Introduction
Exposure to combustion aerosols has been associated with a range of negative
health effects. In particular, wood smoke aerosols have been shown to present
respiratory and cardiovascular health effects (Naeher et al., 2007). Bonfires
and fireworks are one of the main sporadic events with high emissions of
atmospheric pollutants (Vassura et al., 2014; Joshi et al., 2016); even when
these high emissions only last a couple of hours, high pollutant
concentrations may instigate adverse effects on human health (Moreno et al.,
2007; Godri et al., 2010) and severely reduce visibility (Vecchi et al.,
2008). Ravindra et al. (2003) found that the short-term exposure to air
pollutants increases the likelihood of acute health effects.
Due to these adverse effects, different studies have been performed to
analyse air pollution during important festivities around the world, for
instance New Year's Eve celebrations (Drewnick et al., 2006; Zhang et al.,
2010), the Lantern Festival in China (Wang et al., 2007) and Diwali festival
in India (Pervez et al., 2016) as well as football matches such as during the
Bundesliga in Mainz, Germany in 2012 (Faber et al., 2013). In the UK, the
Bonfire Night festivity takes place on 5 November to commemorate Guy Fawkes'
unsuccessful attempt to destroy the Houses of Parliament in 1605 (Ainsworth,
1850). During this celebration, bonfires, usually followed by fireworks, are
lit domestically and on a larger scale communally in public parks. Different
studies have been carried out to assess the air pollution during Bonfire
Night in the UK; for instance targeting the particle size distribution
(Colbeck and Chung, 1996), investigating PM10 concentrations in
different cities around the UK during Bonfire Night celebrations (Clark,
1997) and measuring dioxins in ambient air in Oxford (Dyke et al., 1997);
polycyclic aromatic hydrocarbons were measured in Lancaster in 2000 (Farrar et
al., 2004), potentially toxic elements were measured and their association
with health risks was assessed in London (Hamad et al., 2015).
Receptor modelling has been widely used to determine organic aerosol (OA) sources in urban
environments. However, it has been used in just a small number of studies
with sporadic events of high pollutant concentrations. For instance, Vecchi
et al. (2008) were the first to analyse measurements taken during firework
displays using positive matrix factorisation (PMF). Tian et al. (2014) did a
PMF analysis of PM2.5 components, identifying five different sources:
crustal dust, coal combustion, secondary particles, vehicular exhausts and
fireworks. In Riccione, Italy, Vassura et al. (2014) determined that
levoglucosan, organic carbon (OC), polycyclic aromatic hydrocarbons (PAHs), Al and Pb, emitted from bonfires during St. Joseph's
Eve, can be used as markers for bonfire emissions.
Particulate organic oxides of nitrogen (PONs), a term we use here to encompass
nitro-organics and organic nitrates, have been found to absorb light near the
ultraviolet (UV) region (Mohr et al., 2013) and to present potential toxicity to human
health (Fernandez et al., 1992; Qingguo et al., 1995). PONs also act as a
NOx reservoir at night, releasing NOx concentrations when the sun
rises with the possibility of increasing O3 production (Perring et al.,
2013; Mao et al., 2013). PONs are important components of OAs;
for instance Day et al. (2010), in measurements taken during winter at an
urban location, found that PON concentrations accounted for up to 10 % of
organic matter. Kiendler-Scharr et al. (2016) concluded that, on a
continental scale, PONs represent 34 to 44 % of aerosol nitrate. Organic
oxides of nitrogen can be categorised, according to their origin, into two
types: primary and secondary. Primary organic nitrates are related to
combustion sources (Zhang et al., 2016) such as fossil fuels (Day et al.,
2010) and biomass burning emissions (Kitanovski et al., 2012; Mohr et al.,
2013). Secondary organic oxides of nitrogen are produced in the atmosphere,
for example when NO3 reacts with unsaturated hydrocarbons (Ng et al.,
2017). Nitrophenols are produced from reactions of phenols, both during the
day reacting with OH + NO2 and at night reacting with
NO3 + NO2 (Harrison et al., 2005; Yuan et al., 2016).
The Aethalometer (Magee Scientific, USA) has been widely used to measure
light absorbing carbon, proving to be a robust instrument that can operate in
a variety of environments and is currently being used at many different
locations around the world. The European Environment Agency, in a technical
report published in 2013 (EEA, 2013), states that there are at least 11
European countries using Aethalometers. The UK has a black carbon (BC)
network comprising of 14 sites covering a wide range of monitoring sites
(https://uk-air.defra.gov.uk/networks/network-info?view=_ukbsn) and, in 2016,
India started a BC network with 16 Aethalometers
(Laskar et al., 2016). Commonly, Aethalometers have been used to separate
sources of light-absorbing aerosols following Sandradewi et al. (2008). The
approach separates absorption from traffic, predominately resulting from BC,
which absorbs light in the infrared region and from wood burning, which
includes BC and absorbing organic matter that also absorbs near the
UV region. The Aethalometer model is based on the differences in
aerosol absorption, using the absorption Ångström exponent, at a
specific wavelength of light chosen to run the model. Absorption
Ångström exponent values range from 0.8 to 1.1 for traffic and 0.9–3.5
for wood burning (Zotter et al., 2017). It is known that brown carbon (BrC)
is organic matter capable of absorbing light near the UV region (Bones et
al., 2010; Saleh et al., 2014) and that PONs are a potential contributor to BrC
(Mohr et al., 2013). However, the mechanistic principle that links this
behaviour to wood burning has not been completely resolved and there may be
other sources such as secondary organic aerosols (SOAs) that can absorb near the UV
region.
Here we present an analysis performed on data collected during Bonfire Night
celebrations in Manchester, UK (29 October to 10 November 2014) using a
compact time-of-flight aerosol mass spectrometer (cToF-AMS) and a high-resolution time-of-flight
chemical ionisation mass spectrometer (HR-ToF-CIMS) along with other instruments to measure both
aerosols and gaseous pollutants with the aim of understanding the night-time
chemical processes and their atmospheric implications. Very high
concentrations of pollutants occurred as a result of the meteorological
conditions, which presented a good opportunity to investigate the detailed
phenomenon as a case study, particularly the possibility to determine PON
concentrations, their nature and interaction with Aethalometer measurements.
Results
Meteorology and pollutant overview
During Bonfire Night festivities on 5 November, a temperature of 4 ∘C
and wind speed of 1.5 m s-1 were observed (Fig. 1a),
causing stagnant conditions which facilitated pollutant accumulation. Looking
at the time series for the whole sampling time (Fig. 1b), it was possible to
observe four separate events with different pollutant behaviour (marked with
coloured lines over the x axis in Fig. 1), driven by different meteorological
conditions: one event had high secondary concentrations (HSC, yellow line)
from 30 October to 1 November, which experienced a relatively
high temperature of 17–20 ∘C; another event of low pollutant
concentrations (LC, grey line) from 1 to 3 November was observed
when continental air masses were present; Bonfire Night (bfo, blue line),
with a temperature of 4 ∘C; and a winter-like episode (WL, purple
line) from 8 to 10 November, with temperatures of 5–6 ∘C and
high primary pollutant concentrations. Figure S3 shows
back trajectories of the different events.
Aerosol concentrations during Bonfire Night were particularly high (Fig. 1c),
with the highest peak concentrations of 65.0, 19.0, 6.8, 6.0, 5.9 and
3.2 µg m-3 for OA, BC, SO4, Cl, NH4 and NO3
respectively measured around 20:30 LT (local time)
on 5 November. It is worth
noting how high these concentrations are compared to concentrations before
and after Bonfire Night (Fig. 1b), where aerosol concentrations ranged from
0.5 to 7.0 µg m-3. Measured PM1 concentrations (sum of
BC, organic and inorganic aerosols) of 115 µg m-3 (Fig. 1c)
were observed during Bonfire Night.
Looking at the daily concentrations (Fig. 1d), it is possible to observe
PM1 daily concentrations of 25 µg m-3 on Bonfire Night
compared to the low concentrations observed between 1 and 2 November
with concentrations ranging between 3 and 4 µg m-3. The
impact of the emissions during Bonfire Night is present even during the next
day with PM1 concentrations of 14 µg m-3.
Time series of gases measured at Whitworth observatory.
![]()
Gas phase pollutants were measured at the Whitworth observatory. Figure 2
shows high SO2, CO and NOx concentrations during Bonfire Night;
these primary pollutants are well-known combustion-related pollutants. The
high SO2 concentrations during Bonfire Night are expected as solid fuels
such as wood emit SO2 when burned. This can also explain the SO2
peak on the night of 10–11 November when SO2
concentrations may be related to solid fuels used for domestic heating as a
result of the low temperatures (6 ∘C). CO and NO were present at
higher concentrations during Bonfire Night compared to previous days with
concentrations reaching 1600 ppb (CO) and 99 ppb (NO) during Bonfire Night
compared to 1 November with concentrations of 230 ppb of CO and 16 ppb
of NO. Some O3 concentrations were measured during Bonfire Night but
given the very high NO concentrations, these are considered to be an
interference with the measurement.
Bonfire Night analysis
Traffic and wood burning contributions to BC
OA concentrations started increasing at 19:30 LT, while BC concentrations
started increasing 2 h earlier around 17:00 LT (Fig. 1c). This rise
in BC concentrations may be due to bonfire emissions, although they may also
be related to traffic emissions; thus the Aethalometer model was used to
identify both traffic and wood burning contributions to BC.
Once babs values are corrected, equations shown in Sect. 2.2.1
are used to apply the Aethalometer model, with Ångström absorption
exponents (α) of 1.0 for traffic (αtr), using the
wavelength 470 nm, and 2.0 for wood burning (αwb) using the
wavelength 950 nm, to determine traffic and wood burning contributions.
Figure 3 shows the absorption coefficients for wood burning
babs_470wb (blue) and traffic babs_950tr (red), both increasing around 17:00–18:00 LT to values lower than
100 Mm-1, while babs indicates contributions from wood
burning and traffic during this event. When the majority of bonfire
events are taking place, around 20:00, when babs_470wb
shows the greatest increase, with values reaching 480 Mm-1 compared to
150 Mm-1 for babs_950tr.
Absorption coefficients for Wood burning (wb) and traffic (tr).
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PON identification and quantification
Currently, there is no direct technique to quantify online integrated PON
concentrations. However, it is possible to estimate PON concentrations from
AMS measurements using the m/z 46 : 30 ratios (Farmer et al., 2010) as
explained in Sect. 2.2.2. This event during Bonfire Night 2014, with high
pollutant concentrations provided the opportunity to identify the presence
of PON. Inorganic nitrate from NH4NO3 has been detected at m/z 46 : 30
ratios between 0.33 and 0.5 (Alfarra et al., 2006) and of 0.37 (Fry et
al., 2009), although each instrument-specific ratio is determined during
routine calibrations. PON has been identified with m/z 46 : 30 ratios of
0.07–0.10 (Hao et al., 2014) and 0.17–0.26 (Sato et al., 2010). In this
study, m/z 46 : 30 ratios of 0.11–0.18 were observed during Bonfire Night
(Fig. 4), confirming the presence of PON during this event. Figure 4 shows
PON concentrations of up to 2.8 µg m-3 during Bonfire Night,
which are over the detection limit of 0.1 µg m-3 reported by
Bruns et al. (2010). PON concentrations are considered high compared to
previous studies with concentrations between 0.03 and
1.2 µg m-3 from a wide variety of sites across Europe (Kiendler-Scharr et al., 2016),
while high PON concentrations of 4.2 µg m-3 were observed
during a biomass burning event in Beijing, China (Zhang et al., 2016).
PON concentrations during Bonfire Night.
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OA source apportionment
This event with high pollutant concentrations during Bonfire Night gave the
opportunity to test the ME-2 factorisation tool under these conditions and
determine the best way to perform OA source apportionment on a case study
event such as this. A number of different approaches for determining the
optimal apportionment were tried and the one that yielded the most
statistically optimal version was treated as a “best estimate”, although it is
acknowledged that even this may not be perfect. Indeed, it may not be
possible to describe these data completely using the PMF data model. Six
different tests were compared: four tests before modifying the fragmentation
table and two tests when modifying the fragmentation table to determine a PON
source. Test2_ON was the optimal “best estimate” solution, a brief
description is given here after being compared to the other
tests (Sect. S7.2). From this analysis, Test2 resulted in being the best way
to deconvolve OA sources, with the lowest parameters analysed: residuals,
Q/Qexp values and Chi square.
After modifying the fragmentation table,
Test2_ON still shows a good performance with low parameters (Fig. S6–S8).
Refer to Sect. S7 for detailed information about the
source apportionment strategy and analysis performed to determine the optimal
solution.
OA sources mass spectra and time series for Test2_ON for bonfire
only (bfo) and not bonfire events (nbf). Figure 6d shows time series of both
events.
![]()
Two steps were involved in Test2_ON: in step (a), PMF/ME-2 were run for the
event before and after the Bonfire Night (named as not bonfire event, nbf).
In step (b), mass spectra from the solution identified in step (a) were used as
TP to analyse the bonfire-only (bfo) event. Finally, both solutions (nbf and
bfo) were merged for further analysis. Different OA sources were identified
in Test2_ON (Fig. 5), five sources were identified during the nbf event: biomass
burning OA (BBOA), hydrocarbon-like OA (HOA), cooking OA (COA), secondary
particulate organic oxides of nitrogen (sPON_ME2) and low volatility OA
(LVOOA). These sources are identified by characteristic peaks in their respective mass
spectra: BBOA, which is generated during the combustion of biomass, has a
peak at m/z 60, related to levoglucosan (Alfarra et al., 2007); HOA,
related to traffic emissions, presents high signals at m/z 55 and m/z 57
typical of aliphatic hydrocarbons (Canagaratna et al., 2004); COA, emitted
from food cooking activities, is similar to HOA with a higher m/z 55 and
lower m/z 57 (Allan et al., 2010; Slowik et al., 2010; Mohr et al., 2012);
LVOOA, identified as a SOA, has a high signal at
m/z 44 dominated by the CO2+ ion (Ng et al., 2010); sPON_ME2 has a
strong signal at m/z 30 and it has been identified as secondary as it
follows the same trend as LVOOA (Fig. 5a). In the case of the bfo event,
six different sources were identified: BBOA, HOA, COA, LVOOA and two factors
with peaks at m/z 30, which is related to PON (Sun et al., 2012). These two
PON factors may have different sources: one may be secondary (sPON_ME2) and
the other primary (pPON_ME2), which has a similar trend as BBOA (Fig. 5b).
Further details about the nature of pPON_ME2 and sPON_ME2 will be explored in Sect. 4.2.
Discussion
OA source apportionment during the bfo event
It is worth noting that while all sources have their characteristic peaks
and no apparent mass spectral “mixing” between sources (for example COA with
a signal at m/z 60), COA, HOA and LVOOA present high concentrations during
Bonfire Night (Fig. 5b). High concentrations of these sources could be
expected as these (traffic and cooking activities) increase before and after
the main bonfire events and the night represented a very strong inversion
(which will trap all pollutants), but given the high concentrations
experienced during the event and known variability for biomass burning
emissions, the “model error” and thus rotational freedom is likely to be
substantial. The result is that these two factors could contain indeterminate
contributions from minor variabilities within the biomass burning profile and
therefore must be interpreted with caution.
babs_470wb has the same source as BBOA, thus the correlation
between these two can be used to evaluate the effectiveness of BBOA
deconvolution from OA concentrations (Fröhlich et al., 2015; Visser et al.,
2015), r2 values are calculated and analysed using the following
considerations: strong correlation (r2≥ 0.75), moderate correlation
(0.5 < r2 < 0.75) and low correlation (r2 ≤ 0.5).
Here r2 values are calculated for the bfo event between
babs_470wb and the two BBOA obtained; BBOA, obtained without
modifying the fragmentation table and BBOA_2 obtained after modifying the
fragmentation table to identify a PON factor. A slightly higher correlation
between babs_470wb and BBOA_2 was observed with r2= 0.880 compared to r2= 0.839 for babs_470wb and BBOA.
While both have strong correlations from a quantitative point of view,
qualitatively there is an improvement in BBOA_2. This improvement in
BBOA_2 is explained by the fact that the PON factor may be mixed with BBOA
and when both sources are separated, a higher correlation between BBOA_2 and
babs_470wb is present. There is the possibility that the lower
r2 between babs_470wb and BBOA is due to having two BBOA
factors in Test2. However, an r2=0.813 between babs_470wb
and the sum of BBOA + BBOA_1 is still lower than 0.880.
This shows the importance of performing OA source apportionment using
different approaches in order to identify the best way to deconvolve OA
sources. PMF and ME-2 source apportionment tools could not completely
deconvolve OA sources during the bfo event. However, due to the strong
correlation between babs_470wb and BBOA_ 2 (r2= 0.880), we consider that while BBOA_2 might not represent the total
OA concentrations from the Bonfire Night event, it does represent the trend
of OA emitted from the biomass burning.
Primary and secondary PONs
PON concentrations obtained from the m/z ratios 46 : 30 (blue line in Fig. 6)
have a similar trend as BBOA, both increasing at the same time, suggesting
a primary origin, but after 22:00 LT, when BBOA concentrations drop, PON
concentrations remain present with a slow decrease and maintaining low
concentrations when BBOA concentrations were not present anymore. This
suggests the hypothesis that there might not be only one type of PON, and it
could be divided into primary and secondary organic nitrate as reported in
previous studies performed in western Europe (Mohr et al., 2013;
Kiendler-Scharr et al., 2016).
Secondary (sPON) and primary (pPON) organic nitrate time series
estimated from PON and BBOA.
![]()
Using this working hypothesis, primary and secondary PON concentrations were
estimated using the slope between PON and BBOA,
calculated from 18:00 to 12:00 LT,
a time when the main Bonfire Night event took
place (Fig. S10). PON concentrations were multiplied by this slope in order
to calculate the primary PON (pPON) and secondary PON (sPON) and were calculated
as sPON = PON - pPON. Figure 6 shows the time series of this estimation
where pPON reaches 2.5 µg m-3 and sPON with concentrations of
0.5 µg m-3.
A similar behaviour with two different PON sources was observed in the source
apportionment analysis performed in Sect. 3.3, where it was possible to
separate two factors with a peak at m/z 30, characteristic of PON. Figure 7
shows that around 02:00 LT concentrations of the pPON_ME2 started to
decrease (green line) while sPON_ME2 concentrations (grey line) increased.
This analysis shows the presence of two different types of PON; pPON_ME2 are
primarily emitted along with BBOA concentrations with the further presence of
a different PON, considered to be secondary, which increase when primary
pollutants start to decrease. Primary and secondary sources of PON have been
previously identified from AMS-PMF analyses; Hao et al. (2014) identified PON
to be secondary in nature, produced from the interaction between forest and
urban emissions, while Zhang et al. (2016) determined PON to be related to
primary combustion sources. In this study, it is worth noticing that the
increase in sPON_ME2 takes place around 02:00 LT, a period when NO
concentrations started decreasing and CIMS-measured N2O5 and
ClNO2 started to increase, suggesting that nitrate radical chemistry was
occurring (Fig. 8), which is possibly the source of the sPON, although the
exact mechanism can only be speculated.
Secondary and primary organic nitrate time series obtained from
ME-2 analysis.
![]()
Time series of gases pollutants during Bonfire Night.
![]()
Nitrate chemistry at night is important as nitrate radicals can be the main
oxidants in polluted nocturnal environments away from enhanced NO and can
create reservoirs and sinks of NOx. The main NOx removal at night
is via the uptake of dinitrogen pentoxide (N2O5) into aerosols, as
at night N2O5 is formed from NO3 and NO2. In the presence
of chloride in the particle phase (e.g. in sea salt particles),
N2O5 reacts to produce nitryl chloride (ClNO2). In the
morning, following overnight accumulation of ClNO2, photochemical
reactions take place to produce Cl and NO2. N2O5 and
ClNO2 processing and interactions with nitrate chemistry have been
previously studied in the UK (Le Breton et al., 2014a; Bannan et al., 2015).
Figure 8 shows N2O5, ClNO2 and O3 concentrations
increasing when NO and NO2 concentrations decrease. All these processes
may facilitate the sPON production at night. N2O5 concentrations
reduce quickly after the sun rises, around 08:00 LT, while ClNO2
concentrations decrease at a slower rate, with the lowest concentrations
observed around 13:00 LT. Along with NO3 chemistry, it was possible
to observe other nitrogen-containing gases during Bonfire Night using the
CIMS such as hydrogen cyanide (HCN) and nitrous acid (HONO), which have been
found to be emitted from fires (Le Breton et al., 2013; Wang et al., 2015).
High HONO concentrations at night are high the next morning when HONO reacts
to produce OH and NO, which impacts both the OH budget and NOx
concentrations early the next morning (Lee et al., 2016).
OA factors and CIMS correlations
Analysing the CIMS measurements and comparing them with the OA factors, it
may be possible to identify gas markers that can be used as inputs (target
time series) to constrain solutions in future ME-2 analyses or as proxies
when AMS data are not available. A linear regression was performed between the
OA sources determined in Sect. 3.4.1 and CIMS peaks that have been
considered positively identified (Priestley et al., in preparation),
performing a coefficient of determination (r2) analysis for the complete
dataset (ALL), and the events HSC, LC, bfo and WL. During the event HSC, none
of the OA sources showed an r2 higher than 0.6. HOA did not have an
r2 higher than 0.6 with any of the different events analysed. There were
no specific markers identified for COA, while COA showed r2 values
higher than 0.6 for the bfo event, these r2 values were also observed with BBOA
with even higher values. Table S4 shows the r2 values, higher or equal
to 0.4, obtained in this analysis. It is worth noting that r2 values in
the ALL event seem to be influenced by the bfo event; this is the case for BBOA,
COA and LVOOA, which show similar r2 values in both events. Thus, the
analysis will only be explained for the individual events (bfo, LC and WL).
As expected, during bfo, BBOA is the OA source that shows the highest number
of correlations during Bonfire Night. During the bfo episode, strong
correlations were observed with BBOA and methacrylic acid (r2=0.92),
acrylic acid (0.90), nitrous acid (0.86), propionic acid, (0.85) and hydrogen
cyanide (0.76), which have been previously determined as biomass burning
tracers (Veres et al., 2010; Le Breton et al., 2013). Formic acid presented a
strong correlation (r2= 0.86) with BBOA during Bonfire Night; however,
this value drops to 0.52 for the complete dataset, which suggests formic acid
during Bonfire Night is mainly primary, while formic acid concentrations
measured for the whole dataset may be related to primary and secondary
sources. This agrees with Le Breton et al. (2014b) who explored both primary
and secondary origins of formic acid.
During the bfo event, LVOOA did not show a characteristic gas marker, as all
the r2 values were also observed with BBOA. This suggests two
hypotheses: that the LVOOA was mixed with BBOA, in the form of humic-like
material (Paglione et al., 2014), which cannot be differentiated from
secondary OA in the mass spectra (Fig. 5c); or it could also be that
secondary LVOOA may actually be present at the same time as BBOA
concentrations, as during high relative humidity and low temperatures,
enhanced partitioning of semi-volatile material to the particle phase occurs,
where subsequent oxidation and oligomerisation may occur. Moreover, due to
the high aerosol concentration present during Bonfire Night, there is a
greater surface available for gases to be condensed and more particulate bulk
to absorb into, thus it could be speculated that there would be high
secondary aerosol concentrations. However, this is deemed unlikely as there
may be little gas phase oxidation occurring in the presence of such high NO
concentrations, which will remove ozone and nitrate radicals, the main source
of oxidants at night.
During the bfo event, pPON_ME2 showed high r2 values with carbon
monoxide (0.78) and hydrogen cyanide (0.77) and moderate correlations with
methylformamide (0.65) and dimethylformamide (0.63), all of which are typical
primary pollutants related to combustion processes (Borduas et al., 2015,
and references therein). sPON_ME2 showed moderate correlations with
ClNO2 (0.52) and ClNO3 (0.53). Moderate r2 values were also
observed during the LC episode between ClNO2–ClNO3 and LVOOA (0.67–0.66)
and sPON (0.74–0.69) proving their secondary origin. Cl2, which
has previously been identified to be related to both primary and secondary
sources (Faxon et al., 2015), shows low correlations with pPON_ME2 (0.44)
during the bfo event and sPON_ME2 (0.55) during the LC event.
PON and its relationship with <inline-formula><mml:math id="M239" display="inline"><mml:mrow><mml:msub><mml:mi>b</mml:mi><mml:mrow><mml:mi mathvariant="normal">abs</mml:mi><mml:mi mathvariant="normal">_</mml:mi><mml:mn mathvariant="normal">470</mml:mn><mml:mi mathvariant="normal">wb</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula> and
BBOA
Organic oxides of nitrogen, originating from biomass burning, have been
previously found to absorb light near the UV region (Jacobson, 1999; Flowers
et al., 2010; Mohr et al., 2013). However, there is still a question of
whether this absorption is due to primary or secondary PON. Here, the
relationship between babs_470wb, PON and BBOA will be
analysed to determine if PONs absorb at 470 nm, which would interfere
with Aethalometer measurements.
Multilinear (MLR) and linear regression analysis between
babs_470wb and OAs.
ALL
HSC
LC
bfo
WL
MLR 1
A
background
0.000
4.555
1.004
0.000
1.293
B
babs:BBOA
14.340
3.547
18.284
11.926
10.318
C
babs:PON
54.495
9.212
12.046
73.115
21.724
B/C
0.263
0.385
1.518
0.163
0.475
r2_MLR1
0.912
0.064
0.364
0.898
0.760
Linear 1
r2
babs:BBOA
0.861
0.043
0.358
0.839
0.739
babs:PON
0.819
0.060
0.275
0.897
0.311
MLR 2
A
background
0.000
2.527
0.753
0.000
0.079
B
babs:BBOA_2
15.653
27.288
26.481
14.319
10.018
C
babs:PON
42.840
0.000
1.200
54.353
18.982
B/C
0.365
***
22.060
0.263
0.528
r2_MLR2
0.922
0.392
0.480
0.902
0.804
Linear 2
r2
babs:BBOA_2
0.894
0.392
0.480
0.880
0.788
babs:PON
0.819
0.060
0.275
0.897
0.311
ALL
HSC
*HSC
LC
*bfo
WL
MLR 3
A
background
0.000
2.527
1.649
0.763
6.093
0
B
babs:BBOA_2
21.545
27.288
22.764
26.668
16.657
8.577
C
babs:sPON_ME2
3.926
0.000
0.000
0.191
0.000
9.017
D
D
1.138
7.357
B/C
5.488
***
***
***
***
0.951
B/D
20.005
2.264
r2_MLR3
0.896
0.392
0.418
0.480
0.910
0.803
Linear 3
r2
babs:BBOA_2
0.894
0.392
0.392
0.480
0.880
0.788
babs:sPON_ME2
0.024
0.000
0.000
0.273
0.188
0.647
babs:D
0.225
0.633
ALL = complete dataset; HSC = episode with high secondary concentrations
(30 October to 1 November); LC = episode with low
concentrations (1–3 November); bfo = episode with bonfire-only concentrations
(5 November 17:00 LT to 6 November 12:00 LT); WL = Episode
with winter-like characteristics (8–10 November). PON is the
particulate organic nitrate estimate from 46 : 30 ratios. *Trilinear regression
was performed as in *bfo analysis there were two PON factors from ME-2
analysis; pPON_ME2 and sPON_ME2, with the slope D = babs:pPON and
r2_D is the r2 between babs:pPON. In *HSC analysis; BBOA, sPON
and LVOOA were used, with the slope D = babs:LVOOA and r2_D is the
r2 between babs:LVOOA.
In order to quantitatively determine any contribution from PON to the
Aethalometer data products, a multilinear regression (MLR) analysis was
performed on the complete dataset (ALL), and the events HSC, LC, bfo and WL
(Table 1). This analysis was done in three ways: a multilinear regression
(MLR1) with BBOA from OA source apportionment without modifying
the fragmentation table and PON from m/z 46 : 30 analysis; a multilinear regression
(MLR2) with BBOA_2 from OA source apportionment after modifying
the fragmentation table and PON from 46 : 30 analysis; and a multilinear regression (MLR3)
with BBOA_2 and PON sources from OA source apportionment
after modifying the fragmentation table. The following bilinear regression was used:
babs_470wb=A+B⋅x1+C⋅x2,
with x1= BBOA and x2= PON for MLR1; x1= BBOA_2 and x2= PON for MLR2;
x1= BBOA_2 and x2= sPON_ME2 for MLR3. Additionally, a trilinear
regression was performed to *HSC and
*bfo with x3= LVOOA in *HSC and x3= pPON in *bfo. A is the origin and the
partial slopes B, C and D represent the contribution of x1, x2 and x3 to
babs_470wb, respectively.
As used in previous studies (Elser et al., 2016; Reyes-Villegas et al.,
2016), multilinear regression analysis allows for the relationship of one
parameter between two or more variables to be determined. Here we are
analysing the partial slopes and origin to determine the correlation of
babs_470wb with the other variables. Table 1 shows the MLR
outputs where; A represents the background, B, C and D represent the partial
slope between babs_470wb and the respective OA.
B/C
represents the ratio between B and C partial slopes, with the following
considerations: if B/C < 1, then there is a higher contribution of
PON to babs_470wb; if B/C > 1, then there is a higher
contribution of BBOA to babs_470wb. Looking at the coefficient
of determination of the multilinear regression (r2_MLR) for the three
MLR analyses, it is possible to observe that, on the one hand, HSC and LC events present low
r2_MLR values ranging from 0.064 and 0.480;
On the other hand, bfo and WL events have strong correlations with values
between 0.760 and 0.910, which shows that when high
primary OA emissions are present a strong correlation
between babs_470wb and BBOA and PON is observed.
These high r2 values, particularly during the bfo event which presented
the highest r2 (0.910), are consistent with previous studies that found
organic nitrates to absorb at short wavelengths; Mohr et al. (2013)
identified correlation values of 0.65 between nitrophenols and
babs_370wb. Teich et al. (2017), in a recent study from offline
filters, determined nitrated aerosol concentrations with further analysis of
the light absorption of aqueous filter extracts (babs_370) and
identified r2 values between babs_370 and nitrated aerosol
concentrations of 0.67 to 0.74 depending on acidic or alkaline conditions,
respectively.
In MLR3, it is possible to observe that, during the bfo event, the main
contribution to babs_470wb is attributed to both BBOA_2
(16.657) and pPON_ME2 (7.357), while babs:sPON_ME-2 values were zero, with
an optimum r2 of 0.910. This lack of correlation between babs and sPON
is observed in the linear regression babs:sPON_ME2 with an r2 of 0.188.
These results show that while there is evidence of pPON_ME2 absorbing at 470 nm,
with a partial slope of 16.657, sPON_ME2 did not show to be absorbing
at 470 nm. The implication of the background not going to zero (6.093) is
that there is still an unexplained contribution to the absorption at 470 nm,
unrelated to sPON_ME2.
In order to further explore the possibility of sPON_ME-2 absorbing at 470 nm,
the HSC event was analysed, where sPON_ME2 was shown to be
non-absorbing at 470 nm with a partial slope of zero. BBOA_2 had a partial
slope of 27.288 and background a value of 2.527. This
background value suggests there is another component related to
babs_470wb that is not sPON. Thus, a trilinear regression
was performed to
*HSC between babs_470wb and BBOA_2,
sPON and LVOOA. Here, the background value drops to 1.649, sPON partial slope
is zero and LVOOA presents a partial slope of 1.138. These results confirm
that sPON do not absorb light at 470 nm while LVOOA, or at least part of the
components of LVOOA, do absorb at 470 nm during the HSC event and pPON_ME2
during the bfo event.
These results agree with previous studies that found biomass burning BBOA to
contain important concentrations of light absorbing BrC and
that certain types of SOA are effective absorbers near UV light (Bones et
al., 2010; Saleh et al., 2014; Washenfelder et al., 2015). The fact that
pPON_ME2 and LVOOA were shown to be absorbing light at a short wavelength
(470 nm) will have a direct impact on Aethalometer model studies; while
pPON_ME2 could be considered a component of the wood burning aerosol
apportioned using the Aethalometer, it may be that there is an interference
from other forms of BrC in SOA. However, this work would suggest that sPON
specifically does not contribute to the latter, so a different component of
LVOOA would have to be responsible. As well as this Aethalometer
interpretation, it is also worth mentioning that these findings may have
implications for studies on the radiative properties of the atmosphere, as
BrC is also thought to affect climate (Jacobson, 2014).
Conclusions
In order to better understand the aerosol chemical composition and variation
in source contribution during periods of nocturnal pollution, online
measurements of gases and aerosols were made in ambient air between 29 October
and 10 November 2014 at the University of Manchester, with detailed
analysis of the special high pollutant concentrations during Bonfire Night
celebrations on 5 November. High aerosol concentrations were observed
during the Bonfire Night event with 115 µg m-3 of PM1.
Important nitrogen chemistry was present with high HCN, HCNO and HONO
concentrations primarily emitted with the further presence of N2O5
and ClNO2 concentrations from nocturnal nitrate chemistry taking place
after NOx concentrations decreased.
OA source apportionment was performed using the ME-2
factorisation tool. The particular high pollutant concentrations together
with the complex mix of emissions did not allow the running of ME-2 for the
complete dataset, thus the dataset was divided into different events. The
best way to perform source apportionment was found to be to (a) analyse the
event before and after Bonfire Night using BBOA, HOA and COA from a previous
study in Paris as TP, and (b) conduct a further ME-2 analysis of the Bonfire
Night event using BBOA, HOA and COA mass spectra from (a) as TP. Moreover, a
slight improvement in the source apportionment was observed after modifying
the fragmentation table in order to identify PON sources, increasing the r2 value
from linear regressions between babs_470wb (absorption
coefficient of wood burning at 470 nm) and BBOA from 0.839 to 0.880. PMF and
ME-2 source apportionment tools could not completely deconvolve OA sources
during the bfo event as LVOOA, COA and HOA may be mixed with BBOA
concentrations. However, due to the strong correlation between
babs_470wb and BBOA (r2= 0.880) we consider that while
BBOA might not represent the total OA concentrations from the Bonfire Night
event, it does represent the trend of OA emitted from the biomass burning.
The combination of CIMS measurements and OA sources determined from AMS
measurements provided important information about gas tracers to be used as
inputs (target time series) to improve future ME-2 analyses, particularly
gases correlating with BBOA, LVOOA and sPON. However, the use of these
species as target time series should be used with care as their time
variation is greatly affected by meteorological conditions.
The presence of two classes of PON, secondary (sPON_ME2) and primary
(pPON_ME2), was identified both from looking at the BBOA:PON relationship
and from the ME-2 analysis after modifying the fragmentation table. It is clear that,
during Bonfire Night, pPON_ME2 concentrations increased when BBOA
concentrations are present and sPON_ME2 concentrations started evolving when
the primary concentrations decreased.
It was determined that pPON_ME2 absorbed light at a wavelength of 470 nm
during Bonfire Night, where the multilinear regression performed between
babs_470wb, BBOA and pPON_ME2 showed a strong r2 of 0.910,
while sPON_ME2 did not contribute to light absorption at 470 nm. During the
HSC episode, LVOOA showed a partial slope of 1.138 in the multilinear
regression and an r2 from linear regression with babs_470wb
of 0.225, implying secondary LVOOA (associated with SOA) may be absorbing at
470 nm and sPON_ME2 was not absorbing at this wavelength. These results
will help us to understand the mechanistic contributions to UV absorption in
the Aethalometer and will have direct implications for source apportionment
studies, which may need to be corrected for SOA
interferences near the UV region.