Terpenoid emissions above urban areas are a complex mix of
biogenic and anthropogenic emission sources. In line with previous studies
we found that summertime terpenoid fluxes in an alpine city were dominated
by biogenic sources. Inter-seasonal emission measurements revealed
consistency for monoterpenes and sesquiterpenes but a large difference in
isoprene between the summers of 2015 and 2018. Standardized emission potentials
for monoterpenes and sesquiterpenes were 0.12 nmol m-2 s-1 and 3.0×10-3 nmol m-2 s-1 in 2015 and 0.11 nmol m-2 s-1
and 3.4×10-3 nmol m-2 s-1 in 2018, respectively. Observed
isoprene fluxes were almost 3 times higher in 2018 than in 2015. This
factor decreased to 2.3 after standardizing isoprene fluxes to 30 ∘C air temperature and photosynthetic active radiation (PAR) to 1000 µmol m-2 s-1. Based on emission model parameterizations, increased
leaf temperatures can explain some of these differences, but standardized
isoprene emission potentials remained higher in 2018 when a heat wave
persisted. These data suggest a higher variability of interannual isoprene
fluxes than for other terpenes. Potential reasons for the observed
differences such as emission parameterization, footprint changes, water
stress conditions, and tree trimming are investigated.
Introduction
Biogenic and anthropogenic volatile organic compounds (BVOCs, AVOCs) in the
atmosphere can contribute to surface air pollution due to both their
influence on tropospheric ozone formation and their potential to act
as precursors for secondary organic aerosol (Derwent et al., 1996;
Fehsenfeld et al., 1992; Fuentes et al., 2000; Goldstein et al., 2009;
Laothawornkitkul et al., 2009; Riipinen et al., 2012). BVOCs play a
particularly important role globally, as their emission strength is
estimated to be 10 times larger than AVOCs (Guenther et al., 2012; Piccot et
al., 1992). Also, many BVOCs are characterized as highly reactive (Atkinson
and Arey, 2003; Fuentes et al., 2000), resulting in rapid peroxy radical
chemistry important for ozone and ultrafine particle formation processes
(Simon et al., 2020). Of the total global BVOC emissions, terpenes dominate,
with 50 % attributed to isoprene, 15 % to monoterpenes, and about 0.5 %
to sesquiterpenes (Guenther et al., 2012). In predominantly
isoprene-emitting forests isoprene was found to be responsible for 50 %–100 % of
the tropospheric ozone production (Duene et al., 2002; Tsigaridis and
Kanakidou, 2002; Poisson et al., 2001). In coniferous forests monoterpene
and sesquiterpene emissions often dominate (Johansson and Janson, 1993;
Thunis and Cuvelier, 2000; Juráň et al., 2017). It has been shown
that RO2 self-reactions of monoterpenes and sesquiterpenes can rapidly
create highly oxidized matter (HOM) and are a key player for new particle
formation (NPF) events in forests under low NOx conditions (Simon et al.,
2020).
In urban environments where the mixture of BVOCs and AVOCs is more complex,
several recent studies point out the importance of biogenic emissions for
local air quality (Simon et al., 2019, Bonn et al., 2018; Churkina et al.,
2017; Ren et al., 2017; Papiez et al., 2009; Chameides et al., 1988) and
that the BVOC influence is especially high during summertime heat waves
(Churkina et al., 2017).
Particularly in summer, biogenic sources dominate in urban
environments. For example, Yadav et al. (2019) found an increased importance of
biogenic isoprene in an urban site in western India during the pre-monsoon
season when temperatures and PAR were high, and Hellen et al. (2012) found a
strong biogenic influence on isoprene and monoterpene concentrations in
Helsinki in July. Summertime isoprene in two large Greek cities was
determined by positive matrix factorization (PMF) to mainly (60 %–70 %) originate from vegetation
(Kaltsonoudis et al., 2016). Yang et al. (2005) showed a strong seasonal and
daily cycle in isoprene, therefore attributing it to biogenic sources in an
urban region in Taiwan. Borbon et al. (2002) showed that biogenic sources
strongly superimpose the traffic emissions of isoprene in summer in an urban
area in France. Wagner and Kuttler (2014) found that during summer
afternoons in an urban area in Germany anthropogenic influences on isoprene
concentrations were negligible. Chang et al. (2014) and Wang et al. (2013)
showed that in a tropical–subtropical metropolis biogenic contributions
overwhelmed anthropogenic contributions of isoprene in summer and that
biogenic sources started to dominate in all seasons above a threshold
temperature of 17–21 ∘C. Whereas all the studies cited above were
based on concentration measurements for which the influence can be both local
and regional as well as strongly modulated by atmospheric dilution, the following
studies were based on eddy covariance flux tower sites. At temperatures over
25 ∘C more than 50 % of the isoprene flux was found to be
biogenic in origin in London with a mean daytime flux of
0.18 mg m-2 h-1 (Langford et al., 2010). Similarly, Valach et al. (2015) in a
different study in London found a mean daytime flux of 0.2 mg m-2 h-1. Kota et al. (2014) found a daytime median flux of 2.1 mg m-2 h-1 over Houston, Texas, and attributed it to mostly biogenic sources.
Park et al. (2010) also found in Houston, Texas, a daytime isoprene flux of
0.7 mg m-2 h-1. Rantala et al. (2016) found that 80 % of the
measured 10 ng m-2 s-1 summer daytime isoprene flux near Helsinki
could be attributed to biogenic sources by comparing emissions at low and
high temperatures.
While there is evidence for urban trees having a positive influence on urban
environments such as by mitigating the urban heat island effect, sequestering
CO2 and particles, and intercepting storm water (Escobedo et
al., 2011; Connop et al., 2016; Livesley et al., 2016), BVOC emissions of
urban trees and their subsequent effect on air pollution are very
plant-species-dependent (Corchnoy et al., 1992; Steinbrecher et al., 2009;
Fitzky et al., 2019) and should be taken into account when planting urban
trees (Calfapietra et al., 2013; Churkina et al., 2015; Ren et al., 2017).
Emerging evidence that isoprene-derived RO2 competes with RO2
radicals from higher-molecular-weight terpenes in the formation of new
particles highlights the need to study emissions in different environments
(Berndt et al., 2018).
Few studies characterize the interannual changes in BVOCs and even fewer
such studies are available in urban environments. Vaughan et al. (2017)
report airborne flux measurements over southern Sussex for two consecutive
summers, showing different isoprene fluxes that can be explained by different
temperature and cloud cover conditions. Warneke et al. (2010) tried to
explain the measured interannual differences of a factor of 2 in fluxes of
isoprene and monoterpene over Texas by temperature, drought effects, or
influences from changes in leaf area index (LAI). Palmer et al. (2006) found
a maximum of 20 %–30 % interannual difference in isoprene emissions using
satellite-based isoprene quantification from formaldehyde measurements over
North America. A model study by Steinbrecher et al. (2009) found only a
10 % annual difference in biogenic emissions from cold to hot years.
Gulden et al. (2007) found that, on a regional scale, variations in leaf
biomass density driven by variations in precipitation are, together with
temperature and shortwave radiation variations, the most important factors
for variations in BVOC emissions. Tawfik et al. (2012) found in a model
study that interannual variation of isoprene emission is strongest in July
with temperature and soil moisture explaining 80 % of the variations,
whereas the influences of variations in photosynthetic active radiation
(PAR) and LAI were negligible. In a 3-year study over a northern
hardwood forest, Pressley et al. (2005) found that total cumulative isoprene
fluxes varied only by 10 %.
Given the current lack of multiyear urban VOC flux measurements and our
limited understanding of the interannual variability of biogenic and
anthropogenic emission sources, the objective of the present study was to
quantify the interannual variation of the urban ecosystem–atmosphere
exchange of the three major isoprenoids, isoprene, monoterpenes, and
sesquiterpenes, as well as to analyze the underlying drivers. We hypothesized (i)
that the exchange of these BVOCs can be largely attributed to the
spatiotemporal variability of biogenic sources and (ii) that differences in
environmental forcings are the main drivers of interannual variability. To
address these hypotheses, urban eddy covariance BVOC flux measurements
during two growing seasons above the city of Innsbruck (Austria) are blended
with bottom-up emission estimates based on a process-based model and a
detailed urban tree inventory.
Materials and methodsField site and instruments
VOC concentrations and flux measurements were conducted during two
comparable summer periods (10 July–9 September 2015 and 27 July–2 September 2018) close to the city center of Innsbruck on the rooftop of one of the
tallest buildings in the area. The data record in 2018 is continuous, and in
2015 the data record has a gap between 31 July and 3 August. Details on the
Innsbruck Atmospheric Observatory (IAO) measurement site and instrument
performance were published by Karl et al. (2018) and Striednig et al. (2020). Therefore, we give only a short summary of the study location
and measurement details here. The measurement location (47∘15′51.66′′ N, 11∘23′06.82′′ E) is shown in Fig. 1a on a 2000×2000 m map surrounding the site. The
dominant wind direction at the IAO is from the NE during the daytime and
from the SW during nighttime (Karl et al., 2020; Striednig et al., 2020).
Within 500 m from IAO, the mean building height is 17.3 m, whereas the model
building height of about 19 m corresponds to the five- to seven-story buildings, which
are more important in terms of their form drag. For this reason, the
displacement height, zd, is estimated as 13.3 m (0.7 m × 19 m). The
roughness length, z0, is 1.6 m.
(a) Map surrounding the Innsbruck Atmospheric Observatory
(indicated with a red cross in the center) depicting trees, short
vegetation, water, roads, paved areas, and buildings in dark green, light
green, blue, white, light grey, and dark grey, respectively. Black dots
represent individual trees from the city tree inventory. The study area is
indicated with the red rectangle. The 2015 footprint density lines from
30 %–90 % are plotted as blue lines. (b) Same map as (a) with 2018
footprint density lines in black. (c) Diurnal cycle of average and standard
error of PAR in 2015 (blue) and 2018 (black). (d) Diurnal cycle of average
and standard error of ambient temperature in 2015 (blue) and 2018 (black). Maps
were created in MATLAB (https://www.mathworks.com/, last access: 1 August 2021) and are based on OpenStreetMap
(https://www.openstreetmap.org/copyright, last access: 1 August 2021) under the CC BY 3.0
AT license.
3D sonic wind, CO2, and H2O were measured with a CPEC200 (Campbell
Scientific) eddy covariance system at a sampling frequency of 10 Hz on a
tower on top of the building 42 m above street level. In 2015 the tower was
at a provisional location at the north of the building; the heading
direction of the sonic anemometer was 76∘. Flow distortions for
westerly winds due to the building and the support structure cannot be
excluded. In the course of the establishment of the IAO lab the CPEC200 the
inlets were moved ∼50 m to the southern edge of the building
with an anemometer heading of 129∘ and minimal flow disturbances.
For comparability isoprenoid fluxes in this study are limited to the
northeastern sector of [0∘, 120∘] in both years.
A heated inlet line led from the tower to a nearby laboratory hosting a
PTR-QiTOF-MS instrument (IONICON Analytik, Sulzer et al., 2014), which allows
for the acquisition of full, high-resolution, mass spectral information at 10 Hz. Residence time of air samples in the turbulently purged Teflon inlet
line (Teflon PFA, 1/4” ID x 12.7 m heated at 30 ∘C)
is about 0.4 s to keep wall loss and chemical transformation of
isoprenoids negligible. Both summers the PTR-QiTOF-MS was operated in
H3O+ mode with standard drift tube conditions of 112 Townsend (E/N
electric field strength). Regular instrument calibrations and zeroing
revealed typical acetone and isoprene sensitivities of 1550 and 950 Hz ppbv-1, respectively.
Incident PAR was calculated from shortwave radiation measured by a
pyranometer (Schenk 8101, Schenk, Wien) by applying the relationship derived by
Jacovides et al. (2003) (PAR / shortwave radiation ∼0.46
during summer daytime conditions).
Precipitation data were collected 400 m south of our field site by a tipping
bucket precipitation gauge (MPS TRWS 503) and a precipitation monitor (Thies
5.4103.10.000), mounted at 1.5 m above a grass surface, both operated by
Zentralanstalt für Meteorologie und Geodynamik (ZAMG, Austrian
Met-Service) at the station Innsbruck Universität (WMO SYNOP number
11320).
Due to the lack of directly measured city-scale soil moisture data, plant
available soil moisture for 2015–2019 was retrieved as the SMAP level 4
3-hourly 9 km root zone soil moisture product (Reichle et al., 2018) via the
AppEEARS interface (https://lpdaacsvc.cr.usgs.gov/appeears/, last access: 1 August 2021).
Due to the large spatial footprint of this product, the corresponding data
will only be used to interpret interannual differences in precipitation on
soil moisture.
Eddy covariance fluxes
This study focuses on biogenic fluxes collected during summer 2015 and
summer 2018. The presented eddy covariance flux measurements are used to
constrain BVOC flux parameterizations. Biogenic emissions, in particular
isoprene, are strongly light- and temperature-driven. As a consequence we
selected daytime flux data. During daytime the flux footprint density points
towards the east sector imposed by the local valley wind system. In order to
test BVOC emission parameterizations we therefore selected daytime hours (06:00–18:00 local time) and mean wind directions from 0–120∘. Data with wind direction from the south and exceeding a
wind speed >10 m s-1 were excluded as they can be attributed to
foehn events, for which we believe current footprint density calculations
bear too much uncertainty in an urban setting. Eddy covariance fluxes were
calculated using a MATLAB® code described by
Striednig et al. (2020). Figure S1 shows the co-spectral response of the
PTR-QiTOF-MS and inlet system. The loss of covariance of isoprenoid signals
with vertical wind speed due to low-pass filtering is less than 4 % (see
spectral analysis in the Supplement).
As QA / QC criteria for fluxes
we implemented a combination of steady-state filter of the respective
scalar, the integral turbulence characteristics test of the wind components,
and flow sector filtering, similar to the combination described in Chapter
4.2.5. in Foken (2008) with a required overall quality class of 6 or lower.
According to Foken (2008), classes 1–6 can be used for long-term
measurements of fluxes without limitations. Implementing these QA / QC
criteria reduced the available flux data by 29 % and 11 % in 2015 and
2018, respectively.
The footprint density representing the relative contributions of an air mass
sample arriving at the flux tower was calculated following Kljun et al. (2015).
Regarding constraints on the lifetime of reactive terpenes, turbulent timescales (100 s) can be of the order of chemical timescales of some monoterpenes, which can
react fast with ozone. We calculate the chemical loss by the following
equation: c(t)/c0=exp(-tturb/tchem), where tturb is the
turbulent timescale and tchem the chemical timescale. The turbulent
timescale was obtained from the ratio of the measurement height (H) over
the friction velocity (H/u*). For typical turbulent timescales of 100 s,
reaction with OH can be neglected.
Further, our analysis of emissions is primarily focused on the
interpretation of daytime fluxes, when NO3 radical chemistry plays a minor
role compared to ozone. Ozone follows the expected diurnal cycle for an
urban area (30–50 ppbv mixing ratios). Since we do not have speciated
terpene fluxes, we performed a sensitivity study (e.g., estimating realistic
bounds) assuming a fraction of the total sesquiterpene (or monoterpene) flux
was composed of the most reactive compound (rSQT and rMT). For
sesquiterpenes, for example, we can take the estimated rate constant for
ozone and beta-caryophyllene: 1.2×10-14 cm3 molec.-1. A typical
compositional mix of sesquiterpenes was reported by Sakulyanontvittaya et
al. (2008), who assessed reactive terpene fractions between 36 % and 50 %.
Typical reaction rates of less reactive sesquiterpenes (nrSQT) (e.g.,
cedrene, longifolene: Atkinson et al., 1994) are on the order of 1 to 10×1×10-17 cm3 molec.-1. Taking these boundary conditions gives a realistic
range of the reacted fraction of measured SQT fluxes. Similarly, we can do
the analysis for monoterpenes, for which the fraction of reactive terpenes (rMT)
such as ocimene is typically lower (e.g., 10 %–15 % – Sakulyanontvittaya et
al., 2008). For comparison, trans-beta-ocimene, one of the most reactive
monoterpenes known to be emitted from plants, has a reaction rate constant
of 2.6×10-14 cm3 molec.-1. Figures S2 and S3 in the Supplement
show the non-reacted flux for total sesquiterpenes due to reaction with
ozone assuming a 36 to 64 and a 50 to 50 mix (rSQT to nrSQT). With these
scenarios daytime reductions of total sesquiterpene fluxes due to chemistry
would be on the order of 30 %–45 %. For monoterpene fluxes we calculate
losses on the order of 12 % (Fig. S4).
Emission standardization of fluxes
In terms of a big leaf model for standardization of surface fluxes, we standardized
isoprene eddy covariance fluxes, E0,ISO, to a temperature of 303.15 K
and PAR of 1000 µmol m-2 s-1 using a model described in
detail by Guenther et al. (2006): EISO=E0,ISO⋅γT⋅γP, where γT and γP are temperature- and
light-dependent coefficients respectively containing current and past (24
and 240 h) conditions. Monoterpene and sesquiterpene emissions are often
dominated by temperature. Originally the temperature dependence was
described as EMT=E0,MT⋅CT,MT and ESQT=E0,SQT⋅CT,SQT, where CT is a temperature-dependent factor (e.g., Guenther et
al., 1994). Some monoterpene and sesquiterpene emissions have also been
reported to be produced de novo and can therefore show a light-dependent
emission behavior (e.g., Staudt and Seufer, 1995). The light-dependent
portion is included in updated emission algorithms (e.g., Guenther et al.,
2012, Eqs. 3–6), whereby the light-dependent portion is modeled in analogy
to isoprene, and the light-independent fraction is incorporated according to
Guenther et al. (1994). The light-dependent fraction for monoterpenes varies
between 0.2 and 0.8, and for sesquiterpenes it is currently assumed to be
0.5. The temperature and light parameterization was calculated using
Eqs. (3)–(11) from Guenther et al. (2012), who prescribed a 50 % light-dependent fraction for SQT emissions. For monoterpenes we take the average light-dependent fraction from Guenther et al. (2012) (i.e., 50 %), since we
do not have speciated MT fluxes.
In order to investigate the sensitivity of isoprene
emissions to the emission model framework we also set up a five-layer canopy
model according to Guenther et al. (2006), which is the MEGAN five-layer model. The setup was used to conduct a
sensitivity experiment to study potential inter-seasonal changes in isoprene
emissions between 2015 and 2018 based on different model formulations. For
the sensitivity run the model was constrained by measured radiative fluxes as well as
sensible and latent heat fluxes. We prescribed an LAI of 1 to account for
sparse vegetation and mimic a sun-leaf-dominated scenario, with a mean sunlit
fraction of 64 % (40 %–95 %).
Direct LAI measurements are not available for this study. Both campaigns
were conducted in a similar time frame within the year, which should lead to
comparable leaf age. No early senescence in either year was reported by the
city gardeners.
Bottom-up emission potentials
Regarding the city tree inventory, an inventory of all trees planted by the city
municipality is available for the city of Innsbruck, Austria, containing
location, tree species, diameter at breast height, and height. However, this
inventory does not include trees from private gardens. Therefore, all
accessible trees from private gardens were identified and added to the
existing tree inventory in an area 1000×1000 m surrounding the observatory.
This will be referred to as the study area in the following. The locations
of the trees from the city inventory (41 %) and private gardens (59 %)
in the study area are shown in Fig. 1a. Within the study area a total of
1904 registered trees distributed across 129 tree species were counted, and
it is estimated that these cover >90 % of the available trees. A list of the 44 most abundant tree species, of which the species count in
the study area was 6 or more, is given in Table 1.
Literature values of the 44 most abundant tree species found in the
1 km2 area surrounding the measurement site (all values: mg g(dry weight) -1 h-1).
Reference subscripts refer to (a) Stewart et
al. (2003), (b) Kesselmeier and Staudt (1999), (c) Karl et al. (2009), (d) Noe et
al. (2008), (e) Nowak et al. (2002), (f) Wang et al. (2007), (g) Baghi et al. (2012), (h)
Li et al. (2009), and (i) Owen et al. (2003). The asterisk (*) indicates a standard value of 0.1, as no
literature value is available.
For emission potentials, literature values of plant-species-specific emission
potentials of isoprene and monoterpene (µg compound g-1
dry-weight h-1) standardized to 303.15 K and PAR of 1000 µmol m-2 s-1 were assigned to the 44 most abundant species in the study area.
This includes all tree species with an occurrence larger than six individuals
within the 60 % footprint density and accounts for ∼90 %
of the total counted trees. Emission potential assignment was based, if
available, on the detailed work by Stewart et al. (2003). Other emission
potentials were taken from other literature, and if more than one literature
value was available, an average was taken. All species, emission potentials,
and references thereof are shown in Table 1. Sesquiterpene (SQT) emission
potentials were taken from Karl et al. (2009) and if not reported therein
the average value of 0.1 µg compound g-1 dry-weight h-1 was
assigned.
Relative ISO, MT, and SQT emission ratio maps
To generate emission ratio maps, the study area was divided into a 100 m by
100 m grid, and tree species were counted in each grid tile and multiplied by
their emission potential listed in Table 1. The resulting map
(µg compound g-1 dry-weight h-1) neglects the actual, but
unknown, amount of dry leaf weight of each individual tree.
Due to the unknown amount of emitting leaf material, it is difficult to
compare bottom-up estimates from this method with direct eddy covariance
flux measurements. A more robust comparison is possible when relative
emission maps are investigated such as ISO / MT, ISO / SQT, and SQT / MT. For this
we first added up all individual tree emission factors in each tile (e.g.,
ISOtile=∑ISOtree) and then divided these by the tile
emission factors, e.g. ISOtile/ MTtile. For simplicity this is called ISO / MT in
the following. This is a bottom-up ISO / MT ratio expected at
the measurement site. The authors acknowledge that leaf age, phenology, and
LAI or individual trees affect this ratio, but these are unknown for the tree
inventory and are therefore a source of uncertainty of this estimate.
Doubling and halving the emission potential of the highest 20 emitters
resulted in average study area emission ratio changes on the order of
5 %–15 %, giving an estimate of the robustness of this analysis.
Diurnal cycles of average isoprene (a), monoterpene (b), and
sesquiterpene (c) fluxes for the summers of 2015 (blue) and 2018 (black);
shaded areas indicate the standard error. Nighttime fluxes are shown here
for completeness of the diurnal cycle, but grey shaded areas indicate that
these data were not used for further analysis. (d–f) Daytime (06:00–18:00)
isoprene, monoterpene, and sesquiterpene fluxes are plotted vs. theoretical
temperature and light dependencies (Guenther et al., 2006, 2012) including T24, T240, P24, and P240. The 2015 data are depicted in blue and
2018 data in black. The lines indicate a linear fit, with fit parameters
displayed within the plot. The slope of the fit parameter represents the
standardized (303.15 K and 1000 PAR) emission factors.
Results and discussionFlux footprint, light, and temperature conditions
The flux footprint density at the IAO is shown in Fig. 1a and b for 2015
and 2018, respectively. Flux footprint density lines from 30 %–90 % are
plotted on a map of 2000 m × 2000 m surrounding the flux tower location.
A total of 60 % of the flux footprint density lay, in both years, entirely within the
study area (1000 m × 1000 m). The relative contribution of the land cover
types within the study are was similar in both years with 40 %–41 %
buildings, 23 % paved areas, 25 %–28 % roads, 5 % trees, 5 % short
vegetation, and <1 % water. Within the 60 % flux footprint
density area in 2015 and 2018 lay 148 and 89 individual trees of the tree
inventory distributed over 33 and 24 tree species, respectively. Combining
the tree inventory with literature values on basal emission factors (Table 1) and the footprint density calculated for each tree location revealed that
60 % and 70 % of the bottom-up isoprene emissions arriving at the flux
tower were from 12 trees in 2015 and 2018, respectively. These were trees
closest to the footprint density maximum and trees with high isoprene basal
emission factors. The tree species were Populus nigra, Platanus acerifolia, Sophora japonica, and Quercus robur. As the 60 % footprint
density area was smaller in 2018 compared to 2015, the relative importance
of the emission of these trees was higher in 2018 than in 2015. Bottom-up
monoterpene emissions were distributed more evenly among different tree
species: 19 trees in the study area accounted for ∼50 % of
the bottom-up MT emissions arriving at the flux tower. The most important
species were Aesculus carnea, Pinus sylvestris, Larix decidua, and Acer platanoides. Sesquiterpene bottom-up emissions were even more
equally distributed over the tree species: 38 trees accounted for 50 % and
60 % of bottom-up SQT emissions arriving at the flux tower in 2015 and
2018, respectively. Betula pendula and Sophora japonica contributed 20 % and 12 % to the emissions
arriving at the tower in 2015 and 22 % and 19 % in 2018. Diurnal cycles
of PAR and air temperature, two of the strongest biogenic emission drivers,
are shown in Fig. 1c and d, respectively. While PAR was very similar
during the two summers, mean air temperatures in 2018 were 2 K higher during
daytime and 1.5 K higher during nighttime compared to 2015. The higher
temperatures in 2018 coincided with an intense heat wave. Monthly average
temperatures in August 2018 were 3 K above the climatological mean values
(1981–2010).
Two summers of urban isoprenoid fluxes
Karl et al. (2018) showed that isoprene and monoterpene at this measurement
site are linked to biogenic processes. Figure 2a–c show the average diurnal
cycles of isoprene, monoterpene, and sesquiterpene fluxes. Mean daytime
maxima of isoprene fluxes were 0.4 and 1.2 nmol m-2 s-1 in 2015 and 2018, respectively. The large interannual
difference and its potential reasons are discussed further Sect. 3.3.
Anthropogenic contributions to isoprene emissions from traffic were, on the
one hand, estimated using the COPERT emission model
(https://www.emisia.com/utilities/copert/, last access: 1 August 2021) and 1,3-butadiene as a proxy. We
use the ratio of 1,3-butadiene to isoprene from road tunnel studies (Reimann
et al., 2000) and multiply this by the modeled 1,3-butadiene to benzene
ratio. There is no significant modeled difference between warm and cold
seasons because unsaturated hydrocarbons and benzene primarily
originate from combustion-related emissions. Relative to benzene we
calculate that anthropogenic isoprene emissions contribute on the order of
5 % during daytime (Fig. S5 for the summer season). At night the
contribution can be larger (e.g., up to 20 %) as biogenic emissions
decrease more rapidly than benzene fluxes. On the other hand, we used the
measured wintertime isoprene to benzene flux ratio, which revealed a
conservative limit of 20 % due to anthropogenic origin. Overall isoprene
emissions are dominated by biogenic emissions at this site. This is in good
accordance with previous studies conducted in urban environments (Kota et
al., 2014; Park et al., 2010; Rantala et al. 2016).
Maximum average daytime monoterpene fluxes were 0.13
and 0.18 nmol m-2 s-1 for 2015 and 2018, respectively, and average
daytime sesquiterpene fluxes were 5×10-3 nmol m-2 s-1 in
both years.
The theoretical temperature and light parameters are plotted vs. the
observed fluxes in Fig. 2d–f based on the MEGAN big leaf approach
(Guenther et al., 2006, 2012.). The slope of the fit
parameters represents the standardized (303.15 K and 1000 PAR) emission
factors. The slopes in Fig. 2d–f can be interpreted as standardized
fluxes, removing the variability due to current and past temperature and
light conditions, and allow for interannual comparison as well as comparison
to other studies. Standardized isoprene fluxes were 0.26±0.02 nmol m-2 s-1 and 0.67±0.02 nmol m-2 s-1 in 2015 and
2018, respectively. The interannual difference is further discussed in
Sect. 3.3. Isoprene fluxes from both years were lower than what Rantala et
al. (2016) found for an urban flux site in Helsinki, where the standardized
emission potential was 125 ng m-2 s-1 (equal to 1.8 nmol m-2 s-1). The Helsinki flux site had a larger vegetation cover of 38 %–59 %
compared to our study area, where the vegetation cover was estimated to be
10 % within the flux footprint. Park et al. (2010) reported a standard
emission rate of isoprene of 0.53 mg m-2 h-1 (equal to 2.2 nmol m-2 s-1) over Houston, Texas, which is higher than both our 2018
and 2015 measurements. This is potentially due to a higher vegetation cover
in Houston as well as strong isoprene-emitting oaks within the footprint of
the measurement site. Valach et al. (2015) reported a daytime average flux
in August of 0.3 mg m-2 h-1 (equal to 1.2 nmol m-2 s-1)
at an urban site in London and Acton et al. (2020) a summer daytime average
isoprene flux of 4.6 nmol m-2 s-1 at an urban site in Beijing;
however, neither can be directly compared to our measurements as their values
were not standardized to temperature and PAR.
Average daytime standardized monoterpene fluxes were, at 0.04 and 0.05 nmol m-2 s-1 in 2015 and 2018, respectively, relatively similar
between the two summers. Average daytime standardized sesquiterpene fluxes
were over a magnitude smaller than standardized monoterpene fluxes and were
comparable between the two summers with midday values on the order of 3.0×10-3 and 3.5×10-3 nmol m-2 s-1 in 2015 and 2018, respectively. Both monoterpene and sesquiterpene
flux measurements could be underestimated due to loss with reaction to
ozone. The values given here could be underestimated by 10 % for
monoterpenes and 35 %–45 % for sesquiterpenes (see Sect. 2.2).
Monoterpene and sesquiterpene fluxes measured at lower temperatures
(280–295 K) were higher than the predicted values based on biogenic emission
parameterizations (data not shown). This could be an indication that at
lower temperatures other non-biogenic sources contributed to monoterpene
and sesquiterpene fluxes at this site. At temperatures higher than 295 K, MT
and SQT fluxes followed known temperature dependencies. To test this
hypothesis we considered footprint variations and relative distributions
between grasses and trees, which were minor. Variations in flux footprint
and a relative distribution with higher grassland MT emissions can be
excluded as an explanation for MT and SQT excursions. Instead we find that
the residual of non-explained MT and SQT fluxes correlates well with
aromatic fluxes. We find a significant positive correlation (R2∼0.75; RMSE: 0.006204) of the residual MT flux with the
benzene flux (Fig. S5). It suggests that emission of volatile chemical
products (VCPs) (e.g., Gkatzelis et al., 2021) is the most likely explanation
for MT and SQT flux enhancements that are not being reproduced by biogenic
emission parameterizations.
Isoprene flux differences between 2018 and 2015 binned by
temperature and PAR; positive differences are shown in red, negative in blue,
and bins with no available data are colored grey. Grey numbers in the
temperature and PAR fields indicate the number of observations for each
temperature and PAR value pair.
Isoprene flux anomaly
The isoprene flux difference measured between the two summers of 2015 and
2018 is shown in Fig. 2a. Daytime maximum isoprene fluxes in 2018 were up
to 2.7 times higher than in 2015. Isoprene fluxes are temperature-dependent
(Guenther et al., 1993) and light-dependent (Monson and Fall, 1989), and
past 24 and 240 h temperature and light conditions play a role (e.g.,
Guenther et al., 2006). These theoretical temperature and light parameters
are plotted vs. the observed isoprene flux in Fig. 2d based on the MEGAN
big leaf approach (Guenther et al., 2006). Even after including both actual
and past temperature and light parameters the difference in isoprene fluxes
between the two summers could not be resolved, and standardized emission
factors were still a factor 2.3 higher in 2018 than in 2015. Figure 3b shows
that the difference increased with higher temperature and higher PAR
values.
In contrast to monoterpene and sesquiterpene fluxes, which exhibited
comparable emission potentials between the two years and are mainly driven
by evaporative emissions from storage reservoirs (e.g., Kesselmeier and
Staudt, 1999), it remains a puzzle why the isoprene emission potential was
substantially higher in 2018 compared to 2015. As neither actual temperature
and light dependencies nor 24 and 240 h past temperature and light could
fully explain the observed differences in isoprene fluxes, we investigated
the following potential reasons: (a) variation in the flux footprint, (b) tree
trimming, (c) water availability and/or drought, and (d) emission parameterization.
Figure 1 (left and middle panel) shows differences in the flux footprint
densities between 2015 and 2018. Possible reasons for this are a change in
flux tower position between the two years by ∼50 m and different meteorological conditions. In 2015, for westerly winds the flow
regime may have been affected by the support structure and the building, and
consequently the analysis of isoprenoid fluxes was limited to the
northeastern wind sector of [0∘, 120∘]. Median daytime
wind speed and direction in 2018 (affected by a heat wave) are similar to
those in 2015 (Table S1). Median sonic temperature, sensible heat flux, and
friction velocity (see Table S1) were higher in 2018, resulting in stronger
turbulent vertical transport with mostly friction velocity being responsible
for the differences of the flux footprint density function between 2015 and
2018 (Fig. 1). Multiplying the footprint density at each tree location by
the basal emission factor of each tree species revealed a potential
difference of 24 % higher isoprene emissions in 2018 than in 2015. Even
though the actual leaf area of each individual tree is not known and
therefore neglected, this 24 % of potential emission difference due to
footprint density changes cannot explain the factor of 2.3 in observed
fluxes between the two years. Also, growth of juvenile trees between the
study years is unlikely to play a significant role, as just 8 % of the
strong isoprene emitters were younger than 5 years in 2015. This analysis
assumes that the trees from the tree inventory were responsible for the
majority of measured isoprene fluxes and that they were more important than
emissions from short vegetation (e.g., lawn). Further supporting evidence
that the flux footprint change cannot fully explain the observed differences
derives from the fact that both monoterpenes and sesquiterpenes did not show
significant interannual variations in their normalized emission potentials.
A second possible explanation for the isoprene flux difference could be
differences in LAI in the two seasons, for example due to pruning, early
leaf senescence, or insect and/or pathogen damage. Personal communications from city
gardeners revealed that of the trees most important for isoprene emissions
in the study area (Populus nigra, Populus alba, Quercus robur) only poplar trees were cut differently in 2015 than
in 2018. In 2015 only dead wood was removed from the poplars, whereas trees
were cut more substantially in 2018. This would, however, lead to an expected
smaller flux in 2018 than in 2015 due to reductions in leaf area. No
observations on early leaf senescence or leaf damage by insects and/or pathogens
were reported by the city gardeners during the two study years.
A third possible explanation is that the growing season of 2018 was
exceptionally dry with lower-than-average precipitation and large-sale,
satellite-derived root zone soil moisture (Fig. 4a and b). Concurrent
water flux observations, however, shown in Fig. 4c, indicate that on average
the 2018 daytime summer water flux was 0.2 mmol m-2 s-1 higher
than in 2015. Also, the total surface water vapor conductance was 50 mmol m-2 s-1 higher in 2018 than in 2015. Higher water fluxes observed
in 2018 agree with anecdotal reports of city trees being artificially
watered throughout the summer. Water fluxes in urban areas (maximum Bowen
ratios observed in Innsbruck are 6; Karl et al., 2020) are generally very low
(e.g., 5–6 times lower) when compared to measurements over purely vegetated
surfaces and therefore notoriously difficult to interpret. As such we
cannot exclude the possibility of processes other than evapotranspiration
from city trees contributing to higher water fluxes observed in 2018. An
obvious explanation is that a significant water runoff during extensive
watering operations resulted in increased evaporation over hot asphalt and
other non-vegetated surfaces, leading to higher water fluxes in 2018. Water
was also applied to asphalt surfaces more frequently during mornings to
minimize the effect of urban aerosol pollution. The cumulative precipitation
for July, August, and September 2015 was 340 and 258 mm for 2018. When
taking just overlapping campaign duration data (27 July–2 September), the
cumulative precipitation was 158 mm in 2015 and 155 mm in 2018. The
precipitation data confirm an overall drier meteorological summer in 2018.
It is well established that isoprene production in plants can decouple from
photosynthesis during periods of drought and can be sustained by alternative
metabolic carbon sources (e.g., Bertin and Staudt, 1996; Pegoraro et al.,
2004a, b; Fortunati et al., 2008; Genard-Zielinski et al., 2014; Potosnak et
al., 2014; Wu et al., 2015). The exact reason for biochemical regulation of
isoprene emissions during drought is not fully unraveled but has been
suggested to represent a response for coping with heat stress (Loreto et
al., 1998). Isoprene fluxes were observed to increase during the very early
onset of drought conditions. For example, Seco et al. (2015) reported an
increase in the ecosystem-scale isoprene emission potential about 1 month
before significant changes in pre-dawn leaf water potential were observed
but when CO2 uptake was already decreasing. Additionally, they observed
that the closing of stomata had a bigger effect on CO2 than water
fluxes because gradual increases in vapor pressure deficit during the
evening offset reduced leaf conductance. Isoprene is not controlled by
stomata and would not be influenced by any changes in stomatal opening. In
addition, their canopy-scale observations suggested a shift of the
temperature maximum of isoprene emissions towards higher temperatures from
pre-drought to drought conditions. Otu-Larbi et al. (2019) reported a 2.5
fold increase in the isoprene emission potential during the same 2018 heat
wave in a UK oak forest. They observed a strong temperature dependence of
isoprene concentrations during the heat wave and discuss potential causes
such as leaf temperature or rewetting-enhanced emissions. While we do not
have representative soil moisture data available for this study, we looked
at precipitation data. Otu-Larbi et al. (2019) observed large increases in
within- and above-canopy isoprene mole fractions in response to rainfall
events after a 6-week drought in a temperate broadleaf forest, which they
interpreted to result from enhanced isoprene emissions following the
rewetting. We consider rewetting events an unlikely explanation for the
observed higher isoprene fluxes in 2018 because, even though rainfall was
reduced by half compared to 2015, rain-free time intervals were quite short
(between 2 and 7 d), and thus no pronounced rewetting occurred after a
long dry period. In fact, the isoprene flux time series suggests lower
emissions following rain events.
We would like to note that both mono- and sesquiterpene emissions are also
controlled by stomatal conductance, which could be expected to affect
emission rates during drought periods (see, e.g., Niinemets and Reichstein,
2003). We did not observe significant differences of mono- and
sesquiterpene fluxes between the seasons.
We also examined the impact of the emission model framework on isoprene
emissions. Due to the lack of directly measured soil moisture data, which
would be hard to interpret in an urban context, the drought effect was not
included in the emission model parameterization. Precipitation (Fig. 4a) and
large-scale satellite-derived soil moisture data (Fig. 4b) suggest 2018
being drier than 2015, corresponding to a significant heat wave in the
summer of 2018. Severe drought conditions would reduce isoprene emissions
further and therefore could not explain an increased isoprene emission
potential in 2018. However, the fact that evaporative water fluxes were
comparable between 2015 and 2018 (and if at all were somewhat higher in
2018) suggests that the trees might not have undergone a severe drought
episode in the two years. Mild drought has been observed to lead to increases
in isoprene emissions (e.g., Otu-Larbi et al., 2019). To investigate relative
changes between emission model frameworks we also set up a MEGAN five-layer
canopy model (Guenther et al., 2006) for different scenarios. We recognize
that the concept of an LAI for the five-layer model is based on the assumption
of a homogeneous vegetation distribution. The resulting fraction of sun vs. shade leaves for urban vegetation might therefore not be fully constrained
without complex 3D radiative transfer simulations in urban situations with
sparsely distributed vegetation. The prescribed setup, however, was chosen to
mimic a high sunlight fraction of the biomass with an overall fraction of
64 %. The model setup was in turn only used to see whether differences
between 2015 and 2018 could theoretically be explained by a high sunlight
fraction or different temperature response curves. We observed that a shift
in Topt towards higher temperatures helped minimize the observed
difference between the two years (e.g., 10 % to 40 %) best. So, for
example Topt set to 313 K could explain about half of the flux
enhancement. This would leave predicted isoprene emission fluxes
underestimated by about 50 % in 2018. The combination of footprint
(24 %) and Topt (50 %) could bring the isoprene emission potential
between 2015 and 2018 to within 37 % uncertainty.
(a) Cumulative precipitation for the growing seasons of 2014–2019.
(b) Annual SMAP satellite soil moisture of the root zone from 2014–2019. (c)
Diurnal cycle of water fluxes measured in 2015 (blue) and 2018 (black).
Top-down flux and bottom-up isoprenoid emission ratios
Standardized top-down flux ratios were calculated to allow for a better
comparison with bottom-up emission estimates based on literature values of
branch-level emissions and a city tree inventory. Top-down (eddy covariance)
ISOS/ MTS flux ratios were on the order of 5 in 2015 and 12 in
2018, again revealing a strong difference between the two years. Top-down
MTS/ SQTS flux ratios were of the order of 30–40 before factoring
in losses of sesquiterpenes due to reactions with ozone. Factoring in the
upper bound of chemical loss correction, MTS/ SQTS flux ratios
could have been as low as 12–16. Top-down ISOS/ SQTS flux ratios
lay on the order of 190 in 2015 and 380 in 2018, which was mostly caused by
the difference in ISOS flux between the two years. The lower bounds
of the ISOS/ SQTS flux ratios due to fast reaction of sesquiterpene
with ozone were 80 and 150 for 2015 and 2018, respectively.
Branch-level standardized emissions are collected from the literature in
Table 1 and used to calculate a bottom-up emission map shown in Fig. 5a–c. The 2018 footprint area (Fig. 1a) and therefore footprint density were
different to 2015. Multiplying bottom-up emission estimates by footprint
density functions, the theoretically expected ISO / MT, MT / SQT, and ISO / SQT
ratios in 2015 were 3.6, 5.1, and 18.7, respectively. Multiplying the 2018
footprint density, the values were slightly different at 4.2, 4.6, and 19.2
for ISO / MT, MT / SQT, and ISO / SQT ratios, respectively.
(a–c) Bottom-up estimates of standardized ISO / MT, MT / SQT, and
ISO / SQT emission ratios based on literature values (see Table 1). (d) Tree
count. Maps were created in MATLAB (https://www.mathworks.com, last access: 1 August 2021) and are based on
OpenStreetMap (https://www.openstreetmap.org/copyright, last access: 1 August 2021) under
the CC BY 3.0 AT license.
The bottom-up ISO / MT emission ratio was close to the top-down ratio of 2015.
This again indicates that 2018 was an exceptional year. In contrast, the
bottom-up MT / SQT and ISO / SQT emission ratios were significantly lower than the top-down measured flux ratios of
both summers. Even after accounting for the
chemical loss of sesquiterpene before it reached the point of measurement at
the top of the building, the bottom-up estimates were still higher than the
top-down measured flux ratios. Literature values for leaf-level
sesquiterpene emissions are rare and were for many species estimated in
Table 1. Further extensive studies on sesquiterpene standardized emissions
for a large variety of plant species are needed to close the gap between
bottom-up emission ratios and top-down flux ratio estimates.
Summary
In this study we found a strong correlation of isoprene fluxes with
temperature as well as isoprene fluxes following the previously observed
leaf-level light dependency. Assuming the same correlation between isoprene
and benzene fluxes in early spring before the start of the vegetation period
and the summer months results in a maximum of 20 %–30 % influence of
anthropogenic sources on isoprene emissions during both the 2015 and 2018 summer
measurement periods. A PMF analysis at this site (Karl et al., 2018) has
previously revealed two biogenic factors: one light- and
temperature-dependent for isoprene and a second mostly temperature-dependent
including monoterpenes and sesquiterpenes. Bottom-up emission estimates
based on a city tree inventory and emission factors from the literature showed
reasonable agreement with standardized ISO / MT flux ratios and an
underestimation of standardized MT / SQT and ISO / SQT flux ratios. Interannual
comparison of biogenic fluxes revealed up to 3 times higher isoprene
fluxes in 2018, when a heat wave persisted, than in 2015. Monoterpene fluxes
were an order of magnitude lower than isoprene fluxes, and sesquiterpene
fluxes were another order of magnitude lower than monoterpene fluxes;
however, both summer fluxes were comparable for these two terpenoid classes
after standardization. Our findings show a higher interannual variability of
isoprene emissions compared to monoterpenes and sesquiterpenes. Normalizing
isoprene fluxes to standard light conditions did not fully remove the
interannual difference but decreased the factor from 3 to 2.3. The
difference increased with higher temperature and higher PAR values. Analysis
of footprint, precipitation, a coarse-scale satellite-based soil moisture
product as a proxy for plant water availability, and pruning activity
differences of the two summers did not completely resolve the observed
differences in isoprene fluxes. Detailed analysis using standard emission
modeling concepts suggested a higher-than-expected variation of urban
isoprene emission potentials during the heat wave in 2018. While water flux
measurements did not indicate a severe drought in 2018, the effect of an
intense heat wave in 2018 (2 K higher temperatures on average compared to
2015) likely resulted in enhanced isoprene emissions. Isoprene emissions
during drought stress have been grouped into two distinct phases (Niinemets,
2010; Potosnak et al., 2014) and can be enhanced under pre-drought
conditions (Seco et al., 2015; Otu-Larbi et al., 2019). Enhanced leaf
temperatures (e.g., Potosnak et al., 2014) can explain part of the variance
in isoprene emissions, but significant differences remained. In addition to
the leaf temperature effect, Tattini et al. (2015) reported an upregulation
of isoprene emissions during drought stress as antioxidant defense in
Platanus x acerifolia plants. Here a change in Topt towards a higher temperature optimum
could explain another 50 % of the observed isoprene emission flux
difference between 2015 and 2018. In conjunction with changes in flux
footprints (24 %) these two effects could account for about
∼75 % of the difference. If generalized, our observations
suggest distinct differences that urban trees experience, possibly due to
significantly altered environmental conditions (e.g., stresses, light, and
temperature environment). Vegetation in urban areas is exposed to a variety
of different atmospheric conditions, for example the urban heat island
effect, high levels of NOy, heavy metal deposition, or high loadings of
aerosols (e.g., black soot). Isoprene emissions have been linked to the
plant's nitrogen metabolism (e.g., Rosenstiel et al., 2008); higher
leaf nitrate can lead to lower isoprene emissions. Nitrogen dioxide
concentrations have been falling in Innsbruck and were 20 % lower in 2018
than in 2015. Effects of air pollutants on leaf surface characteristics and
senescence were also reported in the past (Jochner et al., 2015; Honour et
al., 2009), but a quantitative understanding of the impacts on isoprene
emissions remains unclear. Our observations suggest that more work is needed
to improve our understanding of urban biogenic isoprene emissions.
Code and data availability
The eddy covariance flux code used to analyze fluxes was published by Striednig et al. (2020) and can be accessed via the following link: https://git.uibk.ac.at/acinn/apc/innflux. Data can be
shared upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-5603-2022-supplement.
Author contributions
LK and TK designed and conceived the paper. MG led the
instrumental operation of the PTR-TOF-MS for the 2015 and 2018 campaigns.
MG, SJ, AP, and MS performed the raw data processing of NMVOC data. MS, TK,
and MG performed the NMVOC flux analysis. GW provided input on tree species
information. TK and LK performed analysis regarding BVOC emission modeling.
SJ aided in the operation of the PTRTOFMS and raw data processing of NMVOC
data for the 2018 campaign. All authors provided input and contributed to
writing the paper.
Competing interests
At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This work was primarily funded by the Hochschulraum-Strukturmittel (HRSM)
sponsored by the Austrian Federal Ministry of Education, Science and
Research (https://www.bmbwf.gv.at/, last access: 1 August 2021), the EC Seventh Framework
Program (Marie Curie Reintegration Program, “ALP-AIR,” grant 334084), and
partly by the Austrian National Science Fund (FWF) under grants P30600 and P33701. Lisa Kaser received funding through the University of Innsbruck. Stanislav Juráň was
supported by the project SustES – Adaptation strategies for sustainable
ecosystem services and food security under adverse environmental conditions
(CZ.02.1.01/0.0/0.0/16_019/0000797). We used atmospheric data
from the Innsbruck/University TAWES station, provided by the Austrian
Weather Service ZAMG and the Department of Atmospheric and Cryospheric
Sciences, Universität Innsbruck. The city of Innsbruck is acknowledged for
making the city tree inventory available, as is Michael Steiner for complementing
it with trees in private spaces.
Financial support
This research has been supported by the Austrian Science Fund (grant no. P30600, P33701).
Review statement
This paper was edited by Drew Gentner and reviewed by three anonymous referees.
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