
(5.5×3.5 km) and temporal resolution (observations once per day) data from this instrument has
been used to examine the FNR in a number of previous analyses (Tan and Wang 2022; Tao et al
2022; Acdan et al 2023; Jin et al 2023). Finally, we note that in addition to the existing NOx and
VOC data, we expect there will be new observations collected during the summer of 2024 by
NOAA and other university groups. To the greatest extent possible, these data will be integrated
into our analysis.

Expected Outputs and Outcomes:
1. Identification of smoke days for 2018-2024 for all Utah sites with sufficient data.
2. Daily results from GAM analysis and R-code for GAMs.
3. Maps of the spatial distribution formaldehyde, NO2 and FNR from all surface
observations and the TEMPO satellite.
4. Quarterly and final reports.
5. Peer-reviewed publication(s).

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Deliverables:
• Quarterly progress reports
• A final technical report
• Presentations at the 2025 Air Quality: Science for Solutions conference
• A peer-reviewed publication
We will comply with UDAQ’s data-sharing requirement by posting final data at the Utah State
University Digital Commons website (https://digitalcommons.usu.edu/) or the University of
Washington’s data archive service (https://digital.lib.washington.edu/researchworks/) within
eight months of project completion. Data posted to digital commons are assigned a permanent
DOI and made public in perpetuity.

Schedule
Item Completion date
Project start July 1, 2024
Task 1 November 30, 2024
Task 2 June 31, 2025
Final project report and
publication
August 31, 2025

Personnel Roles and Responsibilities–
Daniel Jaffe, University of Washington: Prof. Jaffe is a Professor in the UW Bothell School of
STEM and UW Seattle Department of Atmospheric Sciences. He has more 30 years of
experience leading major field campaigns to study O3, NOx, mercury and other pollutants and
has published more than 200 peer-reviewed journal articles. He will coordinate this project and
supervise a post-doctoral fellow and an undergraduate student on the project.

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Budget
Category Calculation Task 1
(40%)
Task 2
(60%)
Total
Personnel
Dan Jaffe, PI 1 month effort @ $18,008 $7,203 $10,805 $18,008
Post Doc 3 months effort @ $5,819.10 $6,983 $10,474 $17,457
Undergraduate Students 300 hours @ $20/hr * 2 students $4,800 $7,200 $12,000
Fringe Benefits
Dan Jaffe, PI 22.6% * $18,008 $1,628 $2,442 $4,070
Post Doc 22.6% * $17,457 $1,578 $2,367 $3,945
Undergraduate Students 21.2% * $12,000 $1,018 $1,526 $2,544
Travel $1,600 $2,400 $4,000
Other Direct Costs
Publications $800 $1,200 $2,000
Total Direct Costs $25,610 $38,414 $64,024
F&A Costs 10% of Total Project Costs $2,846 $4,268 $7,114
Total Project Costs $28,456 $42,682 $71,138

Personnel
Dan Jaffe, PI – Dr. Jaffe will oversee all aspects of the project and will be involved in the analysis for
both Tasks 1 and 2. He will complete all project reports, participate in the science meetings and lead or
co-lead a publication from the project.

TBD, Post Doc – The post-doctoral fellow will assist with the GAMs and the TEMPO satellite data for
formaldehyde and NO2. He will participate in the science meetings and lead or co-lead a publication
from the project.

TBD, Undergraduate Students – Two (2) undergraduate students will assist Dr. Jaffe on the project by
collecting and catenating data for the GAMs (Task 1) and VOC/NOx (Task 2) portions of the project.

Fringe Benefits
The University of Washington has negotiated fringe benefit rates with the Department of Health and
Human Services. The fringes, applicable based on position, include FICA, retirement, health insurance,
workers’ compensation. The rates for this proposal are faculty/post doc – 22.6% and undergraduate
students – 21.2%.

Travel
The PI plans to attend conferences for collaboration and dissemination of the project work. Lodging and
per diem are calculated based on GSA rates. All other costs are calculated on historical costs for similar
travel. We use Washington DC as a placeholder.

Other Direct Costs
Publications – Funding is requested to publish in journals that require page charges.

F&A Costs
The University of Washington has a negotiated indirect cost rate agreement with the Department of
Health and Human Services. However, due to the solicitation for this program, the university has limited
these costs to 10% of total project costs.

14

References
Acdan, J. J. M., Pierce, R. B., Dickens, A. F., Adelman, Z., and Nergui, T.: Examining
TROPOMI formaldehyde to nitrogen dioxide ratios in the Lake Michigan region:
implications for ozone exceedances, Atmos. Chem. Phys., 23, 7867–7885,
https://doi.org/10.5194/acp-23-7867-2023, 2023.
Buysse, C.E., Kaulfus, A., Nair, U., and Jaffe D.A.: 2019. Relationships between particulate
matter, ozone, and nitrogen oxides during urban smoke events in the western US. Environ
Sci Technol 53, 21, 12519-12528, doi: 10.1021/acs.est.9b05241.
Chance, K., and Coauthors, 2019: TEMPO green paper: Chemistry, physics, and meteorology
experiments with the Tropospheric Emissions: Monitoring of Pollution instrument. Proc.
SPIE, 11151, 111510B, https://doi.org/10.1117/12.2534883.
Duncan, B. N.; Yoshida, Y.; Olson, J. R.; Sillman, S.; Martin, R. V.; Lamsal, L.; Hu, Y.;
Pickering, K. E.; Retscher, C.; Allen, D. J.; Crawford, J. H. (2010). Application of OMI
Observations to a Space-Based Indicator of NOx and VOC Controls on Surface Ozone
Formation. Atmos. Environ. 44, 2213−2223.
Gong, X., Kaulfus, A., Nair, U., Jaffe D.A.: 2017. Quantifying O3 impacts in urban areas due to
wildfires using a Generalized Additive Model. Environ Sci Technol 51, 22, 13216-13223,
doi: 10.1021/acs.est.7b03130.
Jaffe D.A., Ninneman M., Nguyen L., Lee H., Hu L., Ketcherside D., Jin L., Cope E., Lyman
S., Jones C., O’Neil T. and Mansfield M.L. Key results from the salt lake regional smoke,
ozone and aerosol study (SAMOZA), Journal of the Air & Waste Management
Association, DOI: 10.1080/10962247.2024.2301956, 2024.
Jaffe, D.A., 2021. Evaluation of ozone patterns and trends in 8 major metropolitan areas in the
U.S. Final project report for CRC Project A-124. Coordinating Research Council, Alpharetta,
GA. Available at http://crcao.org/wp-content/uploads/2021/04/CRC-Project-A-124-Final-
Report_Mar2021.pdf.
Jaffe, D.A., O’Neill, S.M., Larkin, N.K., et al., 2020. Wildfire and prescribed burning impacts on
air quality in the United States. J Air Waste Manage Assoc 70, 6, 583-615, doi:
10.1080/10962247.2020.1749731.
Jin X., Fiore A.M., Boersma K.F., De Smedt I. and Valin L. (2020) Inferring Changes in
Summertime Surface Ozone–NOx–VOC Chemistry over U.S. Urban Areas from Two
Decades of Satellite and Ground-Based Observations. Environ. Sci. Technol. 2020, 54, 11,
6518–6529. https://doi.org/10.1021/acs.est.9b07785
Jin X., Fiore A.M. and Cohen R.C. Space-Based Observations of Ozone Precursors within
California Wildfire Plumes and the Impacts on Ozone-NOx-VOC Chemistry. Environmental
Science & Technology (2023) 57 (39), 14648-14660. DOI: 10.1021/acs.est.3c04411.
Jung J., et al., (2022). Changes in the ozone chemical regime over the contiguous United States
inferred by the inversion of NOx and VOC emissions using satellite observation,
Atmospheric Research 270. https://doi.org/10.1016/j.atmosres.2022.106076.
15

Kaulfus, A.S., Nair, U., Jaffe, D., et al., 2017. Biomass burning smoke climatology of the United
States: implications for particulate matter air quality. Environ Sci Technol 51, 20, 11731-
11741, doi: 10.1021/acs.est.7b03292.
Lee H. and Jaffe D.A. Impact of wildfire smoke on ozone concentrations using a Generalized
Additive model in Salt Lake City, Utah, USA, 2006–2022, Journal of the Air & Waste
Management Association, DOI: 10.1080/10962247.2023.2291197, 2023.
Leys, C., Ley, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not use
standard deviation around the mean, use absolute deviation around the median. Journal of
Experimental Social Psychology, 49(4), 764–766. doi:10.1016/j.jesp.2013.03.013
Liu S., et al. (2023). Strong Dependence of Atmospheric Formaldehyde Concentration on Air
Temperature. Abstract A42B-02, presented at AGU23, 11-15 Dec.
Louisiana Department of Environmental Quality (LDEQ, 2018). Louisiana Exceptional Event of
September 14, 2017: Analysis of Atmospheric Processes Associated with the Ozone
Exceedance and Supporting Data. Available at
https://www.epa.gov/sites/production/files/2018-
08/documents/ldeq_ee_demonstration_final_w_appendices.pdf.
Martin, R. V.; Fiore, A. M.; Van Donkelaar (2004). A. Space-Based Diagnosis of Surface Ozone
Sensitivity to Anthropogenic Emissions. Geophys. Res. Lett. 2004, 31, No. L06120.
McClure C.D. and Jaffe D.A. Investigation of High Ozone Events due to Wildfire Smoke in an
Urban Area. Atmos. Envir. https://doi.org/10.1016/j.atmosenv.2018.09.021, 2018.
Naeger et al; 2021. Revolutionary Air-Pollution Applications from Future Tropospheric
Emissions: Monitoring of Pollution (TEMPO) Observations. Bull Amer. Met Soc. Sept.
2021; DOI: https://doi.org/10.1175/BAMS-D-21-0050.1
Ninneman, M., Jaffe, D.A., 2021. The impact of wildfire smoke on ozone production in an urban
area: Insights from field observations and photochemical box modeling. Atmos Environ 267,
118764, doi: 10.1016/j.atmosenv.2021.118764.
Ninneman M., Lyman S., Hu U., Cope E., Ketcherside D. and Jaffe D. Investigation of Ozone
Formation Chemistry during the Salt Lake Regional Smoke, Ozone, and Aerosol Study
(SAMOZA). ACS Earth and Space Chemistry. 7 (12), 2521-2534 DOI:
10.1021/acsearthspacechem.3c00235, 2023.
Rolph, G.D., Draxler, R.R., Stein, A.F., et al., 2009. Description and verification of the NOAA
Smoke Forecasting System: the 2007 fire season. Weather Forecast 24, 2, 361-378, doi:
10.1175/2008waf2222165.1.
Smithsonian Institute (2023). Tropospheric Emissions: Monitoring of Pollution.
https://tempo.si.edu/. Last accessed Jan. 29, 2024.
Souri A.H. et al. (2020) Revisiting the effectiveness of HCHO/NO2 ratios for inferring ozone
sensitivity to its precursors using high resolution airborne remote sensing observations in a
high ozone episode during the KORUS-AQ campaign. Atmospheric Environment 224.
https://doi.org/10.1016/j.atmosenv.2020.117341.
16

Tan, Y. and Wang, T. (2022). What caused ozone pollution during the 2022 Shanghai lockdown?
Insights from ground and satellite observations, Atmos. Chem. Phys., 22, 14455–14466,
https://doi.org/10.5194/acp-22-14455-2022.
Tao M., et al. (2022) Investigating Changes in Ozone Formation Chemistry during Summertime
Pollution Events over the Northeastern United States. Environmental Science &
Technology 56 (22), 15312-15327. DOI: 10.1021/acs.est.2c02972
Texas Commission of the Environment (TCEQ), 2017. El Paso Ozone Exceptional Event: June
21, 2015, Addendum 4: Update, May 17, 2017.
Zoogman P., et al. 2017. Tropospheric emissions: Monitoring of pollution (TEMPO), Journal of
Quantitative Spectroscopy and Radiative Transfer 186.
https://doi.org/10.1016/j.jqsrt.2016.05.008.