AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-11-5643-2018An in situ flow tube system for direct measurement of N2O5
heterogeneous uptake coefficients in polluted environmentsDirect measurement of γN2O5 in polluted environmentsWangWeihaohttps://orcid.org/0000-0001-9809-9771WangZhez.wang@polyu.edu.hkhttps://orcid.org/0000-0002-5627-6562YuChuanXiaMenhttps://orcid.org/0000-0002-8534-3357PengXianghttps://orcid.org/0000-0002-0741-4914ZhouYanYueDingliOuYuboWangTaocetwang@polyu.edu.hkhttps://orcid.org/0000-0002-4765-9377Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, ChinaEnvironment Research Institute, Shandong University, Ji'nan, Shandong, ChinaGuangdong Environmental Monitoring Center, State Environmental Protection Key Laboratory of Regional Air Quality Monitoring, Guangzhou, ChinaZhe Wang (z.wang@polyu.edu.hk) and Tao Wang (cetwang@polyu.edu.hk)16October20181110564356555June201821June201817September201827September2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://amt.copernicus.org/articles/11/5643/2018/amt-11-5643-2018.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/11/5643/2018/amt-11-5643-2018.pdf
The heterogeneous reactivity of dinitrogen pentoxide
(N2O5) on ambient aerosols plays a key role in the atmospheric fate of
NOx and formation of secondary pollutants. To better understand the
reactive uptake of N2O5 on complex ambient aerosols, an in situ
experimental approach to direct measurement of N2O5 uptake
coefficient (γN2O5) was developed for application in
environments with high, variable ambient precursors. The method utilizes an
aerosol flow tube reactor coupled with an iterative chemical box model to
derive γN2O5 from the depletion of synthetically
generated N2O5 when mixed with ambient aerosols. Laboratory
tests and model simulations were performed to characterize the system and the
factors affecting γN2O5, including mean residence time,
wall loss variability with relative humidity (RH), and N2O5 formation and titration
with high levels of NO, NOx, and O3. The overall uncertainty was
estimated to be 37 %–40 % at γN2O5 of 0.03 for RH
varying from 20 % to 70 %. The results indicate that this flow tube
coupled with the iterative model method could be buffered to NO
concentrations below 8 ppbv and against air mass fluctuations switching
between aerosol and non-aerosol modes. The system was then deployed in the
field to test its applicability under conditions of high ambient
NO2 and O3 and fresh NO emission. The results demonstrate that the
iterative model improved the accuracy of γN2O5
calculations in polluted environments and thus support the further field
deployment of the system to study the impacts of heterogeneous
N2O5 reactivity on photochemistry and aerosol formation.
Introduction
Dinitrogen pentoxide (N2O5) is a nocturnal reactive intermediate
in the atmospheric oxidation of nitrogen oxides (NOx), which
plays an important role in atmospheric photochemistry and the production of
secondary pollutants (e.g., Chang et al., 2011). N2O5 is formed
from the reaction of nitrogen dioxide (NO2) and nitrate radical
(NO3). Because NO3 is photolytically unstable, it (and
therefore N2O5) only accumulates under dark conditions (i.e., at
night). The heterogeneous reactions of N2O5 on aerosols have
been recognized as a major sink for NOx, affecting the
atmospheric lifetime of NOx and the formation of ozone and
other secondary pollutants (e.g., Brown et al., 2007; Wang et al., 2016). The
heterogeneous N2O5 loss rate on aerosols (kaerosols)
depends on the uptake coefficient of N2O5 (γN2O5) and the available aerosol surface area, and can be
expressed using Eq. (1) when the gas-phase diffusion effect is negligible
(Fuchs and Sutugin, 1971; Tang et al., 2014).
kaerosols=14cN2O5SaγN2O5,
where cN2O5 (m s-1) is the mean molecular speed of N2O5 and Sa
(m2 m-3) is the aerosol surface area concentration. γN2O5 is the reaction probability of a N2O5 molecule
colliding with the aerosol surface, resulting in net removal via reactions on
aerosols. Because γN2O5 is a critical parameter
for determining N2O5 uptake on aerosols, it is necessary to develop
reliable methods to measure it.
γN2O5 has typically been determined in the laboratory using
different types of flow tubes and reactors to measure the decay rate of
N2O5 in the presence of pure inorganic and organic aerosols or
mixed aerosols under different conditions (e.g., Thornton et al., 2003; Tang
et al., 2017, and references cited therein). The γN2O5
has been shown to be highly dependent on aerosol composition, temperature,
and relative humidity; different parameterizations of varying degrees of
complexity have thus been proposed to relate γN2O5 to aerosol composition (Anttila et al., 2006; Bertram and Thornton,
2009; Davis et al., 2008; Evans and Jacob, 2005; Riemer et al., 2009). In
ambient conditions, several methods have been developed to derive γN2O5 directly from atmospheric concentrations of
N2O5. Brown et al. (2007) utilized steady-state approximation of
NO3 and N2O5 to derive γN2O5 based on the
correlation of inverse N2O5 steady-state lifetime with NO2
concentration and aerosol surface area; Phillips et al. (2016) assumed a
conserved air mass and used the production rates of NO3- and
ClNO2 to derive γN2O5; and Wagner et al. (2013) applied
an iterative chemical box model to derive the appropriate γN2O5 to match the predicted N2O5 concentration to the
measured values with the assumption of the reaction time starting at sunset
and with no interception of other NOx emissions.
Bertram et al. (2009a) introduced an approach to directly measure γN2O5 on ambient aerosols by utilizing an entrained aerosol flow
reactor coupled with a chemical ionization mass spectrometer (CIMS). By
switching sampling between filtered and unfiltered ambient air, the
reactivity of N2O5 was determined based on a comparison of the
pseudo-first-order loss rate of N2O5 in ambient air with and
without aerosols. The loss rate of N2O5 to aerosols
(kaerosols) could be derived from the concentration ratio at the exit
of the flow reactor, with the assumption that the wall loss of
N2O5 is constant in the successive two measurements and that all
losses are first order (Bertram et al., 2009a):
kaerosols=-1Δtln[N2O5]Δtw/aerosols[N2O5]Δtwo/aerosols,
where Δt is the mean residence time in the flow tube reactor and
[N2O5]Δt is the N2O5 concentration measured
at the exit of the flow reactor in the two modes (i.e., the presence and
absence of aerosols). This flow tube apparatus was deployed at two urban
sites in Boulder and one coastal site in La Jolla to measure γN2O5 on ambient aerosols (Bertram et al., 2009b; Riedel et al.,
2012). They found that the fluctuation of relative humidity (RH) and
NO3 reactivity (mainly dominated by NO) could lead to great uncertainty
in measured γN2O5, and they therefore applied some screening
criteria, including only data with a RH fluctuation of less than 2 % and
NO concentration lower than 750 pptv. This constraint resulted in about
20 % of the data being used for further analysis. It was necessary to
adopt these criteria because only first-order loss is considered in the flow
tube reactor and other reactions involving ambient NO, NO2, and O3
are not. The latter treatment is suitable when ambient concentrations are
low and the air mass is relatively stable, but it may be problematic in
polluted environments with high fresh NOx emissions, high O3
concentrations, and rapidly changing air mass.
Schematic diagram of the aerosol flow tube system.
Several recent studies have revealed active N2O5 heterogeneous
processes on aerosols at polluted sites and its significant impacts on
photochemistry and secondary aerosol formation due to abundant NOx,
O3, and aerosols (e.g., Li et al., 2016; Tham et al., 2016; Wang et al.,
2016; X. Wang et al., 2017; Z. Wang et al., 2017; Yun et al., 2018). The γN2O5 derived from ambient concentration measurements showed
different characteristics and dependence compared to previous measurements
in relatively clean environments (Morgan et al., 2015; Z. Wang et al., 2017). To better understand
the reactive uptake of N2O5 on complex ambient aerosols, a flow
tube reactor approach was developed for direct N2O5 reactivity
measurement under highly polluted conditions. In the following sections, we
describe in detail the method used for determining the N2O5 uptake
coefficient with an iterative box model and discuss the factors affecting
the system's performance and uncertainty. Laboratory tests and field
deployment of the method are presented to demonstrate its application under
conditions with high ambient concentrations of NO2 and O3 and fresh NO
emission.
MethodologyFlow tube reactor
The flow tube system consists of an N2O5 generation part, a
sample inlet with aerosol filter manifold, a flow tube reactor, and detection
instruments. A schematic diagram of the experimental apparatus is given in
Fig. 1. The sample inlet with an aerosol filter manifold is made of
1/4 in. outer diameter (OD) stainless-steel tubing. By switching two
stainless-steel ball valves, ambient air can be introduced directly into the
flow tube or through a PTFE membrane (Pall Life Sciences) to remove aerosols.
The flow tube is a Teflon-coated stainless-steel tube, 120 cm in length with
an internal diameter of 12.5 cm. The ambient or filtered air enters and
exits the flow tube via 10 cm deep 60∘ tapered end caps. The total
flow rate through the flow tube is 4.6 standard liter per minute (SLPM) and
includes 120 standard cubic centimeter per minute (SCCM) of N2O5
flow, which is introduced through an orthogonal entry to minimize the
entrance length of the injected flow. The air pressure in the flow tube
reactor is around 730 torr. The adopted flow rate and pressure give a
Reynolds number of 55 (i.e., laminar flow) in the flow tube reactor. At the
exit of the flow tube reactor, several detection instruments are used to
measure the concentrations of N2O5, O3,
NOx, and aerosol surface area.
Generation of N2O5
N2O5 is generated in situ from the reaction of O3 with excess
NO2 at room temperature via Reactions (R1) and (R2), which has been
used in many previous lab and field measurements (e.g., Bertram et al.,
2009a).
O3+NO2→NO3+O2NO3+NO2+M↔N2O5+M
In this study, ozone was generated from O2 photolysis with a mercury
lamp in a commercial calibrator (model 4010, Sabio Instrument Inc.). One hundred SCCM of produced O3 flow was mixed with 20 SCCM of NO2 (10 ppmv
balanced in N2; Arkonic, USA) in a Teflon reaction chamber (volume = 68 cm3) for about 28 s prior to injection into the flow tube reactor.
Under conditions of excessive NO2, the system was expected to shift the
R2 equilibrium towards N2O5. Concentrations of synthesized
N2O5 were calculated from observed changes in NO2 (before and
after addition of O3), and the N2O5 content had also been
inter-validated with a cavity ring-down spectrometer (CRDS) in our previous
studies (Wang et al., 2016). Prior to the N2O5 generation, the
system was purged with dry zero air and NO2 for at least 2 h, to
minimize the water content level and stabilize the NO2 source. This
system was shown to be able to produce N2O5 concentrations from 1
to 10 ppbv (after dilution in the flow tube). In a typical experiment used in
the present study, the input of the N2O5 source to the top of the flow
tube contained 4.3 ppbv of N2O5, together with 106 ppbv of O3
and 57 ppbv of NO2. The stability of the synthetic-N2O5 source
was tested continuously for 8 h, and the variation of the signal was
within ±2 % in each hour. More detailed description of the
N2O5 generation can be found in Wang et al. (2016).
Detection instruments
At the exit of the flow tube reactor, O3 was measured by a UV
photometric analyzer (Thermo, Model 49i) and NO2 was measured by a
chemiluminescence NOx analyzer (Thermo, Model 42i) equipped
with a blue-light photolytic converter (BLC). The aerosol number
concentration and size distribution (10 nm to 10 µm) were measured
by a wide-range particle spectrometer (WPS, model 1000XP, MSP Corporation,
USA) to determine the aerosol surface area. The uncertainty of the aerosol
surface area measurement was 20 %–30 % (Z. Wang et al., 2017; Tham
et al., 2018). The transmission of aerosols in the flow tube was evaluated
using laboratory-generated (NH4)2SO4 particles. The passing
efficiency was around 50 % for particles with a size of 20 nm and more
than 90 % for particles larger than 100 nm. The total surface area loss
in the flow tube was around 10 %–25 %. The N2O5 and
ClNO2 concentrations were quantified by an iodide-adduct chemical
ionization mass spectrometer (CIMS; THS Instrument, Atlanta). The CIMS has
been deployed in several field campaigns, and the setup and operation have
been previously described (Tham et al., 2016; Wang et al., 2016; X. Wang et
al., 2017; Z. Wang et al., 2017). Briefly, the primary ion I- was
generated from ionization of CH3I diluted in N2 flow
through a 210Po source. The N2O5 and ClNO2
were detected as ion clusters of I(N2O5)- and
I(ClNO2)- at 235 and 208 m/z by the quadrupole mass
spectrometer. Because of the higher pipeline resistance in the flow tube
reactor compared to ambient measurement, a smaller orifice with a 0.0135 in
diameter was utilized in the CIMS inlet to reduce the sample flow, and
another orifice was added before the scroll pump to keep the pressure in the
ionization reaction chamber at 50 torr. The corresponding sample flow was
0.4 SLPM. The detection limit of the instrument was estimated to be 2 pptv
(1 min averaged data), and the uncertainty of the CIMS measurement was
estimated as ±25 % (Tham et al., 2016). The ambient volatile organic compounds (VOCs) were
determined using an online gas chromatograph (GC) coupled with a flame
ionization detector (FID) and a mass spectrometer (MS). The VOC
concentrations were used to determine the kNO3-VOC in
the aerosol flow tube system, which was treated as constant during the
short time period of flow tube measurement. The ambient NO level was measured
by another chemiluminescence NOx analyzer (Thermo,
Model 42i) equipped with a molybdenum converter.
Determination of residence time
The mean residence time that represents the average reaction time of the
gases in the flow tube reactor is an essential parameter in calculation of
the reactive uptake coefficient. In a previous flow reactor studies (e.g.,
Thornton et al., 2003), the average residence time has usually been
calculated from the flow rate and flow tube volume assuming an ideal laminar
flow. To determine the mean residence time for non-ideal flow more
accurately, the residence time distribution (RTD) method introduced by
Danckwerts (1953) was used in the present study. The RTD method involves
introduction of an inert tracer species into the reactor and detection of
its transient concentration leaving the reactor outlet, and it has been
widely used in previous lab studies to characterize the mixing and flow
behavior of non-ideal aerosol flow reactors (e.g., Lambe et al., 2011).
Pulse injection of highly concentrated ClNO2 was used in the present
study to measure the RTD and hence determine the mean residence time.
ClNO2 is an inert gas within the dark Teflon-coated flow tube reactor
and can be measured by CIMS with high time resolution (>1 Hz).
ClNO2 was synthesized in situ via passing the N2O5 through a
NaCl slurry in the Teflon tubing reactor (Wang et al., 2016). The pulse
injection was controlled by a solenoid valve. At t=0 s, 120 SCCM (the same
flow as N2O5 injection during the uptake measurement) of
ClNO2 was directly injected into the flow tube reactor; at t=2 s,
the solenoid valve switched and the ClNO2 flow was passed through a
charcoal filter to provide zero gas into the flow tube reactor. The
RTD function E(t) is defined by the following equation:
E(t)=C(t)∫0∞C(t)dt,
where C(t) represents the ClNO2 concentration measured at
time t. Then the mean residence time can be calculated as follows:
Δt=∫0∞tEtdt.
The measurement result of the residence time test is shown in Fig. 2. With a
flow rate of 4.6 SLPM in the flow tube reactor, the mean residence time
determined from the RTD method was 149±2 s. In comparison, the
residence time calculated using the flow rate and reactor volume gives a
value of 159±5 s, which is 6.7 % higher than that given by the RTD
method, and could lead to underestimation of the rate constant. The RTD
function in Fig. 2 is clearly different from the ideal laminar flow reactor.
Bertram et al. (2009a) have suggested that the
determined rate constant would be underestimated by up to 25 % due to
non-ideal plug flow conditions. More discussion of the uncertainty in γN2O5 calculation associated with residence time distribution is
presented in Sect. 5.
The measured residence time distribution of the injected
ClNO2 in the flow tube reactor. The blue line represents the fitted
residence time distribution of the ClNO2 pulse injection
experiment. The pink line represents the expected residence time distribution
of an ideal laminar flow reactor without diffusion.
Iterative box model for determination of loss rate and uptake
coefficient
As described previously, the reactivity of N2O5 can be
investigated using the aerosol modulation by comparing the loss rate of
generated N2O5 in the flow tube reactor with and without ambient
aerosols. Previous studies (e.g., Bertram et al., 2009a) utilized the
exit-concentration ratio of N2O5 to obtain the N2O5 loss
rate on aerosols. However, air mass changes lead to different NO3 loss
rates and production rates over a short time period (i.e., a typical
sampling cycle for about 1 h), and high background NO2 and O3 in
the ambient air would affect the exit N2O5 concentration and hence
bias the measurement of loss rate and uptake coefficient from the flow tube
experiments. To minimize the potential influences of high levels of ambient pollutants
and rapidly changing air mass, a time-dependent box model constrained by the
real measurement data was used in the present study to directly calculate
the N2O5 loss rate in both aerosol and non-aerosol mode,
considering multiple reactions describing the production and loss of
NO3 and N2O5 (Reactions R1–R6) under ambient conditions.
O3+NO→NO2+O2;k3NO3+NO→2NO2;kNO3-NONO3+VOC→products;kNO3-VOCN2O5+aerosols/wall→products;khet=kwall+kaerosols
The rate constants for Reactions (R1) to (R4) recommended by the National
Aeronautics and Space Administration–Jet Propulsion Laboratory (Sander et
al., 2009) were used. The loss rate coefficient kNO3-VOC from
NO3 reactions with VOCs (Reaction R5) was determined by ambient measured VOC
concentrations and rate coefficients from Atkinson and Arey (2003). The
N2O5 heterogeneous loss rate coefficient khet (Reaction R6)
including heterogeneous loss on both aerosol and reactor surfaces was the
only adjustable parameter, while other parameters such as N2O5, NO,
NO2, and O3 concentration were constrained by concurrent
measurements. The model simulated the reactions starting from the entrance
of the reactor after mixing the ambient air sample and synthetic-N2O5 source. The initial concentrations of [NO2]t=0 and
[O3]t=0 were calculated from the ambient measured levels of
NO2 and O3 and those from the N2O5 source. Given the
constraint of measured parameters at the entrance of the flow tube reactor –
including [NO]t=0, [NO2]t=0, [O3]t=0,
[N2O5]t=0, [VOCs]t=0, temperature, and pressure – these
reactions could be integrated in time (performed in Matlab with the Kinetic
PreProcessor using a Radau5.integrator) (Damian et al., 2002) to obtain the
exit concentrations of NO2, O3, and N2O5. The calculated
concentrations were then compared with the measured concentrations at the
exit of the flow tube reactor, and the N2O5 loss rate coefficient
was tuned until the N2O5 concentration predicted by the box model
agreed with the measured N2O5 concentration,
[N2O5]Δt. Assuming that kwall are constant between
successive flow tube experiments with and without aerosols, the loss rate
coefficient on aerosols surfaces can be determined from the differences
between two modes: kaerosols=khetw/aerosols-khetwo/aerosols. Then the uptake
coefficient of N2O5 on aerosol surfaces (γN2O5)
can be calculated by the following equation:
γN2O5=4khetw/aerosols-khetwo/aerosols/(cSa).
In circumstances without concurrent ambient measurement of NO2 and
O3 and when accurate measurements are only available at the flow
tube outlet, as in the present study, an iterative box model including both
backward and forward simulation is needed. Following the method suggested by
Wagner et al. (2013), the relevant reactions can be integrated backward
starting with the measured concentrations at the exit of the reactor
(t=Δt) to obtain the initial concentrations. As the cycle between
NO3 and N2O5 is fast and quickly established in
high-NOx conditions, the NO3 and N2O5 are
considered as one singular N2O5∗ species by assuming
NO3 and N2O5 are in equilibrium (Brown et al., 2003).
Doing this also makes backward reaction simulation possible by avoiding
unstable equilibrium in the box model. The NO at the entrance of the flow
tube could react quickly with O3 and NO3, with a short
lifetime of a few seconds, resulting in near-zero concentration at the exit
of the flow tube. To initialize the simulation, a time-dependent NO
concentration in the flow tube must be derived. An approximate [NO] profile
can be estimated from a forward simulation with inputs of measured initial
NO, N2O5, guessed khet, and estimated initial
NO2 and O3 concentrations from the following equations.
The measured initial NO data used 3 min earlier data as input data
considering the mean residence time of 150 s.
[NO2]0=[NO2]Δt×eΔtk1[O3]Δt[NO]0[O3]0=[O3]Δt×eΔtk1[NO2]Δt+[NO]0NOt=[NO]0×e-tk3O30+k4N2O50KeqNO20
The estimated [NO] profile was then constrained in the backward model
simulation – together with inputs of measured concentrations of
N2O5, NO2, and O3 at the exit of the flow tube reactor
and the initially guessed khet – to derive the initial mixing ratios.
The box model was run forward and backward iteratively with updated values
and adjusted khet until simulated N2O5 concentration matched
the measurement at the exit of the flow tube reactor. The agreement of
simulated NO2 and O3 concentrations with measurements was also
used as a check to validate the model calculation. Thus, the uptake
coefficient of N2O5 was determined from Eq. (5). An example of the
iterative box model calculation is shown in Fig. 3.
An example of the iterative box model simulation to derive
khet from the measured concentrations of NO2,
O3, and N2O5 at the exit of the flow tube reactor. The
concentration profiles obtained from the simulation in 10 iterations are
shown for (a)O3, (b)NO2,
(c)N2O5, and (d) NO. In the upper
part of (e), the adjusted N2O5 loss rate is shown for each
iteration. The lower part of (e) shows the concentration
differences between the model simulation and measurements of
N2O5, O3, and NO2 at the exit of the reactor
for each iteration. (f) shows the comparison between measured
initial concentrations from laboratory test and predicted initial
concentrations from the iterative model.
For some conditions, the iterative box model returns a negative
N2O5 loss rate coefficient. This non-physical result might
result from much larger fluctuations of kNO3 or kwall in the
system during each measurement cycle. When kaerosol is small due to the
low Sa or insignificant uptake, the kNO3 or kwall may
dominate the N2O5 loss in flow tube reactor, and the fluctuations
of kNO3 or kwall due to the air mass or temperature/RH changes
would bias the kaerosol determination and lead to large uncertainty or
negative values. This situation often occurred under conditions of fresh NO
emission; more discussion of the influence of NO is presented in Sect. 6.
Laboratory test and overall uncertainty
Laboratory tests of N2O5 uptake on (NH4)2SO4
aerosols were also performed with different NO, NO2, and O3
conditions, and the uptake coefficients were determined from the iterative
box model analysis described above with input of measured concentrations.
The determined uptake coefficient ranged from 0.018 to 0.026 (Table S1 in
the Supplement), which are similar to previous laboratory study results with
(NH4)2SO4 aerosols (Davis et al., 2008). The consistency
also can serve as a validation of the applicability of the introduced system
and method. In addition, we also compared the measured initial concentration
of NO2 and O3 during the lab tests with that predicted from the
iterative model (Fig. 3f). The NO2 concentration matched well between
model prediction and measurement, while O3 was a little lower from
the model simulation, which might be due to the wall loss or other loss ways
of O3 in the flow tube reactor.
In the present work, the determination of kaerosols is independent of
the magnitude of kwall, but the stability of kwall is critical for
the accurate retrieval of kaerosols. kwall depends on RH, and
the variability in RH on the timescale of the measurement can introduce
additional uncertainty (Bertram et al., 2009a). Laboratory experimental
tests have been conducted to investigate the variability of kwall with
RH in the current flow tube system. kwall can be determined from the
previously described iterative model with the measurement of N2O5
loss through the flow tube in a zero air flow in the absence of aerosols. As
shown in Fig. 4, kwall has a strong positive relationship with RH and
increases with RH, especially when RH is higher than 50 %. The consistent
kwall at each RH condition with different initial N2O5 concentrations suggests that kwall in the current system is
relatively stable under different chemical conditions but varies as a
function of RH.
The sample air exiting the flow reactor was continuously measured by a RH
probe, and the results showed that the RH variation between the aerosol
presence and absence modes was within 1 % more than 80 % of the time
during the ambient measurement cases. This result would translate into an
uncertainty of (±0.15×10-3) to (±2.4×10-3) in γN2O5 with RH of 20 % to 70 %,
respectively, and a Sa of 1000 µm2 cm-3. To minimize the
magnitude of the variability in kwall, the wall of the reactor was
coated with Teflon PFA, and the flow tube reactor was cleaned daily with
distilled water. Ultrasonic baths were also utilized after a 1-week period
of ambient measurement to remove aerosol buildup from the wall of the flow
tube reactor.
Relative humidity dependence of the wall loss rate coefficient
(kwall) of N2O5 in the flow reactor.
a 1σ standard deviation for the varied
parameters. b The kwall is calculated from RH, using
the relation fitting equation in Fig. 4. c The variation of
kwall is calculated as RH varied 1 %.
The partial uncertainty in γN2O5 determination
associated with kwall changes, VOC variation, and the variation
of different parameters during the measurement cycles derived from Monte
Carlo simulations for three individual sets with 400 simulations at
(a) RH =40 % and (b) different RH values. In these
three data sets, the condition was set as follows: surface
area =1000µm2 cm-3, reaction time =150 s,
initial O3=80 ppbv, initial NO2=50 ppbv,
initial NO =2 ppbv, initial N2O5=5 ppbv, temp =25∘C, and kNO3-VOC=0.01 s-1.
In addition to kwall being affected by RH, uncertainty in
kaerosols determination can also result from N2O5 source
variability, NO3 reactivity with VOCs, and precision as well as accuracy
associated with the measurement of all parameters. The long measurement cycle
may also bring about uncertainty due to variation in concentrations
in two operation modes. As described in Sect. 2.2, the stability of the
N2O5 generation source was within ±2 % over an hour. In
the present study, online VOCs were measured with a time resolution of 1 h.
A ±0.01 s-1 variation of kNO3-VOC would lead to a
single-point uncertainty in γN2O5 of ±0.4×10-3 for Sa= 1000 µm2 cm-3. NO reacts at a faster rate
with NO3, having a larger impact on the γN2O5
calculation compared to VOCs. With a constrained real-time NO concentration,
the iterative model can buffer against small NO changes. Stability of NO,
NO2, O3, and N2O5 for a period of at least 5 min for
each mode is required to ensure that the flow tube reactor measurement and
iterative model yield reasonable results. The measurement precision and
variation of these species during each cycle might also introduce
uncertainty in the iterative model calculation.
The uncertainty in the γN2O5 determination associated with
kwall changes, VOC variation, and the variation of the different
parameters during the measurement cycles was estimated with a Monte Carlo
approach, as described in Groß et al. (2014), by assessing the
uncertainty from individual key parameters (shown in Table 1) in the
calculation model. γN2O5 was found to be most sensitive to
RH, which was closely related to kwall as discussed before. Figure 5a
shows the partial uncertainty of γN2O5 derived from Monte
Carlo simulations with RH at 40 %. The single-point uncertainty in γN2O5 was estimated to be ±4.1×10-3 for
γN2O5 around 0.03 and ±3.6×10-3 for
γN2O5 around 0.01, with RH of 40 %. The uncertainty
increased with RH and would be 9 % to 17 % at γN2O5 around 0.03 for RH ranging from 20 % to 70 % (Fig. 5b).
Sensitivity tests with the iterative model calculation were performed to
evaluate the uncertainty associated with measurement accuracy of
N2O5 and VOCs, by varying the input N2O5
concentrations and kNO3-VOC in both modes. It is found
that the N2O5 measurement uncertainty of 25 % (Tham et al.,
2016; Z. Wang et al., 2017) would
translate into an uncertainty of 12 % in the γN2O5
(shown in the Supplement). The VOC measurement uncertainty, however, has
negligible influence on γN2O5 calculation. In a previous
flow tube method introduced by Bertram et al. (2009a),
the homogeneous reaction was expected to be
independent of the aerosol and non-aerosol modes and was thus able to be canceled
out in the calculation. Only strong atmospheric variation in VOC in a short
time period would influence the N2O5 uptake measurement. The
uncertainty introduced by the aerosol surface area measurement including
aerosol loss influence would be propagated to an uncertainty in the γN2O5 calculation of 30 %.
As mentioned in Sect. 3, the use of mean residence time rather than RTD
function by assuming an ideal reactor and ignoring diffusion and dispersion
processes would also introduce uncertainties. In order to evaluate the
magnitude of this bias, we performed a simplified test by comparing a
first-order loss rate from mean residence time with a residence time
distribution range. Briefly, the mean concentration of N2O5 at the
exit of the reactor could be expressed by
N2O5‾=∫0∞N2O5tEtdt=∫0∞N2O50e-ktEtdt,
where [N2O5]t is the average concentration exit from the reactor
between t and t+dt, E(t) is the residence time distribution function,
and k is the first-order loss rate coefficient of N2O5. The
results showed that the first-order loss rate calculated from the
distribution function was higher than that with a mean residence time and
was about 5 % or 16 % higher when the ratio of N2O5tN2O50 was
0.6 or 0.2 in the flow tube system, respectively. When all of
these factors are incorporated, the estimated total uncertainty is propagated to be 37 % to
40 % at γN2O5 around 0.03 with 1000 µm2 cm-3Sa for RH ranging from 20 % to 70 %.
Demonstration of γN2O5 measurements under polluted
conditions
In polluted environments, high concentrations of NO2, O3, or NO in
ambient air would affect the determination of the N2O5 loss rate
and uptake coefficient in the flow tube experiments. To investigate the
effect of multiple reactions of these species in polluted conditions, a
series of tests with different conditions were simulated to compare the
derived loss rate and uptake coefficient with and without consideration of
N2O5 regeneration and NO titration in the flow tube system. Using
the forward box model described in Sect. 4, the process in the flow tube
reactor was simulated with an assumed fixed Sa of 1000 µm2 cm-3, γN2O5 of 0.03, kwall of
0.004 s-1, and kNO3-VOC of 0.01 s-1. Various conditions were
simulated with different O3, NO2, and NO levels introduced into the
flow tube, and the resulting concentrations of N2O5, NO2, and
O3 at the exit of the reactors with and without aerosols modes were
obtained. The loss rate and uptake coefficients of N2O5 were
then calculated using the simple exit-concentration ratio approach (Eq. 2)
and time-dependent iterative box model, respectively. The difference in
γN2O5 obtained from these two methods reflects the effect
of N2O5 regeneration and NO titration on uptake coefficient
determination.
The influence of multiple reactions resulting from high ambient
NO2 and O3 levels under different ambient NO2
levels from 0 to 40 ppbv. The colors indicate the NO3 production
rate (pNO3) at the entrance of the flow tube reactor after
mixing with 106 ppbv of O3 and 57 ppbv of NO2 from the
N2O5 source.
(a) Simulation results of NO titration effect on γN2O5. The γN2O5 ratio represents (γN2O5 from the iterative model) / (γN2O5 from the method of ignoring multiple reactions). Initial NO and
initial N2O5 represent the respective initial concentrations of
NO and N2O5 in the flow tube reactor. The lines represent the
simulation result, and the cubes represent the lab test result.
(b)khet calculated via the iterative model in
laboratory experiments with constant RH of 21 %, different initial
N2O5, and varied NO additions.
Figure 6 shows the simulation results for the derived uptake coefficients
regarding the effect of N2O5 formation in the flow tube reactor,
with O3 varying in the range of 0–100 ppbv and NO2 in the range
of 0–40 ppbv without NO presence in the ambient air. The N2O5
source input was fixed at 4.3 ppbv, as measured in the laboratory, together
with 106 ppbv of O3 and 57 ppbv of NO2 from the N2O5
source. The N2O5 regeneration effect on γN2O5
calculation was significant when O3 and NOx levels in the
ambient air were high. For example, at NO2=40 ppbv and O3=100 ppbv, which may frequently be encountered in city cluster regions in
China, neglecting N2O5 formation in the flow tube would result in
underestimating γN2O5 by 42 %.
To demonstrate the influence of NO titration, simulation tests were
performed with NO varying from 0 to 8 ppbv. Because the reaction rate of NO
with NO3 is 2 orders of magnitude faster than that of NO with
O3, the initial N2O5 level would affect the NO titration
process. We performed the simulation with different initial N2O5
concentrations injected into the flow tube reactor. As the green line in Fig. 7a indicates, the calculated γN2O5 will be greatly
underestimated when NO concentration increases, up to 55 % at a NO level
of 8 ppbv with an initial N2O5 level of 3 ppbv compared to a NO
level of 0. During the laboratory experiments, two initial N2O5
conditions with the input of an additional 5 ppbv of NO were also tested. The
determined γN2O5 from the iterative model simulation and
exit-concentration method was compared and is shown as cubes in Fig. 7a. The
model results lie within the uncertainty range of the measurements, further
cross-validating the NO influences and the model simulation. Figure 7a also
shows that a lower initial N2O5 leads to a larger underestimation
of γN2O5 in the presence of NO. It is not desirable to use
N2O5 concentrations above 5 ppbv to minimize the NO effect,
because of other potential artifacts associated with working at high
concentration (Thornton et al., 2003).
To explore which NO level would leave an extremely low N2O5
concentration in the exit of the reactor and make N2O5 loss rate
measurement impossible, a series of experiments in clean air with additional
NO was conducted in the laboratory to investigate NO titration effects and
the performance of the iterative model in buffering against high NO. As
shown in Fig. 7b, the derived khet showed consistent results for zero
NO and NO <6 ppbv conditions when RH and other parameters were
unchanged. With higher NO addition and a lower initial N2O5 level,
the calculated khet, however, could be underestimated due to greater
uncertainty when NO3 and N2O5 were insufficient to titrate
with NO. Figure 7b also shows that the introduced box model method could
buffer against NO below 8 ppbv with an initial N2O5 level of 4.3 ppbv. For future development, an activated-carbon scrubber in the inlet to
reduce the gas-phase interferers (NO, NO2, O3, VOCs) but transmit
aerosols could be a complementary approach to apply the flow tube system
coupled with iterative box model analysis to even more polluted
conditions.
In summary, the simulation and laboratory results demonstrate that neglecting
the formation and titration reactions in a flow tube reactor will result in
underestimating γN2O5. To reduce the NO titration
effect, a relatively high level of N2O5 (but less than 5 ppbv)
should be introduced to the flow tube reactor. Consideration of the multiple
reactions in the iterative model is sufficiently robust to encourage further
development to improve the accuracy of γN2O5
calculations.
Ambient measurement
During winter 2017, the flow tube system was deployed to measure the
N2O5 uptake coefficient at a sub-urban site in Heshan,
Guangdong, in southern China. The sampling time for each mode with and
without ambient aerosols lasted for at least 15 min to ensure 5 min stable
data at the exit for subsequent modeling analysis. The measured 5 min
average concentrations of initial NO and exit N2O5,
NO2, and O3 were used as the inputs in the iterative box
model to derive khet and γN2O5. Most
measurements were conducted during the daytime to avoid interruption of
nighttime ambient N2O5, and daytime N2O5 levels
could be neglected. The average ambient temperature, RH, NO, NO2,
and O3 during the field campaign were 23 ∘C, 51 %,
3.2 ppbv, 23 ppbv, and 32 ppbv, respectively. As discussed previously,
changes in RH and temperature can influence the stability of
kwall and N2O5–NO3 equilibrium, and thus
upset γN2O5 measurement. In the cases where γN2O5 measurement was affected by extreme fluctuations in NO
(above 8 ppbv), temperature and RH (fluctuation >2 %) were discarded
from the analysis.
In addition to the iterative box model approach, we also used the
exit-concentration ratio approach (cf. Eq. 2) to calculate the γN2O5. Figure 8 exhibits the comparison of γN2O5
obtained using these two methods. Fifteen out of 51 measurements occurred
under relatively “clean and stable” conditions (defined as ambient NO <1 ppbv, fluctuation of NO <0.3 ppbv, NO3 production
rate <0.8 ppbv min-1, and fluctuation of NO2 and O3<4 ppbv), and the corresponding values of γN2O5
from the two methods show good correlation, with an average ratio of 1.34,
which is consistent with our previous simulation results that the
exit-concentration ratio approach could underestimate γN2O5 mainly due to N2O5 regeneration reaction. For
conditions with higher precursor concentrations and fluctuations, a larger
discrepancy between γN2O5 from two methods was found (see
Fig. 8). As described previously, greater uncertainty in the
exit-concentration ratio approach could result from multiple reactions and
air mass changes. The fluctuations of NO, NO2, and O3 could
greatly affect the exit N2O5 concentration ratio. For example, a
lower NO level and higher NO2 and O3 levels in the aerosol mode
relative to the non-aerosol mode would result in a higher exit
N2O5 concentration ratio, which would lead to underestimation of
γN2O5 and even negative values (see Fig. 8 and Supplement). As even
a 1 ppbv fluctuation of NO concentration could largely affect the exit
N2O5 concentration, it would result in significant uncertainty for the
exit-concentration ratio approach. When NO concentration is much higher, for
example in the aerosol-existing mode, the measured N2O5
concentration is lower due to NO titration; thus the
uptake coefficient is overestimated when only the end concentration ratio of
N2O5 in two modes is compared.
Comparison of γN2O5 determined from the
exit-concentration ratio approach and the iterative model approach for all
available data measured in the Heshan campaign. The blue points represent the
data obtained under “clean and stable condition”, while green points are
data obtained from other condition. The “clean and stable condition” is
defined as follows: ambient NO <1 ppbv, the change of NO <0.3 ppbv,
the NO3 production rate <0.8 ppbv min-1, and the change
of NO2 and O3<4 ppbv. The error bar represents the
uncertainty calculated by the Monte Carlo approach under the measurement
condition.
Two sample cases are shown. In the upper panel, the blue and light
brown dots represent 1 min ambient O3 and NO2 data,
respectively. In the middle panel, the brown dots represent 1 min ambient NO
data. In the lower panel, the pink dots represent 1 min average of
N2O5 concentration normalized to the initial N2O5
concentration in the flow tube reactor. The calculated total
N2O5 loss rate derived from the iterative model with 5 min
average input data (the blue bar) is also shown for each cycle.
Two example cases with large air mass changes are shown in Fig. 9. In Fig. 9a, a case with high and fluctuating NO emission was observed on the night
of 21 March 2017, with average ambient concentrations of NO of 6 ppbv,
NO2 of 27 ppbv, O3 of 2 ppbv, and Sa of 1880 µm2 cm-3. γN2O5 was determined to be 0.028 from
the iterative model approach, and a higher γN2O5 value of
0.036 was obtained from the exit-concentration ratio approach. The
overestimated γN2O5 from the exit-concentration ratio
approach could be explained by the increased NO level (∼1.5 ppbv) in the aerosol mode. For comparison, another two periods of data
points in the 21 March case (Fig. 9a) with different NO levels were
also selected to derive the khet, and the results showed good
consistency (0.0136–0.0140 s-1) (Fig. S2 in the Supplement), also demonstrating the
applicability of the iterative model in buffering against fluctuating NO. In
Fig. 9b, another case with fluctuating NO2 and O3 levels was
observed on 26 March 2017, and the NO2 level was about 5 ppbv
higher, but the O3 level was about 11 ppbv lower in aerosol mode. With Sa of
681 µm2 cm-3, γN2O5 was determined to be
0.020 from the iterative model approach and a much lower value of 0.008 from
the exit-concentration ratio approach. The consideration of multiple
reactions in the iterative model approach was able to buffer against small
fluctuations of precursors in switching between aerosol and non-aerosol
modes. The results demonstrated the applicability of the iterative model
approach to directly measuring the N2O5 heterogeneous uptake
coefficient under conditions of high NO2 and O3 and fresh NO emission.
Summary and conclusion
An in situ experimental approach for direct measurement of N2O5
heterogeneous reactivity in a polluted environment was developed and
introduced in the present study. The method uses an aerosol flow tube
reactor combined with an iterative box model, to determine the heterogeneous
loss rate of synthesized N2O5 on ambient aerosols with
consideration of multiple reactions affecting N2O5 in the flow
tube. A series of laboratory and model simulations were conducted to test
the applicability of the system under different conditions. The overall
γN2O5 uncertainty from the variations of parameters during
two operation modes and uncertainties associated with measurements of
gaseous and aerosol species was propagated to be 37 %–40 % at γN2O5 around 0.03 with Sa of 1000 µm2 cm-3 and RH
ranging from 20 % to 70 %. Field deployment of this system at a polluted
suburban site in southern China demonstrated the applicability of the
introduced method in measuring N2O5 uptake coefficients in
polluted environments with high ambient levels of O3, NO, and NO2
and rapid air mass changes. Both field results and simulation tests
demonstrate that neglecting multiple reactions within the flow tube reactor
leads to underestimating γN2O5 values. The introduced
approach could also be used to investigate the heterogeneous reactivity of
other trace gases on ambient aerosols in polluted environments.
The data used in this study are available upon request from
the corresponding author (z.wang@polyu.edu.hk or cetwang@polyu.edu.hk).
The supplement related to this article is available online at: https://doi.org/10.5194/amt-11-5643-2018-supplement.
TW and ZW designed the research in this study; WW and ZW
developed the method; WW and XP carried out the lab testing; WW, CY, and MX
conducted the filed measurement and analyzed the data; YZ, DY, and YO
contributed to the field measurement and data analysis; and WW, ZW, and TW wrote
the manuscript. All authors contributed to discussion and commented on the
manuscript.
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was funded by the National Natural Science Foundation of China
(grant nos. 91544213 and 41505103); the Research Grants Council of Hong Kong Special
Administrative Region, China (grant nos. C5022-14G and 15265516); and the National Key
R&D Program of China (grant no. 2016YFC0200500). The authors also acknowledge
the support of the Research Institute for Sustainable Urban Development
(RISUD).
Edited by: Lisa Whalley
Reviewed by: two anonymous referees
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