Ground-level ozone (O3) is an important pollutant that
affects both global climate change and regional air quality, with
the latter linked to detrimental effects on both human health and ecosystems.
Ozone is not directly emitted in the atmosphere but is formed from chemical
reactions involving volatile organic compounds (VOCs), nitrogen
oxides (NOx= NO + NO2) and sunlight. The photochemical
nature of ozone makes the implementation of reduction strategies challenging
and a good understanding of its formation chemistry is fundamental in order
to develop efficient strategies of ozone reduction from mitigation measures
of primary VOCs and NOx emissions.
An instrument for direct measurements of ozone production rates (OPRs) was
developed and deployed in the field as part of the IRRONIC (Indiana Radical,
Reactivity and Ozone Production Intercomparison) field campaign. The OPR
instrument is based on the principle of the previously published MOPS
instrument (Measurement of Ozone Production Sensor) but using a different
sampling design made of quartz flow tubes and a different Ox (O3
and NO2) conversion–detection scheme composed of an O3-to-NO2
conversion unit and a cavity attenuated phase shift spectroscopy (CAPS) NO2 monitor.
Tests performed in the laboratory and in the field, together with model
simulations of the radical chemistry occurring inside the flow tubes, were
used to assess (i) the reliability of the measurement principle and (ii) potential biases associated with OPR measurements.
This publication reports the first field measurements made using this
instrument to illustrate its performance. The results showed that a
photo-enhanced loss of ozone inside the sampling flow tubes disturbs the
measurements. This issue needs to be solved to be able to perform accurate
ambient measurements of ozone production rates with the instrument described
in this study. However, an attempt was made to investigate the OPR
sensitivity to NOx by adding NO inside the instrument. This type of
investigations allows checking whether our understanding of the turnover
point between NOx-limited and NOx-saturated regimes of ozone
production is well understood and does not require measuring ambient OPR but
instead only probing the change in ozone production when NO is added. During
IRRONIC, changes in ozone production rates ranging from the limit of
detection (3σ) of 6.2 ppbv h-1 up to 20 ppbv h-1 were
observed when 6 ppbv of NO was added into the flow tubes.
Introduction
Ground-level ozone (O3) is a primary constituent of photochemical smog
that irritates the respiratory system (WHO, 2013) and damages vegetation
(Ashmore, 2005). In addition, ozone is a greenhouse gas and an
important precursor of the hydroxyl radical (OH), a key species controlling
the atmospheric oxidative capacity (Monks, 2005; Rohrer et al.,
2014; Prinn, 2003). Ozone is a photochemical pollutant formed during daytime
and has an average lifetime estimated at 22 ± 2 days (Stevenson et
al., 2006), which is long enough to transport it from polluted regions to
remote areas and between continents. The local production of ozone on top of
the amount advected from elsewhere can lead to exceedances of air quality
standards in urbanized areas, making ozone pollution an issue of global
concern (Akimoto, 2003).
In the troposphere, ozone can be rapidly converted to nitrogen dioxide
(NO2) through reaction with nitric oxide (NO) and back to O3
through NO2 photolysis. This chemistry does not produce new ozone and
is known as the O3–NOx photostationary state (PSS), with NOx
being the sum of NO and NO2. The production of new ozone is driven by
the oxidation of volatile organic compounds (VOCs), which leads to the
production of hydroperoxy (HO2) and organic peroxy (RO2) radicals.
The current understanding of tropospheric ozone chemistry indicates that new
ozone is formed via reactions of these peroxy radicals with NO, which
results in the conversion of NO to NO2 without consumption of ozone
(Monks, 2005; Seinfeld and Pandis, 2006).
When ozone is produced, reactions of peroxy radicals with NO also lead to
the formation of OH, which can then oxidize other molecules of VOCs to
produce more peroxy radicals and, as a consequence, more ozone. The
propagation chemistry between ROx (OH, HO2 and RO2) radicals,
which fuels ozone production, is terminated either by NOx–ROx
reactions or by cross reactions of ROx radicals in NOx-rich and
NOx-poor environments, respectively. These two types of termination
reactions lead to different regimes of ozone production referred to as
NOx-limited or NOx-saturated when the rate of ozone production
increases or decreases with NOx, respectively. The turnover point
between the two regimes depends on NOx concentrations, VOC reactivity
and radical production rates (Kleinman, 2005). Since different air
quality regulations have to be implemented for the two different regimes,
i.e either NOx or VOC emission regulations, investigating the
sensitivity of ozone production rates (OPRs) to its precursors during field
studies, such as NOx, is important to test our understanding of the
turnover point. Understanding this complex and nonlinear radical chemistry
is key for the design of efficient emission control strategies.
The instantaneous ozone production rate, p(O3), can be calculated from
Eq. (1) as the rate of reactions between peroxy radicals and NO. The
instantaneous ozone loss rate, l(O3), can be calculated using Eq. (2),
based on reaction rates for ozone photolysis, reactions of O3 with
HOx and alkenes, and the reaction of OH with NO2, since NO2 is
a reservoir molecule for O3. The net ozone production rate,
P(O3), is then computed as the difference between instantaneous
production and loss rates as shown in Eq. (3).
pO3=kHO2+NOHO2[NO]1+∑i(kRO2,i+NORO2iNO)l(O3)=kO(1D)+H2OO(1D)H2O+kOH+O3OHO3+kHO2+O3HO2O3+∑ikO3+AlkeneiO3[Alkenei]2+kOH+NO2OHNO23P(O3)=pO3-l(O3)
Here kX+Y is the bimolecular reaction rate constant for the two
reagents X and Y. Therefore, the calculation of ozone production rates
requires peroxy radical concentrations, either from ambient measurements
(Green et al., 2006; Liu and Zhang, 2014; Fuchs et al., 2008; Dusanter et
al., 2009a; Griffith et al., 2016) or box model outputs (Goliff et al.,
2013; Stockwell et al., 2011; Saunders et al., 2003).
In most urban and suburban environments, where concentrations of NOx are
significant (10–80 ppbv), ozone production rates can reach a few tens of
ppbv h-1 (Mao et al., 2010). In highly polluted environments, such as
Mexico City or Houston, Texas, P(O3) can even exceed 100 ppbv h-1
(Shirley et al., 2006; Chen et al., 2010). Ozone production rates lower than
10 ppbv h-1 have also been observed in urban atmospheres such as
Phoenix, AZ (Kleinman et al., 2002), likely due to lower initiation rates of
radicals. Ozone production is usually low in more remote areas or forested
environments that are not impacted by anthropogenic activities (less than
2–3 ppbv h-1), due to the low NOx concentrations (Geng et al.,
2011). However, if NOx emission sources are located downwind of a
forested area, highly reactive biogenic VOCs (e.g., isoprene) can lead to an
enhancement of ozone production (Geng et al., 2011; Thornton et al., 2002).
Some studies performed in urban and suburban areas, whose objectives were to
test our understanding of the radical chemistry by contrasting measurements
and model simulations of HOx concentrations, showed that models tend to
underestimate HO2 for NO mixing ratios higher than a few ppbv (Ren
et al., 2003, 2013; Chen et al., 2010; Dusanter et al., 2009b; Kanaya et al.,
2007). In contrast, models tend to overestimate HO2 in
forested areas and regions characterized by large concentrations of biogenic
VOCs (Griffith et al., 2013; Mao et al., 2012; Pugh et al., 2010).
Disagreements are also present in the modeling of OH, with the models
underestimating the measurements at forested environments (Lelieveld et
al., 2008; Tan et al., 2001; Whalley et al., 2011; Hofzumahaus et al., 2009; Lu
et al., 2013; Pugh et al., 2010), while the agreement may be better when
colder temperatures lead to lower concentrations of isoprene and other VOCs
(Griffith et al., 2013).
The discrepancies between models and measurements question our ability to
successfully measure radical species or indicate that there are still
unknowns in our understanding of the radical and ozone production chemistry,
which in turn could lead to erroneous P(O3) calculations by
atmospheric models. These models are widely used for the design of air
quality regulations (Rao et al., 2010; Fu et al., 2006) based on emission
control strategies. It is therefore essential to ensure that chemical
mechanisms used in atmospheric models are accurate enough to simulate the
oxidative capacity of the atmosphere and to predict both absolute rates of
ozone production and the turnover point between the two ozone production
regimes.
In order to address these issues, an instrument for direct ozone production
measurements (MOPS) was developed by Cazorla and Brune (2010). The principle
of MOPS is based on differential ozone measurements between two sampling
chambers made of FEP (fluorinated ethylene propylene),
one exposed to sunlight (referred to as the sampling chamber)
to get an ozone production rate inside the chamber that mimics atmospheric
P(O3) and the other one covered with a UV filter (reference chamber)
to suppress the radical chemistry and, as a consequence, ozone production.
The difference in ozone between the two chambers divided by the exposure time
yields the ozone production rate. However, NO2 can act as a
reservoir molecule for O3 due to the rapid interconversion between
these two species, and NO2 has to be converted into O3 before
measuring ozone. The differential Ox (Ox= O3+ NO2)
measurements yield P(Ox) values, which represent P(O3) when
NO2 is efficiently photolyzed during daytime.
The first version of the MOPS instrument was tested on the campus of
Pennsylvania State University in the late summer of 2008. These tests
demonstrated the feasibility of the MOPS technique, as the instrument
responded to the presence of solar radiation and ozone precursors and yielded
rates of ozone production that were within a range of reasonable values (up
to 10 ppbv h-1) for this area. This instrument was then deployed
during the Study of Houston Atmospheric Radical Precursors (SHARP, 2009)
(Cazorla et al., 2012). The measurements were compared to ozone production
rates calculated using measurements of HO2 and NO (referred to as
calculated P(O3)) as well as modeled radical concentrations from a box
model (referred to as modeled P(O3)). Measured and calculated
P(O3) had similar peak values but the calculated P(O3) tended
to peak earlier in the morning when NO values were higher. Measured and
modeled P(O3) had a similar diurnal profile, but the modeled
P(O3) was only half the measured P(O3). The MOPS deployment
during the SHARP field campaign showed the potential of this instrument for
contributing to the understanding of the ozone-producing chemistry but was
limited by measurement uncertainties due to potential wall effects. The
heterogeneous loss of NO2 under humid conditions
(RH > 50 %) was reported as a main issue for this technique.
Recently, an improved version of the MOPS instrument was deployed during the
NASA's DISCOVER-AQ field campaign in 2013, in Houston, Texas
(Baier et al., 2015). Wall effects were reduced by improving the
design of the sampling chambers and the airflow characteristics. The
measurements made over 1 month were consistent with ambient ozone
observations and model-derived P(O3) values from previous field
campaigns in Houston. The authors, however, highlighted a possible bias due
to surface HONO production followed by its photolysis in the sampling
chamber, as well as unresolved ozone analyzer issues. HONO concentrations in
the sampling chambers were reported as 2 to 5 times higher than ambient
values, which could cause a bias up to 5–10 ppbv h-1 on the
P(O3) measurements.
A recent publication from Sadanaga et al. (2017) also reports the development
and the field deployment of another instrument to measure ozone production
rates. The main differences with MOPS are the use of two quartz flow tubes
instead of Teflon® chambers, an O3-to-NO2 conversion unit and
an NO2 detection by laser-induced fluorescence. While quartz was chosen for
the flow tubes, their inner surface is covered by a Teflon® film. The reported
detection limit is 0.5 ppbv h-1 for 60 s measurements. P(O3)
values ranging from the detection limit up to 11 ppbv h-1 were
reported for 3 days of measurements in a forested area characterized by
low mixing ratios of O3 (< 10 ppbv) and NOx
(< 1ppbv).
In this publication, we present the development and the characterization of
an ozone production rates instrument. The OPR instrument is based on
the principle of the MOPS, using sampling and detection schemes similar to
those proposed by Sadanaga et al. (2017). This publication describes this new
instrument and its characterization in the laboratory. An emphasis is given
to the modeling of the radical chemistry inside the sampling chambers to
assess potential biases on P(O3) measurements associated with
instrumental characteristics and operating conditions. The publication also
reports preliminary field results from the Indiana Radical, Reactivity and
Ozone Production Intercomparison (IRRONIC) campaign, which highlight the
current limitations of this instrument.
Schematic of the OPR instrument. O3 converted into NO2
by reaction with NO. Difference in Ox mixing ratios between the two
flow tubes quantified by CAPS. SV: solenoid valves. MFC: mass flow
controller.
Experimental sectionDescription of the OPR instrument
The principle of the OPR is based on differential Ox measurements
between an ambient flow tube, exposed to sunlight to mimic ambient
photochemistry, and a reference flow tube, covered with an
Ultem® film (polyetherimide, 0.25 mm thick,
CS Hyde Co., USA) to block wavelengths lower than 400 nm, which in turn
should suppress ozone production. As mentioned above for the MOPS instrument,
the fast partitioning between O3 and NO2 requires measuring Ox
instead of O3, assuming that P(O3) is equal to P(Ox) when
NO2 is efficiently photolyzed during daytime. P(Ox) is calculated
from the difference in Ox between the two flow tubes, ΔOx,
divided by the mean residence time (τ) of air inside the tubes as shown
in Eq. (4).
POx=ΔOxτ=Oxamb-Oxrefτ
A detailed schematic of the OPR instrument is shown in Fig. 1. The two
flow tubes exhibit the same geometry and are made of quartz (14 cm i.d. and 70 cm long).
Each flow tube is connected to the inlet and outlet flanges that
are made of anodized aluminum and PTFE. Since a major issue previously
identified for the MOPS instrument was wall effects causing NO2 losses
(Cazorla and Brune, 2010), the inner geometry of the flanges
was designed based on fluid dynamics simulations using STAR-CCM+ v8
(CD-adapco). The geometry was optimized to minimize radial mixing and
recirculation eddies that could increase wall effects. The design of the
flanges can be found in the Supplement (Fig. S1).
Each flange consists of two parts. For both the inlet and outlet, a conical
PTFE piece is screwed inside an external aluminum flange. Four holes are
drilled symmetrically around the aluminum flanges to inject zero air around
the PTFE inlet and to extract air around the PTFE outlet. The lengths of the
inlet and outlet flanges are 25 and 14 cm, respectively. The PTFE inlet has
an external diameter of 2.54 cm which increases to 7 cm over a length of 20 cm.
The PTFE outlet starts from a diameter of 3 cm which decreases to 1.27 cm
over 10 cm. The aluminum flanges exhibit a curved conical inner surface
around the PTFE parts.
Ambient air is sampled through a common inlet (PFA, perfluoroalkoxy, 1.27 cm o.d.) at a flow
rate of 4 L min-1 and is transferred into both flow tubes through the
internal PTFE inlets (2 L min-1), while additional zero air (250 mL min-1)
is injected at the outer periphery of these inlets inside the
flanges. This flow of zero air helps in keeping the ambient airflow forward,
minimizing recirculation eddies, and should therefore reduce wall effects.
The dilution of the sampled air is approximately 10 %. At the outlet, air
is sampled only from the center of the flow tube, through the PTFE outlet
(750 mL min-1), while the rest is extracted by an external pump
(1.5 L min-1). Both the injection and extraction of air are regulated by mass
flow controllers (MFC in Fig. 1).
The Ultem® filter is placed on a rectangular
aluminum frame outside of the reference flow tube, which enables the flow of
ambient air between the filter and the flow tube using fans. This setup
allows the two flow tubes to be kept at the same temperature by extracting
the heat released by the filter. For the same reason, a frame covered by an
FEP film (.005 cm thick, DuPont Teflon® FEP), transparent to the solar
radiation, is used for the ambient flow tube to reduce heat dissipation by
the wind.
The air exiting the two flow tubes is mixed with 10 SCCM of NO (50 ppmv,
Indiana Oxygen, USA), leading to an NO mixing ratio of 650 ppbv in the
conversion unit. The mixing of the gases takes place in two identical pyrex
chambers, providing a reaction time of approximately 22 s at 20 ∘C,
which is long enough to quantitatively titrate O3 into NO2.
Both the relative humidity and temperature are monitored in the airflow
extracted from the flow tubes and at the O3-to-NO2 conversion
unit.
Downstream the conversion unit, Ox (O3+ NO2) is measured by
an Aerodyne cavity attenuated phase shift spectroscopy (CAPS) NO2
monitor (Kebabian et al., 2005, 2008). Since the CAPS is a single-cell
monitor, the measurements from the ambient and reference flow tubes are taken
sequentially, using two solenoid valves (SV1 and SV2 in Fig. 1). When air
from the ambient (or reference) flow tube is sampled by the CAPS monitor
(750 mL min-1), the same flow rate of air is extracted from the other
flow tube by a mass flow controller connected to a pump. The valves switch
every 1 min, alternating the flows that are sampled by the CAPS monitor and
the pump. ΔOx is calculated as the difference between an ambient
flow tube measurement and the average of two surrounding reference
measurements, leading to a P(Ox) measurement every 2 min. The first
15 s of each 1 min measurement is discarded since they describe a
transient regime between ambient and reference flow tube measurements. Ozone
production values are calculated from Eq. (4).
The zero of the monitor was checked frequently during the field campaign
using dry zero air and was found to change by less than 0.3 ppbv over 12 h.
It is worth noting that a slow drift of the zero does not impact the
measurements since the same CAPS monitor was used to measure Ox at the
exit of both flow tubes with a switching time of 1 min. The calculation of
P(Ox) implies a subtraction between the measured Ox
concentrations, which cancels out any offset in the monitor's zero. The
monitor was calibrated with an NO2 standard mixture at 190 ± 3 ppb
(2σ) certified by LNE (French National Metrology Institute). The
detection limit (3σ) for a 1 s integration time was 300 pptv.
The measurement sequence is automated and controlled through a National
Instruments LabVIEW 2013 interface. Three USB data acquisition boards are
used (NI-9264, NI-6008, NI-6009) to control the two solenoid valves and the
seven mass flow controllers, as well as to record signals from the CAPS
monitor and sensors setup for humidity and temperature measurements.
Laboratory and field experiments conducted to characterize the
OPR
Experiments conducted to characterize the OPR instrument include
measurements of the mean residence time, Ox losses, and HONO production
rates in the flow tubes and measurements of the O3-to-NO2
conversion efficiency.
The mean residence time was quantified in each flow tube by injecting short
pulses of toluene (10 s in duration) at the inlet of the flow tubes. A
PTR-ToF-MS (proton transfer reaction time-of-flight mass spectrometer, KORE
Technology Inc.) was connected at the outlets to measure the time it takes
for a pulse introduced at the inlets to exit the flow tubes. The pulse
experiment was repeated five times, and the average was calculated as the mean
residence time.
O3 and NO2 losses inside both flow tubes were measured in the
laboratory and during the field deployment described below by sampling
mixtures of zero air and O3 (or NO2) at known mixing ratios and by
measuring NO2 downstream the conversion unit (or directly at the exit
of the flow tubes). A relative loss was calculated from the difference in
concentrations between the inlet and outlet and was referenced to the inlet
concentration. These tests were performed at relative humidity values
ranging from 0 to 65 %.
The release of HONO from the inner surface of the flow tubes was quantified
using a chemical ionization mass spectrometer (CIMS, Georgia Tech). Mixtures
of NO2 and humid zero air were introduced into the flow tubes, while
HONO was measured both at the inlet and outlet. These experiments were
performed under dark conditions as well as under various irradiated
conditions using artificial UV light provided by two types of fluorescent
lamps: four lamps centered at 312 nm (Vilber, T-15.M) and four lamps centered at
365 nm (Philips, T12).
Finally, the O3-to-NO2 conversion efficiency was measured by
sampling zero air enriched with O3 (3–170 ppbv) through the mixing
chambers of the conversion unit, varying the flow of NO and measuring
NO2 with the CAPS monitor. These tests were performed at various
relative humidities (25–60 %). The conversion efficiency at a specific NO
level was calculated from the ratio of NO2 measured at this NO level to
that measured when 700 ppbv of NO was added, assuming for the latter that
100 % of O3 was converted. This assumption is verified from kinetic
considerations (kNO+O3= 1.80 × 10-14 cm3 molecule-1 s-1
and 23 s of residence time in the conversion
unit) and from the observation of a plateau for NO mixing ratios higher than
500 ppbv.
Modeling experiments conducted to characterize the OPR
As previously mentioned, the measurement principle of ozone production rates
is based on the assumption that (i) P(Ox) in the ambient flow tube is
similar to P(Ox) in the atmosphere and (ii) there is no significant
production of ozone in the reference flow tube. Box model simulations were
performed to check whether this assumption is valid. In addition, simulations
were also conducted to investigate the impact on OPR measurements of (a) an
O3-to-NO2 conversion efficiency lower than 100 %, (b) NO2
and O3 losses and (c) HONO production inside the flow tubes, (d) a
possible increase in the temperature in the reference flow tube due to the UV
filter, (e) the dilution of ambient air by injecting zero air inside the flow
tubes at the periphery of the inlets and (f) reactions of OH with NOz
species producing Ox.
Selected data and chemical mechanism
The simulations were performed using a box model based on the Regional
Atmospheric Chemistry Mechanism (RACM) (Stockwell et al., 1997).
RACM is a gas-phase chemical mechanism developed for the modeling of
regional atmospheric chemistry and includes 17 stable inorganic species, 4
inorganic intermediates, 32 stable organic species and 24 organic
intermediates for a total of 237 chemical reactions. Organic compounds are
grouped together to form a manageable set of compounds. Only 8 organic
species are treated explicitly (methane, ethane, ethene, isoprene,
formaldehyde, glyoxal, methyl hydrogen peroxide and formic acid) and 24 are
surrogates that are grouped based on emission rates, chemical structure and
reactivity with the OH radical.
Measurements from several field campaigns were used for this modeling
exercise, including measurements performed in (i) a megacity as part of the
2006 Mexico City Metropolitan Area (MCMA)
(Dusanter et al., 2009b) and (ii) an
urban area as part of the 2010 California Nexus (CalNex) campaign
(Griffith et al., 2016). Two days characterized by elevated and low Ox concentrations were selected
for each campaign and are presented in the Supplement (Table S1 and Fig. S2).
For both campaigns, ozone was higher by approximately a factor of 2 on high
O3 days (≈ 100 ppbv) compared to low O3 days (≈ 50 ppbv). However, while both high and low ozone levels were similar for
the selected days of these campaigns, large differences were observed for
NOx (6–120 ppbv) and OH reactivity (8–86 s-1). Since OH
reactivity and NOx are main drivers of ozone production, these modeling
results are expected to provide a good assessment of potential biases
associated with P(Ox) measurement for any urban environments.
Modeling of ambient <inline-formula><mml:math id="M211" display="inline"><mml:mi>P</mml:mi></mml:math></inline-formula>(O<inline-formula><mml:math id="M212" display="inline"><mml:mrow><mml:msub><mml:mi/><mml:mi>x</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> values
The model was constrained by 10 min (MCMA) or 15 min (CalNex) average
measurements of temperature, pressure, humidity, organic and inorganic
species, and J values, while the differential equation system was integrated
by the FACSIMILE solver (MCPA Software Ltd.). In total, 24 J values were used
to constrain the model, as derived in
Dusanter et al. (2009b), together with
7 inorganic and 17 organic species or surrogates. Tables reporting the
constrained species and J values can be found in the Supplement (Tables S2
and S3). The integration time was set at 30 h with constrained species
reinitialized every 2 s. Ambient ozone production values were then
calculated from Eqs. (1) to (3) and are referred to as
P(Ox)atm in the following. In total, 18
surrogates of RO2 species were taken into account to calculate
p(O3) from Eq. (1), while 10 unsaturated surrogates were used to
calculate l(O3) from Eq. (2) (Table S4).
Modeling of <inline-formula><mml:math id="M220" display="inline"><mml:mi>P</mml:mi></mml:math></inline-formula>(O<inline-formula><mml:math id="M221" display="inline"><mml:mrow><mml:msub><mml:mi/><mml:mi>x</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> values in the ambient and reference flow
tubes
Modeling OPR measurements requires simulating the chemistry inside each flow
tube. J values used to model the chemistry in the ambient flow tube were the
same as for the ambient modeling since the quartz material used to build the
flow tubes is transparent to solar irradiation. For the reference flow tube,
J values were scaled based on the absorption coefficient of the Ultem® film
(Philipp et al., 1989) as discussed in the Supplement
(Sect. S2.1).
The model was constrained by the same meteorological parameters and chemical
species as for P(Ox)atm. In addition, modeled
concentrations of VOC-oxidation products and peroxy radicals inferred from
the modeling of P(Ox)atm were also constrained in
these simulations (Table S5), assuming that a significant fraction of the
peroxy radicals is not lost in the sampling line. The constrained concentrations were
initialized once, at the entrance of the flow tubes, and the simulations were
run for 10 min without reinitializing the constraints. The simulations were
run separately for each flow tube and P(Ox) was calculated every 15 s
from Eq. (3). An integrated value of P(Ox) was then computed for the
flow tube residence time.
P(Ox)atm is compared to the integrated
P(Ox) value from the ambient flow tube (referred to as
P(Ox)amb) to check whether ozone production in
the ambient flow tube is similar to ambient ozone production. The integrated
value of P(Ox) in the reference flow tube (referred to as
P(Ox)ref) is also scrutinized to check whether
ozone production is negligible in this flow tube.
Modeling of OPR measurements
Since the OPR instrument measures Ox after conversion of O3 into
NO2, NO2 concentrations at the exit of the conversion unit are
calculated from the conversion efficiency C as shown in Eq. (5).
NO2conv=NO2τ+CO3τ
Here the concentrations reflect those observed at the exit of the conversion
unit (subscript: conv) and at the exit of the flow tubes (subscript: τ). The concentrations at the exit of the flow tubes are the model outputs at
the residence time τ. Based on Eq. (4), the ozone production rate
measured by the OPR, P(Ox)OPR, is then calculated from
Eq. (6).
6P(Ox)OPR=NO2conv,amb-NO2conv,refτ=NO2τ,amb-NO2τ,ref+C(O3τ,amb-O3τ,ref)τ
In this equation the subscripts “amb” and “ref” indicate the ambient and the
reference flow tubes, respectively. A bias in OPR measurements can be
quantified by comparing P(Ox)OPR to
P(Ox)atm assuming a conversion efficiency of
100 % for the conversion units.
Sensitivity tests
The simulation performed without Ox losses and HONO production in the
flow tubes, no dilution and no temperature differences between the tubes
will be referred to as the base simulation in the following. All simulations
performed including sensitivity tests are compared to the results from the
base simulation to assess the impact of operating conditions on ozone
production measurements.
To assess the impact of a conversion efficiency lower than 100 %,
P(Ox)OPR is calculated from Eq. (6) by varying
the conversion efficiency using the model outputs from the base simulation.
P(Ox) values inferred when varying the conversion efficiency are
compared to values calculated for a conversion efficiency of 100 %. To
account for Ox losses, a similar sink of O3 or NO2 is
introduced in the model for each flow tube, with a first-order loss rate
ranging from 1.5 × 10-4 to 1.2 × 10-3 s-1.
This range of loss rates corresponds to a relative loss of 4–28 %. The
measured P(Ox)OPR is again calculated by Eq. (6)
assuming a conversion efficiency of 100 % and compared to the base
simulation. Sensitivity tests were also performed assuming that the loss of
NO2 on the quartz surface led to HONO formation with the same
first-order rate as the NO2 loss, or by including a HONO source in the model,
independent of NO2, with production rates comparable to experimental
observations. Additional sensitivity tests focused on decreasing the
constrained species by 5–30 % to assess the impact of diluting ambient
air in the flow tubes, as well as increasing the temperature of the reference
flow tube by 2 to 20 % to simulate a heat release by the UV filter.
Finally, sensitivity tests were performed to investigate whether reactions of
OH with NOz species that produce Ox could significantly impact the
OPR measurements. NOz species producing NO2 or NO3 (NO2
reservoir) in the model when reacting with OH are HONO, HO2NO2,
organic nitrates, HNO3, PANs and unsaturated PANs (peroxyacyl nitrates).
The NO2 and
NO3 products of the reactions mentioned above were removed from the
model for the sensitivity test.
Description of the field measurements
The OPR instrument was deployed in the field, as part of the Indiana
Radical, Reactivity and Ozone Production Intercomparison campaign
in Bloomington, Indiana, during July 2015. The measurements were taken at
the Indiana University Research and Teaching Preserve (IURTP) field
laboratory (39.1908∘ N, 86.502∘ W), 2.5 km northeast of the Indiana University
Bloomington campus.
The site is a mixed deciduous forest containing northern red oaks and
big-tooth aspens, which are known to be strong emitters of isoprene and
monoterpenes (Isebrands et al., 1999; Funk et al., 2005). A highway (E
Matlock Road, State Route 45) is located 1 km southwest, and therefore the
site can be impacted by anthropogenic emissions. The OPR flow tubes were
setup on scaffolding to expose them to the sunlight for the entire day.
The conversion units and the CAPS monitor were housed inside the laboratory
and were connected to the flow tubes using 4 m long heated 1/4′′ PFA lines.
This campaign included measurements of OH, HO2* (HO2+αRO2), total peroxy radicals (HO2+RO2),
total OH reactivity, NOx, O3, anthropogenic and biogenic VOCs,
radiation and meteorological data. For the measurements presented in this
publication, VOCs were measured by an online TD-GC–FID (thermal desorption and gas chromatography
with flame ionization detection), an online
TD-GC–FID-MS (Badol et al., 2004; Roukos et al., 2009), and offline samplers
for dinitrophenylhydrazine (DNPH) cartridges (Waters Sep-Pak) and sorbent
cartridges (Carbopack B and Carbopack C) by IMT Lille Douai. Measurements of NO
(chemiluminescence, Thermo Scientific model 42i-TL), NO2 (cavity attenuated phase
shift spectroscopy, Aerodyne Research) and ozone (2B Tech model 202 sensor)
were also conducted by the University of Massachusetts. Measurements of
J(NO2) were performed using a scanning actinic flux spectroradiometer
(SAFS, METCON) from the University of Houston, while meteorological data,
including temperature, relative humidity, wind speed and wind direction, were
measured with a meteorological station from Montana State University.
The OPR measurements were focused on investigating the sensitivity of
P(Ox) to NOx (see Sect. 3.3). This was achieved by introducing a
certain amount of NO (ppbv range) inside the OPR sampling line for 40 min
and then stopping the NO addition for another 40 min. This pattern was
repeated continuously all along the campaign. The level of NO added in the
flow tubes when the addition was turned ON was kept at a constant level for
several days before changing it for another period of several days. The first
20 min of each 40 min measurement was discarded, since it corresponds to
a transient regime between the disturbed–undisturbed P(Ox)
measurements due to the long air-exchange time in the flow tubes (see
Sect. 3.1.1). The addition of NO in the OPR sampling line was performed
through a 1/8′′ o.d. stainless steel tube using an NO cylinder (3.75 ppmv in
N2) from Indiana Oxygen and a mass flow controller. After the mixing
point, a length of 10 m of 1/2′′ o.d. PFA tubing was used as the sampling line
to ensure a good mixing of NO with the sampled air, leading to a residence
time of approximately 10 s in the line at a total flow rate of
4 L min-1.
Results and discussionLaboratory characterizationQuantification of the flow tube residence time
As described in the experimental section, pulses of toluene were injected in
the flow tubes to quantify the mean residence time. One of the five experiments
that were conducted is shown in Fig. 2. The pulse shape is asymmetric and
exhibits a long tail, indicating that a large range of residence times is
observed in the flow tubes. The toluene pulse is treated as a probability
distribution of the time variable t, with the average residence time in the
flow tubes being the mean of the probability distribution. The latter is
calculated as a weighted average of the possible values that the time
variable can take. The average residence time from the five toluene pulse
experiments was 4.52 ± 0.22 min (1σ). The uncertainty reported
for the residence time will lead to a 4.9 % error (1σ) on the
P(Ox) measurements. While plug flow conditions are not met in the flow
tubes, it is interesting to note that a residence time of 4.79 min would be
expected from plug flow conditions at a total flow rate of
2.25 L min-1 for a volume of 10.8 L in each flow tube.The asymmetry
of the peak indicates that the flow rate at the central axis of the tube is
larger, with the first molecules of toluene being sampled after approximately
2 min (Fig. 2). These observations are similar to those reported by Cazorla
and Brune (2010) for sampling chambers exhibiting a different geometry and
operated under different flow conditions. A similar asymmetric shape is
observed for the pulse. Further work is needed on the OPR instrument to
reduce the skewness of the time distribution.
Example of pulse experiments for the quantification of the flow
tube residence time. The pulse of toluene generated at the entrance of the flow
tube at t=0 s.
Tests were also performed to quantify the air-exchange time in the flow
tubes. These tests were performed by sampling a constant concentration of
Ox species with the OPR instrument until a stable Ox signal was
measured. A quick concentration change in Ox was then induced at the
inlet and the time needed to reach 95 % of a new stable Ox signal
was defined as the air-exchange time. The air-exchange time was quantified at
approximately 20 min, corresponding to a maximum residence time of 1200 s.
As mentioned in Sect. 2.1, a P(Ox) value is recorded every 2 min.
Since the air-exchange time is 20 min, the 2 min P(Ox) values are
not independent from each other and therefore the OPR instrument cannot
detect rapid changes in P(Ox). In order to get independent
measurements of P(Ox), the OPR measurements are therefore averaged
over 20 min.
Quantification of O<inline-formula><mml:math id="M319" display="inline"><mml:msub><mml:mi/><mml:mi>x</mml:mi></mml:msub></mml:math></inline-formula> losses in the flow tubes
The principle of the OPR instrument requires the only difference between
the two flow tubes to be the suppression of gas-phase photolytic reactions
leading to the formation of free radicals in the reference tube. All other
characteristics, including flow pattern and potential gas–wall interactions,
should be the same in the two flow tubes so that they cancel out in the
differential Ox measurement. However, if Ox losses were slightly
different between the two flow tubes, it could significantly impact the
P(Ox) measurements. For example, a 2 % difference in Ox
losses between the flow tubes would lead to a bias of 27 ppbv h-1 on
the measurements for an ambient Ox level of 100 ppbv and a residence
time of 4.5 min.
NO2 and O3 relative losses measured during the IRRONIC
field campaign at different relative humidity values. Losses in the ambient
and reference flow tubes are shown in the top and middle panels,
respectively. The bottom panel reports the difference in relative losses
between the two flow tubes. On 28 July O3 losses were measured under
sunny conditions (orange squares: ambient flow irradiated and reference flow
tube covered by the UV filter; orange triangles: both flow tubes
irradiated) and dark conditions (orange circles: both flow tubes covered by
an opaque cover).
Figure 3 shows the results of NO2 and O3 loss tests for the two
flow tubes, performed at different dates during 1 month of field operation
during the IRRONIC campaign and at different relative humidity values. All
NO2 loss tests were performed under dark conditions, i.e., with both flow
tubes covered by an opaque cover. Figure 3a, c and e show that the NO2
loss is lower than 5 % in both flow tubes and is close to 3 % on
average. When the two flow tubes are operated under the same conditions, the
relative loss in the reference tube seems to be higher than the loss in the
ambient tube by only 1 % at most (Fig. 3e). For an ambient NO2
mixing ratio of 30 ppbv, a difference of 1 % in NO2 losses between
the flow tubes would lead to a 4 ppbv h-1 bias in the P(Ox)
measurements.
Cazorla and Brune (2010) reported an uncertainty of ±14 % for the
MOPS instrument due to potential differences in relative humidity between the
two sampling chambers, which in turn leads to different NO2 losses. This
was mainly due to a higher temperature in the reference chamber, which is
covered by the UV filter. However, the fans used on the OPR instrument to
drive the flow of ambient air between the UV filter and the flow tube minimize the
temperature differences between the two tubes, leading to relative humidity
differences lower than 4 %, as observed during the field testing.
Figure 3e also shows that a decrease in relative humidity from 65 to 0 %
only leads to a small decrease in the NO2 loss by 1–2 %. A small
difference of 4 % in relative humidity between the two flow tubes is
therefore not expected to lead to additional errors in the P(Ox)
measurements. Further analysis of the impact of NO2 losses on the
P(Ox) measurements is discussed in the modeling results section.
Ozone loss tests were mainly performed under dark conditions during this
campaign. On 28 July, however, O3 losses were measured with (a) the
ambient flow tube exposed to the sunlight and the reference tube covered by
the UV filter (orange squares), (b) both flow tubes exposed to the sunlight
(orange triangles) and (c) both tubes covered by a dark cover (orange
circles). For the first days of the campaign (29 June–8 July), a close
inspection of the measurement scatter shown in Fig. 3b, d indicates
that the relative loss of O3 is at most close to 5 %. However, ozone
loss tests performed on 28 July, after 1 month of operation in the field,
revealed an increase in the relative loss of up to 13–15 %.
Particular attention should be paid to the three different tests performed on
28 July regarding the irradiation conditions. When the losses are quantified
under dark conditions (orange circles in Fig. 3f), the losses are equal
between the two flow tubes and close to 13 %. However, when the ambient
flow tube is irradiated and the reference is covered by the UV filter (orange
squares), it can be seen that the relative loss in the ambient tube is higher
than in the reference by approximately 3 %. Box modeling has shown that
the gas-phase photolysis of O3 in the ambient flow tube could at most
account for 0.05 % of this additional ozone loss. Therefore, there seems
to be a photo-enhanced ozone loss that takes place when the ambient flow tube
is irradiated. For an ambient O3 level of 50 ppbv, this difference in
O3 losses would lead to a negative P(Ox) bias of approximately
20 ppbv h-1.
Additional tests were performed after the campaign under different
conditions of illumination, RH and ozone mixing ratios to thoroughly
investigate the loss of ozone on the quartz material. Overall, these tests
showed that the dark loss can be reduced below 5 % for several days of
ambient measurements if the quartz flow tubes are conditioned with elevated
O3 mixing ratios at high relative humidity. These results indicate that
the low value observed for the loss after the conditioning period may be due
to (i) a cleanup of the surfaces, removing unsaturated organic species that
may be absorbed on the quartz surface; or (ii) a chemical treatment of the
surface, deactivating sites where ozone could be lost during ambient
measurements. Tests were also performed to investigate the potential
photo-enhanced loss of ozone discussed above. These tests were performed by
irradiating the two flow tubes with UV lamps (312 and 365 nm), introducing
known mixtures of ozone–zero air in the flow tubes and varying humidity
and/or light conditions. While a photo-enhanced loss of ozone was not
observed in the reference flow tube covered with the UV filter, a
significant photo-enhanced loss of up to 7.5 % was observed for the
ambient flow tube when the 312 nm lamps were used, with a dependence on
light intensity. In contrast, irradiating the ambient flow tube with the 365 nm
lamps did not lead to a photo-enhanced loss, indicating that lower
wavelengths are inducing the loss process responsible for the photo-enhanced
loss. This issue is further discussed in the field deployment Sect. 3.3.
Heterogeneous HONO production in the flow tubes
The formation of HONO in the flow tubes was investigated in the laboratory
by sampling humid zero air (25–80 % RH) enriched with NO2 at various
mixing ratios (0–100 ppbv) and by measuring HONO at the exit of the tubes as
described above in Sect. 2.2. Both clean and contaminated (used for more
than 1 month during the IRRONIC campaign) flow tubes were tested to assess
the magnitude of HONO production rates and to examine whether there is a
dependence on NO2 mixing ratios, humidity and irradiation. Mixing
ratios of HONO up to 250 and 700 pptv were measured under dark conditions
for clean and contaminated flow tubes, respectively. Higher mixing ratios of
up to 1.5 ppbv were measured under irradiated conditions in the ambient flow
tube (J(NO2)= 1.4 × 10-3 s-1;
J(HONO) = 3.1 × 10-4 s-1).
Dividing the measured mixing ratios of HONO by the residence time in the
flow tubes (i.e., 4.5 min), an average production rate can be calculated
under dark and irradiated conditions. It is important to note, however, that
HONO is also photolyzed at the wavelengths emitted by the lamps (312 and
365 nm) and production rates calculated under irradiated conditions
represent lower bounds. It is estimated that, for the J(HONO) value mentioned
above and a negligible loss of HONO from OH + HONO, the HONO production rate
will be underestimated by less than 8 %. The dark HONO production is on
the order of 9 ppbv h-1 in both flow tubes, while the total HONO
production under irradiated conditions (dark + photo-enhanced) can reach
up to 20 ppbv h-1 in the ambient flow tube. In the reference flow tube,
the UV light did not impact the formation of HONO, since wavelengths below
400 nm are blocked by the UV filter.
The HONO production rate was not observed to depend on NO2 or humidity
and HONO could even be released when no NO2 was introduced into the
contaminated flow tubes. These results strongly suggest that
nitro-containing compounds and organic photosensitizers were adsorbed on the
walls of the flow tubes and that the HONO production rate depends on
contamination levels. Indeed, it was observed that flowing humid zero air in
the flow tubes for a few days could reduce the HONO production rate to
negligible levels.
Quantification of the conversion efficiency
Based on kinetic considerations for the titration reaction of O3 by NO,
i.e., a rate constant of 1.80 × 10-14 cm3 molecule-1 s-1 at 298 K
(Atkinson et al., 2004), a
reaction time of 23 s and the addition of 500 ppbv of NO in the
conversion unit, an O3-to-NO2 conversion efficiency of 99.5 %
is expected. These calculations are shown in Fig. 4 (black solid line) for
different mixing ratios of NO (50–800 ppbv) together with laboratory
measurements (symbols) made at different O3 levels. This figure shows
that a plateau of almost 100 % conversion is observed at NO mixing
ratios higher than 500 ppbv. These experimental results are in good
agreement with the calculated curve, although the measurements performed at
a low O3 mixing ratio of 3.5 ppbv slightly underpredict the curve for
NO mixing ratios lower than 500 ppbv. However, the conversion plateau is
reached for all Ox levels and both conversion units (one for each flow
tube) for NO mixing ratios higher than 500 ppbv. During the field deployment
of the instrument, an NO mixing ratio of 650 ppbv was used to ensure that
the difference in conversion efficiency between the two mixing chambers was
lower than 0.1 % and could be assumed to be 100 % for both chambers.
O3-to-NO2 conversion efficiency for various NO mixing
ratios, Ox levels and relative humidity values. The black curve was
calculated from the reaction rate constant between O3 and NO and a
reaction time of 23 s. Open symbols (3.5 ppbv O3) are hidden behind the
plain symbols for NO > 500 ppbv. “Ref.” and “Amb.” refer to
the conversion units coupled to the reference and ambient flow tubes,
respectively.
In the first version of MOPS (Cazorla and Brune, 2010) the
NO2-to-O3 conversion was performed by photolyzing NO2 using a
light-emitting diode, achieving a maximum conversion efficiency of 88 % at
17 ppbv of NO2. In the most recent version of the instrument
(Baier et al., 2015), the conversion efficiency was increased to
88–97 % for NO2 mixing ratios lower than 35 ppbv using a
highly efficient UV lamp that provided 10 times more photons than the
light-emitting diodes. In the MOPS instrument, however, the conversion
efficiency depends on NO2 levels, as well as on the intensity of the
lamp that could drift during a long period of use in the field. In the OPR
instrument, the conversion efficiency is stable and does not depend on
O3 mixing ratios. On the other hand, an NO cylinder is required to
perform the conversion and possible NO2 impurities in the cylinder have
to be monitored. Indeed, NO2 impurities coming either from the NO
mixture or from NO oxidation in the lines were observed but were kept at low
levels of approximately 6–10 ppbv. Since this impurity is present in both
the ambient and reference channel, it does not affect the P(Ox)
determination.
Detection limit of the OPR
The detection limit (DL) of the CAPS monitor was quantified by sampling zero
air for several hours after several days of conditioning with ambient air.
The time resolution was set to 1 s and the zero measurements were averaged
over 45 s segments, corresponding to the OPR measurement averaging time. The
detection limit (3σ) for a 45 s integration time was quantified at
34 pptv. This detection limit for NO2 together with a residence time of
4.5 min in the flow tubes should lead to a detection limit of
0.6 ppbv h-1 for 2 min P(Ox) measurements (1 min measurement
from each flow tube). However, nighttime measurements made during the IRRONIC
field campaign revealed that the measurement scattering for the complete
setup (flow tubes + O3-to-NO2 conversion unit + CAPS) was
significantly larger than that expected from the noise of the CAPS monitor.
Based on the observed nighttime 1σ variability of
2.1 ppbv h-1, a limit of detection (3σ) of 6.2 ppbv h-1
was inferred for the OPR instrument. The scatter in P(Ox) measurements
does not only depend on the precision of the CAPS monitor, but also depends
on how fast each flow tube responds to variations in Ox at the inlet.
Indeed, if the time constant for the response is slightly different between
the two flow tubes, fluctuations of Ox species at the inlet will introduce
some scatter in the OPR measurements. In addition, small changes in
temperature and humidity may evenly affect Ox losses in each flow tube,
leading to additional scatter in the P(Ox) measurements.
Numerical modeling
As mentioned in the experimental section, several days from different field
campaigns were selected to model ambient P(Ox), P(Ox) in both
flow tubes and the impact of some operating conditions on the OPR
measurements. The results from 30 May 2010 of the CalNex field campaign were
selected to illustrate the discussion, and results from the other days are
shown in the Supplement (Figs. S4, S5, S7–S9). A detailed analysis of the
chemistry occurring in each flow tube is discussed below to assess the
reliability of OPR measurements.
OH (a, b) and total peroxy (HO2+RO2;
c, d) radical budgets for 30 May 2010 of the CalNex 2010 campaign.
Radical budgets modeled for the ambient (a, c) and the
reference (b, d) flow tubes. The OH chain length is also presented
in an insert (a, b) for each flow tube. “Init” in the legend
indicates initiation reactions and “hv” represents photons.
Radical budget in flow tubes
An analysis of the radical budget was performed in each flow tube to gain
insights into the processes driving radical production and loss routes.
Figure 5 shows the production and loss rates of OH (upper panel) and peroxy
radicals (lower panel) for each flow tube on 30 May 2010 during CalNex. The
production and loss rates were calculated taking into account initiation,
propagation and termination processes as described below.
OH production rates were calculated from photolytic reactions involving
closed shell molecules (O3, HONO, H2O2, HNO3,
HO2NO2 and organic peroxides), reactions of O3 with alkenes
and the propagation of HO2 by reaction with NO. Loss routes of OH
include propagation reactions to HO2 and RO2 by reaction with CO
and VOCs and termination reactions of OH with NO2 and other species
(NO, PANs, HNO3, HONO and HNO4). For peroxy radicals, production
routes include the photolysis of organic species (carbonyls, organic
peroxides and organic nitrates), the ozonolysis of alkenes, PAN
decomposition and the propagation of OH. Loss routes were calculated from
reactions of peroxy radicals with NOx, self or cross reactions between
peroxy radicals, and propagation of HO2 to OH.
Figure 5 clearly shows that the UV filter covering the reference flow tube
leads to a decrease in the initiation rates of all radicals by more than a
factor of 10 and a decrease in their propagation rates by at least a factor
of 30. In the ambient flow tube, photolytic reactions of oxygenated VOCs are the most
important initiation routes of peroxy radicals, with a contribution of
approximately 95 %. HONO and O3 photolysis are the most important
initiation routes of OH, contributing approximately 45 % each. In the
reference flow tube, the primary route of radical initiation is
O3–alkenes reactions since wavelengths below 400 nm are suppressed.
The propagation reactions are important in both flow tubes for the
production and loss of OH and peroxy radicals. However, the partitioning
between initiation and propagation processes is different in the two tubes,
which in turn leads to different OH chain lengths. The OH chain length is
calculated as the rate of propagation of HO2 to OH divided by the total
initiation of ROx radicals. As can be seen from Fig. 5, the OH chain
length is fairly constant at a value of 3 in the ambient flow tube, while in
the reference flow tube it quickly decreases to unity for most of the day
and to values lower than unity in the late afternoon. Therefore, in addition
to lowering initiation rates of radicals, the UV filter allows the reduction
of ozone production by lowering the cycling efficiency within the pool of
ROx radicals.
A close inspection of the radical termination rates in Fig. 5 indicates that
the peroxy–NOx termination reactions are almost suppressed in the
reference flow tube. This observation is also supported by Fig. S6, which
shows time series of the peroxy radicals (HO2 and RO2) and NO in
each flow tube at a residence time of 4.5 min. Since NO2 photolysis is
almost eliminated in this tube, the O3–NOx PSS is shifted towards
NO2 due to the reaction of NO with O3. As a result, NO mixing
ratios in the reference flow tube are at least 1 order of magnitude lower
than in the ambient flow tube. The propagation rate from HO2+ NO is
therefore reduced and the OH + NO2 loss route is enhanced, leading
to the shorter OH chain length discussed above. It is also interesting to
note that peroxy radical mixing ratios in the reference flow tube are of the
same order of magnitude as in the ambient flow tube. This counterintuitive
observation is also due to the consumption of NO in the reference flow tube
that leads to a longer lifetime for the peroxy radicals, as shown in Fig. S6.
Calculating P(Ox) from Eqs. (1) to (3) results in ozone production rates
in the ambient flow tube, P(Ox)amb, in good
agreement with the modeled P(Ox)atm values, as
shown in Fig. 6, with a small underestimation of approximately 10 % on
average. However, significant ozone production rates are also observed in the
reference flow tube, which can reach up to 4 ppbv h-1 on this day,
while higher values were observed on other days (e.g., 30 ppbv h-1 on
21 March 2006 of the MCMA 2006 campaign, Fig. S10). Ozone production rates in
the reference flow tube are about 10–15 % of that observed in the
ambient flow tube for most of the day. It is important to note, however, that
this ozone production is in reality Ox (= O3+ NO2)
production, since NO2 photolysis is almost suppressed in the reference
flow tube. These results indicate that the assumptions initially made on the
principle for P(Ox) measurements, i.e that P(Ox) in the ambient
flow tube mimics P(Ox) in the atmosphere and P(Ox) in the
reference flow tube is not significant, are not completely fulfilled. Based
on the modeling results discussed above, the accuracy of the measurements
could be significantly impacted by Ox production in the reference flow
tube.
Modeling comparison of P(Ox) values. (a) Ozone
production rates modeled for the atmosphere, P(Ox)atm; the
ambient flow tube, P(Ox)amb; and the reference flow tube,
P(Ox)ref, for 30 May 2010 of the CalNex 2010
campaign. (b) Comparison of modeled ozone production rates for the
OPR, P(Ox)OPR, and the atmosphere,
P(Ox)atm, for 30 May 2010. Inserts: correlations between
P(Ox)atm and P(Ox)amb(a) as well as
between P(Ox)atm and P(Ox)OPR(b),
color-coded by the time of day.
P(Ox)OPR was calculated from Eq. (6), using an
O3-to-NO2 conversion efficiency of 100 %, and is also shown in
Fig. 6. As discussed above, P(Ox)OPR
underestimates the modeled P(Ox)atm, mainly due
to significant Ox production in the reference flow tube. The scatterplot shown as insert in this figure indicates that a negative bias of
approximately 20 % would be observed for P(Ox) measurements
performed on this day. A negative bias ranging from 15 to 20 % was observed
during the other 3 days that were modeled (Fig. S11).
As mentioned in the experimental section, concentrations of peroxy radicals
obtained as model outputs from the modeling of
P(Ox)atm were constrained for the simulations
inside the flow tubes, assuming that most of these species are not lost if a
short high-flow rate sampling inlet is used. However, simulations were also
performed without constraining the peroxy radicals to assess the impact on
the simulation results. These simulations have shown that P(Ox) values are
lower by 10 and 30 % in the ambient and reference flow tubes,
respectively, when peroxy radicals are not constrained. Overall, the measured
ozone production, which is the difference between P(Ox) in the two
flow tubes, would only decrease by 2–4 %. Therefore, not constraining
peroxy radicals in the simulations does not impact the comparison between
P(Ox)atm and
P(Ox)OPR, with
P(Ox)OPR underestimating
P(Ox)atm by 15–20 %.
However, the reason for this disagreement depends on whether peroxy radicals
are constrained. When peroxy radicals are constrained, the disagreement is
mainly caused by Ox production in the reference flow tube. In contrast,
when peroxy radicals are not constrained, this disagreement is due
to an underestimation of P(Ox)atm by P(Ox)amb. This
underestimation is the result of a latency in the first part of the ambient
flow tube due to the time needed to reproduce the radicals, which is on the
order of 1–2 min. It is very likely that only a fraction of the peroxy
radicals will be transferred to the flow tubes and a combination of the two
issues discussed above will lead to the negative bias of 15–20 %.
Sensitivity tests performed for 30 May 2010 (CalNex 2010) to assess
the impact on the P(Ox) measurements of the (a) O3-to-NO2 conversion efficiency, (b) NO2
and (c) O3 dark losses, (d) heterogeneous HONO
formation, (e) dilution of ambient air and (f) NO2
loss towards HONO production in the flow tubes. The results presented here
correspond to the 2 h of the day identified as lower (blue squares) and
upper (orange squares) limits of the impact on the P(Ox) measurements.
The daily average behavior is also shown using green triangles.
Sensitivity tests – assessment of the impact of operating conditions on OPR
measurements
Figure 7 shows the dependence of P(Ox)OPR on the
O3-to-NO2 conversion efficiency, O3 and NO2
surface losses, surface production of HONO and a dilution of the sampled
air. The results are displayed for two different times of the day,
characterized by different O3 and NO2 mixing ratios, which have
been identified as upper (orange squares) and lower (blue squares) limits for
the impact on the P(Ox) measurements. In addition, these results are
also displayed using daily averaged values (green triangles), which are more
representative of the average impact of a particular parameter on
P(Ox) measurements. The figures described below are for the CalNex
campaign during 30 May 2010. Results from the other days are shown in the
Supplement (Figs. S12–S14).
Figure 7a shows that P(Ox)OPR is very sensitive
to the O3-to-NO2 conversion efficiency. For instance, a conversion
efficiency of 85 % would lead to an underestimation of the P(Ox)
measurements by 20–60 % (≈ 35 % on average), depending on
the chemical composition of the air mass. It is interesting to see that the
change in P(Ox)OPR, expressed as the ratio
between P(Ox)OPR at a conversion efficiency lower
than 100 % and P(Ox)OPR at a conversion
efficiency of 100 % (base simulation), changes linearly with the
conversion efficiency. The slope of the straight line can be used as an
indicator to gauge the impact of the conversion efficiency on P(Ox)
measurements throughout the day. As can be seen from Eq. (6), for the
limiting case of C= 0, the measured P(Ox) is determined by the
absolute NO2 difference between the two flow tubes. The
O3–NOx PSS is shifted towards NO2 in the reference flow tube,
due to the lack of NO2 photolysis, reducing the NO2 difference
between the two tubes and lowering the measured P(Ox). These results
stress the need to reach a conversion efficiency better than 98 % to
keep this artifact below 5 %. The OPR instrument described in this study
exhibits a conversion efficiency higher than 99.9 % and is not impacted
by this issue.
Relative surface losses of 3 and 5 % have been observed for NO2 and
O3, respectively, during the laboratory and field testing (Sect. 3.1.2).
Figure 7b shows that a relative NO2 loss of 3 % in the flow tubes
can lead to an overestimation of up to 8 % (≈ 3 % on
average). On the other hand, Fig. 7c shows that a 5 % relative loss of
O3 can lead to an underestimation of up to 30 % (≈ 5 %
on average). These contrasting effects can be explained as follows: ozone in
the reference flow tube is lower than in the ambient flow tube, due to the
conjunction of a lower production rate of ozone and a shift of the
O3–NOx PSS towards NO2. A similar relative loss of ozone in
the two flow tubes will therefore lead to a larger absolute loss of Ox
species in the ambient flow tube, which in turn will lead to an
underestimation of the P(Ox) measurements (Eq. 6). In contrast,
NO2 is higher in the reference flow tube and a loss of NO2 will
lead to a larger absolute loss of Ox species in the reference flow tube
and, as a consequence, to an overestimation of the P(Ox) measurements.
Sources of errors on P(Ox) measurement. Upper limits and
campaign averages of errors assessed from modeling the selected days of the
MCMA 2006 and CalNex 2010 field campaigns (see text). FT: flow
tube.
Sources of errorsValueNegative bias on P(Ox)Positive bias on P(Ox)average(upper limit)average(upper limit)Residence time (s)271 ± 13a-4.9 %a(-4.9 %a)+4.9 %a(+4.9 %a)O3 production in reference FT and latency in ambient FT-18 %b(-20 %b)–O3 loss5 %a-10 %b(-25 %b)–NO2 loss< 3 %a–5 %b(+11 %b)HONO productionup to 20 ppbv h-1a–+27 %b(+40 %b)Dilution of sampled air10 %a-8 %b(-9 %b)–Temperature increase in reference FT5 %c-3 %b(-5 %b)–Ox formation from OH + NOz––+3 %b(+3 %b)Conservative sum of biases-44 %(-64 %)+40 %(+59 %)
a From laboratory testing; b from model
simulations; c from estimation.
Time series of selected trace gases, J(NO2), measured ΔOx and ΔP(Ox) values during 4 days of the IRRONIC
campaign when 6 ppbv of NO was intermittently added in the flow tubes. The
light colors on ΔOx correspond to 2 min measurements while the
darker colors are 20 min averaged values. Error bars on ΔP(Ox)
are 1σ on the averaged 20 min measurements.
Figure 7d shows how a heterogeneous production of HONO can impact the
P(Ox) measurements. In these simulations, a HONO source was added in
the model, with a production rate of 10 ppbv h-1 in both flow tubes
(dark HONO production) and an additional varying production rate in the
ambient flow tube (enhanced HONO production). The x axis presents
the HONO production rate in the ambient flow tube, where 10 ppbv h-1
corresponds to the dark production only. Moreover, this figure indicates that
HONO production rates of 20 ppbv h-1 in the ambient flow tube, similar
to experimental observations, can lead to an overestimation of the
P(Ox) measurements by up to 40 % (≈ 27 % on
average). This overestimation results from HONO photolysis in the ambient
tube, which leads to additional OH production, which in turn leads to an
enhancement of VOC-oxidation rates and ozone production. Additional
simulations were also performed assuming that NO2 molecules lost on the
surface were equally converted into HONO in both flow tubes (Fig. 7f),
although it is unlikely that the conversion yield of NO2 into HONO is
100 %. The results indicate that, for a relative NO2 loss of
3 %, P(Ox) could be overestimated by up to 15 % (10 % on
average). Note that the impact of this HONO formation adds up to the
previously discussed overestimation due to the NO2 loss.
Figure 7e displays how the injection of zero air at the periphery on the PTFE
inlets impacts P(Ox) measurement through a dilution of the sampled
air. As can be seen from this figure, a 10 % dilution leads to a less than
9 % underestimation of P(Ox).
Additional sensitivity tests (not shown) were performed to test the impact
of a temperature increase in the reference flow tube due to heat release by
the UV filter, as well as reactions of OH with NOz species that produce
NO2. A temperature increase of 5 % in the reference flow tube
(1 ∘C increase at 20 ∘C) can lead to an underestimation of up to
5 %, while the Ox production from reactions of OH with NOz
species can lead to an overestimation of up to 3 %.
Conclusions on potential biases on <inline-formula><mml:math id="M677" display="inline"><mml:mi>P</mml:mi></mml:math></inline-formula>(O<inline-formula><mml:math id="M678" display="inline"><mml:mrow><mml:msub><mml:mi/><mml:mi>x</mml:mi></mml:msub><mml:msub><mml:mo>)</mml:mo><mml:mi mathvariant="normal">OPR</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>
measurements
From the above discussion, we can conclude that there are two main sources of
errors. The first source of errors is due to Ox production in the
reference flow tube and the latency for ROx reformation in the ambient flow
tube, with the extent of each depending on the fraction of ambient peroxy radicals
that is transmitted into the flow tubes. The combination of these two issues
can lead to an underestimation of ambient P(Ox) by 15–20 % on
average for the conditions observed during the MCMA 2006 and CalNex 2010 campaigns. The
second main source of errors is caused by a surface production of HONO in the
ambient flow tube. Based on a HONO production rate of 20 ppbv h-1,
P(Ox) would be overestimated by approximately 30 % on average.
Additional sources of errors are due to the 4.9 % uncertainty on the flow
tube residence time, 5 % O3 and 3 % NO2 surface losses, the
dilution by 10 % of the sampled air, a possible temperature increase of
5 % in the reference flow tube and Ox production from reactions of
OH with NOz species. Daily averaged values and upper bounds of errors
associated with these factors, as derived from all modeled days, are reported
in Table 1.
Based on the daily average values reported in Table 1, direct sums of the
potential negative and positive biases lead to -44 and +40 %,
respectively. However, the magnitude of each error will depend on the
atmospheric composition and positive errors will, to some extent, cancel out
with negative errors. A quadratic sum of all these potential errors leads to a
range of ±36 %. The estimation of these errors is based on ambient
conditions observed in two different environments, with different air
compositions for 4 different days. It is safe to assume that similar error
values would be observed in other urban environments.
Current limitations for field operation
As mentioned in Sect. 2.4, OPR measurements were performed during the IRRONIC
field campaign. Figure 8 displays time series for a subset of measurements
performed from 10 to 14 July 2015, including two anthropogenic VOCs (toluene
and acetylene), a biogenic VOC (isoprene) and inorganic species (O3, NO
and NO2). It is clear from this figure that the measurement site was
mainly impacted by biogenic emissions, with isoprene reaching at least
5 ppbv most of the days, while anthropogenic VOCs were low
(< 500 pptv). In addition, NOx levels were lower than 3 ppbv
on these days, confirming the low impact of anthropogenic emissions. These
observations indicate that the photochemistry was mainly driven by the
oxidation of biogenic VOCs under low NOx conditions, similar to those
observed in other forested areas (Griffith et al., 2013). Isoprene is very
reactive with the hydroxyl radical and the strong diurnal variation in this
species led to a large range of OH reactivity (from a few
s-1 up to 30 s-1,
not shown). The conjunction of the latter with low levels of NOx makes
this a site of particular interest to study the sensitivity of ozone
formation to NOx by adding NOx in the OPR instrument as described in
the experimental Sect. 2.4.
Due to the low levels of ambient NOx, ozone production rates at the site
were lower than the OPR detection limit of 6.2 ppbv h-1 (Sect. 3.1.5).
Indeed, P(Ox) calculations based on total peroxy radical measurements
performed using the peroxy radical chemical amplifier technique indicated
peak ozone production rates of approximately 2 ppbv h-1 (not shown).
Ambient measurements performed by the OPR instrument without addition of NO
should therefore be scattered around zero within the measurement precision.
Figure 8 also displays ΔOx values (difference in Ox mixing
ratios between the two flow tubes) measured by the instrument without the
addition of NO (ΔOxzero, blue diamonds). While ΔOxzero was scattered around zero during nighttime, it
consistently exhibited large negative values during daytime (-1 to
-5 ppbv), indicating that Ox mixing ratios in the ambient flow tube
were lower than in the reference flow tube.
It is interesting to note that ΔOxzero values are
anticorrelated with J(NO2) (Fig. 8). Covering the ambient flow tube
with a similar UV filter than the reference flow tube, i.e., operating the
two tubes under similar irradiation, showed that ΔOx increases
towards less negative values and ultimately reaches zero. This behavior
indicates that the higher loss rate of Ox species in the ambient flow
tube is due to the solar irradiation and points towards a photo-enhanced
surface loss of Ox species initiated by photons at wavelengths lower
than 400 nm. As ambient NO2 mixing ratios were much lower than the
observed loss of Ox, this photo-enhanced loss involves a loss of ozone.
For an ambient O3 level of 40 ppbv, as usually observed during the
field measurements, a ΔOxzero of -3 ppbv corresponds to a
7.5 % difference in O3 losses between the two flow tubes and an ozone
loss rate higher by approximately 39 ppbv h-1 in the ambient flow tube
compared to the reference flow tube. This issue was further investigated in
the laboratory. As mentioned in Sect. 3.1.2, tests performed using
artificial irradiation and mixtures of humid air and ozone confirmed that
light-induced processes at wavelengths lower than 400 nm lead to a loss of
ozone at the surface of the ambient flow tube. It was found that this loss
depends on ambient ozone levels, J values and absolute humidity.
This version of the OPR instrument is therefore not suitable to perform
ambient P(Ox) measurements since the measured ΔOx is a
combination of ambient ozone production and surface-O3 losses in the
ambient flow tube. For this reason, the OPR measurements were focused on
investigating the sensitivity of P(Ox) to NOx, by recording the
relative change in P(Ox) when the chemical composition of ambient air
was perturbed by an addition of NO. For these measurements, it is assumed
that ΔOxzero is representative of the instrumental
zero and ΔOxzero measurements are referred to as
the “baseline” in the following. ΔOx measurements performed with
an addition of NO are assumed to deviate from ΔOxzero
due to a change in ozone production in the ambient flow tube, while the
surface loss of ozone is assumed to be unchanged. This measurement step is
denoted ΔOxNO. The difference between ΔOxzero and ΔOxNO divided by the
residence time in the flow tubes therefore provides a quantification of the
change in P(Ox), referred to as ΔP(Ox), due to the addition
of NO. The validity of the assumption that the O3 photo-enhanced
surface loss is not disturbed by the addition of NO is discussed below.
Investigating the ozone production sensitivity to NO is outside the scope of
this paper and we only present measurements performed when 6 ppbv of NO was
added in the instrument to illustrate its current performances and
limitations. Figure 8 displays time series of ΔOxNO
(orange diamonds) when 6 ppbv of NO was added in the flow tubes. When NO is
added, there is almost no change in ΔOx during nighttime. In
the absence of sunlight, NO only converts O3 into NO2 and the
amount of Ox measured by the CAPS monitor does not change. During
daytime, ΔOxNO is higher than ΔOxzero,
suggesting production of ozone in the ambient flow tube. The difference
between ΔOxNO and ΔOxzero, divided by
the residence time in the flow tubes, represents the change in ozone
production rates and is displayed in the bottom panel of Fig. 8 as ΔP(Ox). Changes in ozone production of up to 20 ppbv h-1, well
correlated with J(NO2), are observed for these days. With ozone production
being NOx limited in this environment, a positive change in
P(Ox) is indeed expected when a small amount of NOx is added to
the flow tubes.
However, the assumption that the photo-enhanced surface loss of ozone does
not change when NO is added may breakdown for large NO mixing ratios.
Indeed, the addition of NO in the flow tubes leads to the conversion of a
significant fraction of O3 into NO2, which in turn reduces the
absolute loss of O3 in the ambient flow tube, leading to a shift of the
ΔOxzero baseline to less negative values. ΔP(Ox) values reported in Fig. 8 will therefore be the combination of
a change in ozone production and a change in the absolute loss of O3.
If the change in the ozone loss rate is significant compared to the change
in the ozone production rate, this could lead to an overestimation of the
change in ozone production. An assessment of this measurement bias requires
modeling the chemistry in both flow tubes to separate the two contributions,
i.e the changes in (i) ozone production and in (ii) ozone loss. While this
work is outside the scope of this publication, which focuses on the
performances and limitations of the OPR instrument, it is interesting to
note that preliminary modeling indicates a bias lower than 5 pbbv h-1
when 6 ppbv of NO is added.
The field deployment during IRRONIC revealed an additional bias in
P(Ox) measurements due to a photo-enhanced loss of ozone at the inner
surface of the ambient flow tube and the difficulty in probing changes in
P(Ox) when the sampled air mass is perturbed by an addition of NO.
Ambient measurements of P(Ox) with the current version of the OPR
would necessitate performing frequent zeros of the instrument to track the
ozone loss, and unfortunately a simple solution to do so was not found. This
work shows that the sampling part of the OPR instrument needs to be rethought
to remove (or reduce to a negligible level) the photo-enhanced surface loss
of ozone, which is a prerequisite to acquiring an instrument capable of reliable
measurements of ozone production rates.
Comparison to previously published instruments and potential improvements
for the OPR instrument
Previous studies (Cazorla and Brune, 2010; Baier et al., 2015) have shown
that measurements of ambient ozone production rates are feasible.
Baier et al. (2015) reported that the zero of their MOPS
instrument was achieved by removing the UV filter from the reference chamber
for a full day to record a diurnal profile of ΔOx, which was
then subtracted from the raw ΔOx measurements on other days.
This zeroing procedure was also tested on the OPR instrument, but led to
unrealistically high ambient P(Ox) values of approximately
40 ppbv h-1 for the low-NOx forested environment of IRRONIC. This
result also suggests that altering the irradiation conditions of the OPR flow
tubes leads to a wrong zero of the instrument. This zeroing technique seems
to provide better results for the MOPS instrument and it is possible that the
design used for the MOPS sampling chambers or the material used to build them
(FEP) make it less sensitive to light-dependent surface reactions.
The instrument design reported by Sadanaga et al. (2017) does not seem to be
significantly impacted by a photolytic loss of ozone on the quartz flow
tubes whose inner surface was coated with Teflon®. Interestingly, these
authors report dark losses of ozone on the order of 8–10 % on the uncoated
quartz surface for a residence time of 21 min in the tubes, which are
consistent with the reported dark loss of less than 5 % observed in our
study for O3-conditioned flow tubes and a residence time of 4.5 min.
The Teflon® coating seems to remove or to reduce the photolytic loss of ozone to a negligible level on this instrument.
Since the main artifacts on the OPR instrument are caused by heterogeneous
surface reactions in the flow tubes, i.e., HONO production (Sect. 3.2.2)
and ozone losses (Sect. 3.2.2 and 3.3), the flow tubes should be
redesigned to reduce the impact of physicochemical processes occurring near
the quartz surface on the ozone production chemistry occurring at the center
of the tubes. A solution worth investigating would be to minimize surface
reactions by coating the inner surface of the flow tubes with Teflon® as in
Sadanaga et al. (2017) or by applying a chemical treatment on the quartz
surface, which should help in removing reactive sites. The latter has already
been applied for laboratory kinetic experiments to clean reactor surfaces.
Interestingly, it was reported that this type of treatment can also reduce
HONO production on quartz surfaces (Laufs and Kleffmann, 2016).
Other potential solutions would be to (i) increase the diameter of the tubes
to reduce the surface-to-volume ratio and (ii) shorten their lengths together
with an increase in the total flow rate to reduce the contact time between
trace gases and the walls. A shorter residence time would also lead to a
shorter air-exchange time, which in turn would help in minimizing the scatter in
ΔOx measurements and would help improve the time resolution
necessary to generate independent P(Ox) measurements. However, a
shorter residence time would also lead to a lower detection limit and a
tradeoff between these two parameters will likely have to be made.
Regarding the deployment of these OPR instruments in the field, a reliable
zeroing method would be suitable for both ambient P(Ox) and
P(Ox) sensitivity measurements. An interesting solution would be to
introduce a radical scavenger in the flow tubes to suppress ozone production,
but a suitable compound has yet to be identified.
Conclusions
An instrument dedicated to direct measurements of ozone production rates
was developed and consists of two quartz flow tubes, an
O3-to-NO2 conversion unit and an Aerodyne CAPS NO2 monitor.
This setup, compared to the NO2-to-O3 conversion approach
previously published in the literature, presents the advantage of a
conversion efficiency higher than 99.9 %, which is independent of ambient
Ox levels. Laboratory and field testing performed to characterize the
performance of this instrument showed that dark losses of O3 and
NO2 inside the flow tubes are lower than 5 and 3 %, respectively.
However, it was shown that dark ozone losses can increase after a long
exposure of the flow tubes in the field and frequent reconditioning steps
should be performed during nighttime by flowing humid air and O3 through the
tubes to keep the loss below 5 %.
A modeling exercise taking advantage of measurements from previous urban
field campaigns showed that a latency in ozone production in the ambient flow
tube and a net ozone production in the reference flow tube can lead to an
18 % measurement underestimation of ambient P(Ox) on a daily
average for the conditions of the MCMA 2006 and CalNex 2010 field
campaigns. However, the magnitude of this underestimation depends on the
chemical composition of ambient air, and it is recommended to assess this
potential bias for future campaigns.
Sensitivity tests performed during the modeling exercise highlighted the
importance of a high conversion efficiency, since a conversion of 95 %,
which is only 5 % lower than the maximum, could lead to an
underestimation of ambient P(Ox) by approximately 20 % on a daily
average for the two selected field campaigns. A dark surface loss of ozone in
the flow tubes would lead to an underestimation of ambient P(Ox),
while an NO2 loss would lead to an overestimation. On a daily average, an
underestimation of 10 % and an overestimation of 5 % were assessed
for an O3 loss of 5 % and an NO2 loss of 2 %, respectively.
A photo-enhanced production of HONO in the ambient flow tube on the order of
20 ppbv h-1 would also lead to an overestimation of ambient
P(Ox) by 27 % on a daily average. Overall, a quadratic sum of
these potential biases for the conditions of the two urban field campaigns
leads to a range of errors of ±37 % on a daily average.
As shown from the first deployment of the OPR instrument, there is an
additional bias due to a photo-enhanced loss of O3 taking place in the
ambient flow tube. This requires improving the sampling design to be able to
perform reliable ambient measurements. The first field deployment of the OPR
instrument was performed in a low NOx environment, allowing the focusing
of the study on the sensitivity of ozone production to NOx. Significant
changes in ozone production rates were observed (up to 20 ppbv h-1)
when 6 ppbv of NOx was added in the flow tubes, consistent with an
NOx-limited production regime.
Data availability
Contact the corresponding author for data.
The supplement related to this article is available online at: https://doi.org/10.5194/amt-11-741-2018-supplement.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was supported by grants from the Regional Council
Nord–Pas-de-Calais through the MESFOZAT project, as well as the French
National Research Agency (ANR–11–LABX–0005–01) and the European
Regional Development Fund (ERDF) through the CaPPA (Chemical and Physical Properties of the
Atmosphere) project. The authors thank the Région Hauts-de-France and the
Ministère de l'Enseignement Supérieur et de la Recherche (CPER
Climibio) and the European Fund for Regional Economic Development for their
financial support. The authors are grateful to William Bloss and
Leigh Crilley (Birmingham University) for sharing their experience on the OPR
technique and for the idea of using quartz flow tubes as sampling chambers
for the OPR instrument. The authors are also grateful to Vinod Kumar and
Vinayak Sinha (IISER Mohali) who provided support and assistance during the
initial development stage of the OPR instrument. Finally, the authors thank
the Mechanical Instrument Services at Indiana University for the construction
of the flow tube flanges. Edited by: Lisa
Whalley Reviewed by: two anonymous referees
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