A Zeppelin airship was used as a platform for in situ measurements of greenhouse gases and short-lived air pollutants within the planetary boundary layer (PBL) in Germany. A novel quantum cascade laser-based multi-compound gas analyzer (MIRO Analytical AG) was deployed to simultaneously measure in situ concentrations of greenhouse gases (CO2, N2O, H2O, and CH4) and air pollutants (CO, NO, NO2, O3, SO2, and NH3) with high precision at a measurement rate of 1 Hz. These measurements were complemented by electrochemical sensors for NO, NO2, Ox (NO2+ O3), and CO, an optical particle counter, temperature, humidity, altitude, and position monitoring. Instruments were operated remotely without the need for on-site interactions. Three 2-week campaigns were conducted in 2020 comprising commercial passenger as well as targeted flights over multiple German cities including Cologne, Mönchengladbach, Düsseldorf, Aachen, Frankfurt, but also over industrial areas and highways.
Vertical profiles of trace gases were obtained during the airship landing
and take-off. Diurnal variability of the Zeppelin vertical profiles was
compared to measurements from ground-based monitoring stations with a focus
on nitrogen oxides and ozone. We find that their variability can be
explained by the increasing nocturnal boundary layer height from early
morning towards midday, an increase in emissions during rush hour traffic,
and the rapid photochemical activity midday. Higher altitude (250–450 m)
NOx to CO ratios are further compared to the 2015 EDGAR emission
inventory to find that pollutant concentrations are influenced by
transportation and residential emissions as well as manufacturing industries and construction activity. Finally, we report NOx and CO concentrations from one plume transect originating from a coal power plant and compare it to the EURopean Air pollution Dispersion-Inverse Modell (EURAD-IM) model to find agreement within 15 %. However, due to the increased contribution of solar and wind energy and the impact of lockdown measures the power plant was operating at max. 50 % capacity; therefore, possible overestimation of emissions by the model cannot be excluded.
Introduction
Favorable meteorological conditions can trigger severe pollution episodes in
which anthropogenic emissions of pollutant concentrations accumulate and
drastically exceed the World Health Organization (WHO) guideline values.
Meteorologically induced air pollution is consistently observed globally in
Asia (He et al., 2017; Li et al., 2019; Cai et al., 2017; Zhao et al.,
2019), America (Jury, 2020; Zhao et al., 2011; Lin and McElroy, 2010), and
Europe (Dupont et al., 2016; Pernigotti et al., 2012) even during periods
when certain anthropogenic emission sectors are diminished (Gkatzelis et
al., 2021a). With air quality being the number one environmental health risk
globally (WHO, 2021; Lelieveld et al., 2015), there is an increasing need to
monitor pollutant concentrations in time and amplitude in order to identify
the driving factors for degraded air quality. An essential first step
towards this goal is to accurately determine the effect of local
meteorological parameters such as surface relative humidity, wind speed,
turbulence, and planetary boundary layer (PBL) depth development on
pollutant concentrations. Up to date, various studies highlight the need for
accurate PBL depth data as they pose the most uncertain parameter for
efficient air quality forecasts (e.g., Dupont et al., 2016; Lin and McElroy,
2010; Silcox et al., 2012; Horel et al., 2016). Vertical mixing of air
tracers within the PBL can influence their tropospheric distributions with a
turbulent mixed layer leading to a more uniform vertical distribution and a
stable boundary layer resulting in greater vertical gradients.
Numerous European ground-based networks (e.g., the European Environment
Agency, EEA) together with the European Monitoring and Evaluation Programme,
(EMEP; http://ebas.nilu.no, last access: 13 June 2022) and infrastructure such as the Aerosols,
Clouds and Trace gases Research Infrastructure (ACTRIS;
https://www.actris.eu/, last access: 13 June 2022) provide data for criteria pollutant concentrations
worldwide. However, there is still a lack of information for the vertical distribution.
Satellite retrievals allow global coverage of pollutant concentrations but
only obtain the nadir total column with limited information on the vertical
distribution of pollutant concentrations (e.g., Veefkind et al., 2012). On
the other hand, aircraft campaigns provide pollutant concentrations at
various altitudes (e.g., Molina et al., 2010; Ryerson et al., 2013; Benedict
et al., 2019); however, obtaining vertical profiles is challenging and the
data availability is limited due to high rental aircraft costs. A way to
overcome such a limitation has been to deploy instrumentation in commercial
airliners, as has been done in the last decades by the In-service Aircraft
for a Global Observing System (IAGOS; https://www.iagos.org, last access: 13 June 2022; Marenco et al., 1998; Petzold et al., 2015). Such measurements provide regular data on the
PBL dynamics but are limited to areas in proximity to airport locations
during the aircraft's landing and take-off (Boschetti et al., 2015).
Commercial airborne measurements have also been extended to routine
helicopter flights to monitor vertical profiles for pollutant concentrations
in Utah, USA (Crosman et al., 2017). Balloon-borne (e.g., Ouchi et al.,
2019) and small unmanned aerial vehicles (i.e., drones; Villa et al., 2016)
are also frequently used for vertical profile measurements; however, they
cover a limited number of pollutants due to weight restrictions. Finally,
ground-based LIDAR measurements can provide a diagnosis on the PBL height
and vertical concentration profiles but are often limited to only one
pollutant (e.g., Dang et al., 2019).
A Zeppelin is an ideal airborne platform to capture the vertical
distribution of pollutant concentrations and gain insights into their origin
and emission sources (Lampilahti et al., 2021; Li et al., 2014; Nieminen et
al., 2015). It offers enough room to deploy equipment and flies precisely
and slowly at desired heights. Such airborne measurements provide unique
opportunities to compare to modeling efforts and evaluate and update air
quality forecasts and emission inventories for single point sources.
Here, we present commercial and targeted Zeppelin flights in Germany using
state-of-the-art instrumentation to investigate the vertical, spatial, and
temporal distribution of pollutant concentrations including nitrogen oxides
(NO, NO2, and NOx), ozone (O3), carbon monoxide (CO), carbon
dioxide (CO2), and others. We compare these results to observations
from ground-based monitoring stations and emission inventory estimates.
Finally, we report emissions from a coal power plant and compare our
measurements to the EURopean Air pollution Dispersion-Inverse Modell (EURAD-IM) model hindcast (Elbern et al., 2007).
MethodsZeppelin platform
The airborne platform used in this study was the Zeppelin New Technology
(NT) developed by Zeppelin Luftschifftechnik GmbH & CO. KG (ZLT) in
Friedrichshafen, Germany, in 1997. Zeppelin NT is an economical airship with
a length of 75 m, a diameter of about 14 m, and a maximum payload of around
1.8 t. It offers a unique combination of capabilities not available in
other airborne platforms including a high scientific payload, high
maneuverability in all directions due to a vectored thrust propulsion
system, flight speeds from 0–115 km h-1, a horizontal reach of up to more than 600 km, operating altitude of 20–1500 m, and a maximum flight
endurance of 15 h.
FDH: Friedrichshafen, BNJ: Bonn-Hangelar, BadH: Bad Homburg airfield.
Figure 1a shows the Zeppelin flights over Germany and Fig. 1b the vertical
and diurnal distribution of these flights. Detailed information on the
take-off and landing times, airport locations, and flight paths are provided
in Table 1. In this study, 14 d of commercial flights, 4 d of targeted
flights, and 6 d of transect flights with overall 172 take-offs and
landings were performed and analyzed. The Zeppelin flew over various cities,
including Cologne, Mönchengladbach, Düsseldorf, Aachen, Jülich,
and Frankfurt, but also over industrial areas and highways. The majority of the
measurements ranged from 200 to 450 m in altitude. Measurements below 200 m
were predominantly during the Zeppelin landing and take-off periods and
higher altitude measurements above 400 m were during transect and targeted
flights. Flights were distributed in summer 2020 to 9, 7, and 10 flight days
in May, June, and September, respectively, ranging from 3–10 flight hours
per day. Four airports were chosen to refuel the Zeppelin, namely,
Friedrichshafen, Bonn-Hangelar, Mönchengladbach, and the Bad Homburg
airfield.
Zeppelin NT has been previously used as an airborne platform fully equipped
with instrumentation to conduct atmospheric research during the PEGASOS
project (Li et al., 2014; Nieminen et al., 2015). Here, measurements were
predominantly performed during commercial passenger flights providing low
cost but limited space to deploy instrumentation. Two main instrument
setups were fitted in the cabin of the Zeppelin: the MIRO instrument and the
hatch box with diverse low-cost sensors as discussed in the following
sections.
MIRO instrument
We deployed a MIRO MGA10-GP multi-compound gas analyzer, a newly
available commercial instrument (MIRO Analytical AG, Wallisellen,
Switzerland). The analyzer measures 10 trace gases NO, NO2, O3,
SO2, CO, CO2, CH4, H2O, NH3, and N2O with a time
resolution of 1 s and precisions (1σ) as summarized in Table 2. The
stated precisions were determined by Allan–Werle variance (Werle et al.,
1993). A detailed description of the measurement principle and the
instrument's data processing and characterization of the instrument can be
found elsewhere (Liu et al., 2018; Hundt et al., 2018).
The instrument is a quantum cascade laser-based (QCL) spectrometer
containing five distributed feedback (DFB) QCLs. The gas mixing ratios are
measured by direct laser absorption spectroscopy of selected vibrational
absorption lines of the target molecules. Beer–Lambert's law provides the
relation between the light transmission and the mixing ratios of an
absorbing species. To obtain the transmission spectra, the laser light is
steered through an astigmatic Herriott cell to a mercury cadmium telluride
(MCT) detector. The Herriott cell is constantly flushed with the sampled gas
providing an online in situ measurement. The pressure in the cell was
maintained at 95 hPa using a pressure controller in combination with a
membrane pump. For temperature stabilization of the QCLs, an external water
chiller is connected to the instrument to minimize drifts caused by ambient
temperature variations. During the Zeppelin flights, the instrument's inlet
was switched to a zero-air supply of NOx- and O3-free air by
sampling cabin air through zero air cartridges for 2 min every 20 min. The
obtained zero points were used to apply a background correction to the NO,
NO2, and O3 data to reduce their drift. For consecutive zero air
measurements median background drift values were 0.56, 0.34, and 2.9 ppb for NO, NO2, and O3, respectively.
Instrumentation onboard the Zeppelin aircraft
(Zeppelin picture by Michael Häfner).
The instrument's software runs on an integrated portable computer, which
offers remote access and full remote control over the analyzer and its
settings if an internet connection is provided. For operation on the
Zeppelin, the analyzer and its peripheral devices were integrated into a
standardized 48 cm wide rack as shown in Fig. 2. The instrument is four rack units (18 cm)
high and 61 cm deep. The sample inlet line connected to the MIRO consisted
of an unheated 8 m long PFA (perfluoroalkoxy alkane) tube with an internal
diameter of 4 mm. The sample air was drawn from the inlet located at the
hatch box below the Zeppelin cabin at a flow rate of 1.2 L min-1 resulting in a residence time of around 5 s.
The performance of the MIRO with its sampling line to measure sticky
molecules including NH3 and H2O was further examined by laboratory
measurements which mimicked the conditions during the Zeppelin flights. Fast
changes of pollutant concentrations were applied to determine the response
times (t90) of the measurement system, which were 240 and 9 s for
NH3 and H2O, respectively (see Fig. S1 in the Supplement). This highlights the
future need for a heated sampling line in order to provide quality assured
data, especially for NH3. We therefore omitted NH3 from our
discussion. For H2O and less sticky molecules, response times below 9 s
result in a spatial horizontal resolution of < 150 m considering a
horizontal Zeppelin flight speed of 60 km h-1 and a vertical resolution of < 15 m for a vertical speed of 1.7 m s-1. This provides the upper limits of the spatial resolution of pollutant concentration but is sufficient for the analysis included in this work. Finally, the instrument detection limit for SO2 is 1.7 ppb i.e., 4.9 µg m-3 (1σ) and the expected SO2 concentrations in European urban areas are mostly below 5 µg m-3 (Henschel et al., 2013). Therefore, SO2 measurements are omitted from further discussions within this paper.
Hatch box for low-cost sensors and optical particle counter
Figures 2 and S2 show the hatch box arrangement for multiple sensors
deployed below the Zeppelin cabin. Six setups were installed each including
an amperometric electrochemical gas sensor (ECS) for CO, NO, NO2, and
Ox (O3+ NO2) measurements (Baron and Saffell, 2017), a
ChipCap2 sensor for temperature (T) and relative humidity (RH) measurements, and a GPS locator for latitude, longitude, and altitude measurements.
Particle-phase size distribution measurements were performed by two optical
particle counters (OPCs). Currently, the optical counter has been used based
on the available instrument recommendations with and without an isokinetic
inlet. The isokinetic inlet was a 3-D-printed L-shaped inlet of 10 cm
length and an internal diameter of 5 mm. The second OPC was used without any
inlet line, sampling perpendicular to the flight direction. No further
sample preparation or quality assurance has been performed. We therefore
exclude the OPC data in its current state from further discussion. Two long
term evolution (LTE) antennas were used for real-time communication to the
instruments from the ground and wireless communication for onboard
decisions. Details on the performance of the sensors including their time
resolution, the limit of detection, and references are found in Table 2.
Furthermore, the potential of the ECSs to measure nitrogen oxides is shown
in Fig. S3. On average, ECS NOx data are higher by 20 % compared to
the MIRO for concentrations above 15 ppbv which is the limit of detection
for NOx measured by ECS. This makes the ECSs ideal for the
identification of high NOx emission sources during the Zeppelin flights
but limited in determining NOx variability at low-NOx
environments. Calibrations, sensitivity analysis, and associated
uncertainties of the ECSs measurements will be further discussed in a
separate publication and are not the focus of this work. In the following,
all measured pollutant concentrations are acquired from the MIRO instrument.
The EURAD-IM model
The EURAD-IM model output was compared to the Zeppelin observations by
focusing on the emissions and evolution of an industrial plume as discussed
in Sect. 3.4. Details of the model are provided by Elbern et al. (2007).
Briefly, the regional emission inventory provided by the Copernicus
Atmosphere Monitoring Service (CAMS) (Kuenen et al., 2014; Errera et al., 2021) was used and further refined using land use information. The Weather Research and Forecasting (WRF) model version 4.0.3 (Skamarock et al., 2008; Powers et
al., 2017) was initialized using the global analysis of the European Centre
for Medium-Range Weather Forecasts (ECMWF) to account for the meteorological
effects whereas theRegional Atmospheric Chemistry Mechanism - Mainz Isoprene Mechanism (RACM-MIM) (Pöschl et al., 2000)
was applied to account for the effects of atmospheric chemistry on pollutant
concentrations. Here, we focus on 1 h time resolution model concentrations
for NOx and CO on a 1 km horizontal grid. The observational average
flight height during the comparison periods was around 300 m and the model
level centered at about 268 m was chosen as the closest vertical grid point
in proximity to these measurements. A challenge in accurately determining
the location of different emission sources in the model is that the
horizontal resolution of the inventory emissions is coarser (approx. 7 km in
western Germany) than the model resolution (1 km). When refining the
emissions to match the model grid, it is often hard to match single emission
patterns with an operational forecast model. This was the case for the
modeled industrial emission source investigated in Sect. 3.4 that was
offset by 3 km to the southeast compared to the original location of the
power plant. Here, NOx concentration fields that are associated with
the significant point source were reallocated to match the location of the
industry and improve the comparison to observations. Reallocation of the CO
concentrations was not applied since this studied power production facility
had negligible CO emissions. CO background concentrations were variable due
to the numerous other point sources and the longer CO lifetime.
Results and DiscussionVertical profiles in Frankfurt and Bonn
Zeppelins can climb and descend slowly at confined locations to obtain the
vertical distribution of pollutants. Each day, multiple flights were
performed with vertical measurements obtained during the Zeppelin landing
and take-off at the airports. Figure 3 shows the vertical profiles of
NOx and O3 at different times of the day for measurements
performed nearby Frankfurt in September and Bonn in May, June, and
September. For Bonn, larger variability in pollutant concentrations due to
the broader seasonal coverage was not evident with measurements in May and
June showing on average similar vertical trends as in September (Fig. S4).
Zeppelin data below 25 m were excluded from the analysis as they were
affected by the Zeppelin engine exhaust emissions. In the early hours from
06:00 to 08:00 UTC, NOx mixing ratios close to the ground were higher
with a median (25th–75th percentile) of 8 (5–19) ppbv and 5.7
(3–20) ppbv for Frankfurt and Bonn, respectively. In Frankfurt, the median
NOx concentration sharply decreased down to 1 (0.27–6) ppbv when above
125 m, whereas in Bonn, a moderate decrease to 5 (2.7–10) ppbv was
observed. O3 showed the opposite trend with low mixing ratios close to
the ground and an O3 increase above 125 m in height. During the period
from 08:00 to 10:00 UTC, an increase of NOx at all heights compared to
06:00 UTC was evident in Frankfurt, whereas in Bonn, NOx was similar to
earlier hours with a slight increase of ground-level NOx to 9.2
(3.1–14.5) ppbv. In parallel, O3 decreased at higher altitudes and
increased closer to the ground for Frankfurt and Bonn compared to earlier
hours. From 10:00 to 18:00 UTC, the lower PBL was well mixed with NOx
and O3 concentrations agreeing within their variability at all heights
from 25 up to 375 m. NOx concentrations decreased throughout the day
down to less than 1 ppbv, whereas O3 increased up to more than 60 ppbv
for both Frankfurt and Bonn. Same trends were observed for other criteria
pollutants including CO, CO2, and CH4 (Fig. S5), whereas
N2O, H2O, and NH3 were less variable (Fig. S6).
Vertical profiles for different time-periods (UTC) for
NOx and O3 in Bonn and Frankfurt. Circle and square markers
correspond to the median NOx and O3 mixing ratios, respectively.
The shaded areas represent the 25th and 75th percentiles. Numbers
correspond to the data points used to generate the NOx and O3
medians.
Changes in PBL dynamics, anthropogenic emissions, and atmospheric chemistry
are the drivers of the observed diurnal and vertical variability. During
nighttime, a shear develops between the residual layer and the more stagnant
nocturnal boundary layer that grows higher and reaches a morning maximum
(see Fig. S7). Shortly after sunrise, the surface heats up and a mixed
layer evolves increasing with height until the former nocturnal boundary
layer and the residual layer are finally fully mixed. From 08:00 to 10:00 UTC, the Zeppelin captured the evolving convective mixed layer that
developed after sunrise and reached on average up to 125 m. The increased
ground-level NOx concentrations during the early hours are the result
of fresh emissions from ground sources into a shallow mixed layer. As the
convective mixed layer increases, ground-level NOx concentrations are
expected to decrease due to dilution effects. Higher altitude concentrations
increase as more concentrated ground-level NOx is distributed
vertically. The morning anthropogenic emissions including rush-hour traffic
increase the ground-level NOx, but dilution mitigates the level of
NOx concentrations at low altitudes in Frankfurt and Bonn during the
morning hours. O3 is a secondary product from the interplay of
NOx, volatile organic compound emissions, and meteorology. Under dark
conditions, O3 is expected to react away and its concentration
decreases, whereas during the day, it is expected to reach maximum
concentrations midday when photochemistry peaks. These trends are verified
by the Zeppelin flights in Frankfurt and Bonn where early morning O3
titration is followed by photochemical O3 production/increase midday
with uniform vertical distributions obtained after 10:00 UTC.
NOx vertical profiles compared to ground-based monitoring stations
The above field-derived vertical profiles show the influence of PBL dynamics
in diluting pollutant concentrations but also highlight the influence of
anthropogenic emissions. Comparison of these profiles to ground-based
measurements provides insights into their origin and location. Figure 4
compares the NOx vertical profiles in Frankfurt to ground-based
observations from various monitoring stations (provided by Unweltbundesamt,
Table S1). Twelve ground-based monitoring stations were chosen located in
the broader Frankfurt metropolitan area as shown in Fig. 4a. Monitoring
stations in the inner city namely, Frankfurt-Höchst, Frankfurt-Niedwald,
Frankfurt-Ost, Frankfurt-Schwanheim, Frankfurt-Friedberger Landstraße,
and Offenbach-Untere Grenzstraße are categorized as urban. Monitoring
stations in the outer Frankfurt area including Hanau, Raunheim,
Wiesbaden-Süd, Wiesbaden-Ringkirche, and Wiesbaden-Schiersteiner
Straße were categorized as suburban. Finally, Kleiner Feldberg was
considered a remote station at a higher altitude, 700 m above the city
center of Frankfurt and 300–400 m above the Zeppelin flight track. Figure 4b
shows a detailed comparison of the NOx diurnal variability of the
ground stations and the Zeppelin measurements. For the urban and suburban
stations, a NOx peak was evident with an average (±1σ)
concentration of 46.3 (±4.9) ppbv and 35.6 (±10.1) ppbv in the
morning hours. Midday, the NOx concentrations decreased due to the
increasing convective mixed layer height as well as reduced traffic
emissions and increased again in the evening due to rush-hour traffic
reaching a maximum of 56.6 (±25) ppbv and 41.3 (±23.7) ppbv
for the urban and suburban stations, respectively. At the remote station,
the NOx concentrations were at background levels and no significant
anthropogenic contribution was evident. Zeppelin data followed the same
morning increase as the urban and suburban monitoring stations with maximum
NOx at 21.7, 21.3, 11.4, and 5 ppbv for measurements at 50, 100, 150,
and 200 m, respectively, during the time from 08:00–10:00 UTC. For higher
altitude measurements at 250 and 300 m, the NOx concentrations were the
highest from 10:00–12:00 UTC with concentrations of 6.3 and 8.85 ppbv,
respectively.
At lower altitudes, Zeppelin flew close to the outer urban airfield while at
higher altitudes it was located closer to the city center of Frankfurt as
shown in Fig. 4a. It is therefore expected that measurements taken at low
altitudes are comparable to those from suburban monitoring stations in
particular during the early morning hours when the convective mixing layer
starts evolving and the dilution of fresh emissions is less pronounced.
However, ground-based (sub-)urban stations are located close to roads or
even in road canyons catching fresh emissions and observed concentrations of
primary emissions were higher on average than the respective low altitude
Zeppelin data (Fig. 4b). In the morning hours, the effect of rush hour
traffic emissions on both the Zeppelin and urban/suburban stations is
evident with a peak in NOx concentrations at altitudes below 200 m at
08:00–10:00 UTC. For higher altitude measurements (250–300 m) the NOx concentrations peak at 10:00–12:00 UTC which highlights the effect of PBL dynamics, where morning emissions are distributed vertically to generate a well-mixed layer resulting in a dilution of the mixed air masses. This results in a later and weaker peak of the NOx concentration at high
altitudes. As the convective layer reaches altitudes above the upper flight
range of the Zeppelin, this observed change in dynamic is no longer
captured. In the afternoon, no significant concentration differences are
observed between the different heights. The evening peak captured by the
monitoring stations is only partially measured by the Zeppelin. Future
Zeppelin campaigns to capture the nocturnal boundary layer development in
the evening hours will provide further insights into the diurnal variability
of the PBL.
These results provide evidence of effective vertical mixing in particular
during the afternoon but limited to the heights captured by the Zeppelin.
Future studies to convey whether mixing is as efficient at higher altitudes,
which have been previously achieved by aircraft studies (e.g., Flynn et al.,
2014, 2016; Choi et al., 2020; Li et al., 2021), will be of
great interest. Furthermore, comparison of the Zeppelin flights not only to
ground-based monitoring stations, but also to modeling efforts and
satellite measurements, would be of great value as geostationary satellites
come on-line in the future.
Comparison to the EDGAR 2015 emission inventory
Airborne Zeppelin measurements are an ideal platform to investigate
pollutant concentrations on the vertical; however, the majority of flight
hours were at heights ranging from 250–450 m. During these periods, the
Zeppelin flew over various cities, including Cologne, Mönchengladbach,
Düsseldorf, Aachen, and Frankfurt, but also over industrial areas and
highways. This provided the opportunity to better characterize anthropogenic
emissions and compare observations to emission inventory estimates for
Germany. Here, we use the 2015 Emissions Database for Global Atmospheric
Research (EDGAR v5) (Crippa et al., 2020), which is the most recent year for
which data are publicly available. Emissions in EDGAR are provided in
Gg yr-1 while pollutant concentrations detected onboard the Zeppelin are measured in ppbv. A direct comparison of the emission inventories and
observations is challenging. From emission to detection, measured pollutant
concentrations can drastically change due to dilution as well as chemical
and physical loss processes whereas the annual inventory emission estimates
may differ from daily or even hourly emission rates. A common strategy to
reduce the above described uncertainties has been to focus on pollutant mass
ratios (Gkatzelis et al., 2021b; Coggon et al., 2021). For example, if CO
and a volatile organic compound are co-emitted from a pollution source their
ratio is constant as they travel downwind of the source if their physical
and chemical loss pathways are not different and they do not have other
sources.
Distribution of the NOx to CO ratio (g g-1) during the Zeppelin flights shown as a violin plot for measurements ranging from 250 to 450 m in height. These ratios are compared to the ratios of
different pollution sources following the EDGAR 2015 emission inventory. The
size and color of the markers indicate the NOx emission strength for
each EDGAR source.
Figure 5 shows the diurnal variability of the NOx to CO slope for all
Zeppelin flights between 250 and 450 m. A sensitivity analysis was
performed to derive the observed NOx to CO slope by applying a linear
fit function every 60, 100, or 1000 s. Linear fits obtained from each
time step were further filtered depending on the goodness of fit with data
discarded if the coefficient of determination R2 of the linear fit was
below 0.6, 0.7, or 0.8, respectively. An overview of this sensitivity
analysis to the different time steps and R2 thresholds is given in
Fig. S8. Overall, the NOx to CO slopes were within ±0.05 g g-1 independent of the chosen time steps and R2 thresholds. Therefore, for Fig. 5 we choose a 1 min time step and an R2 threshold of 0.6 to discuss the observed variability of the NOx to CO slopes. Figure 5 also shows the NOx to CO emission ratios from numerous
pollution sources based on the EDGAR 2015. The EDGAR emission inventory was
separated into different emission sectors including transportation,
industry, building and miscellaneous, and other sources by lumping the Intergovernmental Panel on Climate Change (IPCC)
emission categories. Road transportation with no resuspension is the
dominant source of NOx in the inventory accounting for 40 % of the
NOx emitted in Germany with a NOx/ CO ratio of 0.68 g g-1. Other transportation categories are not expected to contribute more than 6 % to the total NOx emissions, however they have drastically higher NOx/ CO ranging from 3–10 g g-1. Industrial emissions are dominated by the categories “main activity electricity and heat production” and “manufacturing industries and construction” accounting for 22 % and 18 % of the total NOx, with NOx/ CO at 1.37 and 0.56, respectively. The remaining industrial emissions account for 3 % of the total NOx and their NOx/ CO ranges from 0.017–24.32 g g-1. Building and
miscellaneous sources account for 11 % of the total NOx in EDGAR and
are predominantly related to residential emissions and off-road vehicles
with NOx/ CO at 0.08. NOx to CO slopes during the Zeppelin flights were relatively constant with a daily average of 0.36 (±0.03) g g-1.
These values were in the range of the average emission ratio of road
transportation, building and miscellaneous emissions, and specific
industrial sources when compared to EDGAR 2015. Higher NOx to CO levels
by a factor of 2 to 5 compared to the daily average were evident promoting
sporadic detection of high NOx emission sources compared to CO. Figure 6 shows that the spatial distribution of these higher NOx to CO
emissions is predominantly related to petroleum refinery and chemical
industries along the flight tracks in North Rhine-Westphalia and Hessen
further promoting the potential of Zeppelin flights to locate different
pollution sources in space and time. A characteristic example of higher
NOx to CO industrial emissions is highlighted in Sect. 3.4 and
further investigation of individual emission sources is the focus of a
future study.
The observed NOx/ CO slope can be influenced by the longer lifetime of CO compared to NOx (Seinfeld and Pandis, 2006) and therefore bias the measurements low. The longer CO lifetime also leads to higher background levels; however, when focusing on the NOx/ CO slope this background is accounted for in
the offset of the linear fit. The major NOx daytime loss pathway is the
reaction of NO2 with the OH radical yielding nitric acid. The net
chemical loss of NOx in the atmosphere is challenging to directly
observe. Observational methods to determine the lifetime of NOx have
shown that under typical midday conditions in an isoprene-dominated forest
it was 11 h ± 5 h (Romer et al., 2016), whereas for studies focused on the outflow of isolated emissions the average range of NOx lifetimes
was around 5–8 h (Ryerson et al., 1998; Liu et al., 2016; Dillon et al.,
2002; Alvarado et al., 2010; Valin et al., 2013). The vertical trajectory
time for emissions to be detected onboard the Zeppelin is expected to be
below the above lifetime thresholds due to the midday vertical mixing with
vertical wind speeds in the range of 1 to 2 m s-1 (Stull, 1988). However, the net chemical loss of NOx cannot be neglected, especially if shorter NOx
lifetimes are evident due to the efficient production of multifunctional
nitrates (e.g., Valin et al., 2013). Therefore, the observational ratios
presented here are a lower estimate compared to the ratios close to the
emission source.
Uncertainties can also exist in the 2015 EDGAR emission inventory estimates
compared to the expected emissions in 2020 when the Zeppelin flights were
performed. The European Environmental Agency reports a drastic decrease in
NOx emissions by almost 60 % from 1990 to 2019 (EEA, 2021).
Assuming the decrease in NOx emissions is stronger compared to the
decrease of CO from 2017 to 2020, this could lead to an overestimation of
the EDGAR NOx/ CO ratios presented here. Furthermore, 2020 was the year that the COVID-19 pandemic led to unprecedented government restrictions to limit the spread of the disease. Gkatzelis et al. (2021a) show that reduced NOx and CO emissions correlate with stricter government responses that could affect the NOx and CO observations. Although transportation and
industrial emissions are expected to decline during lockdown conditions,
building emissions could have risen. The increased contribution of building
emissions could therefore explain why observational NOx/ CO ratios fall along with the EDGAR transportation/industry and building emission ratios.
Targeted flights to identify coal power plant emissions
As highlighted in the previous section, emissions from industrial energy
production are a major contributor to pollutant concentrations in Germany.
Industrial pollution sources were identified in proximity to the Zeppelin
airports using the EURAD-IM model prior to flights performed in North
Rhine-Westphalia. The coal power plant in Weisweiler (50.838∘ N,
6.321∘ E) was chosen to investigate the vertical and horizontal
evolution of industrial emissions on 8 May 2020, at 06:00–14:00 UTC.
Evolution of industrial plume emissions of NOx and
CO based on the EURAD-IM model overlaid by the field-derived Zeppelin
observations.
Figure 7 shows the EURAD-IM model results (see Sect. 2.4) for NOx and
CO concentrations overlaid with observations on-board the Zeppelin for one
transect crossing the industrial plume at 07:30–08:30 UTC. Comparison of the EURAD-IM model and observations at different industrial plume heights and
different time periods are the focus of future work; here we highlight the
potential of Zeppelin measurements as valuable input for model evaluation or
data assimilation approaches. EURAD-IM NOx concentrations were at 25 ppbv close to the industrial emissions and decreased moving downwind of the industry due to dilution. The Zeppelin flew in circles around the industry during the same period. NOx background concentrations were at 5–10 ppbv and increased to 10–25 ppbv when flying through the industrial plume. Model concentrations ranged from 5 to 10 ppbv upwind of the industry location in the southern and eastern regions and were in good agreement with
observational trends. The model NOx concentrations were on average
18–20 ppbv for this industry and agreed within 12 % with the Zeppelin
measurements for this transect. Industrial CO emissions were minimal both in
the model and observations and background CO levels agreed within less than
10 %.
Industrial emissions during the period of the measurements were lower than
the business-as-usual emission scenario due to the increased contribution of
solar and wind energy (Burger, 2021) and/or the impact of lockdown
measures. Particularly, the net electricity generation from the Weisweiler
coal power plant during the Zeppelin flight was 50 % less compared to the
weekly average generation for 2020 (Burger, 2022). The good agreement between the model and
observations for NOx concentrations could therefore be due to an
overestimation of NOx emissions at this given time by the model.
However, the model NOx and CO background concentration levels are in good agreement with observations. Although the period of the measurements was
during lockdown conditions, the background NOx and CO levels seem to be
unaffected by the stay-at-home orders.
Conclusions
We report in situ measurements of air pollutant concentrations within the
planetary boundary layer on board the Zeppelin NT airship in Germany. A
novel quantum cascade laser-based multi-compound gas analyzer (MIRO
Analytical AG) is deployed to simultaneously measure the concentration of
greenhouse gases (CO2, N2O, H2O, and CH4) and air
pollutants (CO, NO, NO2, O3, SO2, and NH3) with high
precision at a measurement rate of 1 Hz. Electrochemical sensors for NO,
NO2, Ox (NO2+ O3), and CO, an optical particle counter, temperature, humidity, altitude, and position monitoring are attached to a hatch box below the Zeppelin cabin. In total, 14 commercial flights, 4 targeted flights, and 6 transect flights were performed in May, June, and September 2020, and include flights over urban, remote, and also industrial areas and highways.
Vertical profiles of pollutants are obtained with a focus on NOx and
O3 during the airship landing and take-off close to the airports of
Bonn and Frankfurt. In the early hours from 06:00 to 08:00 UTC, NOx
mixing ratios are higher close to the ground and sharply decrease when above
125 m. O3 has the opposite trend with low mixing ratios close to the
ground and an O3 increase above 125 m. This is due to a developing
convective mixing layer in which dilution of fresh emissions is less
pronounced leading to an increase in NOx concentrations close to the
ground, and the subsequent titration and decrease of ground-level O3.
From 08:00 to 10:00 UTC, an increase of NOx mixing ratios is evident at
all heights due to the morning rush hour traffic emissions. From 10:00 to
18:00 UTC, the convective mixing layer is fully developed within the max.
flight height of 450 m above ground and well mixed with NOx and O3
concentrations agreeing within their variability at all heights. During
these periods, NOx concentrations decrease throughout the day due to
vertical dilution, whereas O3 increases due to increased photochemical
activity. We compare the diurnal variability of the Zeppelin vertical
profiles to measurements from ground-based monitoring stations in Frankfurt
to find that Zeppelin vertical concentrations are predominantly affected by
suburban emissions and only the higher altitude measurements are influenced
by urban Frankfurt emissions.
NOx to CO slopes at higher altitudes (250–450 m) are compared to the
2015 EDGAR emission inventory. A daily average of 0.36 (±0.03) g g-1 is
found for the Zeppelin measurements in the range of the inventory emission
ratios for transportation, residential emissions, manufacturing industries,
and construction activity. Sporadic high NOx to CO slopes (2–5 g g-1) close to industrial sources are observed including a coal power plant in Weisweiler. We compare dedicated measurements in this industrial facility to the EURAD-IM model to find agreement within less than 15 % for NOx and CO concentrations during one plume transect. However, due to the increased contribution of solar and wind energy and/or the impact of lockdown measures the power plant was operated at 50 % capacity; therefore, possible overestimation of emissions by the model cannot be excluded. Nevertheless, an agreement between the model and observations for background NOx and CO concentrations promotes that emissions were not drastically affected due to lockdown restrictions as they are adequately
represented by the model calculations, in which no emission reductions to
account for the lockdown have been included.
From obtaining vertical pollutant distributions to evaluating emission
inventories and modeling efforts the findings of this work highlight the
unique scientific insights obtained aboard a Zeppelin platform and promote
the importance of frequent airship measurements in Europe in the following
years. Furthermore, the low costs of commercial flights provide an
affordable and efficient method to improve our understanding of changes in
emissions in space and time. Future efforts to include volatile organic
compound measurements along with the greenhouse gases and air pollutants
obtained by the MIRO MGA10-GP multi-compound gas analyzer will further
expand the capabilities of this platform and provide insights into primary
and secondary pollution observations.
Data availability
Data are available at https://doi.org/10.26165/JUELICH-DATA/7ZZIXJ (Tillmann, 2022).
The supplement related to this article is available online at: https://doi.org/10.5194/amt-15-3827-2022-supplement.
Author contributions
RT, FR and AKS designed the experiments and flight campaigns. BW, CW, TS, and FR carried them out. OA and MH provided profound instrumentation support. ACL,
PF, and EF provided the model data. GIG, TS, and MD visualized the data. GIG
and RT prepared the paper with contributions from all co-authors. RW,
FR, PF, and AKS commented on the paper.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We acknowledge the support of Deutsche Zeppelin Reederei (DZR) and Zeppelin
Luftschifftechnik GmbH (ZLT); Jeff Peischl and Matthew M.
Coggon for fruitful discussions and IGOR software development support. The
authors gratefully acknowledge the computing time granted through JARA on
the supercomputer JURECA at Forschungszentrum Jülich (Jülich Supercomputing Centre, 2018).
Financial support
This research has been supported by funding from the Helmholtz Association within the framework of MOSES.The article processing charges for this open-access publication were covered by the Forschungszentrum Jülich.
Review statement
This paper was edited by Eric C. Apel and reviewed by two anonymous referees.
ReferencesAlvarado, M. J., Logan, J. A., Mao, J., Apel, E., Riemer, D., Blake, D., Cohen, R. C., Min, K.-E., Perring, A. E., Browne, E. C., Wooldridge, P. J., Diskin, G. S., Sachse, G. W., Fuelberg, H., Sessions, W. R., Harrigan, D. L., Huey, G., Liao, J., Case-Hanks, A., Jimenez, J. L., Cubison, M. J., Vay, S. A., Weinheimer, A. J., Knapp, D. J., Montzka, D. D., Flocke, F. M., Pollack, I. B., Wennberg, P. O., Kurten, A., Crounse, J., Clair, J. M. St., Wisthaler, A., Mikoviny, T., Yantosca, R. M., Carouge, C. C., and Le Sager, P.: Nitrogen oxides and PAN in plumes from boreal fires during ARCTAS-B and their impact on ozone: an integrated analysis of aircraft and satellite observations, Atmos. Chem. Phys., 10, 9739–9760, 10.5194/acp-10-9739-2010, 2010.Baron, R. and Saffell, J.: Amperometric Gas Sensors as a Low-Cost Emerging
Technology Platform for Air Quality Monitoring Applications: A Review, ACS
Sensors, 2, 1553–1566, 10.1021/acssensors.7b00620, 2017.Benedict, K. B., Zhou, Y., Sive, B. C., Prenni, A. J., Gebhart, K. A., Fischer, E. V., Evanoski-Cole, A., Sullivan, A. P., Callahan, S., Schichtel, B. A., Mao, H., Zhou, Y., and Collett Jr., J. L.: Volatile organic compounds and ozone in Rocky Mountain National Park during FRAPPÉ, Atmos. Chem. Phys., 19, 499–521, 10.5194/acp-19-499-2019, 2019.Boschetti, F., Chen, H., Thouret, V., Nedelec, P., Janssens-Maenhout, G.,
and Gerbig, C.: On the representation of IAGOS/MOZAIC vertical profiles in
chemical transport models: contribution of different error sources in the
example of carbon monoxide, Tellus B, 67, 28292, 10.3402/tellusb.v67.28292, 2015.Burger, B.: Öffentliche Nettostromerzeugung in Deutschland im Jahr 2020, Fraunhofer-Institut für Solare Energiesysteme ISE, https://www.energy-charts.info/downloads/Stromerzeugung_2020_1.pdf, (last access: 13 June 2022), 2021.Burger, B.: Energy-Charts, Fraunhofer-Institut für Solare Energiesysteme ISE,
https://www.energy-charts.info/charts/power/chart.htm?l=de&c=DE&stacking=stacked_absolute_area, last access: 13 June 2022.Cai, W., Li, K., Liao, H., Wang, H., and Wu, L.: Weather conditions
conducive to Beijing severe haze more frequent under climate change, Nat.
Clim. Change, 7, 257–262, 10.1038/nclimate3249, 2017.Choi, S., Lamsal, L. N., Follette-Cook, M., Joiner, J., Krotkov, N. A., Swartz, W. H., Pickering, K. E., Loughner, C. P., Appel, W., Pfister, G., Saide, P. E., Cohen, R. C., Weinheimer, A. J., and Herman, J. R.: Assessment of NO2 observations during DISCOVER-AQ and KORUS-AQ field campaigns, Atmos. Meas. Tech., 13, 2523–2546, 10.5194/amt-13-2523-2020, 2020.Coggon, M. M., Gkatzelis, G. I., McDonald, B. C., B.Gilman, J., Schwantes,
R. H., Abuhassan, N., Aikin, K. C., Arendde, M. F., Berkoff, T. A., Brown, S.
S., Campos, T. L., Dickerson, R. R., Gronoff, G., Hurley, J.,
Isaacman-VanWertz, G., Koss, A. R., Li, M., A.McKeen, S., Moshary, F.,
Peischl, J., Pospisilova, V., Ren, X., Wilson, A., Wude, Y., Trainer, M.,
and Warneke, C.: Volatile chemical product emissions enhance ozone and
modulate urban chemistry, P. Natl. Acad. Sci. USA,
118, e2026653118, 10.1073/pnas.2026653118, 2021.Crippa, M., Solazzo, E., Huang, G., Guizzardi, D., Koffi, E., Muntean, M.,
Schieberle, C., Friedrich, R., and Janssens-Maenhout, G.: High-resolution
temporal profiles in the Emissions Database for Global Atmospheric Research,
Scientific Data, 7, 121, 10.1038/s41597-020-0462-2, 2020.Crosman, E. T., Jacques, A. A., and Horel, J. D.: A novel approach for
monitoring vertical profiles of boundary-layer pollutants: Utilizing routine
news helicopter flights, Atmos. Pollut. Res., 8, 828–835,
10.1016/j.apr.2017.01.013, 2017.Dang, R., Yang, Y., Hu, X.-M., Wang, Z., and Zhang, S.: A Review of
Techniques for Diagnosing the Atmospheric Boundary Layer Height (ABLH) Using
Aerosol Lidar Data, Remote Sens., 11, 1590, 10.3390/rs11131590, 2019.Dillon, M. B., Lamanna, M. S., Schade, G. W., Goldstein, A. H., and Cohen,
R. C.: Chemical evolution of the Sacramento urban plume: Transport and
oxidation, J. Geophys. Res.-Atmos., 107, ACH 3-1–ACH 3-15, 10.1029/2001JD000969, 2002.Dupont, J. C., Haeffelin, M., Badosa, J., Elias, T., Favez, O., Petit, J.
E., Meleux, F., Sciare, J., Crenn, V., and Bonne, J. L.: Role of the
boundary layer dynamics effects on an extreme air pollution event in Paris,
Atmos. Environ., 141, 571–579, 10.1016/j.atmosenv.2016.06.061, 2016.EEA: Air pollutant emissions data viewer (Gothenburg Protocol, LRTAP Convention) 1990–2019, European Environment Agency, https://www.eea.europa.eu/data-and-maps/dashboards/air-pollutant-emissions-data-viewer-4 (last access: 13 June 2022), 2021.Elbern, H., Strunk, A., Schmidt, H., and Talagrand, O.: Emission rate and chemical state estimation by 4-dimensional variational inversion, Atmos. Chem. Phys., 7, 3749–3769, 10.5194/acp-7-3749-2007, 2007.Errera, Q., Ramonet, M., Sudarchikova, N., Schulz, M., Eskes, H. J., Basart, S., Benedictow, A., Bennouna, Y., Blechschmidt, A.-M., Chabrillat, S., Christophe, Y., Cuevas, E., El-Yazidi, A., Flentje, H., Fritzsche, P., Hansen, K. M., Im, U., Kapsomenakis, J., Langerock, B., Richter, A., Thouret, V., Wagner, A., Warneke, T., and Zerefos, C.: Validation report of the CAMS near-real-time global atmospheric composition service: Period
March–May 2021, Copernicus Atmosphere Monitoring Service (CAMS) report, https://atmosphere.copernicus.eu/sites/default/files/publications/26_CAMS84_2018SC3_D1.1.1_MAM2021.pdf, last access: 15 June 2021.Flynn, C. M., Pickering, K. E., Crawford, J. H., Lamsal, L., Krotkov, N.,
Herman, J., Weinheimer, A., Chen, G., Liu, X., Szykman, J., Tsay, S.-C.,
Loughner, C., Hains, J., Lee, P., Dickerson, R. R., Stehr, J. W., and Brent,
L.: Relationship between column-density and surface mixing ratio:
Statistical analysis of O3 and NO2 data from the July 2011 Maryland
DISCOVER-AQ mission, Atmos. Environ., 92, 429–441, 10.1016/j.atmosenv.2014.04.041, 2014.Flynn, C. M., Pickering, K. E., Crawford, J. H., Weinheimer, A. J., Diskin,
G., Thornhill, K. L., Loughner, C., Lee, P., and Strode, S. A.: Variability
of O3 and NO2 profile shapes during DISCOVER-AQ: Implications for satellite observations and comparisons to model-simulated profiles, Atmos. Environ., 147, 133–156, 10.1016/j.atmosenv.2016.09.068,
2016.Gkatzelis, G. I., Gilman, J. B., Brown, S. S., Eskes, H., Gomes, A. R.,
Lange, A. C., McDonald, B. C., Peischl, J., Petzold, A., Thompson, C. R.,
and Kiendler-Scharr, A.: The global impacts of COVID-19 lockdowns on urban
air pollution: A critical review and recommendations, Elementa: Science of
the Anthropocene, 9, 00176, 10.1525/elementa.2021.00176, 2021a.Gkatzelis, G. I., Coggon, M. M., McDonald, B. C., Peischl, J., Aikin, K. C.,
Gilman, J. B., Trainer, M., and Warneke, C.: Identifying Volatile Chemical
Product Tracer Compounds in U.S. Cities, Environ. Sci. Technol., 55, 188–199, 10.1021/acs.est.0c05467, 2021b.He, J., Gong, S., Yu, Y., Yu, L., Wu, L., Mao, H., Song, C., Zhao, S., Liu,
H., Li, X., and Li, R.: Air pollution characteristics and their relation to
meteorological conditions during 2014–2015 in major Chinese cities,
Environ. Pollut., 223, 484–496, 10.1016/j.envpol.2017.01.050, 2017.Henschel, S., Querol, X., Atkinson, R., Pandolfi, M., Zeka, A., Le Tertre,
A., Analitis, A., Katsouyanni, K., Chanel, O., Pascal, M., Bouland, C.,
Haluza, D., Medina, S., and Goodman, P. G.: Ambient air SO2 patterns in 6 European cities, Atmos. Environ., 79, 236–247,
10.1016/j.atmosenv.2013.06.008, 2013.Horel, J., Crosman, E., Jacques, A., Blaylock, B., Arens, S., Long, A.,
Sohl, J., and Martin, R.: Summer ozone concentrations in the vicinity of the
Great Salt Lake, Atmos. Sci. Lett., 17, 480–486, 10.1002/asl.680, 2016.Hundt, P. M., Tuzson, B., Aseev, O., Liu, C., Scheidegger, P., Looser, H.,
Kapsalidis, F., Shahmohammadi, M., Faist, J., and Emmenegger, L.:
Multi-species trace gas sensing with dual-wavelength QCLs, Appl. Phys.
B, 124, 108, 10.1007/s00340-018-6977-y, 2018.Jülich Supercomputing Centre (JURECA): Modular supercomputer at
Jülich Supercomputing Centre, Journal of Large-Scale Research
Facilities, 4, A132, 10.17815/jlsrf-4-121-1, 2018.Jury, M. R.: Meteorology of air pollution in Los Angeles, Atmos. Pollut. Res., 11, 1226–1237, 10.1016/j.apr.2020.04.016, 2020.Kuenen, J. J. P., Visschedijk, A. J. H., Jozwicka, M., and Denier van der Gon, H. A. C.: TNO-MACC_II emission inventory; a multi-year (2003–2009) consistent high-resolution European emission inventory for air quality modelling, Atmos. Chem. Phys., 14, 10963–10976, 10.5194/acp-14-10963-2014, 2014.Lampilahti, J., Manninen, H. E., Nieminen, T., Mirme, S., Ehn, M., Pullinen, I., Leino, K., Schobesberger, S., Kangasluoma, J., Kontkanen, J., Järvinen, E., Väänänen, R., Yli-Juuti, T., Krejci, R., Lehtipalo, K., Levula, J., Mirme, A., Decesari, S., Tillmann, R., Worsnop, D. R., Rohrer, F., Kiendler-Scharr, A., Petäjä, T., Kerminen, V.-M., Mentel, T. F., and Kulmala, M.: Zeppelin-led study on the onset of new particle formation in the planetary boundary layer, Atmos. Chem. Phys., 21, 12649–12663, 10.5194/acp-21-12649-2021, 2021.Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., and Pozzer, A.: The
contribution of outdoor air pollution sources to premature mortality on a
global scale, Nature, 525, 367–371, 10.1038/nature15371, 2015.Li, J., Wang, Y., Zhang, R., Smeltzer, C., Weinheimer, A., Herman, J., Boersma, K. F., Celarier, E. A., Long, R. W., Szykman, J. J., Delgado, R., Thompson, A. M., Knepp, T. N., Lamsal, L. N., Janz, S. J., Kowalewski, M. G., Liu, X., and Nowlan, C. R.: Comprehensive evaluations of diurnal NO2 measurements during DISCOVER-AQ 2011: effects of resolution-dependent representation of NOx emissions, Atmos. Chem. Phys., 21, 11133–11160, 10.5194/acp-21-11133-2021, 2021.Li, R., Wang, Z., Cui, L., Fu, H., Zhang, L., Kong, L., Chen, W., and Chen,
J.: Air pollution characteristics in China during 2015–2016: Spatiotemporal
variations and key meteorological factors, Sci. Total Environ.,
648, 902–915, 10.1016/j.scitotenv.2018.08.181, 2019.Li, X., Rohrer, F., Hofzumahaus, A., Brauers, T., Haseler, R., Bohn, B.,
Broch, S., Fuchs, H., Gomm, S., Holland, F., Jager, J., Kaiser, J., Keutsch,
F. N., Lohse, I., Lu, K. D., Tillmann, R., Wegener, R., Wolfe, G. M.,
Mentel, T. F., Kiendler-Scharr, A., and Wahner, A.: Missing Gas-Phase Source
of HONO Inferred from Zeppelin Measurements in the Troposphere, Science,
344, 292–296, 10.1126/science.1248999, 2014.Lin, J.-T. and McElroy, M. B.: Impacts of boundary layer mixing on
pollutant vertical profiles in the lower troposphere: Implications to
satellite remote sensing, Atmos. Environ., 44, 1726–1739, 10.1016/j.atmosenv.2010.02.009, 2010.Liu, C., Tuzson, B., Scheidegger, P., Looser, H., Bereiter, B., Graf, M.,
Hundt, M., Aseev, O., Maas, D., and Emmenegger, L.: Laser driving and data
processing concept for mobile trace gas sensing: design and implementation,
Rev. Sci. Instrum., 89, 065107, 10.1063/1.5026546, 2018.Liu, F., Beirle, S., Zhang, Q., Dörner, S., He, K., and Wagner, T.: NOx lifetimes and emissions of cities and power plants in polluted background estimated by satellite observations, Atmos. Chem. Phys., 16, 5283–5298, 10.5194/acp-16-5283-2016, 2016.Marenco, A., Thouret, V., Nédélec, P., Smit, H., Helten, M., Kley,
D., Karcher, F., Simon, P., Law, K., Pyle, J., Poschmann, G., Wrede, R. V.,
and Cook, C. H. T.: Measurement of ozone and water vapor by Airbus
in-service aircraft: The MOZAIC airborne program, an overview, J. Geophys. Res.-Atmos., 103, 25631–25642, 10.1029/98JD00977, 1998.Molina, L. T., Madronich, S., Gaffney, J. S., Apel, E., de Foy, B., Fast, J., Ferrare, R., Herndon, S., Jimenez, J. L., Lamb, B., Osornio-Vargas, A. R., Russell, P., Schauer, J. J., Stevens, P. S., Volkamer, R., and Zavala, M.: An overview of the MILAGRO 2006 Campaign: Mexico City emissions and their transport and transformation, Atmos. Chem. Phys., 10, 8697–8760, 10.5194/acp-10-8697-2010, 2010.Nieminen, T., Yli-Juuti, T., Manninen, H. E., Petäjä, T., Kerminen, V.-M., and Kulmala, M.: Technical note: New particle formation event forecasts during PEGASOS–Zeppelin Northern mission 2013 in Hyytiälä, Finland, Atmos. Chem. Phys., 15, 12385–12396, 10.5194/acp-15-12385-2015, 2015.Ouchi, M., Matsumi, Y., Nakayama, T., Shimizu, K., Sawada, T., Machida, T., Matsueda, H., Sawa, Y., Morino, I., Uchino, O., Tanaka, T., and Imasu, R.: Development of a balloon-borne instrument for CO2 vertical profile observations in the troposphere, Atmos. Meas. Tech., 12, 5639–5653, 10.5194/amt-12-5639-2019, 2019.Pernigotti, D., Georgieva, E., Thunis, P., and Bessagnet, B.: Impact of
meteorology on air quality modeling over the Po valley in northern Italy,
Atmos. Environ., 51, 303–310, 10.1016/j.atmosenv.2011.12.059, 2012.Petzold, A., Thouret, V., Gerbig, C., Zahn, A., Brenninkmeijer, C. A. M.,
Gallagher, M., Hermann, M., Pontaud, M., Ziereis, H., Boulanger, D.,
Marshall, J., Nedelec, P., Smit, H. G. J., Friess, U., Flaud, J. M., Wahner,
A., Cammas, J. P., Volz-Thomas, A., and Team, I.: Global-scale atmosphere
monitoring by in-service aircraft – current achievements and future
prospects of the European Research Infrastructure IAGOS, Tellus, 67, 28452, 10.3402/tellusb.v67.28452, 2015.Pöschl, U., von Kuhlmann, R., Poisson, N., and Crutzen, P. J.:
Development and Intercomparison of Condensed Isoprene Oxidation Mechanisms
for Global Atmospheric Modeling, J. Atmos. Chem., 37,
29–52, 10.1023/A:1006391009798, 2000.Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C. A., Dudhia, J.,
Gill, D. O., Coen, J. L., Gochis, D. J., Ahmadov, R., Peckham, S. E., Grell,
G. A., Michalakes, J., Trahan, S., Benjamin, S. G., Alexander, C. R.,
Dimego, G. J., Wang, W., Schwartz, C. S., Romine, G. S., Liu, Z., Snyder,
C., Chen, F., Barlage, M. J., Yu, W., and Duda, M. G.: The Weather Research
and Forecasting Model: Overview, System Efforts, and Future Directions,
B. Am. Meteorol. Soc., 98, 1717–1737,
10.1175/BAMS-D-15-00308.1, 2017.Romer, P. S., Duffey, K. C., Wooldridge, P. J., Allen, H. M., Ayres, B. R., Brown, S. S., Brune, W. H., Crounse, J. D., de Gouw, J., Draper, D. C., Feiner, P. A., Fry, J. L., Goldstein, A. H., Koss, A., Misztal, P. K., Nguyen, T. B., Olson, K., Teng, A. P., Wennberg, P. O., Wild, R. J., Zhang, L., and Cohen, R. C.: The lifetime of nitrogen oxides in an isoprene-dominated forest, Atmos. Chem. Phys., 16, 7623–7637, 10.5194/acp-16-7623-2016, 2016.Ryerson, T. B., Buhr, M. P., Frost, G. J., Goldan, P. D., Holloway, J. S.,
Hübler, G., Jobson, B. T., Kuster, W. C., McKeen, S. A., Parrish, D. D.,
Roberts, J. M., Sueper, D. T., Trainer, M., Williams, J., and Fehsenfeld, F.
C.: Emissions lifetimes and ozone formation in power plant plumes, J. Geophys. Res.-Atmos., 103, 22569–22583, 10.1029/98JD01620, 1998.Ryerson, T. B., Andrews, A. E., Angevine, W. M., Bates, T. S., Brock, C. A.,
Cairns, B., Cohen, R. C., Cooper, O. R., de Gouw, J. A., Fehsenfeld, F. C.,
Ferrare, R. A., Fischer, M. L., Flagan, R. C., Goldstein, A. H., Hair, J.
W., Hardesty, R. M., Hostetler, C. A., Jimenez, J. L., Langford, A. O.,
McCauley, E., McKeen, S. A., Molina, L. T., Nenes, A., Oltmans, S. J.,
Parrish, D. D., Pederson, J. R., Pierce, R. B., Prather, K., Quinn, P. K.,
Seinfeld, J. H., Senff, C. J., Sorooshian, A., Stutz, J., Surratt, J. D.,
Trainer, M., Volkamer, R., Williams, E. J., and Wofsy, S. C.: The 2010
California Research at the Nexus of Air Quality and Climate Change (CalNex)
field study, J. Geophys. Res.-Atmos., 118, 5830–5866,
10.1002/jgrd.50331, 2013.
Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry And Physics: From Air Pollution to Climate Change, second edition, Wiley-Interscience
Publication, Hoboken, NJ, USA, ISBN 9781118947401, 2006.Silcox, G. D., Kelly, K. E., Crosman, E. T., Whiteman, C. D., and Allen, B.
L.: Wintertime PM2.5 concentrations during persistent, multi-day cold-air
pools in a mountain valley, Atmos. Environ., 46, 17–24, 10.1016/j.atmosenv.2011.10.041, 2012.Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D., Duda,
M. G., Huang, X., Wang, W., and Powers, J. G.: A Description of the Advanced
Research WRF Version 3 (No. NCAR/TN-475+STR), University Corporation for
Atmospheric Research, 10.5065/D68S4MVH, 2008.Stull, R. B.: An Introduction to Boundary Layer Meteorology, Kluwer Academic
Publishers, Dordrecht, Boston, London, 666 pp., 10.1007/978-94-009-3027-8, 1988.Tillmann, R.: Replication Data for: Zeppelin flights 2020: Air
quality observations, Jülich DATA, V1 [data set], 10.26165/JUELICH-DATA/7ZZIXJ, 2022.Valin, L. C., Russell, A. R., and Cohen, R. C.: Variations of OH radical in
an urban plume inferred from NO2 column measurements, Geophys. Res.
Lett., 40, 1856–1860, 10.1002/grl.50267, 2013.Veefkind, J. P., Aben, I., McMullan, K., Förster, H., de Vries, J.,
Otter, G., Claas, J., Eskes, H. J., de Haan, J. F., Kleipool, Q., van Weele,
M., Hasekamp, O., Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingmann,
P., Voors, R., Kruizinga, B., Vink, R., Visser, H., and Levelt, P. F.:
TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global
observations of the atmospheric composition for climate, air quality, time
periods and ozone layer applications, Remote Sens. Environ., 120,
70–83, 10.1016/j.rse.2011.09.027, 2012.Villa, T. F., Gonzalez, F., Miljievic, B., Ristovski, Z. D., and Morawska,
L.: An Overview of Small Unmanned Aerial Vehicles for Air Quality
Measurements: Present Applications and Future Prospectives, Sensors, 16,
1072, 10.3390/s16071072, 2016.Werle, P., Mücke, R., and Slemr, F.: The limits of signal averaging in
atmospheric trace-gas monitoring by tunable diode-laser absorption
spectroscopy (TDLAS), Appl. Phys. B, 57, 131–139, 10.1007/BF00425997, 1993.WHO: WHO global air quality guidelines: particulate matter (PM2.5 and PM10, ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide), World Health Organization, Geneva, ISBN 978-92-4-003422-8, 2021.
Zhao, S., Yu, Y., Qin, D., Yin, D., Dong, L., and He, J.: Analyses of
regional pollution and transportation of PM2.5 and ozone in the city
clusters of Sichuan Basin, China, Atmos. Pollut. Res., 10,
374–385, 10.1016/j.apr.2018.08.014, 2019.Zhao, Z., Chen, S.-H., Kleeman, M. J., and Mahmud, A.: The Impact of Climate
Change on Air Quality–Related Meteorological Conditions in California. Part
II: Present versus Future Time Simulation Analysis, J. Climate, 24,
3362–3376, 10.1175/2010JCLI3850.1, 2011.