ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-8529-2018Detection and variability of combustion-derived vapor in an urban basinDetection and variability of combustion-derived vaporFiorellaRichard P.rich.fiorella@utah.eduhttps://orcid.org/0000-0002-0824-4777BaresRyanLinJohn C.https://orcid.org/0000-0003-2794-184XEhleringerJames R.https://orcid.org/0000-0003-2050-3636BowenGabriel J.https://orcid.org/0000-0002-6928-3104Department of Geology and Geophysics, University of Utah, Salt Lake City, Utah 84112, USADepartment of Atmospheric Sciences, University of Utah, Salt Lake City, Utah 84112, USAGlobal Change and Sustainability Center, University of Utah, Salt Lake City, Utah 84112, USADepartment of Biology, University of Utah, Salt Lake City, Utah 84112, USARichard P. Fiorella (rich.fiorella@utah.edu)18June201818128529854730November201730January20185June20188June2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/8529/2018/acp-18-8529-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/8529/2018/acp-18-8529-2018.pdf
Water emitted during combustion may comprise a significant portion of ambient
humidity (> 10 %) in urban areas, where combustion emissions are
strongly focused in space and time. Stable water vapor isotopes can be used
to apportion measured humidity values between atmospherically transported and
combustion-derived water vapor, as combustion-derived vapor possesses an
unusually negative deuterium excess value (d-excess,
d=δ2H- 8δ18O). We investigated the
relationship between the d-excess of atmospheric vapor, ambient CO2
concentrations, and atmospheric stability across four winters in Salt Lake
City, Utah. We found a robust inverse relationship between CO2 excess
above background and d-excess on sub-diurnal to seasonal timescales, which
was most prominent during periods of strong atmospheric stability that occur
during Salt Lake City winter. Using a Keeling-style mixing model approach,
and assuming a molar ratio of H2O to CO2 in emissions
of 1.5, we estimated the d-excess of combustion-derived vapor in Salt Lake
City to be -179 ± 17 ‰, consistent with the upper limit of
theoretical estimates. Based on this estimate, we calculate that vapor from
fossil fuel combustion often represents 5–10 % of total urban humidity,
with a maximum estimate of 16.7 %, consistent with prior estimates for Salt
Lake City. Moreover, our analysis highlights that changes in the observed
d-excess during periods of high atmospheric stability cannot be explained
without a vapor source possessing a strongly negative d-excess value. Further
refinements in this humidity apportionment method, most notably empirical
validation of the d-excess of combustion vapor or improvements in the
estimation of the background d-excess value in the absence of combustion, can
yield more certain estimates of the impacts of fossil fuel combustion on
urban humidity and meteorology.
Introduction
Fossil fuel combustion releases carbon dioxide and water to the atmosphere.
Annual carbon emissions are estimated to be 9.4 Pg C yr-1, which suggests annual water emissions from combustion of
∼ 21.1 Pg, assuming a mean molar emissions ratio between
H2O and CO2 of 1.5 Sect. 2, and also.
This water flux is negligible in the hydrologic cycle on global and annual
timescales e.g.,, but it may be significant to urban
hydrologic cycling and meteorology as fossil fuel emissions are tightly
concentrated in space and time . In turn,
water vapor from fossil fuel combustion may impact urban air quality and
meteorology, for example, through direct changes in radiative balance by
increased water vapor concentrations , impacts
on aerosols and cloud properties ,
and altered local or downwind precipitation amounts .
Where combined with atmospheric stratification, these changes can potentially
lengthen or intensify periods of elevated particulate pollution in cities,
which would directly impact public health through increased incidence of
acute cardiovascular or respiratory
illness. However, using standard meteorological
measurements it remains difficult to isolate combustion-derived vapor (CDV)
from “naturally occurring” water vapor, or vapor from other
anthropogenically influenced fluxes (e.g., snow sublimation from buildings),
making the impact of CDV on the urban atmosphere difficult to assess.
Stable water vapor isotopes represent a promising method to partition
observed water vapor between combustion and advection sources
. Combustion of hydrocarbons produces water from the
reaction of atmospheric oxygen, which is 18O-enriched relative to the
international standard, Vienna Standard Mean Ocean Water (VSMOW) +23.9 ‰,, and structurally bound fuel hydrogen, which
is 2H-depleted relative to VSMOW due to preference for 1H over
2H during biosynthetic reactions e.g.,. The
reaction of 18O-enriched oxygen with 2H-depleted fuels produces vapor
with an unusually negative deuterium excess value
d-excess, d=δ2H- 8δ18O; that is distinct compared to the d-excess value
in the “natural” hydrological cycle. Deuterium excess is ∼ 10 ‰, on average, in precipitation
, and ranges in natural waters from
+150–200 ‰ in vapor in the upper troposphere
to ∼-60 ‰ in
highly evaporated surface waters e.g.,. In contrast,
estimated CDV d-excess values for fuels in Salt Lake
Valley (SLV) ranging from -180 to -470 ‰, depending on the
isotopic composition of the fuel and the degree of equilibration of oxygen
isotopes between CO2 and H2O in combustion emissions.
The Salt Lake City, Utah, metropolitan area (population of ∼ 1.15 million) is
located within SLV. SLV (∼ 1300–1500 m) is bounded on the
west by the Oquirrh Mountains (∼ 2200–2500 m), on the east by the
Wasatch Mountains (> 3000 m), and on the south by the Traverse
Mountains (< 2000 m). The northwest corner of the basin is bounded by
the Great Salt Lake. During the winter, cold air often pools in SLV,
increasing atmospheric stability and limiting transport of combustion
products away from the city and impairing air quality. Previous work in
SLV indicated that CDV comprised up to ∼ 13 % of urban specific humidity
during strong inversion events in winter 2013–2014 . Here
we combine those data with three additional winters of water vapor isotope
measurements in Salt Lake City, Utah (DJF 2014–2017), to refine our estimate of
the d-excess of CDV, update estimates of the contributions of CDV to the
urban atmosphere, and identify the largest sources of error that can be
addressed or reduced in future studies.
Stoichiometric relationships between CO2 and CDV and fuel use in SLV
The ratio of CO2 to CDV in fossil fuel emissions depends on the
stoichiometry of the fuels used. The chemical reaction for the idealized
combustion of a generic hydrocarbon is
CxHy+(x+y/4)O2→×CO2+(y/2)H2O.
The molar ratio of H2O and CO2 in product vapor is defined
here as the emissions factor (ef), and arises directly from the molar ratio
of hydrogen and carbon in the fuel as y/2x. Of simple hydrocarbons, methane (CH4)
has the greatest ef value of 2. Longer chained hydrocarbons,
such those in gasoline, have lower ef values. Octane (C8H18) has
an ef value of 1.125, for example .
Fuels burned within SLV are generally petroleum products and natural gas,
with the latter being extensively used in the winter for residential heating.
Seasonal patterns of fuel use emerge from both “top-down” and “bottom-up”
style emissions estimates. A high-resolution, bottom-up, building-level
emissions inventory has been produced for Salt Lake County as part of the
HESTIA project . On an annual basis,
on-road transport represents 42.9 % of Salt Lake County emissions, followed by
the residential (20.8 %) and industrial (12.6 %) sectors
. The commercial, electric generation, and non-road
transport sectors comprise the remaining 23.7 % of Salt Lake County
emissions. In winter, however, the residential sector is a much larger
contributor to Salt Lake County emissions (34.4 %), followed by the on-road
transport (34.3 %) and commercial sectors (13.1 %) (Table ).
The remaining 18.2 % of emissions arise from the
non-road transport, electricity production, and industrial sectors. The
increased prominence of residential and commercial sector emissions during
the winter, primarily at the expense of on-road and industrial emissions,
likely results from a greater heating demand and a concomitant increase in
natural gas use. Top-down observations of stable carbon isotope
compositions in atmospheric CO2 in SLV reflect this seasonal
change in carbon inputs primarily from gasoline combustion and
respiration in the summer to a much stronger signal from natural gas in the
winter .
HESTIA Emissions Estimates and estimated ef values for Salt Lake County.
Economic sectorDecemberJanuaryFebruaryDJF sumNatural gasPetroleumCoalEstimated ef(Gg C) (%) Airport8.478.748.0425.240.0100.00.01.05Commercial45.3047.4735.16127.9283.316.70.01.80Electricity generation10.016.506.8423.36100.00.00.01.95Industry33.2133.8133.21100.2446.735.118.21.37Non-road8.908.598.9326.420.0100.00.01.05On-road113.50113.41108.94335.850.0100.00.01.05Railroad1.171.171.063.400.0100.00.01.05Residential116.14125.6494.48336.26100.00.00.01.95Weighted average ef1.521.531.481.51
From these considerations, we estimate a valley-scale ef value using the
HESTIA emissions inventory and appropriate emissions
factors for natural gas, petroleum, and sub-bituminous coal resources.
Natural gas was assumed to be composed of 90 % methane, 8 % ethane, and 2 %
propane , yielding an ef value of 1.95. Petroleum
products, such as gasoline, jet fuel, and fuel oil, were assumed to be 85 % C
and 15 % H by mass , yielding an ef
value of 1.05. Finally, an ef value of 0.5 was assigned to coal, assuming a
molar ratio of hydrogen to carbon of 1 . Fuels or fuel
mixtures were assigned to each economic sector in the HESTIA data set
(Table ). Mobile emissions (airport, on-road, non-road, and
railroad) were assigned petroleum sources, while the residential and
electricity generation sectors were assigned natural gas sources
(Table ). Coal combustion supplies the majority of electricity in
Utah and in SLV, but the power plants supplying SLV are outside of the
valley to the south. Electricity generation facilities within SLV are
primarily natural gas facilities. Commercial and industrial source emissions
were apportioned using the state-wide ratios of carbon emissions across fuel
sources for these economic sectors collected by the US Energy Information
Administration . Commercial sector emissions were assumed to
be 83.3 % natural gas and 16.7 % petroleum, while industrial emissions were
assumed to arise from a combustion mixture of 46.8 % natural gas, 35.1 %
petroleum, and 18.1 % coal (Table ). Weighting these
economic sectors and fuel sources by their relative emissions amounts yields
a Salt Lake County scale estimate of an ef of 1.51 for winter, with individual
months ranging from 1.48 to 1.53. Based on this analysis, we consider an
estimate for an ef of 1.5 going forward.
MethodsEstimates of atmospheric stratification
SLV experiences periods of enhanced atmospheric stability each winter
when cold air pools in the valley under warmer air aloft
. Atmospheric stratification is present when
potential temperature increases with height. Nocturnal stratification is
common in many settings due to more rapid radiative cooling near the surface
than aloft, but SLV and other topographic basins can experience periods
of extended atmospheric stability lasting longer than a diurnal cycle
. These periods are commonly
referred to as persistent cold air pools (PCAPs) .
We assess large-scale SLV vertical stability using twice-daily atmospheric
soundings from the Salt Lake City Airport (ICAO airport code KSLC, 00:00 and 12:00 UTC, coordinated universal time, or 05:00 and
17:00 LT, local time). Sounding profiles were obtained from the Integrated Global Radiosonde
Archive (IGRA) and interpolated to 10 m resolution
between the surface (∼ 1290 m) and 5000 m. We calculate
two metrics of atmospheric stability from the radiosonde data: a bulk valley
heat deficit (VHD) and an estimated mixing height. The VHD is the energy that
must be added between the surface and some height to bring this portion of
the atmosphere to the dry adiabatic lapse rate (e.g., ∂θ∂z= 0.0 K km-1
or ∂T∂z=-9.8 K km-1). VHD is calculated following prior studies of winter stability in SLV :
VHD=cp∑1290m2200mρ(z)θ2200m-θ(z)Δz,
where cp is the specific heat capacity at constant pressure for dry air
(1005 J kg-1 K-1), ρ(z) is the air density as a function of
height (kg m-3), θ2200m and θ(z) are the
potential temperatures at 2200 m a.s.l. (above sea level) and at height z
respectively (K), and Δz is the thickness of each layer
(10 m). The upper bound in the VHD calculation (2200 m) is
determined by the elevation of the Oquirrh Mountain ridgeline, which forms
the western valley boundary. Following , we define a PCAP
as three or more consecutive soundings with a VHD > 4.04 MJ m-2.
This VHD threshold of 4.04 MJ m-2 corresponds to the mean VHD in
days on which SLV daily fine particulate matter concentration (PM2.5)
exceeds half of the US National Ambient Air Quality Standard for PM2.5
(17.5 µg m-3) , and has been used in
subsequent studies of SLV air quality and atmospheric stability
. We have retained this convention for
intercomparison with prior studies.
Mixing height estimates depend on whether a surface-based temperature
inversion is present or absent. If the sounding features a surface-based
inversion, the mixing height is estimated as the height at the top of the
surface-based inversion . If there is no surface-based
inversion, the mixing height is estimated using a bulk Richardson number
method . The bulk Richardson number, which
is a measure of the ratio of buoyancy to shear production of turbulence, is
calculated as
Ri(z)=g/θvsθv(z)-θvsz-zsu(z)-us2+v(z)-vs2+bu*2,
where Ri(z) is the bulk Richardson number as a function of height, g is
the acceleration due to gravity (9.81 m s-2), θv is the
virtual potential temperature (K), z is the altitude (m a.s.l.),
u and v are the zonal and meridional wind components
(m s-1), and bu*2 is the effect of surface friction. A
subscript “s” indicates these are surface values. As u* is not
available from radiosonde observations, we assumed frictional effects were
negligible . This assumption is particularly well justified
during stable atmospheric conditions , such as during
PCAPs. The mixing height was identified as the lowest altitude at which
Ri(z) was greater than a critical value of 0.25.
Water vapor isotope data
Water vapor isotope data were collected using a Picarro L2130-i water vapor
isotope analyzer (Santa Clara, CA, USA). Vapor was sampled from the roof of the
eight-story (∼ 35 m above the ground) William Browning Building on
the University of Utah campus (UOU, 40.7662∘ N,
111.8458∘ W; 1440 m a.s.l.) through copper (prior to winter 2016/2017) or
teflon tubing, using a diaphragm pump operating at ∼ 3 L min-1.
Standards were analyzed every 12 h using the Picarro Standards Delivery
Module (Table ), using lab air pumped through a column
of anhydrous calcium sulfate (Drierite) as a dry gas source.
We calibrated the data using the University of Utah vapor processing scripts,
version 1.2. Calibration of raw instrument values at ∼ 1 Hz on the
instrument scale to hourly averages on the VSMOW scale proceeds across three
stages. (1) Measured isotope values are corrected for an apparent dependence
on cavity humidity, using correction equations developed by operating the
standards delivery module at a range of injection rates, corresponding to
cavity humidity values of 500–30 000 ppm. Instrumental precision is determined
in this step, with uncertainties arising both from a decrease in instrument
precision with decreasing cavity humidity, and uncertainty in the regression
equation to correct for this bias. The humidity correction is determined by a
linear regression of the deviation of isotopic composition from the measured
isotopic composition at a reference humidity against the inverse of cavity
humidity. The reference humidity used is 15 000–25 000 ppm, a range at which the
instrument response is linear and at which liquid water samples are measured
and lab standards are calibrated. Additional details on this correction are
provided in the Supplement. (2) Analyzer measurements are calibrated to the
VSMOW–VSLAP (Vienna Standard Light Antarctic Precipitation) scale using two standards of known isotopic composition delivered
by the standards delivery module (Table ), using
calibration periods that bracket a series of ambient vapor measurements to
correct for analytical drift. (3) Corrected measurements were aggregated to
an hourly time step. Measurement uncertainties are primarily limited by
changes in instrument precision with cavity humidity, and 1σ
uncertainties range from 0.88 ‰ for δ18O,
3.61 ‰ for δ2H, and 7.93 ‰ for d-excess
(assuming error independence) at a humidity of 1000 ppm, to
0.14 ‰ for δ18O, 0.53 ‰ for
δ2H, and 1.24 ‰ for d-excess at a humidity of 10 000 ppm.
Laboratory standard isotopic compositions.
Light standard Heavy standard δ18Oδ2Hδ18Oδ2HPrior to 16 Feb 2017-16.0-121.0-1.23-5.51After 16 Feb 2017-15.88-119.661.6516.9CO2 and meteorological measurements
Meteorological measurements were co-located with water vapor isotope sampling
on the roof of the UOU. Temperature, humidity, wind speed, solar radiation,
and pressure measurements are all made at 5 min averages ,
and were averaged to 1 h blocks for analysis.
CO2 measurements were made in two different locations during the
study period. Prior to August 2014, CO2 measurements were made on the
roof of the Aline Skaggs Biology Building (ASB) on the University of Utah
campus, ∼ 0.25 km south of the William Browning Building (coded as
UOU). CO2 and H2O measurements made at ASB were performed
using a Li-Cor 7000. Atmospheric air was drawn through a 5 L mixing
volume and measured every 5 min. Pressure and H2O dilution
corrections were applied by the Li-Cor. All measurements were recorded by a
Campbell Scientific CR23X.
From August 2014 onwards, CO2 measurements have been made at the UOU
where they are co-located with meteorological measurements and the water
vapor isotope described in Sect. 3.2. Atmospheric CO2,
CH4, and H2O measurements were performed using a Los Gatos Research
Off-Axis Integrated Cavity Output Spectroscope (Model 907-0011, Los Gatos
Research Inc., San Jose, CA, USA). Measurements were recorded at 0.1 Hz.
The effects of water vapor dilution and spectrum broadening
were corrected by LGR's real-time software, and were
independently verified through laboratory testing.
At both ASB and UOU, calibration gases were introduced to the analyzer every
3 h using three whole-air, dry, high-pressure reference gas cylinders
with known CO2 concentrations, tertiary to the World Meteorological
Organization X2007 CO2 mole fraction scale .
Concentrations of the calibration gases spanned the expected range of
atmospheric observations. Each standard of known concentration is linearly
interpolated between two consecutive calibration periods to represent the
drift in the averaged measured standards over time. Ordinary least squares
regression is then applied to the interpolated reference values during the
atmospheric sampling periods to generate slope and intercept estimates. These
are then used to correct all uncalibrated atmospheric observations between
calibration periods. Analytical precision is estimated to be ∼ 0.1 ppm.
A total of 7 months of overlapping data were collected at both ASB and UOU and
analyzed to identify any significant difference in measurement locations. The
two locations are highly similar (CO2,UOU= 0.98CO2,ASB+ 8.087,
r2= 0.96), though pollutants appear to “mix out” at the end of a PCAP event
approximately 1 h earlier at ASB relative to UOU. We do not adjust the
ASB time series as the potential time shift is small, and the period of
overlapping records is short and does not span a full annual cycle.
Mixing analysis between meteorological humidity and combustion-derived vapor
CDV can be assessed by considering a two-part isotopic mixing model that
treats meteorological or advected vapor and CDV as the end members. We
develop a schematic demonstrating the natural evolution of d-excess under
atmospheric moistening and condensation conditions, as well as through
moistening via the addition of CDV. The isotopic composition of an air parcel
losing moisture in a Rayleigh condensation process can be modeled as δ=δ0+1qq0α-1-1,
where δ is the isotopic composition, q is the specific humidity, and
α is the temperature-dependent equilibrium fractionation factor
between vapor and the condensate. A subscript zero indicates the initial
conditions of a parcel prior to condensation. Humidity is removed from the
air parcel through adiabatic cooling starting from the parcel's initial dew
point temperature and cooling in 0.5 K intervals to 243 K;
progressive cooling is used to account for changes in α with
temperature. δ18O and δ2H are modeled separately and then
combined to estimate the evolution of d-excess throughout condensation. We
used fractionation factors for vapor over liquid for temperatures above
273 K and for vapor over ice for temperatures below
253 K . We
interpolated α values between 273 and 253 K to account
for mixed-phase processes between these temperatures. As the heavy isotopes
of both oxygen and hydrogen are progressively removed through condensation,
d-excess increases as humidity is decreased, approaching a limit of
7000 ‰ if all 2H and 18O were removed .
We also modeled the isotopic evolution of d-excess in an air parcel in the
absence of CDV experiencing mixing between the moist and dry end members of
the Rayleigh distillation curve. D-excess is modeled throughout this humidity
range as a mass-weighted mixing model average of the d-excess values of both
end members:
dmix=ddryqdry+dmoistqmoistqdry+qmoist.
Likewise, moistening of the lower troposphere by CDV can be modeled as a
mixing process between CDV and the background natural water vapor:
dmix=dCDVqCDV+dbgqbgqmix,
where subscripts CDV, bg, and mix refer to properties of CDV, the atmospheric
moisture in the absence of CDV, and values of the mixed parcel, respectively.
assumed a mean value of -225 ‰ for
dCDV based on a few direct measurements. Adopting this value, we
construct a model framework to explain changes in d-excess relative to
humidity expected from natural condensation and mixing pathways as well as
the addition of moisture via CDV (Fig. ), but also revisit this
assumption based on further analysis of our data (below). Drying the
atmosphere by mixing in a dry air mass in the absence of CDV or by Rayleigh
condensation increases the d-excess of ambient vapor, whereas atmospheric
moistening occurring due to mixing with a moist air mass can decrease the
d-excess of ambient vapor. The response of d-excess due to these natural
processes is nonlinear with respect to changes in humidity, and very similar
between condensation and mixing of natural air masses (Fig. ).
In contrast, small mass additions of CDV (up to 500 ppm)
produce a strong, quasilinear decrease in dmix with
increasing qCDV (Fig. ). Assuming a representative ef value
of 1.5 (Sect. 2), 100 or 500 ppm of CDV correspond to CO2 increases
of 66.7 or 333.3 ppm, respectively. Deviation from the natural air mass
mixing line is greatest at low qbg for a given qCDV,
as CDV comprises a larger fraction of qmix.
Schematic of expected changes in the d-excess of atmospheric vapor
with changes in humidity associated with atmospheric moistening and drying in
the absence of CDV due to Rayleigh distillation (solid black lines) or air
mass mixing (dashed black lines) or the addition of CDV (dotted black lines).
Models for Rayleigh distillation and air mass mixing are shown for two
initial d-excess values of the moist end member: 0 ‰ (thick lines) and
10 ‰ (thin lines). Panel (a) shows this relationship
of d (‰) vs. specific humidity, q (mmol mol-1), where mixing
processes trace hyperbolic pathways, and panel (b) shows the same models
but with axes of qd (‰ mmol mol-1) against
q (mmol mol-1), where mixing processes are linear. Finally, lines
across a red gradient are drawn to show the impact of fixed amounts of CDV
addition ranging from 100 ppm (light) to 500 ppm (dark) as a function of
specific humidity.
Recasting these mixing-model equations following the Miller–Tans
formulation of the Keeling
mixing model, we can estimate
dCDV. In this framework, the product of observed d and q (e.g.,
dobs and qobs) is proportional to qCDV:
dobsqobs=dCDVqCDV+dbgqbg.
If we assume that qCDV is linearly related to the increase in
CO2 above background concentrations, dCDV can be
estimated as the slope of a linear regression between dobsqobs and
observed CO2 concentrations:
dobsqobs=dCDV(ef)CO2-minCO2+dbgqbg,
where ef is the emissions factor, which is the stoichiometric ratio of
H2O to CO2 in combustion products, and [CO2- min(CO2)]
represents the amount of excess CO2 in the atmosphere
above the background value. The ef parameter depends on the molar ratios of
hydrogen to carbon in the fuel source; we estimate a fuel-source-weighted
SLV-scale ef value for winter of 1.5, but note that ef values for
hydrocarbon fuels can vary from < 0.5 to 2. We define the background
CO2 value, min(CO2), to be the seasonal minimum value observed
at the UOU or the ASB. Observations of urban δ13C-CO2 and
atmospheric modeling of SLV indicate that wintertime increases in
CO2 above background concentrations are driven by anthropogenic
emissions, and that the contribution from local respiration to urban
CO2 enhancement is likely negligible
. We apply two linear
mixed models where PCAP-to-PCAP event-scale variability is treated as a
random effect to estimate dCDV: in the first, the slope is
assumed to be constant across all PCAP events but the intercept is allowed to
vary, while in the second, both the slope and intercept are allowed to vary
across PCAP events. These models are constructed to find the best-fit slope,
and therefore the best-fit estimate of dCDV, across all PCAP
events. As a result, they implicitly assume that changes in dCDV
through time are small compared to changes in dbgqbg,
or that changes in the emissions profile of SLV are small compared to
environmental variability in humidity and d-excess. We consider only the
second model in our results as we find it has more support than the first
model, with this selection determined based on lower Akaike and Bayesian
information criteria (AIC and BIC) for the second model. AIC and BIC are both
model selection tools that optimize model parsimony by evaluating a model's
likelihood against a penalty based on the number of model parameters.
Valley heat deficit (MJ m-2, blue polygon) and mixing height
(m, black indicates Richardson mixing height; red indicates surface-based
inversion top) by season. Seven, four, seven, and eight PCAP events are
identified for DJF 2013/14, 2014/15, 2015/16, and 2016/17, and are denoted by light
gray shading.
Finally, the fraction of urban humidity comprised of CDV can be estimated by
solving Eq. () for qCDV/qobs using the constraint that
qobs=qCDV+qbg:
qCDVqobs=dobs-dbgdCDV-dbg.
Using this equation, we estimate a maximum contribution of CDV to boundary
layer humidity for each PCAP for which water isotope data are available using the
minimum dobs value from each PCAP. We assume a constant value
of dCDV, determined from the slope of the linear mixed model described
above. Two estimates of dbg were made for each PCAP based on the
assumptions that dbg reflects (a) the mean observed d value for the
12 h prior to the initiation of the PCAP, or (b) the mean d value for
the 12 h period during which the 12 h moving average CO2 concentration
falls below 415 ppm. For (b), if the 12 h average CO2
concentration fails to fall below 415 ppm between two PCAPs, dbg is
estimated from the minimum CO2 value between these PCAP events.
Results
We observed 26 PCAP events across 4 winters, with 7, 4, 7, and
8 occurring during DJF 2013/14, 2014/15, 2015/16, and 2016/17, respectively
(Fig. ). VHD exceeded 4.04 MJ m-2 for 30, 18, 27,
and 25 % of the observed KSLC soundings during each winter. Variability of
1 to 2 MJ m-2 between consecutive soundings is common, and results
from the diurnal cycle of surface heating during the day and radiative
cooling at night . Calculated mixing heights ranged from
the surface (0 m a.g.l.) to 3390 m a.g.l., with a median value of
270 m a.g.l. The mean mixing height and its variance are low in December and
January, though both increase in February as solar radiation increases and
more energy is available to grow the daytime convective boundary layer.
CO2 concentrations show close inverse associations with measured
d-excess values across diurnal to synoptic timescales (Fig. ).
Paired d-excess and CO2 measurements are available for 76.8 % of the
period of record, including for 22 of the 26 PCAP events. CO2
concentrations and d-excess values were inversely cross-correlated for all
four winter periods (r=-0.589, -0.547, -0.428, and -0.527 for each
consecutive winter). The maximum cross-correlation was observed with zero lag
in DJF 2014/15 and 2016/17, whereas d-excess lagged CO2 by 1 h in
DJF 2013/14 and 2015/16. For each winter season, minimum/maximum hourly CO2
concentrations were 397/637, 400/581, 404/598, and 406/653 ppm, whereas minimum/maximum hourly d-excess
values were -26.4/24.5, -10.5/19.4, -8.0/12.9, and -26.8/14.3 ‰.
The 6 h running-mean CO2 concentrations (ppm, black line)
and water vapor d-excess (‰ VSMOW, red line, 2σ uncertainty
shown in red shading) measured at the UOU for DJF 2013–2017. Persistent cold
air pool events are denoted by gray rectangles. When the lower atmosphere is
stable, CO2 builds up in the boundary layer and d-excess tends to
decrease.
During each PCAP event, CO2 was elevated relative to its background
value. For most PCAP events, d-excess decreased commensurately with the
increase in CO2; however, several exceptions were observed. For
example, PCAPs in February 2016 and 2017 showed diurnal cyclicity in d-excess
and CO2 during the event, but these periods often exhibited a
multi-day period of CO2 increase and d-excess decrease prior to
atmospheric stability reaching the VHD threshold for a PCAP. In these events,
the bulk of the d-excess decrease occurs prior to the onset of the PCAP as
defined by the VHD metric, and d-excess exhibits strong diurnal variability
but with a small longer-term trend during the event before increasing when
the PCAP ends. Additionally, elevated CO2 and depressed d-excess
values were frequently observed in the absence of PCAPs (e.g.,
mid-December 2014 and 2016); these cases are associated with low mixing heights but not
necessarily high VHD values, or of moderate VHD values that fell short of the
VHD-based definition of a PCAP.
Relationship between CO2 and d-excess and estimating d-excess of CDV
Clear distinctions emerged in the distributions of CO2 and d-excess
during PCAP events compared to more well-mixed periods. Non-PCAP periods are
typically defined by lower CO2 values, usually below 450 ppm,
and a broad range of d-excess values averaging around
∼ 10 ‰ and spanning ∼ 0–30 ‰ (Fig. ).
D-excess variability during non-PCAP periods is likely
controlled by natural moistening and dehydration processes, including air
mass mixing, Rayleigh-style condensation, and evaporative inputs from the
Great Salt Lake. In contrast, a strong linear relationship between
CO2 and d-excess is observed during PCAP periods, with d-excess
values decreasing proportionally with increasing CO2. At the highest
CO2 concentrations, d-excess can be > 10 ‰ lower than
when CO2 is at background levels outside of PCAP events.
Relationship of the product of specific humidity and d-excess,
qd (‰ mmol mol-1), against specific humidity
q (mmol mol-1). Points are colored by CO2
concentration (ppm) at the time of measurement, with the shape and opacity
corresponding to whether the data point was collected during a PCAP event
(opaque triangles) or outside of a PCAP event (semitransparent circles).
Moistening and drying by condensation and mixing of natural air masses
occurs along a line with a positive slope, while moistening by CDV occurs along
a line with a negative slope.
These relationships between natural moistening and drying of the boundary
layer and moistening by CDV become apparent from the relationship between
d-excess and humidity (Fig. ). We observe increasing qd
values with increasing q at low CO2 concentrations, but decreasing
qd values with increasing CO2 (Fig. ). Strong
positive d-excess excursions are observed during the first two winters, and
are associated with dry, cold conditions following the passage of a strong
cold front. No equivalent excursions are observed during the last two
winters, perhaps due to a similar magnitude cold front event not occurring
during the observed portions of those winters. Negative excursions are
observed during PCAP events or when CO2 is elevated, and can be seen
across a range of humidity values.
Miller–Tans style plots of qd (‰ mmol mol-1) vs. CO2-excess (the difference between the observed CO2 and
the seasonal minimum CO2) by year during PCAP events. The estimated
d-excess of CDV, assuming CDV is the dominant flux of water into the boundary
layer during PCAP events, is the slope of the best fit line.
We leverage the observed, coupled variability in d-excess and CO2
during periods of enhanced CO2 to test previous theoretical estimates
and limited direct measurements of dCDV using a Keeling-style
approach . The best-fit slope of a linear
mixed model allowing for random variation in both the slope and intercept
between PCAP events yields an estimate of dCDV of
-179 ± 17 ‰ for ef = 1.5 (Fig. ). This
estimate of dCDV is consistent with the upper limit of
theoretical estimates and pilot measurements from , and
could be further validated by a comprehensive survey of fuels in SLV.
Based on this regression, d-excess decreases by
0.18 ± 0.02 ‰ for every ppm increase in CO2, though
this rate of change will vary slightly with background q (Fig. ).
Instrumental precision (1σ) for d-excess is estimated
to be 2.4 ‰ at the mean DJF humidity value of 4 mmol mol-1,
implying that enrichments of ∼ 40 ppm CDV can be
detected at the 2σ level. This estimated detection limit will likely
decrease as instrument precision and calibration routines are improved, and
may change in other locations with different fuel use patterns and ef
values. For individual PCAPs, the slope of the regression and the strength of
the correlation between qobsdobs and CO2 excess
are more variable, with slopes ranging from -25 ± 43 to
-379 ± 63 ‰ and coefficients of determination ranging
from 0.77 to 0.001 (Table ). The wide range of slopes and
coefficients of determination observed hints at a complex relationship
between urban humidity, CO2, and CDV that varies with the nature of
each period of high atmospheric stability. For example, fuel mixtures and
heating demands may change with temperature, inversions based on the valley
floor may trap most pollutants below the UOU observation site, and other
sources and processes such as advection or evaporation over the Great Salt
Lake may also contribute water vapor to the boundary layer and alter the
relationship between qobsdobs and CO2 excess.
Expanding observations beyond a single site (UOU) may help distinguish these possibilities.
Miller–Tans regression parameters for each PCAP event.
Start of PCAPEnd of PCAPRegressionRegressionslopeR2(ef = 1.5)10 Dec 2013 12:0014 Dec 2013 00:00-190 ± 460.3315 Dec 2013 12:0019 Dec 2013 12:00-260 ± 210.7726 Dec 2013 00:0029 Dec 2013 00:00-275 ± 270.6230 Dec 2013 12:0031 Dec 2013 1200-89 ± 450.172 Jan 2014 12:004 Jan 2014 00:00-101 ± 410.1317 Jan 2014 00:0022 Jan 2014 12:00-173 ± 250.3024 Jan 2014 12:0026 Jan 2014 12:00-185 ± 350.3431 Dec 2014 12:003 Jan 2015 12:00-134 ± 220.427 Jan 2015 12:0011 Jan 2015 00:00-241 ± 390.3415 Jan 2015 12:0017 Jan 2015 00:00-228 ± 460.5912 Jan 2016 12:0014 Jan 2016 00:00-128 ± 380.2522 Jan 2016 12:0023 Jan 2016 12:00-199 ± 390.5528 Jan 2016 00:0029 Jan 2016 00:00-206 ± 990.158 Feb 2016 12:0014 Feb 2016 12:00-25 ± 430.00120 Dec 2016 00:0021 Dec 2016 00:00-130 ± 540.0627 Dec 2016 120028 Dec 2016 12:00-45 ± 40.00529 Dec 2016 12:002 Jan 2017 00:00-193 ± 180.527 Jan 2017 12:008 Jan 2017 12:00-189 ± 390.3414 Jan 2017 12:0015 Jan 2017 12:00-379 ± 630.6418 Jan 2017 00:0019 Jan 2017 00:00-41 ± 300.4429 Jan 2017 12:002 Feb 2017 12:00-232 ± 320.0813 Feb 2017 12:0015 Feb 2017 12:00-328 ± 400.62
Using this estimate for dCDV of -179 ± 17 ‰, we
estimate the maximum fraction of CDV for each PCAP event using
Eq. () and estimates of dbg from both the 12 h
period prior to PCAP initiation, or the last 12 h period with a
CO2 minimum. When the former assumption is used for dbg,
estimates of the CDV fraction average 5.0 % across all PCAP events, and range
from -2.1 ± 2.3 to 13.9 ± 1.9 %, while when the latter assumption
for dbg is used, the mean CDV fraction rises to 7.2 % and ranges from
2.2 ± 2.1 to 16.7 ± 3.2 % (Table ). Negative CDV
fraction estimates occur when the estimated dbg is less than the
minimum value of dobs and are only observed when the 12 h
period immediately preceding the initiation of the PCAP is used to
estimate dbg. CO2 concentrations can build up whenever the
atmosphere is stable, even if atmospheric stability has not yet met the PCAP
threshold used here. Therefore, this pattern highlights the importance and
challenge of accurately estimating dbg for this humidity
apportionment method to yield accurate estimates of qCDV/qobs.
Relationships between meteorology, d-excess, and CO2 from
28 December 2014 to 14 January 2015. Time series of temperature (a,
∘C), q, (b, mmol mol-1), wind speed (c,
m s-1), CO2 (d, ppm; 1σ uncertainty in orange
shading), d-excess (e, ‰), and the relationship between dq
and q(f) spanning the same time period, with the same color
gradient used across time in all four panels. Data are plotted as 6 h
running averages.
Case studies28 December 2014–14 January 2015
Two distinct PCAP events were observed between 28 December 2014 and 14 January 2015
(Fig. ). The period prior to the first PCAP is marked
by a cold front passage around 30 December 2014 12:00 UTC, where there are
strong decreases in temperature and humidity (Fig. a and b),
elevated wind speeds (Fig. c), a CO2 minimum
(Fig. d), and an increase in d-excess to
∼ 18 ‰ (Fig. ) that is generally consistent with natural removal of
water from the atmosphere (Fig. f). After onset of the PCAP,
however, d-excess dropped rapidly as CO2 and CDV began to build in
the valley. By 2 January, CO2 had risen to 490 ppm and
d-excess had fallen to -7.4 ‰, an increase of ∼ 60 ppm
and a decrease of 25 ‰ respectively (Fig. d and e).
Atmospheric d-excess through this period closely followed model expectations
of moistening via CDV (Fig. f). After the end of the first PCAP
event, specific humidity and temperature rose daily until the start of the
second PCAP on 7 January 12:00 UTC (Fig. a and b). During this period
in between PCAP events, CO2 remained elevated and exhibited diurnal
variability of 20–40 ppm (Fig. d), but d-excess remained
more consistent (Fig. e). Together, the pattern of d-excess and
CO2 change across between the two PCAP events is consistent with
natural moistening of the boundary layer paired with an incomplete
mix-out of CDV and CO2. The second PCAP event, spanning 7 January
12:00 UTC until January 11:00 UTC, was marked by prominent diurnal cycles in
humidity, temperature, and CO2 (Fig. a, b, and d). Strong
diurnal cyclicity was also observed in d-excess (Fig. e).
CO2 concentrations reached their maximum at the end of the PCAP
event and decreased slowly during the first diurnal cycle after the breakup
of the PCAP, before mixing out nearly completely on 12 January. The d-excess
values followed changes in CO2, remaining low but increasing with
decreasing CO2 during the first diurnal cycle, before rapidly
increasing as CO2 decreased at the end of the observation period
(Fig. e). The spike in CO2 at the end of the PCAP is
likely due to the UOU's location on a topographic bench; strong stability
during the PCAP may have kept the most polluted air below the UOU, which then
was transported to higher altitudes as the PCAP ended.
Relationships between meteorology, d-excess, and CO2 from
3 to 17 February 2016. Time series of temperature (a, ∘C),
q, (b, mmol mol-1), wind speed (c, m s-1),
CO2 (d, ppm; 1σ uncertainty in orange shading), and d-excess (e, ‰), and the relationship between dq
vs. q(f) spanning the same time period, with the same color
gradient used across time in all four panels. Data are plotted as 6 h
running averages.
Estimates of CDV humidity fraction.
Start of PCAPEnd of PCAPMin dobsEstimated dnatEstimated dnatqCDV/qobsqCDV/qobs(12 h mean(last 12 h period with(12 h mean(last 12 h period withbefore PCAP)CO2< 415 ppm)before PCAP)CO2< 415 ppm)10 Dec 2013 12:0014 Dec 2013 00:00-7.0 ± 2.320.8 ± 0.520.3 ± 1.713.9 ± 1.913.7 ± 2.415 Dec 2013 12:0019 Dec 2013 12:00-10.9 ± 2.07.7 ± 1.27.5 ± 1.4c10.0 ± 2.09.9 ± 2.126 Dec 2013 00:0029 Dec 2013 00:00-13.8 ± 1.92.6 ± 1.57.0 ± 1.49.0 ± 2.111.2 ± 2.130 Dec 2013 12:0031 Dec 2013 12:00-4.1 ± 1.84.9 ± 1.40.6 ± 1.4b4.9 ± 1.82.6 ± 1.82 Jan 2014 12:004 Jan 2014 00:00-8.1 ± 1.60.3 ± 1.30.7 ± 1.3c4.7 ± 1.74.9 ± 1.717 Jan 2014 00:0022 Jan 2014 12:00-9.6 ± 1.8-1.0 ± 1.48.3 ± 1.34.8 ± 1.99.6 ± 1.924 Jan 2014 12:0026 Jan 2014 12:00-7.8 ± 2.21.3 ± 1.41.8 ± 1.4b5.0 ± 2.15.3 ± 2.131 Dec 2014 12:003 Jan 2015 12:00-10.5 ± 2.69.7 ± 2.2d9.7 ± 2.2d10.7 ± 2.810.7 ± 2.87 Jan 2015 12:0011 Jan 2015 00:00-3.6 ± 1.33.5 ± 1.212.6 ± 1.3b3.9±1.48.5 ± 1.615 Jan 2015 12:0017 Jan 2015 00:002.2 ± 2.010.8 ± 1.48.4 ± 1.4b4.5±1.83.3 ± 1.812 Jan 2016 12:0014 Jan 2016 00:00-5.9 ± 2.22.6 ± 1.73.2 ± 1.74.7 ± 2.25.0 ± 2.222 Jan 2016 12:0023 Jan 2016 12:00-4.3 ± 1.92.0 ± 3.63.6 ± 1.63.5 ± 2.04.3 ± 2.028 Jan 2016 00:0029 Jan 2016 00:00-3.4 ± 2.1-1.1 ± 1.52.0 ± 1.6b1.3 ± 2.03.0 ± 2.18 Feb 2016 12:0014 Feb 2016 12:00-2.7 ± 1.92.2 ± 1.42.6 ± 1.32.7 ± 1.82.9 ± 1.820 Dec 2016 00:0021 Dec 2016 00:00-9.8 ± 2.3-12.9 ± 2.02.5 ± 1.3-1.9 ± 2.86.8 ± 2.327 Dec 2016 12:0028 Dec 2016 12:00-17.0 ± 2.9-8.4 ± 1.7-3.5 ± 1.45.0 ± 2.87.7 ± 2.629 Dec 2016 12:002 Jan 2017 00:00-23.1 ± 2.3-7.6 ± 1.5-6.0 ± 1.4c9.0 ± 2.49.9 ± 2.47 Jan 2017 12:008 Jan 2017 1200-25.9 ± 3.9-18.0 ± 2.04.7 ± 1.24.9 ± 3.716.7 ± 3.214 Jan 2017 12:0015 Jan 2017 12:00-2.4 ± 1.90.6 ± 1.44.5 ± 1.21.7 ± 1.83.8 ± 1.718 Jan 2017 00:0019 Jan 2017 00:00-4.9 ± 2.3-8.4 ± 1.6-0.9 ± 1.4b-2.1 ± 2.32.2 ± 2.129 Jan 2017 12:002 Feb 2017 12:00-14.7 ± 3.1-7.8 ± 1.73.8 ± 1.34.0 ± 2.810.1 ± 2.613 Feb 2017 12:0015 Feb 2017 12:00-9.4 ± 2.11.0 ± 1.41.2 ± 1.25.8 ± 2.05.9 ± 1.9
adbg estimated with
415 ppm <CO2< 425 ppm. bdbg
estimated with 425 ppm <CO2< 450 ppm. cdbg
estimated with 450 ppm <CO2< 475 ppm. d Both
dbg estimates are from the same observation.
3–17 February 2016
This period was marked by one extended PCAP from 8 February 12:00 UTC to
14 February 12:00 UTC (Fig. ), and has been a major focus of
recent air pollution studies . Conditions
prior to the PCAP were dry and cold for the first two days, before warming by
∼ 5 ∘C (Fig. a), concurrent with an increase
in humidity from ∼3 to ∼ 5 mmol mol-1
(Fig. b). Wind speeds peaked at the beginning of this period, and
remained below 2 m s-1 after 5 February (Fig. c).
CO2 increased from 430 to 480 ppm before decreasing back to
430 ppm (Fig. d). Deuterium excess also decreased by a
few permil while CO2 was elevated, but increased back to
3–5 ‰ until the beginning of the PCAP (Fig. e);
humidity increased rapidly during this period, and followed a path parallel
to moistening by the addition of natural water vapor (Fig. f).
The remainder of the pre-PCAP period through the PCAP event was marked by
slow, steady increases in q and CO2, with prominent diurnal cycling
in temperature, CO2, q, and d-excess. Diurnal cyclicity was
apparent in the relationship between d-excess and CO2 as well, with
periods of increasing (decreasing) CO2 producing rapid decreases
(increases) in d-excess with little change in q. These diurnal patterns are
consistent with daytime growth of a shallow convective boundary layer at the
surface with a stable layer aloft; the same interpretation was made in prior
studies of this event . Diurnal cycle amplitudes of
q, temperature, and CO2 decreased for the second half of the PCAP
(Fig. a, b, and d), and co-occur with a reduction in surface solar
radiation as low-level clouds developed during the event. Superimposed on
these diurnal cycles of d-excess against q, conditions became more moist
across several days (Fig. b and f). Following termination of the
PCAP, conditions became warmer and CO2 decreased back toward its
background value. Humidity increased rapidly for a few days after the event
before falling again. Both the moistening and drying occurred with small
changes in d-excess, consistent with changes expected for changes in q in
the absence of the buildup of CDV. In contrast to the previous case study,
the relationship between d-excess and CO2 excess is weak across this
PCAP event (Table ). Atmospheric soundings indicate the
presence of a shallow convective mixed layer near the surface topped by a
strong temperature inversion during this event
e.g.,, suggesting that the column within which
CO2 and CDV are emitted may be larger than for PCAPs with high
atmospheric stability lower in the column. Although changes in q across
multiple days during this event seem to be driven by processes other than CDV
addition, these observations support a strong CDV contribution on diurnal
timescales as d-excess values and CO2 concentrations are correlated
at diurnal timescales but not necessarily multi-day timescales during this event.
Diurnal cycles of humidity, CO2, and d-excess
In this section, we more closely examine diurnal cycles of d-excess,
CO2, and specific humidity. We define diurnal cycles as deviations
from the 24 h running mean, and indicate them with a capital delta (Δ).
Changes in the diurnal variability of the estimated mixing height
and valley heat deficit were apparent throughout the winter season
(Fig. ). Despite subtle variation of the diurnal cycles of
Δd-excess, ΔCO2, and Δq across years and
months, several robust patterns emerged (Fig. ).
Δd-excess was flat or increased slightly in the early morning hours
(00:00–06:00 LT), decreased throughout the morning until ∼ 11:00 LT,
increased from 11:00 LT until late afternoon (∼ 17:00 LT), and then decreased
again from 17:00 LT until late evening (Fig. a and d). The mean
amplitude of the Δd-excess diurnal cycle was ∼ 6 ‰ during
PCAP events (Fig. a) and closer to ∼ 3 ‰ during
non-PCAP periods (Fig. d).
Seasonal average diurnal cycles of Δd-excess (a, d),
ΔCO2(b, e), and Δq(c, f) for
days in PCAP conditions (a–c) or non-PCAP
conditions (d–f). The diurnal cycle is approximated here as the
deviation from a 24 h moving average. Mean values across all four years are
shown as black symbols, with black vertical lines indicating 1σ
variability. The mean diurnal cycle is modeled for each year independently as
a GAM using cubic cyclic smoothing splines, and regression standard error
shown as shaded ribbons, with the color corresponding to model
year.
Daily minimums in CO2 mirror daily maximums in d-excess, and occurred
during the afternoon, when convective mixing, and therefore exchange
between the surface and air aloft, is greatest (Fig. b and e).
Conversely, daily minimums in Δd-excess occur when ΔCO2
is increasing, likely reflecting the addition of CDV. Like Δd-excess,
the amplitude of the diurnal cycle for ΔCO2 is greater during
PCAP periods (∼ 40 ppm, Fig. b) than during non-PCAP
periods (∼ 20 ppm, Fig. e). Patterns in Δd-excess
diurnal cycles mirrored ΔCO2 patterns, demonstrating the close
association between d-excess and CO2 on short timescales. In
contrast, diurnal cycles of Δq show different patterns apart from
amplitude across PCAP and non-PCAP periods (Fig. c and f). During
PCAP periods, Δq increases from ∼ 06:00 to ∼ 18:00 LT and
decreases from ∼ 18:00 to ∼ 06:00 LT (Fig. c), with an
amplitude of 0.7–0.8 mmol mol-1 through the day. During non-PCAP
periods, the amplitude of the Δq diurnal cycle decreased to
∼ 0.4 mmol mol-1, and features a period stable humidity or slight
humidity decrease during the afternoon, presumably due to greater mixing
between the boundary layer and the free troposphere (Fig. f).
Interannual variability in the diurnal cycles was generally small, with the
largest differences observed during PCAP periods. For example, composite
diurnal cycles for PCAP events varied the most across years (Fig. a–c).
However, given the episodic nature of PCAPs, these
diurnal cycles can often be determined by one or two events in a given year.
Though a consistent pattern emerged across many PCAP events, individual
events were expressed differently in both the CO2 and d-excess
records (e.g., Sect. 4.2). Nonetheless, the close associations between
d-excess and CO2 on diurnal cycles, coupled with the observation that
these cycles are generally not coherent with changes in specific humidity,
further suggest that the observed d-excess variability reflects the addition
or removal of CDV.
Discussion
CDV is evident on sub-diurnal to multi-day timescales in the Salt Lake
City d-excess record. On short timescales, periods of high emission intensity
were apparent in the diurnal cycles of d-excess and CO2. Decreases in
d-excess were coincident with increases in CO2 and occur during the
morning and late afternoon when emissions were likely high and tropospheric
mixing was low. Average diurnal cycles in d-excess and CO2 showed
little change overnight outside of PCAP events (Fig. ), which
was unexpected as heating emissions continued throughout the evening. The
absence of overnight d-excess and CO2 changes was likely a result of
the UOU's location on a topographic bench away from large residential
areas, or due to the injection of cleaner air from above if a surface-based
inversion occurs at an elevation below the UOU site. Long-term records of
CO2 have also been collected in lower elevation areas of SLV and
exhibit a greater buildup of CO2 overnight during the winter than
observed at UOU , which suggests that a stronger trend in
nighttime d-excess and CO2 values might be observed elsewhere in the SLV.
On longer timescales, the impact of CDV was most apparent during PCAP events,
in which CO2 and CDV persist in the urban atmosphere while the
atmosphere in SLV remained sufficiently stable. Some contrasts in the
expression of CDV and CO2 were apparent across the winter season and
likely resulted from changes in insolation and the mechanisms resulting in
stability of the near-surface atmosphere. For example, the most rapid
increases in CO2 and decreases in d-excess were observed during
December and January (Figs. and ), when surface
insolation was lower. In contrast, a strong diurnal cycle but a more muted
multi-day response was observed in February, when higher insolation can drive
higher mixing heights (Fig. ) and mix out a greater proportion
of daily emissions. As a result, changes in d-excess and CO2
exhibited large diurnal cycles superimposed upon slower synoptic trends
during February PCAP events (Fig. ).
Based on changes in d-excess relative to CO2 during PCAP events, and
the HESTIA inventory of fossil fuel emissions for SLV ,
we have estimated the mean d-excess of CDV to be -179 ± 17 ‰.
One assumption of the model used here is that all of the change in d-excess
is driven by the addition of CDV; other sources of vapor to the near surface,
such as sublimation of snow or water evaporated from the Great Salt Lake, may
introduce bias into these estimates. However, both of these sources would
have less negative d-excess values, and therefore, if other sources of vapor
contribute significantly to d-excess change, our estimates of dCDV
are a maximum estimate. Deposition of vapor onto ice in
supersaturated conditions can also promote a decrease in vapor d-excess
. While we do not have any direct observations
of supersaturated conditions, we cannot rule out the possibility of
supersaturated conditions occurring when snow is in the valley or during
cloud formation. However, we expect any potential role for vapor deposition
under supersaturated conditions affecting vapor d-excess to be small, as we
do not typically observe decreases in d-excess concurrent with decreases in
specific humidity (Fig. ).
We have made an estimate of 1.5 for ef through a detailed accounting of
emissions or fuel sources from the HESTIA dataset
e.g.,, but several sources of uncertainty in the net
ef remain. For example, heat exchangers designed to improve heating
efficiency may reduce the H2O concentration in emissions and
potentially alter dCDV as well, through condensation of water in
the emissions stream (Fig. ). Additionally, the portfolio of
fuels contributing to CDV change in both time and space, and respond to
meteorological conditions. For example, colder conditions increase demand for
heating, which may shift the portfolio of fuel sources toward natural gas
e.g.,. Finally, dCDV can be altered by the
temperature and degree of equilibration of 18O between H2O and
CO2 in combustion exhaust. If no equilibration occurs between
H2O and CO2, the δ18O values of both species should
be equal to atmospheric oxygen, 23.9 ‰ . In contrast, equilibration between H2O
and CO2 will lower the δ18O value of H2O; at
100∘, for example, the δ18O value of H2O will be
∼ 29 ‰ lower than the δ18O of CO2 for
complete equilibration . The degree of
equilibration may vary across fuels and combustion systems
, which introduces uncertainty into the δ18O,
and subsequently d, of CDV. Regardless, the highly negative estimated
isotopic composition of the flux into the boundary layer during PCAP events,
which we have assumed is predominantly CDV, precludes other potential sources
of water vapor apart from CDV from explaining the observed isotopic change.
These methods may also be helpful to verify that background CO2
measurements are free from local emissions, as we would not expect to see a
strong correlation between CO2 concentrations and d-excess values in
the absence of local emissions.
Though the most prominent periods of CO2 and CDV buildup occur during
PCAP events, decreases in d-excess coincident with increases in CO2
were apparent outside of PCAPs as well. CO2 and CDV from emissions
built up in the boundary layer whenever atmospheric stability was present
regardless of whether VHD values were high enough to qualify as a PCAP. For a
given quantity of fuel burned, CO2 increases and CDV concentrations
will be higher if the mixed height is lower because the volume these species
mix into is smaller. Atmospheric soundings at the Salt Lake City Airport
occurred at 05:00 and 17:00 LT and were unlikely to capture diurnal extremes in the
mixing height, confounding efforts to develop high-frequency relationships
between mixing height, CO2, and CDV. Mid-afternoon patterns in the
diurnal cycles of d-excess and CO2 suggested that boundary layer
development and entrainment did mix a fraction of combustion products out of
the boundary layer. This pattern held even during PCAP events
(Fig. a and b), though it is not clear whether this reflects a mixing out
of the valley, or just a repartitioning of pollutants within the atmospheric
column below a capping inversion. In contrast, CO2 and CDV build up to
higher concentrations during the early morning and late afternoon
(Fig. ), when boundary layer mixing was decreased and emissions were
likely higher due to elevated traffic.
This technique for measuring water from combustion in urban areas can be
adapted beyond SLV, though different environments will present distinct
challenges. SLV is well suited to detecting the buildup of CDV as it has
a dry climate, features a large urban area in a topographic basin, and
experiences frequent multi-day periods of high atmospheric stability in the
winter. The CDV signal is largest in dry regions or during winter
(Fig. ), and CDV may comprise a larger fraction of urban humidity in
these cities for a given level of emissions intensity. Additionally, CDV may
have a larger impact on the radiative balance of cities in drier regions, as
longwave forcing increases logarithmically with water vapor amount
. However, though the CDV signal is higher at low
humidities, instrumental precision is lower. Therefore, at current
instrumental precision limits, there is a trade-off between precision of the
CDV estimates and the size of the CDV signal. Based on our study, we suggest
two potential refinements to this technique that will improve the accuracy
and precision of this technique to diagnose the fraction of urban humidity
arising from CDV. First, the largest source of known uncertainty in our
estimates is associated with dCDV. While our estimate of
-179 ± 17 ‰ is consistent with theoretical estimates, this
fraction may vary through time as a result of changing fuel mixtures
(affecting both isotopic composition and ef) or measurement footprints, and
has not been rigorously validated with direct measurements of dCDV
from a wide variety of fuel sources and combustion systems.
Additionally, due to spatial variability in the δ2H composition of
fuels, dCDV likely varies for other cities. Second, the estimate
of the urban CDV fraction of humidity is highly sensitive to the estimate
of dbg. In this study, estimates of the CDV humidity percentage were
2.2 % greater on average when a low CO2 threshold was used rather
than one based on the time window immediately preceding the PCAP; in one
case, these assumptions yielded estimates that varied by a factor of 3.4, and
in other cases, even yielded different signs (Table ). In
our uncertainty analysis, we have considered uncertainty arising from
instrumental precision, but the uncertainty in dbg remains
difficult to assess. Paired urban–rural observations may be necessary to
accurately estimate dbg or identify appropriate periods for
estimating dbg from the urban record.
Conclusions
Measurements of ambient vapor d-excess were paired with CO2
observations across four winters in Salt Lake City, Utah. We found a strong
negative association between CO2 and d-excess on sub-diurnal to
seasonal timescales. An elevation of CO2 and CDV was most prominent during
PCAP periods, during which atmospheric stability was high for extended periods. We
outline theoretical models that can discriminate between changes in d-excess
driven by condensation, advection, and mixing processes of the natural
hydrological cycle and those driven by CDV moistening. The CDV signal is
largest when humidity is low, as CDV likely comprises a larger fraction of
total humidity and the anticipated signal between vapor with and without CDV
is large. On shorter timescales, prominent diurnal cycles were observed in
both d-excess and CDV that could be tied to both emissions intensity and
atmospheric processes. These diurnal cycles were decoupled from diurnal
cycles of specific humidity, further strengthening the link between d-excess
and urban CO2.
We estimate the d-excess value of CDV to be -179 ± 17 ‰, assuming
a mean molar ratio of H2O : CO2 in emissions of 1.5
derived from the HESTIA inventory of emissions for Salt Lake County
. This estimate is consistent with
theoretical constraints and a limited number of direct observations of CDV
, though uncertainty remains due to variability in the
valley-scale stoichiometric ratio of H2O and CO2 and the
measurement footprint, and due to uncertainties about the isotopic composition of
fuels and their transit through different combustion systems. The latter of
these uncertainties can be reduced in future studies that seek to generate a
bottom-up estimate of dCDV from direct measurements of fuels
and emissions vapor to complement the top-down estimate made in this
study using a mixing-model approach. We use our dCDV estimate to
calculate the fraction of humidity in SLV comprised of CDV using two
different assumptions for the d-excess of water vapor in the absence of
fossil fuel emissions. We find that CDV generally represents 5–10 % of urban
humidity during PCAP events, with a maximum estimate of 16.7 ± 3.2 %.
Estimates of the urban CDV fraction require an accurate estimate of the d-excess
of water vapor in the absence of emissions, and we find generally higher
estimates of urban CDV when a low CO2 threshold is used to
estimate dbg compared to when pre-PCAP observations alone are used. Further
refinements of these methods may help apportion humidity changes during the
winter between CDV and different advected natural water sources to the
urban environment, and help verify that CO2 measurements that are
taken as backgrounds are not influenced by local emissions. Additionally, our
method is most immediately applicable to cities in arid or semi-arid areas
during the winter, as the potential isotopic signal for detecting CDV is the
largest. However, CDV may have the largest impact on urban meteorology when
humidity is low, as greenhouse forcing by water vapor is logarithmically
proportional to water vapor concentration. Further refinements of this
humidity apportionment technique, such as narrowing the uncertainty in the
isotopic composition of CDV and improving the estimation of dbg
will improve estimates of CDV amount in urban environments, and help assess
relationships between CDV, CO2, urban air pollution, and public health.
IGRA radiosonde data are available from
https://www.ncdc.noaa.gov/data-access/weather-balloon/integrated-global-radiosonde-archive.
UOU meteorological measurements are available for download from
mesowest.utah.edu, and CO2 data are available at
air.utah.edu. Calibrated UOU isotope data products are available from
the Open Science Framework (osf.io/ekty3, ), and codes used to calibrate
the water isotope analyzer measurements are available from GitHub
(https://github.com/SPATIAL-Lab/UU_vapor_processing_scripts/releases/tag/v1.2.0, ).
The Supplement related to this article is available online at https://doi.org/10.5194/acp-18-8529-2018-supplement.
The authors declare that they have no conflicts of interest.
Acknowledgements
Richard P. Fiorella and Gabriel J. Bowen received support from NSF grant EF-1241286.
Ryan Bares, John C. Lin, and the CO2 measurements were supported by
grants from Department of Energy (DOE) grant DESC0010624 and the National
Oceanic and Atmospheric Administration (NOAA) grant NA140AR4310178. We thank
Ben Fasoli for his cross-validation of CO2 measurements between the
ASB and UOU sites. Edited by: Thomas Röckmann
Reviewed by: Ingeborg Levin and one anonymous referee
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