Atmospheric aerosol particles are known to have detrimental
effects on human health and climate. Black carbon is an important
constituent of atmospheric aerosol particulate matter (PM), emitted from
incomplete combustion. Source apportionment of BC is very important, to
evaluate the influence of different sources. The high-resolution soot
particle aerosol mass spectrometer (HR-SP-AMS) instrument uses a laser
vaporiser, which allows the real-time detection and characterisation of
refractory black carbon (rBC) and its internally mixed particles such as
metals, coating species, and rBC subcomponents in the form of HOA
Aerosol particles in the atmosphere are known to have very harmful effects on the air quality, human health, and climate (Highwood and Kinnersley, 2006). An important component of atmospheric aerosol particles is black carbon (BC), i.e. soot, which has extremely detrimental impacts on human health and air quality (Janssen and WHO Joint, 2012). BC's main emission source is through the incomplete combustion of fossil fuel and biomass. Sources include transportation, open biomass burning, power generation sources, and residential heating (Bond et al., 2011; Cooke et al., 1999; US EPA, 2012). In the atmosphere, BC can be mixed with organic and inorganic aerosol species, either at the point of emission or through gas-to-particle conversion processes in the atmosphere.
As well as harmful impacts on human health, BC can also absorb cancer-inducing pollutants such as volatile organic compounds (VOCs) and polycyclic aromatic hydrocarbons (PAHs) due to its carbonaceous nature and large surface area. As a result of its smaller size, it can be deposited in weasands and lungs, leading to severe health problems (Cao et al., 2012; Dachs and Eisenreich, 2000). According to hypothesised mechanisms, the ultrafine BC is the cause of abnormal cardiovascular functions and endothelial senescence at the molecular level (Büchner et al., 2013). Along with being harmful to human health, it also affects visibility, reduces agricultural productivity, harms ecosystems, and exacerbates global warming (Grahame and Schlesinger, 2010).
Most BC sources are of anthropogenic origin, but source apportionment is
important to establish which specific sources are responsible. There are
multiple measurement techniques available for this purpose, but they are subject
to considerable uncertainties (Martinsson, 2014). One of the most
widely used techniques is the multiwavelength Aethalometer, which was first
described by Hansen et al. (1984). Later Sandradewi et al. (2008) described
how the Aethalometer can be used to apportion different sources of
light-absorbing aerosols such as wood-burning, which in contrast to traffic
emissions absorbs additional light in the UV region, over what would be
expected in the near-infrared region. Another source apportionment method is
to measure the radiocarbon (
Positive matrix factorisation can in principle, identify multiple categories
of soot; however, it needs a large data set and relevant chemical data of
several species. A soot-specific instrument that may be able to provide such
data is the soot particle aerosol mass spectrometer (SP-AMS) (Onasch et
al., 2012), which generates online mass spectra of refractory black carbon
(rBC) and its coatings. Using this instrument, Onasch et al. (2015)
distributed the carbon ions in the mass spectrum into small carbon clusters
(C1–C5), larger carbon cluster ions (C6–29), and fullerene (
The current study aims to develop the SP-AMS as a source apportionment tool, which will subsequently improve our understanding of the sources of atmospheric soot. For this purpose, Bonfire Night 2014 in Manchester was taken as a case study because it is known that there were at least three sources of BC (traffic, domestic wood burning, bonfires, and potentially fireworks) and weather conditions that night favoured the high concentrations of primary emissions. This event has been described in previous studies (Liu et al., 2017; Priestley et al., 2018; Reyes-Villegas et al., 2018). In terms of air quality, it has been recognised that Bonfire Night is one of the most polluted days in the UK. Every year, this event is celebrated on 5 November (or on a weekend day near this date) where open fires are lit and fireworks are set off at individual households, as well as large community events. These bonfire activities have a strong flaming segment which roughly starts during the evening and lasts for up to 2 h. The fires after flaming are not refuelled, therefore leading to an extended phase of smouldering as the fires are left to completely burn and die down (Dyke et al., 1997; Mari et al., 2010; Pongpiachan et al., 2015).
Different research case studies have previously been published about
Bonfire Night around the UK. For example, Clark (1997) studied the PM
In order to test the ability of HR-SP-AMS to apportion rBC (with multiple BC types) the data were collected during Bonfire Night from 29 October–11 November 2014 at the University of Manchester. As a result of strong meteorological conditions, very high and mixed concentrations of pollutants were observed. Traditionally the PMF tool is applied to conventional AMS data (as with Reyes-Villegas et al., 2018), but the objective of this study is to demonstrate a new way to source-apportion black carbon based on highly time-resolved mass spectrometric composition data of the population of particles that contain black carbon, and it uses information on the composition of black carbon and information on internally mixed fullerene and condensed material.
Fullerenes are a class of exclusively high-molecular-weight carbon clusters (C60, C70, etc.) having a unique hollow cage-like structure, which were discovered by Kroto et al. (1985) for the first time. They were identified as ionised particles in low-pressure fuel-rich flat premixed acetylene and benzene–oxygen flames by molecular-beam sampling combined with mass spectrometer analysis (Gerhardt et al., 1988). These have been reported previously in HR-SP-AMS data, but whether they are formed in the flame or during the vaporisation process is not clear (Fortner et al., 2012).
Measurements were conducted at the South Campus University of Manchester
(53.467
In this case study, the high-resolution soot particle aerosol mass spectrometer (HR-SP-AMS) was used, which is a combination of single-particle soot photometer (SP2) laser and high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS). The laser vaporiser is an intracavity Nd:YAG (1064 nm) that heats up and vaporises black-carbon-containing particles along with metal nanoparticles (Onasch et al., 2012; Carbone et al., 2015). While the instrument is sometimes operated with the standard AMS tungsten vaporiser present, in this instance it was removed. Ionisation is performed using the same 70 eV electron source as the standard AMS, and like the AMS, the separation of the vaporisation and ionisation stages ensures quantitative measurements. A catalytic stripper was also attached to the aerosol sampling lines, which switched between catalytic stripper and direct measurements every 30 min (Liu et al., 2017). In our case, the results have been analysed by using the direct measurements only.
During the experiment, a measurement of the relative ionisation efficiency
(RIE) was not obtained owing to technical difficulties associated with
generating a suitable test aerosol; however, this only affects absolution
quantification and not the ability of the instrument to apportion fractions
of the signal, which is the subject of investigation here. The RIE, as
defined by Allan et al. (2004), is a
constant factor applied to the signals as part of the conversion from a
signal in the mass spectrometer to an ambient mass concentration. Because
this is a purely multiplicative operation, this will affect all data and
associated errors equally, and therefore the factors derived in the PMF model
described by Ulbrich et al. (2009) will simply by multiplied by the exact
same amount. So, when PMF factors are derived using data that have not had an
RIE applied (and corollary to this, other multiplicative factors such as
ionisation efficiency, collection efficiency, and inlet flow rate), the
relative contributions of the different factors as a function of time will
be the same as data that had this applied. The only difference is
that the absolute units of the factors are as an arbitrary mass spectral
response (in s
The HR-SP-AMS data were analysed using the data analysis toolkit ToF-AMS HR
Analysis 1.20O (DeCarlo et al., 2006). The high-resolution PIKA feature of
toolkit allows the direct separation of most ions from the organic and
inorganic species at the same nominal mass-to-charge ratio and grouping into
families such as C
Positive matrix factorisation (PMF) is an advanced factor-analysis technique developed by Paatero and Tapper (1994). In the previous research, PMF has been used extensively to apportion organics with the standard AMS data but not so often to apportion BC from SP-AMS data (Crippa et al., 2013; Saarikoski et al., 2014). In this research study positive matrix factorisation (PMF) was applied to HR-SP-AMS data to apportion BC in to more than two sources. PMF assumes that a matrix of data can be explained by a linear combination of “factors” with characteristic profiles and varying temporal contributions (Paatero and Tapper, 1994; Ulbrich et al., 2009). The analysis was conducted using the PMF Evaluation Tool (Ulbrich et al., 2009; Zhang et al., 2011).
As with all PMF analysis, error estimates have to be provided but because of
the lower signals and the combination of different data retrieval methods
used for the fullerene signals (UMR rather than HR), greater emphasis had to
be placed on these signals. Corbin et al. (2015) presented a very detailed
error model for HR data employing a Monte Carlo method to explore multiple
sources of error. But because UMR was used in this instance, we were unable
to apply this method, so we took an empirical approach. This was done by
applying an additional “model error” to the error matrix, i.e. an error
term proportional to the signal intensity in addition to its square root, as
per the standard AMS error model (Ulbrich et al., 2009; Comero et al., 2009).
The model error value was increased from 0 to 0.10 to downweight the larger
signals and place more of an emphasis on the fullerene signals. The details about model error value modification are available in the
Supplement (Figs. S3a, b, S4a, b, S5a, b). While the methods of
Corbin et al. (2015) cannot be directly applied here, they are in broad
agreement with the values we have used. According to Corbin et al. (2015),
the peak width “
The weather data are, as presented by Reyes-Villegas et al. (2018), and results showed
quiet stagnant conditions with a low temperature of 4
Meteorological measurements of relative humidity (RH), temperature, wind direction (WD), and wind speed (WS) along with the time series of BC and strontium (Sr), a firework tracer emitted during Bonfire Night.
To attempt to identify a unique tracer for fireworks, the HR-SP-AMS data were
analysed for metals. Reyes-Villegas et al. (2018) concluded that fireworks were not
a major factor in the overall mass concentrations but could not conclusively
prove this assertion with the data available. Fireworks release several
pollutants such as manganese, cadmium, strontium, aluminium, other
suspended particles, carbon monoxide, carbon dioxide, and sulfur dioxide
(Lemieux et al., 2004; Shi et al., 2011). The metal compounds are in the
form of metal salts such as potassium chlorates, perchlorates, strontium
nitrates, potassium nitrates, barium nitrates, sodium oxalate, manganese,
sulfur, iron, and aluminium. These metals are mainly used to give different
bright colours; for example, Sr can be used for giving red colour to the
fireworks (Mclain, 1980). During the analysis, different metal peaks, such
as iron (Fe), strontium (Sr), caesium (Cs), and titanium (Ti), which could be
associated with the fireworks, were detected (Fig. 2a). The Sr was most
unambiguously associated with the fireworks due to the fact that there is no
other signal present in the atmosphere outside of Bonfire Night. Other
metals may have other sources, such as mineral or brake dust in the case of
iron, and may be receiving signal interference from other mass spectral peaks.
The highest peak of Sr concentrations, i.e. 53.6 s
Time series of different variables observed during the bonfire event.
Figure 2b shows that the signals associated with refractory BC (rBC) and its
coating species (Org, SO
The HR-SP-AMS data were compared against those of other instruments such as
AE31, CIMS, MAAP, and AMS presented in the previous studies (Reyes-Villegas et al.,
2018; Priestley et al., 2018), and a statistically significant correlation
(see Table S2, S3, and S4 in the Supplement) was found between the
black carbon measured by three different instruments, i.e. rBC from
HR-SP-AMS, eBC from MAAP, and eBC and BrC from AE31. The BC measured by AE31
and MAAP was named eBC (equivalent BC) according to Petzold et al. (2013) recommendations. Reyes-Villegas et al. (2018) measured eBCwb (equivalent black
carbon emissions from wood burning) and eBCtr (eBC
Reyes-Villegas et al. (2018) used AMS to estimate the concentrations of particulate
organic oxides of nitrogen (PONs), i.e. 2.8
PMF solution.
To provide a baseline result and explore the effect of adding fullerene
signals, the factorisation was first performed in a standard configuration
without the inclusion of fullerene signals in the data matrix. The f-peak
parameter was varied between
Next, PMF was performed with the inclusion of fullerene data, and for the
selection of an optimum number of factors, a
stepwise approach was used, beginning with a two-factor model and
successively adding factors up to a maximum of six. In our case,
five factors gave the best solution based on the criteria of
Explanation of the time series used in Figs. 4 and 5.
Figure 3a shows the signal concentrations and mass spectra of five different
factors. Two of them (HOA
The factor identified as “BC and MO-OOA” (more oxidised oxygenated organic
aerosol) is associated with bonfire and non-bonfire sources and identified
as such due to its similarity to previously reported profiles, in particular
the prominent signal at
Another bonfire factor is the biomass burning OA (BBOA), which had strong
signals at
“HOA
The non-bonfire factor “hydrocarbon-like organic aerosols (HOA)” is related
to traffic emissions (fossil fuel combustion), presenting high
signals at
When inspecting the HOA
In previous AMS studies, cooking could be one of the important sources of
PM
Correlation analysis gives an effective way of quickly gaining an idea of
how variables are related with one another. The data analysis software
“openair” was used to generate the hierarchical cluster analysis chart
(Carslaw and Ropkins, 2012) using the “corplot” function on the Bonfire
Night data only. Hierarchical cluster analysis (HCA) provides an effective
way of understanding the order in which different variables appear due to
their similarity to one another. Variables from this (such as rBC, BC &
MO-OOA, BBOA, domestic burning, hydrocarbon-like OA, and HOA
The similarity between different pollutant time series through hierarchical cluster analysis (HCA).
Time series of the pollutants, grouped according to the hierarchical cluster analysis in Fig. 9.
Panel
The contributions of BC signals in
In Fig. 4, a significant correlation was observed between HOA
A correlation was seen between BrC and eBC
Based on the HCA plot, a time series graph was also plotted to investigate the
timings of all pollutants having strong relationships among one another (Fig. 5). The second group which showed the strong correlation was HCNO and (HOA
Another close correlation was the BC and MO-OOA factor with an
These results can be used to estimate the relative contributions of the
different sources to the overall signal and the black carbon assuming that
the divergence of the aerosol in the beam is the same for all particle types,
and hence the efficiency is same for all particle types. Figure 6a
illustrates the total signal fraction of BC accounted for by each BC source
released during the bonfire event. The total signal fraction was obtained
directly by the PMF analysis. The five factors have been divided into two
different categories, i.e. bonfire factors and non-bonfire factors. The
bonfire factors are HOA
This study has shown that for the first time, the inclusion of fullerene
data in PMF applied to HR-SP-AMS data can be used to apportion soot into
five sources during an event that superimposes emissions from a bonfire
event over urban pollution. The five soot sources can be divided into
Bonfire Night factors (HOA
Data are archived at the University of Manchester and available on request.
The supplement related to this article is available online at:
ZB performed the data analysis and wrote the paper. JDA and PIW designed the experiment and operated the SP-AMS. ERV, MP, and CJP provided measurements and data from other instruments. ZB was supervised by JDA and HC, with ERV and JB assisting with PMF analysis.
The authors declare that they have no conflict of interest.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This research has been supported by the Natural Environment Research Council (COM-PART (grant no. NE/K014838/1)) and the University of Manchester, UK (Deans Scholarship Award). Zainab Bibi's PhD was funded by a Dean's Award Scholarship from the Faculty of Science and Engineering, University of Manchester, UK.
This paper was edited by Eleanor Browne and reviewed by two anonymous referees.