Measurements of atmospheric boundary layer nitrous acid (HONO) and nitrogen
oxides (NOx) were performed in summer 2016 inside a city centre road
tunnel in Birmingham, United Kingdom. HONO and NOx mixing ratios were
strongly correlated with traffic density, with peak levels observed during
the early evening rush hour as a result of traffic congestion in the tunnel.
A day-time ΔHONO/ΔNOx ratio of 0.85 % (0.72 % to 1.01 %, 95 % confidence interval) was calculated using reduced major axis
regression for the overall fleet average (comprising 59 % diesel-fuelled
vehicles). A comparison with previous tunnel studies and analysis on the
composition of the fleet suggest that goods vehicles have a large impact on
the overall HONO vehicle emissions; however, new technologies aimed at
reducing exhaust emissions, particularly for diesel vehicles, may have
reduced the overall direct HONO emission in the UK. This result suggests
that in order to accurately represent urban atmospheric emissions and the OH
radical budget, fleet-weighted HONO/NOx ratios may better quantify HONO
vehicle emissions in models, compared with the use of a single emissions ratio
for all vehicles. The contribution of the direct vehicular source of HONO to
total ambient HONO concentrations is also investigated and results show
that, in areas with high traffic density, vehicle exhaust emissions are
likely to be the dominant HONO source to the boundary layer.
Introduction
Nitrous acid (HONO) is an important atmospheric constituent in the boundary
layer, as its photolysis leads to the formation of OH radicals (Reaction R1), which
drive atmospheric oxidation reactions, pollutant removal and the formation
of secondary species. This is particularly important in urban areas where
the measured HONO mixing ratios can reach up to parts per billion, and HONO
photolysis can be the dominant HOx source (Alicke
et al., 2002; Elshorbany et al., 2009; Kleffmann, 2007; Lee et al., 2016;
Michoud et al., 2012; Villena et al., 2011).
HONO+hv⟶λ<400nmOH+NO
The sources of HONO in the atmosphere can be primary or secondary
(Fig. 1). Primary sources include direct
emissions from combustion processes such as vehicle emissions (Kirchstetter
et al., 1996; Liu et al., 2017; Rappenglück et al., 2013; Trinh et al.,
2017; Xu et al., 2015), soil microbial activity (Laufs
et al., 2017; Maljanen et al., 2013; Meusel et al., 2018; Oswald et al.,
2013; Su et al., 2011) and biocrusts (Maier
et al., 2018; Meusel et al., 2018; Weber et al., 2015). Secondary sources
were thought to be dominated by the homogeneous gas phase reaction of NO and
OH during the day (resulting in a null cycle with Reaction R1), and the heterogeneous
production of HONO from NO2 on surfaces at night (Calvert
et al., 1994; Finlayson-Pitts et al., 2003; Jenkin et al., 1988; Kleffmann
et al., 1998; Stutz et al., 2002). More recently, studies have shown that
heterogeneous reactions are also important sources of HONO during the day
and include photo-enhanced reduction of NO2 on organic substrates
(George
et al., 2005; Monge et al., 2010; Stemmler et al., 2006) and nitrate
photolysis (Reed
et al., 2017; Ye et al., 2017; Zhou et al., 2011). Despite numerous studies
over the last few decades, substantial uncertainties remain regarding the
relative magnitude of these sources, with models frequently unable to
account for total measured HONO concentrations without the inclusion of
unknown sources, typically driven by photolysis (Huang
et al., 2017; Lee et al., 2016; Michoud et al., 2014; Vandenboer et al.,
2014; Vogel et al., 2003; Wang et al., 2017). A photo-stationary steady-state method based on measurements of HONO, OH and NOx has previously
been used to infer missing HONO sources; however, in areas of high spatial
heterogeneity this method breaks down because the lifetime of HONO is much
longer than that of OH, or of NO (with respect to NOx–O3
equilibrium) (Crilley
et al., 2016; Lee et al., 2013).
Sources and sinks of HONO in the troposphere, showing direct
emission (red arrows), secondary sources (blue arrows) and HONO sinks (green
arrows). Dashed arrows represent solar driven reactions.
In areas with high traffic density, HONO emitted directly from vehicle
exhausts is an important source, as indicated by large peaks in ambient HONO
concentrations observed during rush hour periods (e.g.
Qin et al., 2009; Rappenglück et al., 2013; Stutz et al., 2004; Tong et
al., 2016; Wang et al., 2017; Xu et al., 2015). Tong et al. (2016) estimated that direct emissions from
vehicles at an urban site in Beijing contributed 48.8 % of the total
measured HONO, almost 5 times higher than the contribution at a suburban
site (10.3 %) located 50 km north-east of Beijing city centre.
A HONO/NOx emission ratio is often used to parameterize the HONO
contribution from vehicles. Various studies have determined this ratio
either directly via chassis dynamometers (Calvert
et al., 1994; Liu et al., 2017; Nakashima and Kajii, 2017; Pitts et al.,
1984; Trinh et al., 2017) or from ambient roadside and tunnel measurements
(Kirchstetter
et al., 1996; Kurtenbach et al., 2001; Liang et al., 2017; Rappenglück
et al., 2013; Yang et al., 2014). Chassis dynamometer studies benefit from the
direct quantification of HONO from vehicle exhausts under different driving
cycles; however, the number of vehicles tested in these studies is very
limited and may not be representative of the wider fleet or real-world
operational conditions. Both tunnel and ambient roadside measurements on the
other hand allow for analysis of HONO emissions from a larger vehicle fleet,
under real-world driving conditions. Tunnel studies have the additional
benefits of eliminating photolytic loss and photo-enhanced surface sources
of HONO and of reducing dispersion. However, care must be taken to
accurately correct for background levels of HONO at the tunnel entrance and
for other HONO sources, such as heterogeneous reactions on tunnel walls and
emitted particles.
Reports of the HONO/NOx emission ratio from chassis dynamometer and
ambient roadside and tunnel studies are highly variable, ranging between 0.03 % and 2.1 %, depending on fuel type and implementation of technologies aimed at
reducing emissions, such as diesel oxidation catalysts (DOCs) and diesel
particulate filters (DPFs). An emission ratio of 0.8 % is widely used in
modelling studies, which is based on a tunnel study with a fleet comprising
primarily gasoline vehicles (∼75 %) conducted in 1997–1998
(Kurtenbach et
al., 2001). However, a recent chassis dynamometer study by Trinh et al. (2017) showed that HONO/NOx
ratios were higher from diesel vehicles (ranging from 0.16 % to 1.00 %)
compared to petrol vehicles (0 % to 0.95 %), under most driving conditions.
The higher HONO/NOx ratio observed from diesel vehicle exhausts is
thought to be due to a reduction–oxidation reaction, as proposed by Ammann et
al. (1998) and Gerecke et al. (1998), in which NO2 can be converted
heterogeneously to HONO on soot via Reaction (R2).
NO2+C–Hred→HONO+Cox,
where C–Hred is a surface site on the
soot particle.
A number of laboratory studies have supported this reaction, with HONO
yields varying between 20 % and 100 % depending on the fuel type used to
generate the soot, initial NO2 concentrations and soot coatings (Arens
et al., 2001; Aubin and Abbatt, 2007; Gerecke et al., 1998; Guan et al.,
2017; Khalizov et al., 2010; Kleffmann et al., 1999; Lelièvre et al.,
2004; Romanias et al., 2013; Stadler and Rossi, 2000). An observation that
is consistent across many laboratory experiments is that the uptake of
NO2 decreases over time, which has previously been attributed to the
deactivation of soot surface receptor sites (Kalberer et al., 1999). Lelièvre
et al. (2004)
observed complete deactivation after the soot was exposed to ambient outdoor
conditions for approximately 20 h. As a result of the soot surface
deactivation, Reaction (R2) is not expected to have a large impact on
atmospheric HONO levels once soot has been exhausted from the vehicle.
However, Khalizov et al. (2010) found that
if soot was pre-heated up to 300 ∘C, the HONO yield increases, as
a result of the removal of products from incomplete combustion, allowing for
a greater number of reactive sites to reduce NO2 to HONO. Reactions
within the vehicle exhaust pipe, on fresh soot, may still be a potential
source of HONO from vehicles. Determining the magnitude of this source under
real-world conditions, however, is challenging. Two recent studies in Hong
Kong investigated the relationship between soot and HONO directly emitted
from vehicles, with contrasting results. Xu et al. (2015)
found a strong positive correlation (R2=0.83) between HONO/NOx
and black carbon (BC) in fresh traffic plumes sampled at an ambient air
monitoring site, but no correlation was ascertained in a separate road
tunnel experiment by Liang et al. (2017). Data taken at a site in
North Kensington, London, during 2012 as part of the Clean Air for London
(ClearfLo) project (Bohnenstengel
et al., 2015) show a modest correlation between HONO and BC
(R2=0.71); however, there is no clear correlation between
HONO/NOx and BC when sampling fresh pollution plumes (i.e. when
NO/NOx ratios are large) (see Fig. S1 in the Supplement).
This agrees with the results of Liang et al. (2017),
who suggested that HONO formed via NO2 conversion on BC may be
insignificant even in a road tunnel scenario shortly after emission.
The primary aim of this study is to determine HONO/NOx emission ratios
under real-world driving conditions, with a fleet containing a high
proportion of diesel vehicles. In the UK, at the end of 2016, there were
over 38 million vehicles licensed for use on the roads (DfT,
2017). The majority of vehicles were passenger cars (∼83 %) and goods vehicles (11.4 %), with the remainder consisting of
motorcycles, buses/coaches and other vehicles (e.g. agricultural machines,
ambulances). Diesel-fuelled cars and goods vehicles, in total, account for
44 % of the vehicles on the road in the UK in 2016 (data on the
statistics discussed here can be found in Table S1 in the Supplement). To our knowledge this is the first study of direct HONO exhaust
emissions performed in the UK and the first study in any country that has a
diesel composition greater than 40 % of the vehicle fleet. Using
co-located HONO, NOx and CO2 data, we investigated the
HONO/NOx ratio using measurements taken in a road-tunnel in the city
centre of Birmingham and considered the impact of new technologies
implemented in the European emission standards on the measured HONO
emissions. Finally, the contribution of direct HONO emissions to the total
HONO measured in an urban area was determined.
Statistical analysis was conducted in R, version 3.6.1 (R Core Team, 2019), using the openair package (Carslaw and Ropkins, 2012), and the lmodel2 package (Legendre, 2018) for the reduced-major-axis (RMA) regression. Figures were produced using the packages ggplot2 (Wickham, 2016) and leaflet (Cheng et al., 2019).
ExperimentalQueensway Tunnel
Measurements took place in the southbound bore of the Queensway Tunnel
(Fig. 2), a two-bore, twin lane tunnel, 548 m in
length, located in the centre of Birmingham, UK (52∘28′46′′ N,
1∘54′ 20′′ W). The tunnel forms part of the A38 roadway which is
a major route into and out of Birmingham City Centre, and links to the M6
motorway north of the city. The instruments were located at the distant end
of a maintenance area approximately 435 m from the entrance to the
southbound tunnel. The two bores of the tunnel are separated by a solid wall
eliminating any influence in the measurements from vehicles travelling in
the northbound bore. The tunnel was not mechanically ventilated during the
measurement period. Airflow through the tunnel is therefore via natural wind
flow or induced by vehicle movement (piston effect). The average speed of
vehicles through the tunnel during the day-time (06:00 to 19:59 British Summer Time, BST) is 52 km h-1 (33 miles per hour, mph) which drops to 33 km h-1 (21 mph) at
17:00 during peak evening rush hour as a result of congestion (see Fig. S2).
Data from an Automatic Number Plate Recognition (ANPR) camera commissioned
by Birmingham City Council were used to obtain information on the vehicle
fleet passing through the tunnel. ANPR data were taken from 8 to 11 November 2016 and included vehicle type, European emissions standard and capture rate of the
vehicle number plates, on a 15 min timescale. The ANPR capture rate is
based on a comparison of manual classified counts (MCC) with the ANPR data.
The capture rate was typically around 90 % of MCC, with counts missing
from ANPR usually as a result of the number plate being obscured
(Rhead et al., 2012). We assumed the same fleet
proportions for the missing vehicle counts and adjusted the final data
accordingly. Figure 3 shows the mean hourly number of vehicles travelling
through the tunnel during the weekdays and a breakdown of vehicles by type.
The ANPR data were also compared to manual traffic counts performed at the
exit of the southbound tunnel on selected days during the measurement period
with good agreement. Approximately 37 700 vehicles travel through the
southbound bore of the Queensway tunnel during an average weekday. The
majority of these vehicles are petrol (40 %) and diesel (44 %) fuelled
passenger cars, with the remaining fleet comprised primarily of diesel-fuelled light-goods vehicles (LGVs, e.g. vans, small pick-ups) (10.4 %),
ordinary goods vehicles (OGVs, e.g. trucks, articulated vehicles) (2.5 %)
and passenger vehicles (taxis and buses) (1.3 %).
(a) Average hourly vehicle fleet in the southbound tunnel from the
corrected ANPR counts for weekdays (LGV = light goods vehicles, HD = heavy-duty vehicles, e.g. trucks, lorries and buses). Orange points represent
the counts estimated from video footage recorded during the measurement
period. (b) Percentage of vehicle types, determined from all weekday ANPR
data in November 2016.
Instrumental techniques
The instruments were installed in the tunnel in close proximity (1.5 m) to
the roadside and made measurements from 29 July until 8 August 2016. Access to the instruments was only possible when the tunnel was closed
to traffic during maintenance periods, which occurred approximately every 2 weeks. Table 1 provides an overview of the
instrumentation deployed in the tunnel during the campaign.
Overview of instruments deployed in the Queensway Tunnel.
InstrumentMeasurementMethodSampling time (s)UncertaintyTEI: 42cNO (and NOy)Chemiluminescence (Mo converter)60±10 %BBCEAS (open-path)HONO and NO2Broadband cavity enhancedabsorption spectroscopy20±5 ppbv (NO2) ±1.2 ppbv (HONO)LI-COR: LI-820CO2Non-dispersive infrared1±5 %Kestrel 4500anemometerWind speed (WS),temperature (T),relative humidity (RH)Impeller (WS) Solid state sensor (T, RH)600The larger of ±3 % or 0.1 m s-1 (WS) ±0.5∘C, ±3 % RH units
Direct spectroscopic measurements of HONO and NO2 were made by
broadband cavity enhanced absorption spectroscopy (BBCEAS) (Langridge
et al., 2009; Thalman et al., 2015). The BBCEAS instrument operated in the
wavelength range 363 to 388 nm, which includes a highly structured part of
the NO2 spectrum and two HONO absorption bands at 368 and 385 nm.
Ultraviolet light from an LED light source (LEDengin LZ1-10UV00, nominal
peak wavelength = 365 nm) was directed through a cavity formed by two
highly reflective mirrors (Layertec GmbH, 25 mm diameter, 0.5 m radius of
curvature, high reflectivity 370 to 395 nm) separated by 80 cm, giving an
effective path length of approximately 1.4 km. The mirrors were housed in
bespoke mirror mounts (length = 11 cm) which were purged with nitrogen
(0.9 L min-1 divided between the two mounts), and thus BBCEAS data were
corrected for the cavity's length factor (LF = 1.37). The cavity was
operated “open path”; i.e. the portion of the cavity between the mirror
mounts was open to the ambient atmosphere. This open-path configuration has
the advantage that there were no wall losses or heterogeneous production of
HONO within the instrument. A Teflon tube was inserted between the mirror
mounts in order to measure the reference spectrum of light transmitted when
the cavity was purged with nitrogen. The reflectivity of the cavity mirrors
(as a function of wavelength) was characterized in the laboratory before the
campaign; their measured reflectivity peaked at 99.940 % at 387 nm. The
reflectivity was verified in the field by measuring the 380 nm absorption
band of O4 when purging the cavity with pure oxygen, and measurements
taken at the start and end of the campaign agreed to within 4.5 %.
BBCEAS spectra were integrated for 20 s (the average of two 10 s
acquisitions) using an OceanOptics QEPro spectrometer (instrument line shape = 0.20 nm HWHM). Spectra were fitted with reference absorption cross
sections for HONO (Stutz et
al., 2000), NO2 (Vandaele et al., 1998) and O4
(Hermans as given in the HITRAN database, Richard et al., 2012), and
any remaining unfitted broadband absorption was attributed to extinction by
ambient aerosol particles. Typical statistical errors for retrieving HONO
and NO2 concentrations from the spectral structure were ±0.8 and ±0.9 ppbv, (1σ in 20 s) respectively. The BBCEAS
measurements are also affected by systematic uncertainties in the molecules'
reference absorption cross sections (typically ±3 %) and for
determining the mirror reflectivity in the field (±4.5 %).
Extinction by ambient aerosol also reduced the effective path length of the
BBCEAS measurement. HONO retrievals tended to be dominated by statistical
(spectral fitting) errors, whereas retrievals of the much higher ambient
NO2 concentrations were dominated by the systematic uncertainties in
the cross sections and the mirror reflectivity. For the mean HONO (3 ppbv)
and NO2 (75 ppbv) amounts recorded during the campaign, the total
measurement uncertainties (from combining the statistical and systematic
errors) were ±1.2 ppbv for HONO and ±5 ppbv for NO2.
NO was measured by a commercial chemiluminescence NOx analyser (Thermo
Environmental Instruments Inc: Model 42C). The detection limit of the 42C
was determined from 3 times the standard deviation of the measurement in
zero air and was calculated to be approximately 0.2 pbbv for a 1 min
averaging period. The uncertainty of the instrument was estimated to be
±10 % from calibrations. The analyser is capable of measuring
NO2 and NOx. However, the instrument utilizes a molybdenum
converter to covert NO2 to NO, an approach which is known to be
influenced by positive interference from other NOy species such as
HONO, nitric acid (HNO3), peroxyacetyl nitrate (PAN) and alkyl
nitrates (e.g.
Dunlea et al., 2007; Villena et al., 2012); therefore the final NOx
used in this study is calculated from the sum of the NO measured by the 42C
and NO2 by the BBCEAS. The inlet for the 42C instrument was shared with
a non-dispersive infrared instrument (LI-COR: Model LI-820) to measure
CO2. The LI-COR was calibrated with pure N2 (0 ppmv CO2) and
1500 ppmv CO2 to determine the relative uncertainty (±5 %)
and precision (1.2 %, 2σ). The LI-COR and 42C analysers were
evaluated for drift from zero measurements taken before and after the
measurement campaign. The drift for CO2 and NOx were determined to
be 1.45 ppmv and 0.42 ppbv, respectively. The drift for both instruments was
less than 2 % of the minimum measured values in the tunnel (NO = 1.27 %, CO2= 0.35 %), and therefore no correction was deemed
necessary here. A mini-meteorological station anemometer (Kestrel 4500) was deployed
near the top of the tunnel (3 m above ground level and 1 m from the roadside)
to measure the temperature, relative humidity and air flow in the tunnel.
Results and discussionData overview
Figure 4 presents 15 min averaged time series
for gases measured in the tunnel. NO, NO2, HONO and CO2 follow
the same diurnal cycle as each other, indicating that they have a similar
source. There is a clear difference in weekday and weekend diurnal cycles,
with peaks associated with rush hour traffic observed in NO, NO2, HONO
and CO2 during the weekdays, which are not present at the
weekend. The measured concentrations are also lower at the weekend, on
average. On weekdays, the mixing ratios of all gas species increase from
around 06:00 (start of morning rush hour) and drop away in the afternoon. A
large spike in the evening around 17:30 to 18:00 is observed, which
coincides with congestion in the southbound tunnel during peak evening rush
hour. The congestion is also evident in the lower mean vehicle speeds at
17:00 to 18:00 (Fig. S2).
The 15 min averaged measurements of NO, NO2, HONO and CO2
from the Queensway Tunnel between 29 July and 8 August 2016.
Shaded areas indicate the weekends. Spikes in the measured mixing ratios on
weekdays are the result of traffic congestion during morning and evening
rush hour periods.
The mean weekday HONO mixing ratio during the day-time (06:00 to 19:59,
calculated from the 15 min averages) is 3.4±1.0 ppbv (1σ), which decreased to 2.4±0.7 ppbv overnight. The maximum observed
15 min average HONO level occurred during the evening rush hour, reaching
9.7 ppbv on 3 August. Observed HONO levels in the current work are
typically lower than in previous tunnel studies. Liang et al. (2017) measured a mean HONO level of
15.7±4.2 ppbv during their study in Hong Kong, whereas Kurtenbach et
al. (2001) observed
peak HONO levels of 45 ppbv in the day-time. Kirchstetter et al. (1999) only performed measurements
between 16:00 and 18:00 in the Caldecott Tunnel, California, and observed a
mean HONO level of 6.9±1.4 ppbv, higher than the mean level of 4.1±1.4 ppbv for the same hours in this study. The lower levels measured
in the current study may be the result of differences in vehicle fleet
between studies as discussed further in Sect. 3.3, in combination with
shorter tunnel length (Table 2) and shorter
distance of our sampling point into the tunnel.
Comparison of HONO/NOx ratios from different tunnel studies.
LocationYear ofTunnelAvg.DieselPetrolOtherHONO/NOxReferencestudylengthvehicles per dayvehiclesvehicles(%)(%)(m)(%)(%)Queensway Tunnel,201654837 7005940<10.85This studyBirmingham, UK(CI: 0.72–1.01)Shing Mun Tunnel,2015160026 970384715a1.24±0.35Liang et al. (2017)Hong KongKiesbergtunnel,1997/110022 00024.3b74.710.80±0.10Kurtenbach et al. (2001)Wuppertal, Germany1998Caldecott Tunnel,19951100–<0.299–0.29±0.05Kirchstetter et al. (1996)California, USA
a LPG fuelled.
b Includes LGVs and OGVs (assumption made here that these are diesel
fuelled).
The persistence of HONO overnight is unlikely to be due to vehicle exhaust
emissions, as traffic flow is low during this period, but is rather from
background ambient HONO entering at the tunnel's entrance and from the
heterogeneous formation of HONO on particles deposited onto the walls of the
tunnel (Kurtenbach et
al., 2001). This also suggests that vehicles were not the only source of
HONO during the day, and the impact of heterogeneous HONO formation is
considered in emission ratio calculations in Sect. 3.2.1.
From late evening on 1 August until mid-morning on 2 August
the HONO mixing ratios are lower than otherwise observed for a weekday.
Precipitation data from a nearby weather station (Fig. S3) revealed that
it rained continuously from 17:00 on 1 August to 16:00 on 2 August with corresponding high relative humidity recorded inside the tunnel.
It is likely that wet surfaces inside the tunnel, as a result of spray from
tyres, resulted in a loss of HONO. The Henry's law constant (in water) of
HONO (kH=4.8×10-1 mol m-3 Pa-1) is
approximately 4800 times greater than that of NO2 (kH=9.9×10-5 mol m-3 Pa-1)
(Sander, 2015), and therefore HONO is likely to be washed
out more rapidly than NO2 on wet surfaces, resulting in a reduction in
the HONO/NOx ratios. As a result, this precipitation event was excluded
from the final dataset for calculation of emission ratios.
Relative HONO emission ratios
To determine a HONO/NOx emission ratio representative of direct exhaust
emissions, corrections for both heterogeneous formation of HONO from
NO2 and background HONO levels were considered.
Heterogeneous HONO formation from NO2
A number of heterogeneous reactions resulting in the formation of HONO have
been proposed in the literature; however, the formation of HONO from
NO2 on humid surfaces (Reaction R3) is thought to be the main pathway
(Spataro and Ianniello, 2014 and references
therein).
2NO2(g)+H2O(ads)→HONO(g)+HNO3(g)
The rate coefficient for Reaction (R3) (khet) is dependent on the geometric uptake
coefficient of NO2 on the tunnel walls (γgeo) and is given
by
khet=18ν‾NO2SVγgeo,
where ν‾NO2 is the mean molecular velocity of NO2 and
S/V is the surface-to-volume ratio of the tunnel (Kurtenbach et
al., 2001). Kurtenbach et al. (2001) performed
laboratory experiments to measure HONO generated on tunnel wall residue to
directly calculate γgeo and determined khet=3×10-3 min-1.
The rate of formation of HONO from NO2 can then be calculated via Eq. (2).
dHONOdt=-12dNO2dt=khet[NO2],
where dt is the residence time of the gases in the tunnel from the entrance to
the sampling point.
Kurtenbach et al. (2001) calculated
that the heterogeneous conversion on the tunnel wall contributed to
approximately 13 % of the measured HONO during the day and up to 80 %
at night (for a maximum NO2 mixing ratio of 250 ppbv). This result is
in contrast to measurements obtained in the Caldecott Tunnel by Kirchstetter
et al. (1996), who showed that the
HONO/NOx ratio did not change between the middle of the tunnel and the
tunnel exit, suggesting that there was no significant formation of HONO (or
deposition of HONO) on the tunnel walls in their study.
Liang et al. (2017) determined
khet=1.31×10-3 min-1 in the Shing Mun Tunnel, Hong Kong, from using an upper limit of γgeo=10-6 as calculated by Kurtenbach et al. (2001). Using wind
speed data in the tunnel to determine the residence time, Liang et al. (2017) found
that the contribution of HONO/NOx from heterogeneous reactions on the
tunnel walls alone was 0.04 % on average. Since this value was less than
the error when calculating HONO/NOx from direct emissions, the authors
did not perform any corrections to their final HONO/NOx ratio. As there
is no consensus in the literature about the relative contribution from
tunnel walls to measured HONO, we calculated the contribution from
heterogeneous HONO formation in the Queensway tunnel during the measurement
period. Using Eq. (1), a geometric uptake coefficient for NO2 of γgeo=10-6 and the surface-to-volume ratio of the Queensway
tunnel (0.64 m-1), we calculated khet to be 1.9×10-3 min-1.
The Kestrel anemometer used in this study logged data every 20 min, and
as a result, the wind speed measurements inside the tunnel only provide a
“snap-shot” of the data and are dependent on the traffic flow. Care should
be taken when using wind speed measured by an anemometer to calculate the
air residence time in a tunnel. Rogak et al. (1998) compared residence times in a
tunnel calculated using an SF6 tracer and anemometer data and found
that the wind speed in the tunnel measured by the anemometer overestimates
volumetric flow during high wind speeds and underestimates when wind speed
is low, requiring a correction factor for the determination of emission
factors in the tunnel. Tracer measurements were not performed during our
study, and consequently a modelling approach was used to correct the wind speed
data from the anemometer. As outlined below, the wall source contribution to
the observed HONO was small (∼5 % on average) during the
day-time periods used to derive our final results.
“True” wind speeds were inferred from computational fluid dynamics (CFD)
modelling of CO2 profiles measured along the tunnel. After the
campaign, the LI-COR CO2 instrument was installed in a van and driven
repeatedly through the southbound bore of the tunnel at four different
times of the day (late morning, late afternoon, evening and at night). Data
recorded when the van was inside the tunnel were extracted from the CO2
time series. The timestamps of these data points were converted into
distance along the tunnel using the speed of the van and the time that it
entered the tunnel, as recorded by a camera mounted on the van's dashboard.
The CO2 data were then averaged into 50 m bins along the tunnel to
produce a profile of the CO2 concentration increasing with distance
into the tunnel. The CO2 profiles were simulated by a CFD model which
took as its inputs the tunnel's physical dimensions, the anemometer wind
speed, the ANPR traffic counts and traffic composition, and emission factors
from the DEFRA Emissions Factor Toolkit version (EFT v8.0.1)
(DEFRA, 2017). Further information on the DEFRA EFT is
provided in the supplementary material. The wind speeds that produced the
optimum fits between modelled and measured CO2 profiles are shown as
the crosses in Fig. S4. These wind speeds match closely with the
anemometer wind speeds increased by a factor of 3.0 and they correctly
capture the day- and night-time differences. The optimum CFD-inferred wind
speeds at 10:00, 16:00 and 20:00 (3.4 to 3.9 m s-1) are also in
agreement with the 3.6 m s-1 wind speed (06:00 to 23:59) in the tunnel
study by Liang et al. (2017) and are consistent with the empirical correction
factor produced by Rogak et al. (1998) of ×3.5 for an
anemometer-measured wind speed of 1.5 m s-1, applicable during much of
the day-time. The results of CFD modelling of CO2, NO2 and HONO
profiles measured in the Queensway tunnel will be published in a separate
paper.
During the night, the measured wind speed was often below the minimum speed
required to turn the anemometer's impeller (<0.4 m s-1) so the
residence time of the air parcel overnight is difficult to determine
directly. As a result, we focus here on the heterogeneous formation of HONO
in the tunnel during the day-time (see comments below, in Sect. 3.2.3,
regarding use of day-time-only data to infer the fleet average emission
ratios). Between 06:00 and 19:59 the mean wind speed in the tunnel was 3.9 m s-1, giving a residence time of air in the tunnel up to the
sampling point of 1.86 min. If we assume the mean residence time of
NO2 emissions in the tunnel is half of this residence time (Liang
et al., 2017; Pierson et al., 1978), the HONO produced from heterogeneous
formation on the tunnel walls is 0.18 ppbv for a mean daily weekday NO2
mixing ratio of 98 ppbv, which is approximately 5 % of the mean measured
HONO during the day. Although the mean heterogeneous contribution to HONO
during the day-time is small, the heterogeneous contribution is higher (8 %) when the tunnel becomes congested and the residence time of the air in
the tunnel increases. Therefore, the final HONO data used in this study were
corrected for the heterogeneous HONO contribution using measured NO2
and the modelled wind speed, to better represent the direct HONO emissions
in the tunnel.
Background corrections for HONO, NOx and CO2
As vehicles travel through the tunnel, cleaner air from outside is drawn
into the tunnel, which dilutes the vehicle emissions in a process known as the piston effect.
To obtain direct vehicle emission measurements it is necessary to correct
for this dilution by subtracting ambient background levels from the
concentrations measured inside the tunnel. As no concurrent measurements
were available at the tunnel entrance during this campaign, ambient
NOx, HONO and CO2 data from nearby air monitoring stations were
used to correct for the background levels. Hourly averaged background
NOx mixing ratios during the measurement period were taken from Acocks
Green (AG), an Automatic Urban Rural Network (AURN) site located 6.9 km
south-east of the Queensway Tunnel (http://uk-air. defra.gov.uk/, last access: 26 January 2020). The AURN
station does not measure HONO or CO2, so data previously taken at the
Birmingham Air Quality Supersite (BAQS) on the University of Birmingham
campus (52∘27′1′′ N, 1∘55′30′′ W) 3 km south-west of
the tunnel were used in this study. As CO2 is well mixed within the
troposphere, variability in background CO2 mixing ratios is primarily
the result of a changing boundary layer height. Therefore, in this study, an
average diurnal CO2 cycle measured at BAQS was used to correct for
background CO2. The overnight background corrected CO2 (ΔCO2=CO2.tunnel-CO2.bkg) levels dropped to near
zero (see Fig. 5c), suggesting that the procedure was suitable.
HONO on the other hand has a much shorter lifetime than CO2, and HONO
mixing ratios vary depending on local sources and the availability of
NO2 (Crilley et
al., 2016). An average diurnal HONO/NOx cycle (Fig. S5) was
calculated from measurements taken at BAQS between 18 March and
1 April 2015 (Singh, 2017) and applied to the background
NOx from Acocks Green to determine hourly background HONO mixing ratios
(Eq. 3).
HONObkg=NOxbkg(AG)×HONONOxBAQS
Figure 5 shows the hourly time series of NOx,
HONO and CO2 measured in the tunnel and the corresponding data
corrected for background mixing ratios. The results show that mixing ratios
measured in the tunnel were, on average, 73 and 44 times higher than the
background NOx and HONO, respectively, suggesting that direct emissions
from vehicles were indeed the dominant source of these gases. Consequently,
any uncertainties in the background HONO mixing ratios that were subtracted
from the measured HONO have little impact on the final results. In the
following sections, the final HONO dataset (ΔHONO) is corrected for
both background HONO levels and the heterogeneous HONO contribution.
Hourly averaged time series (a)NOx in the tunnel and from
Acocks Green urban background station. (b) HONO in the tunnel and estimated
background HONO. (c)CO2 in the tunnel and background CO2 measured
at BAQS.
HONO emission ratios
To determine the HONO to NOx emission ratio, a reduced-major-axis (RMA)
regression (Fig. 6) was performed using the
weekday hourly averaged data from 06:00 to 19:00, i.e. when there was
minimal contribution from heterogeneous HONO sources and the traffic flow
was high (>1500 vehicles per hour). The slope of the regression
gives an average emission ratio of ΔHONO/ΔNOx=0.85 % (95 % confidence interval = 0.72 % to 1.01 %) for this time
period.
RMA regression for hourly averaged ΔNOx versus
ΔHONO, for weekdays between 06:00 and 19:00. The blue line
represents the RMA slope and the grey lines the 95 % confidence intervals
of the regression.
Figure 7a shows that the ΔHONO/ΔNOx emission ratio varies during the day, from 0.66 % at 07:00 to
1.10 % at 19:00. In this section, we explore the relationship between
ΔHONO/ΔNOx and changes in vehicle fleet composition
using information on fuel and vehicle types from the ANPR dataset.
Figure 7c shows the fraction of diesel and
non-diesel (petrol, LPG and electric) vehicles travelling through the
Queensway tunnel during an average weekday. Between 06:00 and 17:00, the
ΔHONO/ΔNOx ratio appears to vary closely with changes
in the fraction of diesel vehicles, with both variables peaking at 10:00
when the ΔHONO/ΔNOx emission ratio reaches a value of
0.91 % and the percentage of diesel-fuelled vehicles in the fleet reaches
a maximum of 66 %.
(a) Hourly averaged ΔHONO/ΔNOx, (b) hourly
averaged ΔHONO/ΔCO2, and (c) fraction of diesel and
non-diesel (petrol, biofuel, electric) vehicles calculated from the total
petrol and diesel vehicles, for the period between 06:00 and 19:00 during
weekdays. Error bars represent 1σ standard deviation of the mean
across the different days of this study.
The ΔHONO/ΔCO2 ratio (Fig. 7b and Table S2) also follows the change in fleet composition, with a
higher ratio observed at 10:00 (3.3 %) compared to 17:00 (1.5 %).
However, this may also be the result of higher CO2 emissions from petrol-fuelled vehicles compared to diesel vehicles; for example, a study of 149
passenger cars showed that CO2 emissions were 13 % to 66 % higher for
petrol vehicles than diesel (O'Driscoll
et al., 2018). Modelled emission factors from the DEFRA Emissions Factors
Toolkit (EFT v8.0.1) indicate a maximum in the CO2 emissions
(mg per vehicle per kilometre) at 17:00 (Fig. S6), in line with the 46 %
maximum in the non-diesel vehicle fraction at evening rush hour.
From 17:00 to 19:00 the relationship between the ΔHONO ratios and
fuel type is less clear. During this period the fraction of diesel vehicles
remains almost constant; however, the ΔHONO/ΔNOx and
ΔHONO/ΔCO2 emission ratios both sharply increase.
Analysis of the modelled NOx and CO2 emission factors from the EFT
(Fig. S6) indicate a decrease in both NOx and CO2 emission
factors from 17:00 to 19:00, which is likely to be related to the total
vehicle flow and lower vehicle speed at times of peak congestion (because
the fleet composition remains very similar). The EFT does not have the
capability to determine a HONO emission factor, and therefore information on
HONO emission variability with speed and vehicle flow is not available a priori.
However, assuming the HONO emissions do not reduce by the same percentage
after 17:00, this would lead to the increase in the observed ΔHONO/ΔNOx and ΔHONO/ΔCO2 ratios. As no
conclusion can be made at this point, without additional information, we
focus the analysis in the following section on the data between 06:00 and
17:00 only.
Ho et al. (2007)
used Eq. (4) to determine emission factors (mg per vehicle per kilometre) for
diesel- and non-diesel-fuelled vehicles based on a method described by
Pierson et al. (1996).
EF=EFDV-EFNDVx+EFNDV,
where x is the fraction of diesel-fuelled vehicles, EFDV is the
emission factor for diesel vehicles, EFNDV is the emission factor for
non-diesel vehicles and EF is the emission factor for the mixed fleet. In
this format, a linear regression of x versus EF, based on Eq. (4), gives
EFDV at x=1 and EFNDV at x=0.
In this study, instead of calculating emission factors, we investigated the
applicability of Eq. (4) to determine ΔHONO/ΔNOx
emission ratios for diesel (ERDV) and non-diesel (ERNDV) vehicles
(see Fig. S7). Extrapolating the best-fit line in Fig. S7 to x=1
gives a plausible emission ratio for diesel vehicles of ERDV=1.35±0.50 %. Unfortunately, this method resulted in an unrealistically
small negative emission ratio for non-diesel vehicles (ERNDV=-0.04±0.26 %). This method may not be appropriate for the current work
because the range in the fuel fraction of vehicles is small, and thus
extrapolating to x=0 and x=1 resulted in large uncertainties.
An alternative approach is to determine emission ratios using a pair of
simultaneous equations based on the fraction of diesel vehicles and ΔHONO/ΔNOx values for different hours of the day. For example,
using the data as given in Table S2, the equation at 17:00 is given by
ΔHONOΔNOx=0.73%=0.54×ERDV+0.46×ERNDV.
Using another equation for a different hour (i.e. different set of
fractions), the values for ERDV and ERNDV can be determined. Care
must be taken when interpreting these results, however, as the calculated
ERs depend on the selection of pairs of measurement points and some pairs
from our data resulted in negative ER values. Thus average ERs were
determined using many pairs of simultaneous equations. Here we calculated
the average of 10 ERs, using one set of fractions at 17:00 and a second set
for each hour from 06:00 to 15:00. The data from 16:00 were not used here as
there was no change in the diesel fraction between 16:00 and 17:00. The means
(±1σ) for ERDV and ERNDV are 1.04±0.47 % and 0.37±0.55 %, respectively, suggesting that diesel-fuelled vehicles do have higher ΔHONO/ΔNOx ratios.
It has previously been suggested that higher HONO/NOx ratios are
observed when the tunnel fleet contains a greater number of heavy-duty
vehicles (Trinh et al., 2017). A similar
calculation to that outlined in the previous paragraph was performed here to
determine the average emission ratio for cars and for heavy-duty vehicles
(goods vehicles and buses) by apportioning the observed ΔHONO/ΔNOx ratios between the car and heavy-duty vehicle
numbers given in Table S2. The ΔHONO/ΔNOx emission
ratio for heavy-duty vehicles (ERHD) is estimated to be 1.3±0.63 %, approximately 1.9 times higher than the ratio determined for cars
(ERCAR=0.69±0.05 %). The impact of heavy-duty vehicles
on the HONO/NOx emission ratio is discussed further in Sect. 3.3.
It should be noted that the methods described above can only provide an
estimate of the emission ratios for different fuel and vehicle types. In our
study, the variability in the fraction of diesel vehicles and heavy-duty
vehicles across the day was small, and therefore extracting emission ratios for
individual vehicle types is challenging. To obtain more precise emission
ratios for different engine types, a larger dataset (i.e. longer time
series) and fully contemporaneous ANPR data would be required.
Comparison of HONO/NOx emission ratios across tunnel studies
Table 2 shows a comparison of measured
HONO/NOx emission ratios from studies performed in road tunnels, along
with summaries of their reported vehicle fleets. The lowest HONO/NOx
ratio (0.29 %) was measured in the Caldecott tunnel, California
(Kirchstetter et al., 1996). As
99 % of the fleet was comprised of petrol-fuelled vehicles, the low
HONO/NOx ratio is expected because the analysis in Sect. 3.2.3 above
and previous dynamometer studies have both demonstrated that petrol vehicles
typically emit less HONO than diesel vehicles. On the other hand, Liang et al. (2017)
observed a HONO/NOx emission ratio of 1.24±0.35 % in the
Shing Mun Tunnel, Hong Kong, in 2015, approximately 1.4 times higher than in
the current work despite the Queensway Tunnel having a higher proportion of
diesel vehicles compared to the Shing Mun Tunnel (59 % and 38 %
respectively). The differences observed in the HONO/NOx emission ratio
between the Shing Mun and Queensway tunnel studies may therefore be related
to (1) the percentage of heavy-duty and goods vehicles within the fleet, and
(2) exhaust after-treatment technologies that affect NO2 emitted
directly from vehicle exhausts (known as primary NO2).
In addition to these two points, which are discussed further below, it
should also be noted that the fleet in the Shing Mun Tunnel consisted of 15 % vehicles fuelled by liquefied petroleum gas (LPG). On-road sampling of
emissions from buses fuelled by diesel and LPG in Hong Kong have indicated
that LPG vehicles have lower NOx emissions when compared to diesel vehicles (Ning et al., 2012). However, as far as we
are aware there have been no published studies investigating HONO emissions
from LPG-fuelled vehicles, and therefore no conclusions can be made at this
point regarding the impact of the LPG fleet on the observed differences in
HONO/NOx ratios.
Liang et al. (2017) investigated the impact of diesel particle filters
(DPFs) on HONO vehicle emissions and found that HONO emissions ratios were
higher in vehicles equipped with DPFs (based on a higher ΔNO2/ΔNOx ratio) and suggest that this may be due to HONO
formation from heterogeneous reactions involving NO2 and black carbon
within the filter. The diesel oxidation catalysts (DOCs) oxidize
hydrocarbons and CO in excess oxygen over platinum–palladium catalysts;
however, NO can also be oxidized to NO2 during this process. DPFs were
mandatory for all new diesel vehicles from 2009 (Euro class V) and operate
by trapping soot in the exhaust where it is then oxidized at high
temperatures. To ensure the filters do not become blocked from engines that
run at lower temperatures, many DPFs use catalysts to convert NO to NO2
to periodically oxidize the soot (He et al., 2015;
Kim et al., 2010). Consequently, DOCs and DPFs both result in an increase in
NO2 emitted from exhausts, which may also lead to an increase in HONO
emissions.
Liang et al. (2017) estimated that half of the diesel fleet in their study
were buses and goods vehicles (Euro class IV and above) equipped with diesel
particulate filters. In the current work less than a quarter of diesel-fuelled vehicles were goods vehicles or buses, and of those only 58 %
were Euro class V and above (i.e. with DPFs fitted). As a result, the
percentage of diesel vehicles emitting high NOx and HONO levels is
expected to be much lower in the Queensway tunnel than the Shing Mun Tunnel,
and this likely accounts for the lower HONO/NOx emission ratio we
observed. In contrast, in the Kiesbergtunnel (Kurtenbach et
al., 2001), medium and heavy-duty vehicles represented 12 % of the fleet
(6 % heavy-duty trucks, 6 % commercial vans), similar to the fleet in
the Queensway tunnel, which may explain why the observed HONO/NOx
emission ratios were so similar (0.85 % and 0.8 %, for the Queensway
tunnel and Kiesbergtunnel studies, respectively). It should be noted that
the Kiesbergtunnel study was done before the Euro III standard came into effect, so higher emissions
of NOx are expected compared to higher Euro standard vehicle classes.
However, few of these vehicles in the Kiesbergtunnel would have been fitted
with DOCs or DPFs, so this may have offset the amount of NO2 (and thus
HONO) emitted.
Recent trend analysis studies have shown that primary NO2 has been
decreasing over the last decade in the UK (Carslaw
et al., 2016; Matthaios et al., 2019) and across Europe
(Grange et al., 2017). For example, the mean
NO2/NOx emission ratio measured from ambient roadside monitoring
sites in inner London has decreased from a peak value of 25 % in 2009 to
15 % in 2014 (Carslaw et al.,
2016). Although the exact cause of the NO2 reduction is not clear, it
is thought that the introduction of after-treatment technologies, such as
selective catalytic reduction (SCR) installed on buses and heavy-duty
vehicles, and a reduction in the use of platinum group metals in catalysts
may have contributed to the observed decrease (Carslaw
et al., 2016; Grange et al., 2017). With a reduction in NO2 emissions
from diesel vehicles overall, a decrease in HONO may also be expected in the
UK. In contrast, Wang et al. (2018) reported
an increase in the NO2/NOx ratio at the outlet of the Shing Mun
Tunnel, from 9.5 % in 2003 to 16.3 % in 2015. Therefore, the higher
HONO/NOx ratio observed in Hong Kong
(Liang et al., 2017) compared to
our current work may also be related to differences in primary NO2
emissions. It should be noted that in the study by Wang et al. (2018), NO2 was measured using a standard chemiluminescence monitor, which typically
use a molybdenum converter. Molybdenum converters also convert other
NOy species, such as HONO, resulting in an overestimation of the
reported NO2. However, as the HONO/NOx ratio was only 1.24 %,
it is unlikely that HONO has contributed to the factor of 1.7 increase in
primary NO2 recorded near the Shin Mung Tunnel.
Impact of HONO emissions from vehicles on total HONO measured in
Birmingham
Previous work has shown that ambient HONO has high heterogeneity when
sampled close to roads (Crilley et al., 2016).
Here we use the mean HONO/NOx emission factor of 0.85 %, determined
from the tunnel measurements in Sect. 3.2.3, to investigate the
contribution of direct HONO vehicle emissions to ambient HONO levels in
urban and suburban areas. Measurements of HONO and NOx were taken in
a mobile laboratory on a return journey between Birmingham and Leicester,
two cities in the UK Midlands separated by 55 km (straight line measurement
from city centres). NO and NO2 were measured every 60 s by our
chemiluminescence analyser fitted with a molybdenum NO2 converter
(Thermo 42i-TL). As discussed in Sect. 2.3, molybdenum converters are
known to result in interferences from NOy species, and therefore the
NOx measurements presented here represent an upper limit on NO+NO2 concentrations. HONO was measured every 5 min with a Long-Path
Absorption Photometer (LOPAP, Model 03, QUMA). Measurement uncertainties are
primarily the result of uncertainties in the calibrations and are estimated
to be 5 % and 10 %, for NOx and HONO, respectively. Further
details regarding the measurement techniques and the driving route can be
found in Crilley et al. (2016).
(a)NOx, HONO and inferred vehicle exhaust HONO from mobile
measurements taken on 23 October 2015 along a return journey between
Birmingham and Leicester (Crilley et al., 2016). (b) Ratio of
vehicle-produced HONOveh to measured HONO (%) for the same period.
Grey shaded areas represent non-driving periods, i.e. when the mobile
laboratory was parked either at the University of Birmingham or University
of Leicester campus. Other colours represent measurements around the
University of Birmingham campus (light blue), the A38 road through Birmingham
city centre (dark blue), M6 and M69 motorways (red), Leicester Ring Road
(purple) and measurements around the University of Leicester campus
(orange). HONOveh/HONO ratios above 100 % are the result of uncertainties
in the HONO and NOx measurements and the HONO/NOx emission ratio.
Figure 8 shows HONO and NOx mixing ratios
measured during the transect from Birmingham to Leicester and the return
journey on 23 October 2015. Also shown is the directly emitted vehicular
HONO (HONOveh) calculated from the mean ΔHONO/ΔNOx
emission ratio (0.85 %). For the two transects, HONOveh contributed
on average 66 % to the total measured HONO. The highest contribution was
observed on the M6 motorway, where HONOveh typically accounted for 86 % of the measured HONO. During the motorway segments of the journeys, the
mobile laboratory was operating in “chase mode”, i.e. deliberately
sampling plumes from a single vehicle ahead. Therefore, it was likely the
high HONOveh/HONO ratios observed on the motorway were the result of
predominantly sampling vehicle emission plumes with low background
contribution. The lowest HONOveh contribution was observed around the
Birmingham University campus (24 %), which is expected as traffic density
was low in this area. Whilst travelling through Birmingham city centre,
HONOveh/HONO increased to 70 %, indicating that vehicle exhaust
emissions were the dominant source of HONO in this area. This value is
higher than determined by Tong et al. (2016) in
Beijing (48 %); however, in our study we were sampling directly on the
road, whereas the site in Beijing was located in a building at the Institute
of Chemistry, Chinese Academy of Sciences, and therefore significantly less
influenced by direct traffic sources. This estimate neglects the differing
atmospheric lifetimes of HONO and NOx; the longer lifetime of the
latter implies that HONO/NOx ratios will fall as the air mass ages.
Accordingly, estimates of the vehicular contribution derived from
measurements made as close as possible to the emission source, i.e. on the
roadway, as described here, will give the highest estimate of the relative
importance of vehicle emissions.
Overall direct vehicle emissions can be an important source of HONO in urban
areas, particularly near roads with high traffic density. Further
investigation is required to quantify the impact of HONO emissions in these areas on the chemistry of the overlying atmosphere.
Summary
Measurements of HONO, NOx and CO2 were performed in a city centre
road tunnel in Birmingham, UK, for 2 weeks in July and August 2016 to
investigate direct HONO emissions from vehicle exhausts under real-world
driving conditions. HONO mixing ratios peaked when NOx and CO2
peaked during traffic congestion in the weekday evening rush hour (17:30 to
18:00), confirming that vehicle exhausts were the dominant source of HONO in
the tunnel.
A HONO/NOx emission ratio of 0.85 % (0.72 % to 1.01 % at 95 %
confidence interval) was determined for an average weekday fleet comprising
59 % diesel-fuelled vehicles. Our value is similar to the ratio of 0.8 % which is typically used in modelling studies and which was determined
for a predominately petrol-fuelled fleet over 20 years ago in Germany. The
results show that despite an increase in the proportion of diesel-fuelled
vehicles over the past two decades in Europe, and updated emissions control
technologies, the HONO/NOx emission ratio has not varied significantly.
A comparison with a tunnel study in Hong Kong suggested that the
HONO/NOx ratio may be less dependent on the percentage of diesel
vehicles but rather the percentage of large goods vehicles within the fleet,
and the after-treatment technologies implemented on those vehicles.
The HONO/NOx emission ratio determined in this study was used to
investigate the contribution of vehicle exhaust HONO to ambient HONO in
Birmingham. The results show that direct vehicle emissions contribute up to
70 % of the total measured HONO in the city centre. As direct HONO
emissions were found to be the dominant source where traffic density is
high, it is important to obtain fuel-based emission ratios which also take
into account after-treatment technologies, to ensure models can accurately
simulate day-time OH radical production rates.
In this study the focus has been primarily on HONO emissions from diesel and
petrol-fuelled vehicles. However, in countries where alternative fuels such
as liquefied petroleum gas (LPG) and compressed natural gas (CNG) are
becoming more prevalent, in particular in the public transport sector,
further investigation into HONO emissions from these fuel types is needed.
Data availability
Hourly averaged fuel type, vehicle type and emission ratios are available in
the Supplement. The 15 min dataset from the tunnel
measurements is available from the authors on request.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-5231-2020-supplement.
Author contributions
The study was conceived by WJB, FDP and SMB. Measurements were performed by LJK, LRC, TJA,
SMB and FDP. Formal analysis performed by LJK, TJA and SMB. LJK prepared the
paper with contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to thank the staff from Amey for their help in
accessing the measurement site. We would also like to thank Birmingham City
Council for their input and provision of ANPR data. This work was funded by the Natural Environment Research Council (NERC) project “Sources of Nitrous Acid in the Atmospheric Boundary Layer”.
Financial support
This research has been supported by the Natural Environment Research Council (grant nos. NE/M013545/1 and NE/M010554/1).
Review statement
This paper was edited by Andreas Hofzumahaus and reviewed by two anonymous referees.
ReferencesAlicke, B., Platt, U., and Stutz, J.: Impact of nitrous acid photolysis on
the total hydroxyl radical budget during the Limitation of Oxidant
Production/Pianura Padana Produzione di Ozono study in Milan, J. Geophys.
Res.-Atmos., 107, LOP 9-1–LOP 9-17, 10.1029/2000JD000075, 2002.Ammann, M., Kalberer, M., Jost, D. T., Tobler, L., Rössler, E., Piguet,
D., Gäggeler, H. W., and Baltensperger, U.: Heterogeneous production of
nitrous acid on soot in polluted air masses, Nature, 395, 157–160,
10.1038/25965, 1998.Arens, F., Gutzwiller, L., Baltensperger, U., Gäggeler, H. W., and
Ammann, M.: Heterogeneous Reaction of NO2 Diesel Soot Particles,
Environ. Sci. Technol., 35, 2191–2199, 10.1021/es000207s, 2001.Aubin, D. G. and Abbatt, J. P. D.: Interaction of NO2 with hydrocarbon
soot: Focus on HONO yield, surface modification, and mechanism, J. Phys.
Chem. A, 111, 6263–6273, 10.1021/jp068884h, 2007.Bohnenstengel, S. I., Belcher, S. E., Aiken, A., Allan, J. D., Allen, G.,
Bacak, A., Bannan, T. J., Barlow, J. F., Beddows, D. C. S., Bloss, W. J.,
Booth, A. M., Chemel, C., Coceal, O., Di Marco, C. F., Dubey, M. K., Faloon,
K. H., Flemming, Z. L., Furger, M., Gietl, J. K., Graves, R. R., Green, D.
C., Grimmond, C. S. B., Halios, C. H., Hamilton, J. F., Harrison, R. M.,
Heal, M. R., Heard, D. E., Helfter, C., Herndon, S. C., Holmes, R. E.,
Hopkins, J. R., Jones, A. M., Kelly, F. J., Kotthaus, S., Langford, B., Lee,
J. D., Leigh, R. J., Lewis, A. C., Lidster, R. T., Lopez-Hilfiker, F. D.,
McQuaid, J. B., Mohr, C., Monks, P. S., Nemitz, E., Ng, N. L., Percival, C.
J., Prévôt, A. S. H., Ricketts, H. M. A., Sokhi, R., Stone, D.,
Thornton, J. A., Tremper, A. H., Valach, A. C., Visser, S., Whalley, L. K.,
Williams, L. R., Xu, L., Young, D. E., and Zotter, P.: Meteorology, air
quality, and health in London: The ClearfLo project, B. Am. Meteorol.
Soc., 96, 779–804, 10.1175/BAMS-D-12-00245.1, 2015.Calvert, J. G., Yarwood, G., and Dunker, A. M.: An evaluation of the
mechanism of nitrous acid formation in the urban atmosphere, Res. Chem.
Intermediat., 20, 463–502, 10.1163/156856794X00423, 1994.Carslaw, D. C. and Ropkins, K.: openair – An R package for air quality data analysis, Environ. Modell. Softw., 27–28, 52–61, 10.1016/j.envsoft.2011.09.008, 2012.Carslaw, D. C., Murrells, T. P., Andersson, J., and Keenan, M.: Have vehicle
emissions of primary NO2 peaked?, Faraday Discuss., 189, 439–454,
10.1039/C5FD00162E, 2016.Cheng, J., Karambelkar, B., and Xie, Y.: leaflet: Create Interactive Web Maps with the JavaScript
“Leaflet” Library, R package version 2.0.3, available at: https://CRAN.R-project.org/package=leaflet (last access: 28 April 2020), 2019.Crilley, L. R., Kramer, L., Pope, F. D., Whalley, L. K., Cryer, D. R.,
Heard, D. E., Lee, J. D., Reed, C., and Bloss, W. J.: On the interpretation
of in situ HONO observations via photochemical steady state, Faraday
Discuss., 189, 191–212, 10.1039/c5fd00224a, 2016.DEFRA: Emission Factors Toolkit for Vehicle Emissions,
available at: https://laqm.defra.gov.uk/review-and-assessment/tools/emissions-factors-toolkit.html
(last access: 14 July 2019), 2017.DfT: Vehicle licensing statistics: 2016, available at:
https://www.gov.uk/government/statistics/vehicle-licensing-statistics-2016
(last access: 26 April 2019), 2017.Dunlea, E. J., Herndon, S. C., Nelson, D. D., Volkamer, R. M., San Martini, F., Sheehy, P. M., Zahniser, M. S., Shorter, J. H., Wormhoudt, J. C., Lamb, B. K., Allwine, E. J., Gaffney, J. S., Marley, N. A., Grutter, M., Marquez, C., Blanco, S., Cardenas, B., Retama, A., Ramos Villegas, C. R., Kolb, C. E., Molina, L. T., and Molina, M. J.: Evaluation of nitrogen dioxide chemiluminescence monitors in a polluted urban environment, Atmos. Chem. Phys., 7, 2691–2704, 10.5194/acp-7-2691-2007, 2007.Elshorbany, Y. F., Kurtenbach, R., Wiesen, P., Lissi, E., Rubio, M., Villena, G., Gramsch, E., Rickard, A. R., Pilling, M. J., and Kleffmann, J.: Oxidation capacity of the city air of Santiago, Chile, Atmos. Chem. Phys., 9, 2257–2273, 10.5194/acp-9-2257-2009, 2009.Finlayson-Pitts, B. J., Wingen, L. M., Sumner, A. L., Syomin, D., and
Ramazan, K. A.: The heterogeneous hydrolysis of NO2 in laboratory
systems and in outdoor and indoor atmospheres: An integrated mechanism,
Phys. Chem. Chem. Phys., 5, 223–242, 10.1039/b208564j, 2003.George, C., Strekowski, R. S., Kleffmann, J., Stemmler, K., and Ammann, M.:
Photoenhanced uptake of gaseous NO2 on solid organic compounds: A
photochemical source of HONO?, Faraday Discuss., 130, 195–210,
10.1039/b417888m, 2005.Gerecke, A., Thielmann, A., Gutzwiller, L., and Rossi, M. J.: The chemical
kinetics of HONO formation resulting from heterogeneous interaction of
NO2 with flame soot, Geophys. Res. Lett., 25, 2453–2456,
10.1029/98GL01796, 1998.Grange, S. K., Lewis, A. C., Moller, S. J., and Carslaw, D. C.: Lower
vehicular primary emissions of NO2 in Europe than assumed in policy
projections, Nat. Geosci., 10, 914–918, 10.1038/s41561-017-0009-0,
2017.Guan, C., Li, X., Zhang, W., and Huang, Z.: Identification of nitration
products during heterogeneous reaction of NO2 on soot in the dark and
under simulated sunlight, J. Phys. Chem. A, 121, 482–492,
10.1021/acs.jpca.6b08982, 2017.He, C., Li, J., Ma, Z., Tan, J., and Zhao, L.: High NO2/NOx emissions downstream of the catalytic diesel particulate filter: An
influencing factor study, J. Environ. Sci.-China, 35, 55–61,
10.1016/j.jes.2015.02.009, 2015.Ho, K. F., Sai Hang Ho, S., Cheng, Y., Lee, S. C., and Zhen Yu, J.:
Real-world emission factors of fifteen carbonyl compounds measured in a Hong
Kong tunnel, Atmos. Environ., 41, 1747–1758,
10.1016/j.atmosenv.2006.10.027, 2007.Huang, R. J., Yang, L., Cao, J., Wang, Q., Tie, X., Ho, K. F., Shen, Z.,
Zhang, R., Li, G., Zhu, C., Zhang, N., Dai, W., Zhou, J., Liu, S., Chen, Y.,
Chen, J., and O'Dowd, C. D.: Concentration and sources of atmospheric nitrous
acid (HONO) at an urban site in Western China, Sci. Total Environ.,
593–594, 165–172, 10.1016/j.scitotenv.2017.02.166, 2017.Jenkin, M. E., Cox, R. A., and Williams, D. J.: Laboratory studies of the
kinetics of formation of nitrous acid from the thermal reaction of nitrogen
dioxide and water vapour, Atmos. Environ., 22, 487–498,
10.1016/0004-6981(88)90194-1, 1988.Kalberer, M., Ammann, M., Arens, F., Gäggeler, H. W., and Baltensperger,
U.: Heterogeneous formation of nitrous acid (HONO) on soot aerosol
particles, J. Geophys. Res.-Atmos., 104, 13825–13832,
10.1029/1999JD900141, 1999.Khalizov, A. F., Cruz-Quinones, M., and Zhang, R.: Heterogeneous reaction of
NO2 on fresh and coated soot surfaces, J. Phys. Chem. A, 114,
7516–7524, 10.1021/jp1021938, 2010.Kim, J. H., Kim, M. Y., and Kim, H. G.: NO2-assisted sort regeneration
behavior in a diesel particulate filter with heavy-duty diesel exhaust
gases, Numer. Heat Tr. A-Appl., 58, 725–739,
10.1080/10407782.2010.523293, 2010.Kirchstetter, T. W., Harley, R. A., and Littlejohn, D.: Measurement of
Nitrous Acid in Motor Vehicle Exhaust, Environ. Sci. Technol., 30,
2843–2849, 10.1021/es960135y, 1996.Kirchstetter, T. W., Harley, R. A., Kreisberg, N. M., Stolzenburg, M. R., and
Hering, S. V.: On-road measurement of fine particle and nitrogen oxide
emissions from light- and heavy-duty motor vehicles, Atmos. Environ.,
33, 2955–2968, 10.1016/S1352-2310(99)00089-8, 1999.Kleffmann, J.: Daytime sources of nitrous acid (HONO) in the atmospheric
boundary layer, Chem. Phys. Chem., 8, 1137–1144, 10.1002/cphc.200700016,
2007.Kleffmann, J., Becker, K. H., and Wiesen, P.: Heterogeneous NO2
conversion processes on acid surfaces: Possible atmospheric implications,
Atmos. Environ., 32, 2721–2729, 10.1016/S1352-2310(98)00065-X,
1998.Kleffmann, J., Becker, K. H., Lackhoff, M., and Wiesen, P.: Heterogeneous
conversion of NO2 on carbonaceous surfaces, Phys. Chem. Chem. Phys.,
1, 5443–5450, 10.1039/a905545b, 1999.Kurtenbach, R., Becker, K. H., Gomes, J. A. G., Kleffmann, J., Lörzer,
J. C., Spittler, M., Wiesen, P., Ackermann, R., Geyer, A., and Platt, U.:
Investigations of emissions and heterogeneous formation of HONO in a road
traffic tunnel, Atmos. Environ., 35, 3385–3394,
10.1016/S1352-2310(01)00138-8, 2001.Langridge, J. M., Gustafsson, R. J., Griffiths, P. T., Cox, R. A., Lambert,
R. M., and Jones, R. L.: Solar driven nitrous acid formation on building
material surfaces containing titanium dioxide: A concern for air quality in
urban areas?, Atmos. Environ., 43, 5128–5131,
10.1016/j.atmosenv.2009.06.046, 2009.Laufs, S., Cazaunau, M., Stella, P., Kurtenbach, R., Cellier, P., Mellouki, A., Loubet, B., and Kleffmann, J.: Diurnal fluxes of HONO above a crop rotation, Atmos. Chem. Phys., 17, 6907–6923, 10.5194/acp-17-6907-2017, 2017.Lee, B. H., Wood, E. C., Herndon, S. C., Lefer, B. L., Luke, W. T., Brune,
W. H., Nelson, D. D., Zahniser, M. S., and Munger, J. W.: Urban measurements
of atmospheric nitrous acid: A caveat on the interpretation of the HONO
photostationary state, J. Geophys. Res.-Atmos., 118, 12274–12281,
10.1002/2013JD020341, 2013.Lee, J. D., Whalley, L. K., Heard, D. E., Stone, D., Dunmore, R. E., Hamilton, J. F., Young, D. E., Allan, J. D., Laufs, S., and Kleffmann, J.: Detailed budget analysis of HONO in central London reveals a missing daytime source, Atmos. Chem. Phys., 16, 2747–2764, 10.5194/acp-16-2747-2016, 2016.Legendre, P.: lmodel2: Model II Regression, R package version 1.7-3, availabe at: https://CRAN.R-project.org/package=lmodel2 (last access: 28 April 2020), 2018.Lelièvre, S., Bedjanian, Y., Laverdet, G., and Le Bras, G.: Heterogeneous
reaction of NO2 with hydrocarbon flame soot, J. Phys. Chem. A, 108,
10807–10817, 10.1021/jp0469970, 2004.Liang, Y., Zha, Q., Wang, W., Cui, L., Lui, K. H., Ho, K. F., Wang, Z., Lee,
S. C., and Wang, T.: Revisiting nitrous acid (HONO) emission from on-road
vehicles: A tunnel study with a mixed fleet, J. Air Waste Manage.,
67, 797–805, 10.1080/10962247.2017.1293573, 2017.Liu, Y., Lu, K., Ma, Y., Yang, X., Zhang, W., Wu, Y., Peng, J., Shuai, S.,
Hu, M., and Zhang, Y.: Direct emission of nitrous acid (HONO) from gasoline
cars in China determined by vehicle chassis dynamometer experiments, Atmos.
Environ., 169, 89–96, 10.1016/j.atmosenv.2017.07.019, 2017.Maier, S., Tamm, A., Wu, D., Caesar, J., Grube, M., and Weber, B.:
Photoautotrophic organisms control microbial abundance, diversity, and
physiology in different types of biological soil crusts, ISME J., 12,
1032–1046, 10.1038/s41396-018-0062-8, 2018.Maljanen, M., Yli-Pirilä, P., Hytönen, J., Joutsensaari, J., and
Martikainen, P. J.: Acidic northern soils as sources of atmospheric nitrous
acid (HONO), Soil Biol. Biochem., 67, 94–97,
10.1016/j.soilbio.2013.08.013, 2013.Matthaios, V. N., Kramer, L. J., Sommariva, R., Pope, F. D., and Bloss, W.
J.: Investigation of vehicle cold start primary NO2 emissions inferred
from ambient monitoring data in the UK and their implications for urban air
quality, Atmos. Environ., 199, 402–414,
10.1016/j.atmosenv.2018.11.031, 2019.Meusel, H., Tamm, A., Kuhn, U., Wu, D., Leifke, A. L., Fiedler, S., Ruckteschler, N., Yordanova, P., Lang-Yona, N., Pöhlker, M., Lelieveld, J., Hoffmann, T., Pöschl, U., Su, H., Weber, B., and Cheng, Y.: Emission of nitrous acid from soil and biological soil crusts represents an important source of HONO in the remote atmosphere in Cyprus, Atmos. Chem. Phys., 18, 799–813, 10.5194/acp-18-799-2018, 2018.Michoud, V., Kukui, A., Camredon, M., Colomb, A., Borbon, A., Miet, K., Aumont, B., Beekmann, M., Durand-Jolibois, R., Perrier, S., Zapf, P., Siour, G., Ait-Helal, W., Locoge, N., Sauvage, S., Afif, C., Gros, V., Furger, M., Ancellet, G., and Doussin, J. F.: Radical budget analysis in a suburban European site during the MEGAPOLI summer field campaign, Atmos. Chem. Phys., 12, 11951–11974, 10.5194/acp-12-11951-2012, 2012.Michoud, V., Colomb, A., Borbon, A., Miet, K., Beekmann, M., Camredon, M., Aumont, B., Perrier, S., Zapf, P., Siour, G., Ait-Helal, W., Afif, C., Kukui, A., Furger, M., Dupont, J. C., Haeffelin, M., and Doussin, J. F.: Study of the unknown HONO daytime source at a European suburban site during the MEGAPOLI summer and winter field campaigns, Atmos. Chem. Phys., 14, 2805–2822, 10.5194/acp-14-2805-2014, 2014.Monge, M. E., D'Anna, B., Mazri, L., Giroir-Fendler, A., Ammann, M.,
Donaldson, D. J., and George, C.: Light changes the atmospheric reactivity of
soot, P. Natl. Acad. Sci. USA, 107, 6605–6609,
10.1073/pnas.0908341107, 2010.Nakashima, Y. and Kajii, Y.: Determination of nitrous acid emission factors
from a gasoline vehicle using a chassis dynamometer combined with incoherent
broadband cavity-enhanced absorption spectroscopy, Sci. Total Environ., 575,
287–293, 10.1016/j.scitotenv.2016.10.050, 2017.Ning, Z., Wubulihairen, M., and Yang, F.: PM, NOx and butane emissions
from on-road vehicle fleets in Hong Kong and their implications on emission
control policy, Atmos. Environ., 61, 265–274,
10.1016/j.atmosenv.2012.07.047, 2012.O'Driscoll, R., Stettler, M. E. J., Molden, N., Oxley, T., and ApSimon, H.
M.: Real world CO2 and NO2 emissions from 149 Euro 5 and 6 diesel,
gasoline and hybrid passenger cars, Sci. Total Environ., 621, 282–290,
10.1016/j.scitotenv.2017.11.271, 2018.Oswald, R., Behrendt, T., Ermel, M., Wu, D., Su, H., Cheng, Y., Breuninger,
C., Moravek, A., Mougin, E., Delon, C., Loubet, B., Pommerening-Röser,
A., Sörgel, M., Pöschl, U., Hoffmann, T., Andreae, M. O., Meixner,
F. X., and Trebs, I.: HONO emissions from soil bacteria as a major source of
atmospheric reactive nitrogen, Science, 341, 1233–1235,
10.1126/science.1242266, 2013.Pierson, W. R., Brachaczek, W. W., Hammerle, R. H., McKee, D. E., and Butler,
J. W.: Sulfate Emissions from Vehicles on the Road, J. Air Pollut. Control
Assoc., 28, 123–132, 10.1080/00022470.1978.10470579, 1978.Pierson, W. R., Gertler, A. W., Robinson, N. F., Sagebiel, J. C., Zielinska,
B., Bishop, G. A., Stedman, D. H., Zweidinger, R. B., and Ray, W. D.:
Real-world automotive emissions – summary of studies in the Fort McHenry and
Tuscarora Mountain Tunnels, Atmos. Environ., 30, 2233–2256,
10.1016/1352-2310(95)00276-6, 1996.Pitts, J. N., Biermann, H. W., Winer, A. M., and Tuazon, E. C.: Spectroscopic
identification and measurement of gaseous nitrous acid in dilute auto
exhaust, Atmos. Environ., 18, 847–854, 10.1016/0004-6981(84)90270-1,
1984.Qin, M., Xie, P., Su, H., Gu, J., Peng, F., Li, S., Zeng, L., Liu, J., Liu,
W., and Zhang, Y.: An observational study of the HONO-NO2 coupling at an
urban site in Guangzhou City, South China, Atmos. Environ., 43,
5731–5742, 10.1016/j.atmosenv.2009.08.017, 2009.Rappenglück, B., Lubertino, G., Alvarez, S., Golovko, J., Czader, B., and
Ackermann, L.: Radical precursors and related species from traffic as
observed and modeled at an urban highway junction, J. Air Waste Manage.,
63, 1270–1286, 10.1080/10962247.2013.822438, 2013.R Core Team: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, available at: https://www.R-project.org/ (last access: 28 April 2020), 2019.Reed, C., Evans, M. J., Crilley, L. R., Bloss, W. J., Sherwen, T., Read, K. A., Lee, J. D., and Carpenter, L. J.: Evidence for renoxification in the tropical marine boundary layer, Atmos. Chem. Phys., 17, 4081–4092, 10.5194/acp-17-4081-2017, 2017.Rhead, M., Gurney, R., Ramalingam, S., and Cohen, N.: Accuracy of automatic
number plate recognition (ANPR) and real world UK number plate problems, in: Proceedings of the 46th IEEE International Carnahan Conference on Security Technology, Institute of Electrical and Electronics Engineers (IEEE), 286–291,
10.1109/CCST.2012.6393574, 2012.Richard, C., Gordon, I. E., Rothman, L. S., Abel, M., Frommhold, L.,
Gustafsson, M., Hartmann, J. M., Hermans, C., Lafferty, W. J., Orton, G. S.,
Smith, K. M., and Tran, H.: New section of the HITRAN database:
Collision-induced absorption (CIA), J. Quant. Spectrosc. Ra.,
113, 1276–1285, 10.1016/j.jqsrt.2011.11.004, 2012.Rogak, S. N., Green, S. I., and Pott, U.: Use of tracer gas for direct
calibration of emission-factor measurements in a traffic tunnel, J. Air
Waste Manage., 48, 545–552, 10.1080/10473289.1998.10463707,
1998.Romanias, M. N., Bedjanian, Y., Zaras, A. M., Andrade-Eiroa, A., Shahla, R.,
Dagaut, P., and Philippidis, A.: Mineral oxides change the atmospheric
reactivity of soot: NO2 uptake under dark and UV irradiation
conditions, J. Phys. Chem. A, 117, 12897–12911, 10.1021/jp407914f,
2013.Sander, R.: Compilation of Henry's law constants (version 4.0) for water as solvent, Atmos. Chem. Phys., 15, 4399–4981, 10.5194/acp-15-4399-2015, 2015.Singh, A.: Quantifying the effect of atmospheric pollution and meteorology
on visibility and tropospheric chemistry, University of Birmingham, available at:
https://etheses.bham.ac.uk//id/eprint/7828/ (last access: 29 April 2019), 2017.Spataro, F. and Ianniello, A.: Sources of atmospheric nitrous acid: State of
the science, current research needs, and future prospects, J. Air Waste
Manage., 64, 1232–1250, 10.1080/10962247.2014.952846, 2014.Stadler, D. and Rossi, M. J.: The reactivity of NO2 and HONO on flame
soot at ambient temperature: The influence of combustion conditions, Phys.
Chem. Chem. Phys., 2, 5420–5429, 10.1039/b005680o, 2000.Stemmler, K., Ammann, M., Donders, C., Kleffmann, J., and George, C.:
Photosensitized reduction of nitrogen dioxide on humic acid as a source of
nitrous acid, Nature, 440, 195–198, 10.1038/nature04603, 2006.Stutz, J., Kim, E. S., Platt, U., Bruno, P., Perrino, C., and Febo, A.:
UV-visible absorption cross sections of nitrous acid, J. Geophys. Res.-Atmos., 105, 14585–14592, 10.1029/2000JD900003, 2000.Stutz, J., Alicke, B., and Neftel, A.: Nitrous acid formation in the urban
atmosphere: Gradient measurements of NO2 and HONO over grass in Milan,
Italy, J. Geophys. Res., 107, 8192, 10.1029/2001JD000390, 2002.Stutz, J., Alicke, B., Ackermann, R., Geyer, A., Wang, S., White, A. B.,
Williams, E. J., Spicer, C. W., and Fast, J. D.: Relative humidity dependence
of HONO chemistry in urban areas, J. Geophys. Res., 109, D03307,
10.1029/2003JD004135, 2004.Su, H., Cheng, Y., Oswald, R., Behrendt, T., Trebs, I., Meixner, F. X.,
Andreae, M. O., Cheng, P., Zhang, Y., and Pöschl, U.: Soil nitrite as a
source of atmospheric HONO and OH radicals, Science, 333, 1616–1618,
10.1126/science.1207687, 2011.Thalman, R., Baeza-Romero, M. T., Ball, S. M., Borrás, E., Daniels, M. J. S., Goodall, I. C. A., Henry, S. B., Karl, T., Keutsch, F. N., Kim, S., Mak, J., Monks, P. S., Muñoz, A., Orlando, J., Peppe, S., Rickard, A. R., Ródenas, M., Sánchez, P., Seco, R., Su, L., Tyndall, G., Vázquez, M., Vera, T., Waxman, E., and Volkamer, R.: Instrument intercomparison of glyoxal, methyl glyoxal and NO2 under simulated atmospheric conditions, Atmos. Meas. Tech., 8, 1835–1862, 10.5194/amt-8-1835-2015, 2015.Tong, S., Hou, S., Zhang, Y., Chu, B., Liu, Y., He, H., Zhao, P., and Ge, M.:
Exploring the nitrous acid (HONO) formation mechanism in winter Beijing:
Direct emissions and heterogeneous production in urban and suburban areas,
Faraday Discuss., 189, 213–230, 10.1039/c5fd00163c, 2016.Trinh, H. T., Imanishi, K., Morikawa, T., Hagino, H., and Takenaka, N.:
Gaseous nitrous acid (HONO) and nitrogen oxides (NOx) emission from
gasoline and diesel vehicles under real-world driving test cycles, J. Air
Waste Manage., 67, 412–420, 10.1080/10962247.2016.1240726,
2017.Vandaele, A. C., Hermans, C., Simon, P. C., Carleer, M., Colin, R., and
Coquartii, B.: Measurements of the NO2 absorption cross-section from 42 000 cm-1 to 10 000 cm-1 (238–1000 nm) at 220 K and 294 K, J. Quant.
Spectrosc. Ra., 59, 171–184, 1998.Vandenboer, T. C., Markovic, M. Z., Sanders, J. E., Ren, X., Pusede, S. E.,
Browne, E. C., Cohen, R. C., Zhang, L., Thomas, J., Brune, W. H., and Murphy,
J. G.: Evidence for a nitrous acid (HONO) reservoir at the ground surface in
Bakersfield, CA, during CalNex 2010, J. Geophys. Res., 119, 9093–9106,
10.1002/2013JD020971, 2014.Villena, G., Wiesen, P., Cantrell, C. A., Flocke, F., Fried, A., Hall, S.
R., Hornbrook, R. S., Knapp, D., Kosciuch, E., Mauldin, R. L., McGrath, J.
A., Montzka, D., Richter, D., Ullmann, K., Walega, J., Weibring, P.,
Weinheimer, A., Staebler, R. M., Liao, J., Huey, L. G., and Kleffmann, J.:
Nitrous acid (HONO) during polar spring in Barrow, Alaska: A net source of
OH radicals?, J. Geophys. Res.-Atmos., 116, 1–12,
10.1029/2011JD016643, 2011.Villena, G., Bejan, I., Kurtenbach, R., Wiesen, P., and Kleffmann, J.: Interferences of commercial NO2 instruments in the urban atmosphere and in a smog chamber, Atmos. Meas. Tech., 5, 149–159, 10.5194/amt-5-149-2012, 2012.Vogel, B., Vogel, H., Kleffmann, J., and Kurtenbach, R.: Measured and
simulated vertical profiles of nitrous acid – Part II. Model simulations and
indications for a photolytic source, Atmos. Environ., 37, 2957–2966,
10.1016/S1352-2310(03)00243-7, 2003.Wang, J., Zhang, X., Guo, J., Wang, Z., and Zhang, M.: Observation of nitrous
acid (HONO) in Beijing, China: Seasonal variation, nocturnal formation and
daytime budget, Sci. Total Environ., 587–588, 350–359, 10.1016/j.scitotenv.2017.02.159,
2017.Wang, X., Ho, K. F., Chow, J. C., Kohl, S. D., Chan, C. S., Cui, L., Lee, S.
cheng F., Chen, L. W. A., Ho, S. S. H., Cheng, Y., and Watson, J. G.: Hong
Kong vehicle emission changes from 2003 to 2015 in the Shing Mun Tunnel,
Aerosol Sci. Tech., 52, 1085–1098,
10.1080/02786826.2018.1456650, 2018.Weber, B., Wu, D., Tamm, A., Ruckteschler, N., Rodríguez-Caballero, E.,
Steinkamp, J., Meusel, H., Elbert, W., Behrendt, T., Sörgel, M., Cheng,
Y., Crutzen, P. J., Su, H., and Pöschl, U.: Biological soil crusts
accelerate the nitrogen cycle through large NO and HONO emissions in
drylands, P. Natl. Acad. Sci. USA, 112, 15384–15389,
10.1073/pnas.1515818112, 2015.
Wickham, H.: ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag, New York, 2016.Xu, Z., Wang, T., Wu, J., Xue, L., Chan, J., Zha, Q., Zhou, S., Louie, P. K.
K., and Luk, C. W. Y.: Nitrous acid (HONO) in a polluted subtropical
atmosphere: Seasonal variability, direct vehicle emissions and heterogeneous
production at ground surface, Atmos. Environ., 106, 100–109,
10.1016/j.atmosenv.2015.01.061, 2015.Yang, Q., Su, H., Li, X., Cheng, Y., Lu, K., Cheng, P., Gu, J., Guo, S., Hu,
M., Zeng, L., Zhu, T., and Zhang, Y.: Daytime HONO formation in the suburban
area of the megacity Beijing, China, Sci. China Chem., 57, 1032–1042,
10.1007/s11426-013-5044-0, 2014.Ye, C., Zhang, N., Gao, H., and Zhou, X.: Photolysis of Particulate Nitrate
as a Source of HONO and NOx, Environ. Sci. Technol., 51,
6849–6856, 10.1021/acs.est.7b00387, 2017.
Zhou, X., Zhang, N., Teravest, M., Tang, D., Hou, J., Bertman, S., and
Stevens, P. S.: Nitric acid photolysis on forest canopy surface as a source
for tropospheric nitrous acid, Nat. Geosci., 4, 440–443,
10.1038/ngeo1164, 2011.