# Measurements of NO and NO2 exchange between the atmosphere and Quercus agrifolia

Erin R Delaria, Megan Vieira, Julie Cremieux, Ronald C. Cohen

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Erin R Delaria, Megan Vieira, Julie Cremieux, Ronald C. Cohen. Measurements of NO and NO2 exchange between the atmosphere and Quercus agrifolia. Atmospheric Chemistry and Physics, 2018, 18 (19), pp.14161-14173. 10.5194/acp-18-14161-2018. hal-01901853

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# Measurements of NO and NO₂ exchange between the atmosphere and *Quercus agrifolia*

Erin R. Delaria<fnref n="1" />, Megan Vieira<fnref n="1" />, Julie Cremieux<fnref n="2" />, and Ronald C. Cohen<fnref n="1" />,<fnref n="3" />

**Correspondence:** Ronald C. Cohen (rccohen@berkeley.edu)

Received: 1 May 2018 – Discussion started: 31 May 2018

Revised: 14 September 2018 – Accepted: 18 September 2018 – Published: 5 October 2018

**Abstract.** NO₂ foliar deposition through the stomata of leaves has been identified as a significant sink of NOₓ within a forest canopy. In this study, we investigated NO₂ and NO exchange between the atmosphere and the leaves of the native California oak tree *Quercus agrifolia* using a branch enclosure system. NO₂ detection was performed with laser-induced fluorescence (LIF), which excludes biases from other reactive nitrogen compounds and has a low detection limit of 5–50 ppt. We performed both light and dark experiments with concentrations between 0.5 and 10 ppb NO₂ and NO under constant ambient conditions. Deposition velocities for NO₂ during light and dark experiments were 0.123 ± 0.009 and 0.015 ± 0.001 cm s⁻<fnref n="1" />, respectively. Much slower deposition was seen for NO, with deposition velocities of 0.012 ± 0.002 and 0.005 ± 0.002 cm s⁻<fnref n="1" /> measured during light and dark experiments, respectively. This corresponded to a summed resistance of the stomata and mesophyll of 6.9 ± 0.9 s cm⁻<fnref n="1" /> for NO₂ and 140 ± 40 s cm⁻<fnref n="1" /> for NO. No significant compensation point was detected for NO₂ uptake, but compensation points ranging from 0.74 to 3.8 ppb were observed for NO. NO₂ and NO deposition velocities reported here are comparable both with previous leaf-level chamber studies and inferences from canopy-level field measurements. In parallel with these laboratory experiments, we have constructed a detailed 1-D atmospheric model to assess the contribution of leaf-level NOₓ deposition to the total NOₓ loss and NOₓ canopy fluxes. Using the leaf uptake rates measured in the laboratory, these modeling studies suggest that loss of NOₓ to deposition in a California oak woodland competes with the pathways of HNO₃ and RONO₂ formation, with deposition making up 3%–22% of the total NOₓ loss.

Additionally, foliar uptake of NOₓ at these rates could account for ~15%–30% canopy reduction of soil NOₓ emissions.

## 1 Introduction

Nitrogen oxides (NOₓ ≡ NO + NO₂) are a group of highly reactive trace gases that control the oxidative capacity of the atmosphere by regulating the amounts of ozone, hydroxyl radicals, volatile organic compounds, and other key atmospheric species (Crutzen, 1979). NOₓ is also directly toxic in high concentrations, plays a major role in tropospheric ozone production, and serves as a source of NO₃⁻, a key nutrient for ecosystems and a component of acid rain. NOₓ is primarily emitted as nitric oxide (NO) through fossil fuel combustion, biomass burning, lightning, and microbial activity in soils (Seinfeld and Pandis, 2006). NO is rapidly oxidized to nitrogen dioxide (NO₂) through reactions with ozone and peroxy radicals, and in the daytime NO₂ subsequently photolyzes to re-form NO. The interconversion of NO and NO₂ reaches steady state within a few minutes during the daytime (Crutzen, 1979). The effects of NOₓ on urban chemistry, where anthropogenic emissions dominate the NOₓ source, have been extensively studied. However, the processes affecting NOₓ in forested and agricultural regions are less well understood.

In forests and agricultural lands, the major source of NOₓ is NO emitted as a by-product of microbial denitrification and nitrification (Mckenney et al., 1982; Caranto and Lancaster, 2017). Deposition of NO₂ to plant canopies is thought

<footnote n="1">¹Department of Chemistry, University of California Berkeley, Berkeley, CA 94720, USA</footnote>

<footnote n="2">²Department of Chemistry, Université Pierre et Marie Curie, Paris, France</footnote>

<footnote n="3">³Department of Earth and Planetary Science, University of California Berkeley, Berkeley, CA 94720, USA</footnote>
to be an important sink of NOₓ in forests, substantially re-
ducing the contribution of soil-emitted NOₓ to the atmo-
spheric NOₓ budget. Jacob and Wofsy (1990) observed low
NOₓ above the canopy over the Amazon forest during the
wet season. Using a 1-D chemical and transport model con-
strained by observed NOₓ and ozone, they concluded that
a substantial fraction of soil NOₓ must be absorbed by the
canopy. Extrapolation of these ideas to forests with different
leaf area indices suggest that 20%-50% of the global frac-
tion of soil-emitted NOₓ is lost to vegetation (Yienger and
Levy, 1995; Lerdau et al., 2000). Using the framework of Ja-
cob and Wofsy (1990) and Yienger and Levy (1995), global
atmospheric models have been tuned to describe observed at-
mospheric NOₓ concentrations and tropospheric ozone pro-
duction using a canopy reduction factor (CRF). The CRF
is an adjustable parameter that accounts for the difference
between soil NO emissions and the amount of NOₓ venti-
lated through the canopy (Yienger and Levy, 1995; Vinken
et al., 2014). However, CRFs are implemented in an unphys-
ical manner where they act only on soil NOₓ emissions and
not on other NOₓ present in the plant canopy. An improved
understanding is needed of the physical and biochemical pro-
cesses governing the foliar uptake of NOₓ at the ecosystem
and leaf scales.

Many studies have also directly observed the leaf-level up-
take of NO2 (Neubert et al., 1993; Rondon and Granat, 1994;
Hereid and Monson, 2001; Sparks et al., 2001; Teklemariam
and Sparks, 2006; Pape et al., 2009; Chaparro-Suarez et al.,
2011; Breuninger et al., 2013). Isotope labeling experiments
investigating the mechanism of NO2 uptake have demon-
strated that atmospheric NO2 can be absorbed through the
stomata of plant leaves, converted to nitrate (NO3) and ni-
trite (NO2), and eventually assimilated into amino acids
(Rogers et al., 1979; Okano and Totsuka, 1986; Nussbaum
et al., 1993; Weber et al., 1995; Yoneyame et al., 2003). The
mechanism of NO2 assimilation is diffusion into the stom-
ata followed by dissolution into the aqueous phase and dis-
proportionation to NO3 and NO2 in the apoplasm (Lee and
Schwartz, 1981a, b). NO2 can also be transformed to nitrate
and nitrite through scavenging by antioxidants, most notably
ascorbate (Ramge et al., 1993). The influence of ascorbate
on foliar uptake was theoretically calculated by Ramge et
al. (1993) and experimentally demonstrated by Teklemariam
and Sparks (2006). The enzyme nitrate reductase converts
NO3 to NO2 in the cytosol, and NO2 is then transported
into the plastids where it is further reduced by the enzyme
nitrite reductase to ammonium (NH4), the product required
for amino acid synthesis (Ammann et al., 1995; Tischner,
2000; Teklemariam and Sparks, 2006). Alternatively, NO2
can deposit directly onto the leaf cuticles or a leaf-surface
water film (Burkhardt and Eiden, 1994). However, foliar up-
take of NO2 has been demonstrated to be controlled primarily
by the stomata, with deposition to the leaf surface represent-
ing only a small fraction of the total NO2 flux (Thoene et
al., 1991; Gessler et al., 2000; Chaparro-Suarez et al., 2011).

Strong correlations have been observed among NO2 con-
centrations, stomatal conductances, and the NO2 deposition
flux, suggesting foliar uptake is mainly controlled by stom-
atal aperture and internal leaf resistances (Johansson, 1987;
Thoene et al., 1991; Rondon et al., 1993; Meixner et al.,
1997; Chaparro-Suarez et al., 2011; Breuninger et al., 2013).

Despite the large existing body of research on the leaf-
level deposition of NO2 to vegetation, there are still dis-
crepancies present in NO2 exchange rates and the role of
mesophilic processes. Many laboratory experiments have
failed to measure uptake rates necessary to describe the ob-
served 20%-50% reduction of soil-emitted NOx (Hanson
and Lindberg, 1991; Breuninger et al., 2013), despite the
many modeling studies that have suggested dry deposition
makes up most of this reduction (Jacob and Wofsy, 1990;
Yienger and Levy, 1995; Ganzeveld et al., 2002a; Geddes and
Murphy, 2014). Photolysis gradients and reaction of NOx to
form higher nitrogen oxides could account for a large frac-
tion of this reduction in soil NOx, as has been suggested by
Min et al. (2012, 2014), but the relative importance of dry
deposition processes versus in-canopy chemical transforma-
tions is still a matter of considerable uncertainty (Lerdau et
al., 2000; Ganzeveld et al., 2002a). Another controversy is
the existence of a compensation point – a concentration be-
low which leaves would instead act as a source of NOx. Com-
pensation points of 0.1–3.2 ppb NO2 have been observed in
a number of laboratory chamber studies, suggesting trees in-
stead may serve as a large source of NOx in forests (Johans-
son, 1987; Rondon et al., 1993; Hereid and Monson, 2001;
Sparks et al., 2001; Teklemariam and Sparks, 2006). Emis-
sion of NO at these low NOx mixing ratios has also been
detected in laboratory chamber studies (Wildt et al., 1997;
Hereid and Monson, 2001). More recent laboratory studies
of leaf level deposition have, however, questioned the exis-
tence of a compensation point (Chaparro-Suarez et al., 2011;
Breuninger et al., 2013). Most observations of NOx canopy
fluxes and atmospheric models predict or assume substan-
tial NOx deposition at concentrations as low as 0.1 ppb, typ-
ical of NOx mixing ratios in remote areas (Jacob and Wofsy,
1990; Wang and Leuning, 1998; Lerdau et al., 2000; Sparks
et al., 2001; Wolfe and Thornton, 2011; Min et al., 2012;
Geddes and Murphy, 2014). However, some modeling stud-
ies have suggested that an NO2 compensation point is neces-
sary to describe (Seok et al., 2013) or has only a small effect
on canopy fluxes in most regions (Ganzeveld et al., 2002a).
More research is thus needed on leaf and canopy-level pro-
cesses to understand the full complexity of the soil–canopy–
atmosphere system.

To understand the leaf-level processes affecting
ecosystem-scale atmosphere–biosphere NOₓ exchange,
we have conducted laboratory experiments measuring NO
and NO₂ fluxes to the native California tree species *Quercus
agrifolia* (Fig. 1) using a branch enclosure system and
direct laser-induced fluorescence (LIF) detection of NO₂
(Fig. 2). With the LIF technique we are able to measure NOₓ
<figure><img src="image_4.png" /><figcaption>Figure 1. Species distribution map of Quercus agrifolia. Each dot represents an observation of Q. agrifolia occurrence. Data provided by the participants of the Consortium of California Herbaria.</figcaption></figure>

exchange fluxes with high specificity and sensitivity at trace NOₓ mixing ratios relevant to forested environments. We investigated the existence of an NO₂ and NO compensation point and the rate of NOₓ foliar uptake under controlled conditions. To our knowledge this is the first leaf-level uptake experiment that has been performed on a North American tree species.

## 2 Materials and methods

### 2.1 Quercus agrifolia samples

NOₓ uptake by Quercus agrifolia (coastal live oak) was investigated in the laboratory. Three Quercus agrifolia individuals were purchased from a local native California plant nursery (Native Here Nursery), where the plants were grown from seeds and cuttings collected in Contra Costa County. The tree specimens were grown in a nutrient-rich commercial soil mixture (a mixture of orchard potting soil and EB stone cactus mix) at the Jane Grey Research Greenhouse at the University of California, Berkeley. The trees were 2–3 years old when measurements were taken.

### 2.2 Laser-induced fluorescence detection

NO₂ was measured using LIF. A blue diode laser (Z-Laser ZM18H3) centered at a wavelength of 405 nm was focused into each detection cell and made 20 passes in White multipass optical configuration (Fig. 2b) (Thornton et al., 2000; Fuchs et al., 2009). Upon absorption of a visible photon,

NO₂ undergoes a transition from the ²A₁ ground to the ²B₂ excited electronic state. The excited NO₂ molecule is either quenched by collision or emits a red-shifted photon as it relaxes back to ground state (e.g., Thornton et al., 2000). These emitted photons were detected using a red-sensitive photo-multiplier tube (PMT) (Hamamatsu H7421-50). To minimize collisional quenching, each detection cell was maintained at a pressure of around 0.4 kPa. Excitation at 405 nm was chosen because it is near the region of maximum absorption in the NO₂ spectrum and is not subject to interferences from absorption by water vapor or O₃ (Matsumoto and Kajii, 2003).

Calibrations were performed every hour by diluting NO (4.97 ppm ± 5%, Praxair) and NO₂ standard gases (5.08 ppm ± 5%, Praxair) to 1–10 ppb in humidified (RH ~ 60%) zero air. The limit of detection (LOD) for the detection cells is described as follows:

$$ LOD = \frac{S/N}{m} \sqrt{\frac{2b}{t}}, \qquad (1) $$

where m is the slope of the calibration curve constructed from standard dilutions, b is the PMT signal at 0 ppb NO or NO₂, S/N is the desired signal-to-noise ratio, and t is the time of signal averaging. At a S/N of 2 and signal averaging over 5 min, the LOD for detection cells 1–4 was 15, 4, 10, and 30 ppt, respectively. NO₂ in the incoming and outgoing airstreams was measured simultaneously in the first two detection cells. In the second two detection cells, NO was quantitatively converted to NO₂ in the presence of excess ozone, allowing for detection of total NOₓ (Fig. 2a). Ozone was produced using an ozone generator (Jelight 600), and flow rates of ozone delivered were adjusted to achieve unity conversion of NO to NO₂.

### 2.3 Dynamic chamber system

The NOₓ flux measurements were performed with a dynamic branch enclosure system, consisting of a thin transparent double-walled Teflon film (FEP) bag (American Durafilm), which transmits 90% of photosynthetically active radiation. The chamber was illuminated by an LED diode array of 430–475 and 620–670 nm lights (Apollo Horticulture). This light source was selected because it does not emit wavelengths below 420 nm, where NO₂ dissociates, preventing loss of NO₂ to photodissociation and resultant photochemistry. In order to ensure turbulent mixing and minimal aerodynamic and boundary layer resistances, a Teflon-coated fan was installed inside the inner chamber (Meixner et al., 1997; Pape et al., 2009; Breuninger et al., 2013).

Before experiments with Quercus agrifolia individuals, the deposition to an empty chamber was measured and background subtracted from subsequent branch measurements. The measured loss of NO₂ to chamber walls was 5% of the NO₂ mixing ratio flowing into the chamber. This corresponded to a maximum loss of 0.4 ppb at 8 ppb NO₂ and minimum loss of 0.05 ppb at 1 ppb NO₂. Emission of less
<figure><img src="image_5.png" /><figcaption>Figure 2. Schematic of the experimental dynamic chamber (a) and laser-induced fluorescence detection (b) setups.</figcaption></figure>

than 0.05 ppb NO₂ from the Teflon walls was also observed when chamber lights were turned on with 0 ppb NO₂ flowing through the system. It is likely that the chamber walls buffer uptake of NO₂, but this is a minor effect, as the wall emission observed was a tiny fraction of the measured fluxes.

During measurements, the enclosed branch was exposed to known amounts of either NO₂ or NO mixed with zero air. The inner chamber had an inner diameter of 20 cm, a length of 40 cm, and a total volume of 13 L (American Durafilm 200A Teflon FEP). Flow rates into the inner chamber (Q) during experiments were typically 5 L min⁻¹, creating a residence time in the chamber of 3 min. The outer chamber had an inner diameter of 30 cm and a length of 55 cm (American Durafilm 500C20 Teflon FEP). Zero air at a flow rate of 3 L min⁻¹ constantly fumigated the outer bag, serving as a buffer region to ensure the laboratory air, with high mixing ratios of NOₓ, did not diffuse into the bag enclosing the branch.

The photosynthetic photon flux density (PPFD) was monitored outside the chamber with a LiCor quantum sensor (LiCor LI-190SA). The flux density measured above the chamber was 1190 µmol m⁻² s⁻¹, approximately the PPFD for Berkeley, California, at noon during the month of October. This is well above the photon flux required to achieve maximal stomatal aperture for broadleaf evergreen trees (von Caemmerer and Farquhar, 1981; Chaparro-Suarez et al., 2011; Breuninger et al., 2013). We confirmed this assumption by covering the lights with a filter to reduce the intensity by 40% and monitoring CO₂ and H₂O exchange. No reduction in the exchange rates of these gases were observed. The relative humidity of air entering the chamber was maintained at 50%–65% in all experiments by flowing zero air through a bubbler before mixing with NOₓ. Measurements of NOₓ exchange fluxes occurred under a light/dark cycle with a photoperiod of 12 h and a temperature of 26/22±2 °C. No change in NOₓ uptake was observed when heating the chamber with the lights off or cooling the chamber with the lights on. We therefore expect no significant temperature effects caused by the 4 °C difference in temperature between

light and dark periods. We also observed a relative humidity increase in the delivered air of about 2% with the lights off, but do not expect this increase to produce any significant changes in NOₓ deposition or plant physiology (von Caemmerer and Farquhar, 1981; Chaparro-Suarez et al., 2011).

Exchange of CO₂ and H₂O with the leaves were monitored with a LiCor-6262 H₂O/CO₂ analyzer operating in differential mode. Flows of 0.1 L min⁻¹ of air entering and exiting the chamber were diverted to the LiCor analyzer to measure the CO₂ assimilation and transpiration rates. To measure the CO₂ content and relative humidity of air delivered to the chamber, 0.5 L min⁻¹ of the humidified zero air / NOₓ mixture was diverted to a second external 1.5 L cuvette. The temperature and relative humidity of air entering the chamber were measured with a temperature and relative humidity module in the external cuvette (TE Connectivity HTM2500LF). The CO₂ mixing ratios in the external chamber were monitored with a Vaisala CarboCap GMP343 sensor.

## 2.4 NOₓ flux densities

The leaf-level exchange flux of NO or NO₂ (FNOₓ) was calculated according to Eq. (2):

$$F_{NO_x} = \frac{Q \cdot (C_0 - C_i)}{A}, \qquad (2)$$

where Q is the flow rate (m³ s⁻¹), A is the enclosed leaf area (m²), C₀ is the concentration leaving the chamber, and Cᵢ is the concentration entering the chamber (nmol m⁻³). The calculated flux is related to a deposition velocity (VdNOₓ) by Eq. (3):

$$F_{NO_x} = -Vd_{NO_x} \cdot (C_0 + C_{comp}), \qquad (3)$$

where Ccomp is the compensation point, the concentration of NO₂ below which the tree would instead act as a source of NOₓ. The deposition velocities were calculated through weighted least-square regression of calculated fluxes and outlet NOₓ concentrations (C₀). The absolute value of the slope
of the regression line was equal to the deposition velocity, with the x intercept representing the compensation point concentration. The precision error in the NOₓ exchange flux (σF) was calculated through propagation of the error in the inlet (σCᵢ) and outlet (σC₀) concentrations (Eq. 4).

$$ \sigma_F = \frac{Q}{A} \sqrt{\sigma_{C_i}^2 + \sigma_{C_o}^2} \qquad (4) $$

σCᵢ and σC₀ were estimated as the larger of the error in the calibration slopes and the standard deviation of the 5 min signal average. From observations in daily deviations of the flow rate and error in measured leaf area using the ImageJ software (Schneider et al., 2012), we estimate the error in Q/A to be a maximum of 0.005 cm s⁻¹. This usually was only a minor contribution to the total error in the NOₓ exchange flux.

The calculated deposition velocity was used to find the total resistance to deposition, R, via Eq. (5).

$$ Vd_{NO_x} = \frac{1}{R} \qquad (5) $$

The total resistance is described by the canopy stomatal resistance model (Baldocchi et al., 1987) and defined in Eqs. (6)–(7).

$$ R = R_a + R_b + R_{leaf}, \qquad (6) $$

$$ R_{leaf} = \left( \frac{1}{R_{cut}} + \frac{1}{R_{st} + R_{m}} \right)^{-1}, \qquad (7) $$

where Rleaf is the total leaf resistance and Ra, Rb, Rcut, Rst, and Rm are the aerodynamic, boundary layer, cuticular, stomatal, and mesophilic resistances, respectively. The aerodynamic resistance is characterized by the micrometeorology above a surface and is dependent upon the wind speed and turbulence of air flow. The boundary layer resistance describes the diffusion of a molecule through a shallow boundary of air above a surface and is dependent on microscopic surface properties, diffusivity of the gas species, wind speed, and turbulence of air flow (Baldocchi et al., 1987). Rcut, Rst, and Rm are the resistances associated with deposition to the leaf cuticles or through the stomata, and are dependent upon plant physiology.

The chamber fan, installed to create turbulent mixing, allowed for the assumption that Ra was negligible (Pape et al., 2009; Breuninger et al., 2012). Rb is chamber-specific, and has typically not been measured in previous chamber experiments of NO₂ leaf-level deposition (Chaparro-Suarez et al., 2011; Breuninger et al., 2012, 2013). Rb was experimentally measured in this study by placing a tray of activated carbon into the chamber (assumed to have zero surface resistance to deposition of NO₂) and calculating the deposition flux of NO₂. The leaf components to the total deposition resistance were determined through dark and light experiments. During dark experiments, the stomata were closed (confirmed with measurements of CO₂ and H₂O exchange), and the deposition observed was assumed to be entirely driven by deposition to the cuticles.

<figure><img src="image_6.png" /><figcaption>Figure 3. Flux to a 5.1 cm diameter dish filled with activated charcoal. The chemical surface resistance to deposition is approximately zero, so the deposition velocity for deposition of NO₂ to the surface of the charcoal dish is the reciprocal of the boundary layer resistance. The line of best fit is (0.51 ± 0.032) C₀, where C₀ is the concentration of NO₂ in the outgoing airstream.</figcaption></figure>

# 3 Results

## 3.1 Determination of the boundary resistance Rb

To estimate the chamber boundary layer resistance and test the assumption that Rb ≪ Rleaf, a dish of activated carbon, which theoretically has zero chemical resistance to deposition of NO₂, was placed inside the chamber. The boundary layer resistance was considered to be the only component of the total resistance to deposition. The deposition velocity of NO₂ to activated carbon was measured as 0.52 ± 0.06 cm s⁻¹, corresponding to a boundary layer resistance to NO₂ deposition of 1.94 ± 0.02 s cm⁻¹ (Fig. 3). This boundary resistance is approximately double what was measured by Pape et al. (2009) – a reasonable difference given differences in chamber design (Fig. 2). The Rb for NO₂ was scaled with the ratio of diffusivities of NO₂ and NO in air to obtain the resistance to deposition of NO of 2.59 ± 0.03 s cm⁻¹. However, with a branch enclosed inside the chamber, the effective boundary resistance to deposition will likely be reduced, as the surface roughness and surface area for deposition is increased (Galbally and Roy, 1980; Pape et al., 2009). The boundary resistances presented above thus serve as an upper limit for Rb with vegetation inside the chamber.
The boundary resistance was also estimated in an additional experiment (not shown) in which a de-ionized water-soaked Whatman no. 1 filter paper was placed inside the chamber and the evaporation of water vapor into the chamber filled with dry zero air was measured. The emission flux of water vapor from the filter paper was calculated in a similar manner to that of NOₓ deposition flux (Eq. 2). The conductance to water vapor was then calculated via

$$ \frac{Q \cdot (P_{H_2O})}{A} = g_w (P_{sat} - P_{H_2O}), \qquad (8) $$

where Pₐ₅₀ is the partial pressure of water vapor inside the chamber, Pₐ₅₀ is the saturation vapor pressure at the temperature in the chamber, and gₐ₅₀ is the conductance to water vapor. The measured conductance to water vapor was scaled with the ratio of diffusivities of NO₂ to water vapor (Dₐ₅₀/Dₐ₅₀) and inverted to find the NO₂ boundary layer resistance:

$$ R_b = \frac{D_{H_2O}}{D_{NO_2} g_w}. \qquad (9) $$

The boundary resistance to NO₂ deposition by this method was found to be 2 s cm⁻¹, essentially identical to the measurement on the activated-carbon.

## 3.2 NOₓ deposition velocity and compensation point concentration

The deposition velocities and compensation points were respectively calculated as the slope and x-axis intercept of the regression line between NOₓ exchange flux and chamber NOₓ concentrations (Fig. 4). The detection limit was the dominant source of error in the estimation of the NO exchange flux and compensation point. The large relative uncertainties in NO flux measurements were caused by the much slower deposition of NO compared with that of NO₂, inhibiting our ability to observe the very small changes between the NO concentration in the chamber and the incoming airstream (Fig. 4). Additional uncertainty in NO₂ flux measurements because of enhanced water vapor quenching of excited-state NO₂ should be minimal, as calibrations and measurements were performed at equivalent relative humidities. However, transpiration of the enclosed leaves caused the absolute humidity within chamber to be enhanced by 0.3%–0.5% relative to the incoming airstream. We expect this to result in a maximum error in calculated NO₂ mixing ratios of 1%–1.75% (Thornton et al., 2000), resulting in maximum errors in the calculated fluxes and deposition velocities of 2% and 4%, respectively. This 4% error in the calculated deposition velocity during lights-on experiments is less that the uncertainty of the linear fit (Fig. 4).

Correlation coefficients, deposition velocities, compensation points, and statistical testing of the compensation point for NO₂ and NO deposition are shown in Table 1 and Table 2, respectively, and were calculated according to Breuninger

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>**Figure 4.** NO₂ (a) and NO (b) fluxes versus the outlet concentrations for all Quercus agrifolia individuals with the chamber lights on (green) and off (blue). The line of best fit is shown in red and was calculated to minimize the weighted residuals in both the x and y axes. The blue dotted lines show where flux and C₀ are zero. A significantly positive (α = 0.5) x intercept occurs for NO, but not NO₂ experiments.</figcaption></figure>

et al. (2013). For NO₂ experiments, only one dark and one light experiment with Quercus agrifolia 1 were found to have statistically significant (α = 0.05) nonzero intersections with the x axis (Table 1). The range of Cₐ₅₀ measured was −0.02 to 0.300 ppb NO₂, with probabilities of Cₐ₅₀ = 0 ranging from 10.3% to 91.6% (excluding the two Quercus agrifolia 1 experiments) (Table 1). Conversely, all three Quercus agrifolia individuals during all dark and light NO deposition experiments demonstrated compensation points significantly above zero, ranging from 0.74 to 3.8 ppb NO. The average compensation point was calculated as 0.84 ± 0.32 ppb NO during light experiments and 2.4 ± 1.1 ppb NO during dark experiments (Table 2).

Student's t tests (not shown) demonstrated that deposition velocities and compensation points measured during NO
<table><caption>**Table 1.** Parameters of NO₂ bivariate linear least-square fitting regression analysis.</caption><thead><tr><th>Run</th><th>N</th><th>R<sup>2</sup></th><th>[NO<sub>2</sub>]<sub>comp</sub><br>(ppb)</th><th>P([NO<sub>2</sub>]<sub>comp</sub> = 0)<br>(%)</th><th>V<sub>dep</sub><br>(cm s<sup>-1</sup>)</th></tr></thead><tbody><tr><td colspan="6"><em>Q. agrifolia</em> 1, light</td></tr><tr><td>1</td><td>13</td><td>0.979</td><td>0.056 ± 0.013</td><td>42.7</td><td>0.10 ± 0.013</td></tr><tr><td>2</td><td>13</td><td>0.950</td><td>0.046 ± 0.19</td><td>63.7</td><td>0.12 ± 0.023</td></tr><tr><td>3</td><td>16</td><td>0.978</td><td>0.099 ± 0.086</td><td>3.87</td><td>0.15 ± 0.016</td></tr><tr><td>4</td><td>16</td><td>0.958</td><td>0.077 ± 0.14</td><td>28.7</td><td>0.12 ± 0.021</td></tr><tr><td>All</td><td>58</td><td>0.927</td><td>0.080 ± 0.10</td><td>11.6</td><td>0.12 ± 0.012</td></tr><tr><td colspan="6"><em>Q. agrifolia</em> 2, light</td></tr><tr><td>1</td><td>16</td><td>0.963</td><td>0.10 ± 0.12</td><td>10.3</td><td>0.08 ± 0.011</td></tr><tr><td>2</td><td>5</td><td>0.969</td><td>-0.01 ± 0.96</td><td>83.8</td><td>0.12 ± 0.014</td></tr><tr><td>3</td><td>9</td><td>0.997</td><td>0.023 ± 0.032</td><td>20.3</td><td>0.16 ± 0.011</td></tr><tr><td>4</td><td>16</td><td>0.974</td><td>-0.019 ± 0.074</td><td>61.9</td><td>0.14 ± 0.017</td></tr><tr><td>5</td><td>15</td><td>0.979</td><td>0.015 ± 0.082</td><td>72.7</td><td>0.12 ± 0.014</td></tr><tr><td>All</td><td>61</td><td>0.845</td><td>-0.0077 ± 0.091</td><td>91.6</td><td>0.11 ± 0.014</td></tr><tr><td colspan="6"><em>Q. agrifolia</em> 3, light</td></tr><tr><td>1</td><td>11</td><td>0.969</td><td>0.016 ± 0.18</td><td>87.4</td><td>0.12 ± 0.024</td></tr><tr><td>2</td><td>15</td><td>0.961</td><td>0.074 ± 0.16</td><td>39.1</td><td>0.18 ± 0.029</td></tr><tr><td>3</td><td>5</td><td>0.990</td><td>0.30 ± 0.20</td><td>5.9</td><td>0.12 ± 0.038</td></tr><tr><td>All</td><td>31</td><td>0.830</td><td>0.019 ± 0.064</td><td>77.6</td><td>0.14 ± 0.029</td></tr><tr><td>All <em>Q. agrifolia</em>, light</td><td>150</td><td>0.885</td><td>0.030 ± 0.072</td><td>41.3</td><td>0.123 ± 0.0092</td></tr><tr><td colspan="6"><em>Q. agrifolia</em> 1, dark</td></tr><tr><td>1</td><td>16</td><td>0.964</td><td>0.056 ± 0.14</td><td>0.9*</td><td>0.022 ± 0.0034</td></tr><tr><td colspan="6"><em>Q. agrifolia</em> 2, dark</td></tr><tr><td>1</td><td>16</td><td>0.858</td><td>-0.16 ± 0.47</td><td>50.8</td><td>0.016 ± 0.0050</td></tr><tr><td>2</td><td>12</td><td>0.932</td><td>-0.34 ± 0.40</td><td>11.8</td><td>0.013 ± 0.0038</td></tr><tr><td>All</td><td>28</td><td>0.853</td><td>-0.24 ± 0.32</td><td>15.6</td><td>0.015 ± 0.0030</td></tr><tr><td colspan="6"><em>Q. agrifolia</em> 3, dark</td></tr><tr><td>1</td><td>14</td><td>0.900</td><td>-0.30 ± 0.48</td><td>24.1</td><td>0.015 ± 0.0042</td></tr><tr><td>2</td><td>11</td><td>0.909</td><td>-0.001 ± 0.69</td><td>36.7</td><td>0.015 ± 0.0057</td></tr><tr><td>All</td><td>25</td><td>0.898</td><td>-0.22 ± 0.38</td><td>25.3</td><td>0.014 ± 0.0029</td></tr><tr><td>All <em>Q. agrifolia</em>, dark</td><td>69</td><td>0.881</td><td>-0.16 ± 0.24</td><td>12.2</td><td>0.015 ± 0.0018</td></tr></tbody></table>

and NO₂ lights-on and lights-off experiments were not significantly different (to the α = 0.05 confidence level) between different *Quercus agrifolia* individuals. Deposition velocities for NO₂ light experiments were between 0.08 and 0.18 cm s⁻¹, with a deposition of 0.123 ± 0.009 cm s⁻¹ calculated from the regression of all light experiments. Dark experiments resulted in deposition velocities between 0.013 and 0.022 cm s⁻¹, with a deposition velocity of 0.015 ± 0.001 cm s⁻¹ calculated from the regression of all dark experiments (Table 1). NO demonstrated much slower deposition, with deposition velocities from all light and dark experiments calculated as 0.012 ± 0.002 and 0.005 ± 0.002 cm s⁻¹,

respectively (Table 2). Despite the large compensation point measured for NO, the leaf emission fluxes of NO were a maximum of only 8 pmol m⁻² s⁻¹ at 0.1 ppb NO, approximately half of the deposition flux measured for NO₂ at 0.1 ppb (Fig. 4). At typical NO₂/NO ratios and gradients measured in forest canopies, the leaf-level NO₂ and NO exchange fluxes measured make dry stomatal deposition to *Quercus agrifolia* a net sink of NOₓ within the canopy.

### 3.3 Resistances to leaf-level NOₓ deposition

The deposition velocity measured from linear regression of NOₓ exchange fluxes and NOₓ chamber concentrations is the

<footnote>* Significant nonzero compensation point.</footnote>
<table><caption>**Table 2.** Parameters of NO bivariate linear least-square fitting regression analysis.</caption><thead><tr><th>Run</th><th><i>N</i></th><th><i>R</i><sup>2</sup></th><th>[NO<sub>2</sub>]<sub>comp</sub><br>(ppb)</th><th>P([NO<sub>2</sub>]<sub>comp</sub> = 0)</th><th><i>V</i><sub>dep</sub></th></tr></thead><tbody><tr><td colspan="6"><i>Q. agrifolia</i> 1</td></tr><tr><td>Light</td><td>17</td><td>0.874</td><td>0.74 ± 0.65</td><td>3.5*</td><td>0.011 ± 0.0032</td></tr><tr><td>Dark</td><td>13</td><td>0.699</td><td>3.8 ± 2.2</td><td>0.52*</td><td>0.0040 ± 0.0025</td></tr><tr><td colspan="6"><i>Q. agrifolia</i> 1</td></tr><tr><td>Light</td><td>14</td><td>0.954</td><td>0.76 ± 0.49</td><td>0.92*</td><td>0.013 ± 0.0027</td></tr><tr><td>Dark</td><td>10</td><td>0.866</td><td>1.7 ± 1.0</td><td>1.1*</td><td>0.0046 ± 0.0018</td></tr><tr><td colspan="6"><i>Q. agrifolia</i> 1</td></tr><tr><td>Light</td><td>12</td><td>0.936</td><td>1.3 ± 0.60</td><td>0.17*</td><td>0.0123 ± 0.0029</td></tr><tr><td>Dark</td><td>15</td><td>0.803</td><td>2.0 ± 1.0</td><td>2.5*</td><td>0.0074 ± 0.0033</td></tr><tr><td colspan="6">All <i>Q. agrifolia</i></td></tr><tr><td>Light</td><td>13</td><td>0.908</td><td>0.84 ± 0.32</td><td>&lt; 0.01*</td><td>0.012 ± 0.0015</td></tr><tr><td>Dark</td><td>13</td><td>0.602</td><td>2.4 ± 1.1</td><td>&lt; 0.01*</td><td>0.0050 ± 0.0016</td></tr></tbody></table>

inverse of the total resistance to deposition (Eq. 6), with $R_a$ assumed to be zero. The total resistance in the chamber is thus

$$R = R_b + \left( \frac{1}{R_{\text{cut}}} + \frac{1}{R_s^*} \right)^{-1}, \qquad (10)$$

where $R_s^*$ is the sum of $R_m$ and $R_{st}$. The leaf resistance to deposition can then be found by subtracting the boundary layer resistance from the total resistance. Total leaf resistances, $R_{leaf}$, were calculated using the boundary layer resistances for NO₂ and NO of 1.94±0.02 and 2.59±0.03 s cm⁻¹, respectively. During the dark experiments, $R_{leaf}$ is equal to $R_{cut}$, and the deposition velocity measured was estimated as the inverse of the sum of the boundary and cuticular resistances. After calculation of $R_{cut}$ from dark experiments, the sum of the stomatal and mesophilic contributions ($R_s^*$) to the total leaf resistance was determined. $R_b$, $R_{cut}$, and $R_s^*$ are shown in Table 3. It should be noted that since the reported $R_b$ is the maximum possible boundary resistance, the reported $R_{cut}$ and $R_s^*$ are lower limits. If we were to assume the chamber boundary resistance with the branch enclosed is insignificant (~0 s cm⁻¹), this would introduce maximum systematic 30% and 3% errors to the calculated NO₂, $R_s^*$ and $R_{cut}$, respectively (giving an $R_s^*$ of 9.2 ± 0.9 s cm⁻¹ and an $R_{cut}$ of 67 ± 8 s cm⁻¹). The errors in the calculated NO resistances would be negligible.

It is possible that the stomata were not entirely closed during dark experiments. Evidence exists that nocturnal stomatal conductance can be large enough to allow for transpiration (Dawson et al., 2007), and low (within the range of uncertainty observed for the LICOR-6262) emission of water vapor during dark experiments was measured. However, even if

<table><caption>**Table 3.** Summary of deposition resistance parameters of *Quercus agrifolia*.</caption><thead><tr><th></th><th>Gas</th><th><i>R</i><sub>b</sub><br>(s cm<sup>-1</sup>)</th><th><i>R</i><sub>cut</sub><br>(s cm<sup>-1</sup>)</th><th><i>R</i><sub>s</sub><sup>*</sup><br>(s cm<sup>-1</sup>)</th></tr></thead><tbody><tr><td>NO<sub>2</sub></td><td>1.94 ± 0.02</td><td>65 ± 8</td><td>6.9 ± 0.9</td></tr><tr><td>NO</td><td>2.59 ± 0.03</td><td>200 ± 60</td><td>140 ± 40</td></tr></tbody></table>

all the deposition during dark experiments was stomatal, this would cause only a 0.5 s cm⁻¹ reduction in the calculated $R_s^*$ for NO₂ – less than the uncertainty from the error in the measured deposition velocity (~10% error). The cuticular resistances reported here during dark experiments are nonetheless atmospherically relevant to nighttime NOₓ deposition.

# 4 Discussion

## 4.1 NOₓ deposition velocities and compensation points

The strong linear dependence between NO₂ fluxes and NO₂ chamber concentrations that we observe is consistent with previous observations that NO₂ exchange is largely driven by NO₂ concentration differences between the atmosphere and gaseous phase of the leaf (Rondon and Granat, 1994; Gessler et al., 2000; Hereid and Monson, 2001; Sparks et al., 2001; Teklemariam and Sparks, 2006; Pape et al., 2009; Chaparro-Suarez et al., 2011; Breuninger et al., 2012). Our measurements of NO₂ stomatal resistance parameters for *Quercus agrifolia* represent a stomatal deposition velocity (1/R_s^*) of 0.14 ± 0.02 cm s⁻¹. This value is similar to the range of 0.1–

<footnote>* Significant nonzero compensation point.</footnote>
<figure><img src="image_9.png" /></figure>

**Figure 5.** Model predictions of the fraction of NOₓ loss to alkyl nitrate formation, nitric acid formation, and deposition in a *Q. agrifolia* woodland. The model was run using scenarios with only soil emissions and LAI of 1 m² m⁻² (**a**), only soil emissions and LAI of 3 m² m⁻² (**b**), CNOₓ(adv) = 10 ppb and LAI of 1 m² m⁻² (**c**), and CNOₓ(adv) = 10 ppb and LAI of 3 m² m⁻² (**d**).

0.15 cm s⁻¹ that Chapparo-Suarez et al. (2011) found for two European oak tree species, *Quercus robur* and *Quercus ilex*. The deposition velocity measured here for *Quercus agrifolia* is also much larger than 0.007–0.042 cm s⁻¹ range found for Norway spruce (*Picea abies*) by Breuninger et al. (2012), but surprisingly comparable, given the differences in plant species, to the 0.12 cm s⁻¹ deposition velocity found for maize (*Zea mays*) by Hereid and Monson (2001). We also find here an NO₂ flux at 5 ppb of 0.2 nmol m⁻¹ s⁻¹, similar in magnitude to the 0.1, 0.15–1.5, and 0.18 nmol m⁻¹ s⁻¹ fluxes measured for *Fagus sylvatica* (Gessler et al., 2000), tropical Panamanian native trees (Sparks et al., 2001), and periwinkle (*Catharanthus roseus*) (Teklemariam and Sparks, 2006), respectively.

Resistance parameters reported above for NO deposition to *Quercus agrifolia* represent a stomatal deposition velocity of 0.007 ± 0.002 cm s⁻¹ and cuticular deposition velocity of 0.005 ± 0.001 cm s⁻¹. This observation of very minor NO uptake – at least an order of magnitude less than that of NO₂ uptake – is also consistent with previous observations (Hanson and Lindberg, 1991; Hereid and Monson, 2001; Teklemariam and Sparks, 2006). We also detected a statistically significant NO compensation point, with low emissions up to 8 pmol m⁻² s⁻¹ observed below 1 ppb. These observations are similar to the 8–14 pmol m⁻² s⁻¹ emission fluxes of NO reported by Hereid and Monson (2001) and Teklemariam and Sparks (2006) at low NOₓ concentrations.

<figure><img src="image_10.png" /></figure>

**Figure 6.** 24 h average vertical fluxes of NOₓ predicted by the 1-D multibox model for a California oak woodland using the leaf resistances measured in this study. Model runs were conducted for low (red) and high (blue) LAI cases and for a no-deposition scenario (green).

No significant NO₂ compensation point was found for our measurements of *Quercus agrifolia* NOₓ uptake. Many previous studies have reported NO₂ compensation points, ranging from 0.1 to 3.0 ppb, implicating trees as a constant source of NOₓ in forest ecosystems (Gessler et al., 2000; Hereid and Monson, 2001; Sparks et al., 2001; Teklemariam and Sparks, 2006). Our findings of a lack of NO₂ compensation point support field observations and modeling studies that have recognized NO₂ dry deposition to vegetation as an important NOₓ loss process in forests (Jacob and Wofsy, 1990; Ganzeveld et al., 2002b; Geddes and Murphy, 2014). Our results also support the works of Chaparro-Suarez et al. (2011) and Breuninger et al. (2013), who did not find evidence of an NO₂ compensation point.

The primary difference in our experimental setup, compared to previous dynamic chamber studies that have found an NO₂ compensation point, is the use of a direct NO₂ measurement technique. Measurements of a significant NO₂ compensation point have mostly been obtained using techniques requiring conversion of NO₂, followed by chemiluminescence detection of NO (Gessler et al., 2000; Hereid and Monson, 2001; Sparks et al., 2001; Teklemariam and Sparks, 2006). Such methods have utilized either nonspecific photolytic (Gessler et al., 2000; Hereid and Monson, 2001), luminol (Sparks et al., 2001), or catalytic conversion (Teklemariam and Sparks, 2006) techniques, which may have also resulted in the conversion of PAN, HONO, HNO₃, and other organic nitrates, as well as interferences from alkene + ozone reactions (Carter et al., 2005; Reed et al., 2016). If any of these interfering compounds are not excluded from the cham-
ber system, outgas from the chamber itself, or form from re-
actions of biogenic emissions, this would cause an enhance-
ment in the observed NO₂ compensation point, and a sup-
pression of observed deposition velocity. Our measurements
of NO₂ mixing ratios also demonstrate a much higher degree
of precision, due largely to a lower detection limit, than com-
parable experiments with specific photolytic conversion and
chemiluminescence measurement of NO₂ (Chaparro-Suarez
et al., 2011; Breuninger et al., 2012, 2013). Additionally,
previous chamber measurements have sometimes employed
chamber setups that would let in a substantial amount of UV
light, yet did not exclude photochemical reactions between
NO₂, NO, and O₃. Such corrections are excluded here be-
cause of our use of chamber lights with only wavelengths
above 420 nm. To avoid this issue, other experiments have in-
stead involved a setup including a simultaneously measured
blank chamber, which would theoretically allow for correc-
tion for any reactions resulting from photolysis of NO₂, O₂,
or O₃ (Gessler et al., 2000; Hereid and Monson, 2001). Such
corrections might be complicated by secondary chemistry
not present in our experiments.

## 4.2 Implication for canopy NOₓ loss

Resistance parameters reported above (Table 3) were used
in a 1-D seven-layer multibox model representing chemical
reactions, vertical transport, and leaf-level processes scaled
to the canopy level to assess the impacts of NOₓ deposition
velocities on the NOₓ lifetime and fluxes. The model is con-
structed in a manner similar to Wolfe and Thornton (2011)
with the following modifications: the model domain consists
of seven well-mixed vertical layers extending to a planetary
boundary layer height of 1000 m, with the forest canopy rep-
resented by the first three layers; NOₓ cuticular and stomatal
resistances are adjustable input parameters; and the chem-
istry implemented in the model is the simplified reaction
mechanism presented in Brown and Cohen (2012). The 1-
D model was run for meteorological conditions representing
the native habitat of Quercus agrifolia and two different leaf
area indices (LAIs), approximately representing the lower
and upper limits of LAIs found in California oak woodlands.
As shown in Fig. 5a and b, the model predicts NOₓ deposi-
tion to Q. agrifolia accounts for 3%-7% of the total NOₓ
loss within the boundary layer if the only source of NOₓ is
emissions from the soil. This represents a total NOₓ lifetime
of 7-7.5 h in the boundary layer, and a lifetime to deposi-
tion of 4-11 days in the boundary layer and 0.5-1.2 h below
the canopy. Under these scenarios approximately 15-30% of
soil-emitted NOₓ is removed in the canopy (Fig. 6) – on the
lower end of the range of 25%-80% reduction observed in
field studies (Jacob and Wofsy, 1990; Lerdau et al., 2000;
Ganzeveld et al., 2002a; Min et al., 2014).

The coastal regions of California where *Q. agrifolia* is
found frequently experience much higher NOₓ mixing ratios
of 10–50 ppb. This is particularly important for oak wood-

lands of the San Francisco Bay and Los Angeles areas, where
anthropogenic emissions from nearby urban centers are the
majority of the NOₓ source. To account for this extra NOₓ
source, additional model runs were performed with an added
term accounting for NOₓ advection from a more concen-
trated upwind source (CNOₓ(adv)), with advection treated as a
simple mixing process:

$$
\left( \frac{dC_{\text{NO}_x}}{dt} \right) = -k_{\text{mix}} (C_{\text{NO}_x} - C_{\text{NO}_x \text{(adv)}}), \quad (11)
$$

where $k_{\text{mix}} = 0.3 \text{ h}^{-1}$ and $C_{\text{NO}_x \text{(adv)}}$ is 10 ppb.

In this case, deposition to *Q. agrifolia* could account for
10–22 % of the total NOₓ loss in the boundary layer (Fig. 5c,
d), representing a lifetime to deposition of 5–14 days in the
boundary layer and a total NOₓ lifetime of 28–33 h. Depo-
sition in this higher NOₓ scenario decreased the total NOₓ
lifetime by 3–8 h, compared with a no-deposition case.

5 Conclusions

This work constitutes the first measurements of NO₂ and NO
foliar deposition resistance parameters for a North Amer-
ican tree species. We report observations of leaf-level re-
sistances to NO₂ and NO deposition, corresponding to to-
tal deposition velocities of NO₂ and NO of 0.123 ± 0.007
and 0.012 ± 0.002 cm s⁻¹ in the light and 0.015 ± 0.001 and
0.005 ± 0.002 cm s⁻¹ in the dark, respectively. No compen-
sation point was observed for NO₂, but compensation points
of 0.74–3.8 ppb were recorded for NO. The magnitude of
NO emission below the compensation point was significantly
less than the magnitude of NO₂ uptake in the same concen-
tration range, making *Q. agrifolia* an overall large net sink
of NOₓ. The observed deposition is large enough to explain
canopy reduction factors observed in canopy-level studies,
but is at the lower end of estimated global CRFs. The re-
sults of the 1-D multibox model demonstrate that the depo-
sition observed accounts for 5%–20% of NOₓ removal with
a NOₓ lifetime to deposition of 0.5–1.2 h beneath the canopy
of a California oak woodland. We show that foliar deposition
of NOₓ represents a significant removal mechanism of NOₓ
and can have a large impact on NOₓ mixing ratios and fluxes
in such ecosystems. Further investigations of NO₂ deposition
to a larger variety of plant species under a range of environ-
mental conditions are needed to accurately understand the
global impacts of NO₂ deposition across diverse ecosystems.

*Data availability.* The data collected in this study can be obtained from the authors upon request. The details and implications of the 1-D multi-box model presented in this publication are being prepared for discussion in an additional paper. Questions relating to this model can be addressed to the authors.
*Author contributions.* ERD and JC carried out the experiments. ERD performed the data analysis, constructed the 1-D multi-box model, and prepared all figures. MV conceived of the original project idea and constructed the dynamic chamber and laser-induced fluorescence instrument. Modifications to the initial setup were made by ERD. ERD wrote the paper in consultation with RCC. RCC supervised the project.

*Competing interests.* The authors declare that they have no conflict of interest.

*Acknowledgements.* We would like to thank the two reviewers for their constructive comments. We also wish to gratefully acknowledge financial support from the National Science Foundation (NSF, AGS-1352972). Additional support was provided by an NSF Graduate Research Fellowship to Erin R. Delaria.

Edited by: Alex B. Guenther

Reviewed by: Laurens Ganzeveld and one anonymous referee

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