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- Editorial & advisory board
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- For authors
- For reviewers
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**Research article**
11 Apr 2019

**Research article** | 11 Apr 2019

Receptor modelling: a two-step approach

^{1}National Centre for Atmospheric Science, School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK^{a}also at: Department of Environmental Sciences/Center of Excellence in Environmental Studies, King Abdulaziz University, P.O. Box 80203, Jeddah, 21589, Saudi Arabia

^{1}National Centre for Atmospheric Science, School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK^{a}also at: Department of Environmental Sciences/Center of Excellence in Environmental Studies, King Abdulaziz University, P.O. Box 80203, Jeddah, 21589, Saudi Arabia

Abstract

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Some air pollution datasets contain multiple variables with a
range of measurement units, and combined analysis using positive matrix
factorization (PMF) can be problematic but can offer benefits through the
greater information content. In this work, a novel method is devised and the
source apportionment of a mixed unit dataset (PM_{10} mass and number size
distribution, NSD) is achieved using a novel two-step approach to PMF. In the
first step the PM_{10} data are PMF-analysed using a source apportionment
approach in order to provide a solution which best describes the environment
and conditions considered. The time series **G** values (and errors) of the
PM_{10} solution are then taken forward into the second step, where they are
combined with the NSD data and analysed in a second PMF analysis. This
results in NSD data associated with the apportioned PM_{10} factors. We
exemplify this approach using data reported in the study of Beddows et
al. (2015), producing one solution which unifies the two separate solutions
for PM_{10} and NSD data datasets together. We also show how regression of
the NSD size bins and the ** G** time series can be used to elaborate the solution
by identifying NSD factors (such as nucleation) not influencing the
PM

How to cite

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How to cite.

Beddows, D. C. S. and Harrison, R. M.: Receptor modelling of both particle composition and size distribution from a background site in London, UK – a two-step approach, Atmos. Chem. Phys., 19, 4863-4876, https://doi.org/10.5194/acp-19-4863-2019, 2019.

1 Introduction

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It is unquestionable that worldwide, the scientific vista of air quality is expanding, whether it is the increasing number of observatories or the refinement of information mined from the increasing sophistication of measurements often incorporated in campaign work. The number of metrics being measured has increased from simple measurements of particulate matter (PM) mass and gas concentrations, and we can now probe the composition of the PM mass and the size distributions with mass spectrometers, mobility analysers and optical devices.

Studies using positive matrix factorization (PMF) as a tool for source apportionment of particle mass using multicomponent chemical analysis data are published frequently using datasets from around the world. However, they do not always provide consistent outcomes (Pant and Harrison, 2012), and one means by which source resolution and identification can be improved is by inclusion of auxiliary data, such as gaseous pollutants (Thimmaiah et al., 2009), particle number count (Masiol et al., 2017) or particle size distribution (Beddows et al., 2015; Ogulei et al., 2006; Leoni et al., 2018).

Harrison et al. (2011) analysed number size
distribution (NSD) data (merged Scanning Mobility Particle Sizer (SMPS) and
Aerodynamic Particle Sizer (APS) data) with PMF using auxiliary data
(meteorology, gas concentration, traffic counts and speed). The study used
particle size distribution data collected at the Marylebone Road supersite
in London in the autumn of 2007 and put forward a 10-factor solution
comprised of roadside and background particle source factors. Sowlat et al. (2016)
carried out a similar analysis on number size distribution (13 nm–10 µm)
data combined with several auxiliary variables collected in Los
Angeles. These included black carbon (BC), elemental carbon (EC) and organic carbon (OC), PM mass, gaseous
pollutants and meteorological and traffic flow data. A six-factor solution was chosen
comprising of nucleation, two traffic factors, an urban background aerosol, a
secondary aerosol and a soil factor. The two traffic sources contributed up
to above 60 % of the total number concentrations combined. Nucleation was
also observed as a major factor (17 %). Urban background aerosol,
secondary aerosol and soil, with relative contributions of approximately
12 %, 2.1 % and 1.1 %, respectively, overall accounted for approximately
15 % of PM number concentrations, although these factors dominated the PM
volume and mass concentrations, due mainly to their larger mode diameters.
Chan et al. (2011) considered extracting
more source information from an aerosol composition dataset by including
data on other air pollutants and wind data in the analysis of a small but
comprehensive dataset from a 24-hourly sampling programme carried out during
June 2001 in an industrial area in Brisbane. They chose multiple types of
composition data (aerosols, volatile organic compounds (VOCs) and major gaseous pollutants) and wind data
in source apportionment of air pollutants and found it to result in better
defined source factors and better fit diagnostics, compared to when
non-combined data were used. Likewise, Wang et
al. (2017) report an improvement in source profiles when coupling the PMF
model with ^{14}C data to constrain the PMF run as a priori information.

However, while combining, for example, particle chemical composition and size distribution data in a single PMF analysis may assist source resolution, difficulties arise if the two datasets have different and/or ambiguous rotations (discussed in Sect. 2). This tends to result in factors with either mass contributions and small number contributions or number contributions and small mass contributions and rarely a meaningful contribution from both data types. Experimental design can of course circumnavigate this problem, for instance, using chemical data which are already size-segregated, measured using a cascade impactor (Contini et al., 2014). Such an approach is attractive by view of the fact that there is no question as to whether both datasets sufficiently overlap across the size bins. However, cascade impactors do not offer the high time resolution of particle counting instruments, with individual measurements lasting hours or days. Even so, for the case in which two or more instruments are available in a campaign to measure two or more different metrics, e.g. PM mass and particle number (PN), then a combined data analysis is useful. Emami and Hopke (2017) have shown that the effect of adding variables as auxiliary data (with potentially different units) to a NSD dataset is to decrease the rotational ambiguity of a solution from a one-step PMF analysis.

In this study, we present a method for analysing simultaneously collected
PM_{10} composition and NSD data. In the work of Beddows et al. (2015), both
particle composition and NSD data from a
background site in London (2011 and 2012) were analysed using positive matrix
factorization. As part of the methodology development, it was concluded that
it was preferable not to combine these two data types in a single analysis
but to conduct separate PMF analyses for PM_{10} mass and particle number.
This yielded a six-factor solution for the PM_{10} data (diffuse urban,
marine, secondary, non-exhaust traffic and crustal (NET and crustal), fuel oil and
traffic). Factors described as diffuse urban, secondary and traffic were
identified in the four-factor solution for the NSD data, together with a
nucleation factor not seen in the PM_{10} mass data analysis (see Fig. 1).
When combining the PM_{10} and NSD data in a single PMF analysis,
diffuse urban, nucleation, secondary, aged marine and traffic factors were
identified, but the factors were not as clearly separated from each other as
the factors derived from the separate datasets. For example, fuel oil was
now mixed in with marine and called aged marine. This is summarized in
Fig. 1. However, it would still be useful to obtain a number size
distribution for each of the six PM_{10} factors and/or a chemical
composition for the four NSD factors. As a continuation of this work, we
present an alternative method for analysing the combined dataset in a so-called two-step methodology. In the first step, we analyse the mass data
(PM_{10}; units: µg m^{−3}) according to the methodology of
Beddows et al. (2015). This results in a time series factor **G**, which is
carried forward into a second PMF analysis of a combined dataset consisting
of the **G** time series and an auxiliary dataset (i.e. NSD; units:
cm^{−3}). The first step identifies sources and apportions the **G** factors
to their contribution to mass, and in the second step, an **FKEY** matrix is
chosen such that **G** “drives” the model and the NSD data “follow”. This means
that we have PM_{10} factors, each of which is augmented by its number size
distribution. Furthermore, we also consider linear regression (LR) as a second
step in a PMF–LR analysis to show that although the initial analysis is
biased toward mass by analysing PM_{10} factors only, unseen factors
influencing the NSD data (e.g. nucleation) can be identified in the data.

2 Experiment

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With a population of 8.5 million in 2014 (ONS, 2017), the UK city of London
is the focus of study in this work; the North Kensington (NK) site (lat. = 51^{∘}: 31^{′}: 15.780^{′′} N
and long. = 0^{∘}: 12^{′}: 48.571^{′′} W) was considered. NK is part of both the London Air Quality
Network and the national Automatic Urban and Rural Network and is owned and
part-funded by the Royal Borough of Kensington and Chelsea. The facility is
located within a self-contained cabin within the grounds of Sion Manning
School. The nearest road, St. Charles Square, is a quiet residential street
approximately 5 m from the monitoring site, and the surrounding area is
mainly residential. The nearest heavily trafficked roads are the B450
(∼100 m east) and the very busy A40 (∼400 m
south). For a detailed overview of the air pollution climate at North
Kensington, the reader is referred to Bigi and Harrison (2010).

As alluded to, this work is a continuation of the study carried out by
Beddows et al. (2015), which analysed NSD and PM_{10} chemical
composition data collected at the NK receptor site. Number size
distribution (NSD) data were collected continuously every 15 min using a
Scanning Mobility Particle Sizer (SMPS), consisting of a CPC (TSI model 3775)
combined with an electrostatic classifier (TSI model 3080) and air-dried
according to the EUSAAR protocol (Wiedensohler
et al., 2012). The particle sizes covered were 51 size bins ranging from 16
to 604 nm, the 15 min distributions were aggregated up to hourly
averages (when there were at least three 15 min samples per hour) and all
missing values were replaced using a value calculated using the method of
Polissar et al. (1998). Further details of the SMPS settings are given in
Table S1 in the Supplement, and the reader is also referred to Beccaceci et al. (2013a, b) for
an extensive account of how the NSD data were collected and quality-assured.

Accompanying the NSD data from the study of Beddows et al. (2015) was the
PMF output from the analysis of PM_{10} chemical composition data. The
latter data consisted of 24 h air samples taken daily over a 2-year period
(2011 and 2012) using a Thermo Partisol 2025 sampler fitted with a PM_{10}
size-selective inlet. These filters were analysed for total metals,
PM_{metals} (Al, Ba, Ca, Cd, Cr, Cu, Fe, K, Mg, Mo, Na, Ni, Pb, Sn, Sb,
Sr, V and Zn), using a Perkin Elmer/Sciex ELAN 6100DRC following hydrofluoric acid
digestion of GN-4 Metricel membrane filters. Water-soluble ions, PM_{ions}
(Ca^{2+}, Mg^{2+}, K, ${\mathrm{NH}}_{\mathrm{4}}^{+}$, Cl^{−}, ${\mathrm{NO}}_{\mathrm{3}}^{-}$ and
${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$), were measured using a near-real-time URG-9000B (hereafter
URG) ambient ion monitor (URG Corp). The data capture over the 2 years
ranged from 48 % to 100 % as different sampling instruments varied in
reliability. Data gaps were filled by measurements made on daily PM_{10}
filter samples collected continuously at this site using a Partisol 2025;
laboratory-based ion chromatography measurements were made for anions on
Tissuquartz^{™} 2500 QAT-UP filters. No cation measurements
were available from these filters, and this resulted in a lower data capture
for the cations. Again, all missing data were replaced using a value
calculated using the method of Polissar et al. (1998). A woodsmoke metric,
CWOD, was also included. This was derived as PM woodsmoke from the
methodology of Sandradewi et al. (2008) utilizing aethalometer and
EC∕OC data, as described in Fuller et al. (2014). Samples were also collected
using a Partisol 2025 with a PM_{10} size-selective inlet, and
concentrations of elemental carbon (EC) and organic carbon (OC) were
measured through collection on quartz filters (Tissuquartz^{™} 2500
QAT-UP) and analysis using a Sunset Laboratory thermal–optical analyser
according to the QUARTZ protocol (which gives results very similar to EUSAAR
2: Cavalli et al., 2010) (NPL, 2013). We refer to CWOD, EC and OC as
PM_{carbon}. In addition, particle mass was determined on samples
collected on Teflon-coated glass fibre filters (TX40HI20WW) with a Partisol
sampler and PM_{10} size-selective inlet.

This aforementioned PM_{10} data were represented in this work as the PMF
solution for PM_{10}-only data, derived in Beddows et al. (2015) and
consisting of six sources, namely diffuse urban, marine, secondary,
non-exhaust traffic and crustal, fuel oil and traffic. The diffuse urban factor
had a chemical profile indicative of contributions mainly from both
woodsmoke (CWOD) and road traffic (Ba, Cu, Fe, Zn). The marine factor
explained much of the variation in the data for Na, Cl^{−} and Mg^{2+},
and the secondary factor was identified from a strong association with
${\mathrm{NH}}_{\mathrm{4}}^{+}$, ${\mathrm{NO}}_{\mathrm{3}}^{-}$, ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ and organic carbon. For
the traffic emissions, the PM did not simply reflect tailpipe emissions, as
it also included contributions from non-exhaust sources, i.e. resuspension
of road dust and primary PM emissions from brake, clutch and tyre wear. The
non-exhaust traffic and crustal factor explained a high proportion of the
variation in the Al, Ca^{2+} and Ti measurements consistent with particles
derived from crustal material, derived either from wind-blown or
vehicle-induced resuspension. There was also a significant explanation of
the variation in elements such as Zn, Pb, Mn, Fe, Cu and Ba, which had a
strong association with non-exhaust traffic emissions. As there was a strong
contribution of crustal material to particles resuspended from traffic, this
likely reflected the presence of particulate matter from resuspension and
traffic-polluted soils. The last factor was attributed to fuel oil,
characterized by a strong association with V and Ni together with
significant ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$. This output comprised the first-step solution
in the two-step analysis of PM_{10} and NSD data, and in this study we
concentrate on the analysis of the NSD data in the second PMF step, with the
aim of assigning a NSD to each of the six PM_{10} factors.

Positive matrix factorization (PMF) is a well-established multivariate data
analysis method used in the field of aerosol science. PMF can be described
as a least-squares formulation of factor analysis developed by Paatero
(Paatero and Tapper, 1994). It assumes that the ambient aerosol
concentration **X** (represented by *m*×*n* matrix of *m* observations and *n* PM_{10}
constituents or NSD size bins), measured at one or more sites, can be
explained by the product of a source profile matrix **F** and source contribution
matrix **G** whose elements are given by Eq. (1):

$$\begin{array}{}\text{(1)}& {\displaystyle}{x}_{ij}=\sum _{k=\mathrm{1}}^{p}{g}_{ik}\cdot {f}_{kj}+{e}_{ij}\phantom{\rule{0.25em}{0ex}}\phantom{\rule{0.25em}{0ex}}\phantom{\rule{0.25em}{0ex}}i=\mathrm{1}\mathrm{\dots}m;j=\mathrm{1}\mathrm{\dots}n,\end{array}$$

where the *j*th PM constituent (element, size bin or auxiliary
measurement) on the *i*th observation (i.e. hour) is represented by
*x*_{ij}. The term *g*_{ik} is the contribution of the *k*th factor
(of a total of *p* factors) to the
receptor on the *i*th hour, *f*_{kj} is the fraction of the *j*th PM
constituent in the *k*th factor and *e*_{ij} is the residual for the
*j*th measurement on the *i*th hour. The residuals (i.e. difference
between measured and reconstructed concentrations) are accounted for in
matrix **E**, and the two matrices **G** and **F** are obtained using an iterative algorithm
which minimizes the object function *Q* (see Eq. 2).

Using the data and uncertainty matrices for the model, Eq. (1) is
optimized in the PMF algorithm by minimizing the *Q* value (Eq. 2):

$$\begin{array}{}\text{(2)}& {\displaystyle}Q=\sum _{i=\mathrm{1}}^{n}\sum _{j=\mathrm{1}}^{m}{\left({\displaystyle \frac{{e}_{ij}}{{s}_{ij}}}\right)}^{\mathrm{2}},\end{array}$$

where *s*_{ij} is the uncertainty in the *j*th measurement for hour *i*. All
analyses were carried out in robust mode which reduces the impact of
outliers (Paatero, 2002).

PMF is a weighted technique, and the value of *Q*, and hence the model fit, is
determined by the input variables with the lowest values of uncertainty,
*s*_{ij}, thus giving their variables a higher weighting in the analysis.
Input variables with low weight have little effect upon the value of *Q*, even
when their residuals are large. This can be used to the advantage of the
operator; e.g. when apportioning total PM mass in a conventional one-step
PMF, the total PM concentrations are normally input with artificially high
uncertainty, so that they are essentially passive in the PMF analysis and do
not influence its outcome. By doing so, the chemical composition data
determine the apportionment of PM mass to the source-related factors
identified by the PMF. A similar approach can be followed in the PMF
analysis of a combined dataset, whereby higher weightings can be applied to the
main dataset of interest such that it drives the analysis and the
auxiliary dataset follows; i.e. the uncertainties are chosen such that
the balance of total weights from the two datasets is *tipped* towards the
measurement of interest and highest reliability in regards of rotational
unambiguity.

To assess the PMF model, the *Q* value is outputted by PMF and compared to a
theoretical value *Q*_{theory}. which is approximately the difference
between the product of the dimensions of **X** and the product of the number of
factors and the sum of dimensions of **X** (i.e. *m*×*n* – *p*(*m*+*n*)).
For a given number of factors, the whole
uncertainty matrix is scaled by a factor *b*_{scale} until the ratio between
*Q* and *Q*_{theory} is approximately 1 (*r**Q* value = $Q/{Q}_{\text{theory}}=\mathrm{1}\pm \mathrm{0.02}$).

With regards to the final output from PMF, a scaling has to be applied in
order to achieve quantitative results. This is done by scaling either **G** or
**F** to unity such that the units from **X** are carried over to either **F** or
**G** respectively to complete the apportionment. However, different routes have
to be considered depending on whether **X** has homogeneous or heterogeneous
units.

Given a PMF input data matrix **X**, a solution **GF**+**E** can be computed, where
**G** represents the time series of the source profiles **F**, with a residual matrix
**E**. Often **X** comprises columns of PM_{10} component concentrations (e.g. ICPMS
values measured from acid-digested filters collected with a Partisol
2025), and it is common practice to also include a total variable (e.g. column
of PM_{10}, measured using a Tapered Element Oscillating Microbalance, TEOM) in the data matrix. The
resulting PM_{10} profile element value can then be used to scale **G** and
**F** such that **G** carries the units of **X**, with **F** unitless. Note that neither **G** or
**F** is scaled to unity in this approach. Instead, scaling is done after the
analysis using a constant *a*_{k}, determined by the time series of a total
variable (e.g. PM_{10}), downweighted by applying a high uncertainty,
within the input data.

$$\begin{array}{}\text{(3)}& {\displaystyle}{x}_{ij}=\sum _{k=\mathrm{1}}^{p}\left({a}_{k}{g}_{ik}\right)\left({\displaystyle \frac{{f}_{kj}}{{a}_{k}}}\right)\end{array}$$

The resulting value for the PM_{10} contribution for each factor within
the **F** matrix is then used as a scaling constant *a*_{k} in Eq. (3). Such
scaling results in unitless factors **F** which describe the characteristics of
the sources and time series **G** with units of µg m^{−3}. Apportionment
can then be carried out by averaging the **G** values for each source factor, or
a fully quantified time series of each factor can be presented, e.g. in
bivariate plots. Of course, the **G** and **F** can be normalized such that **G** is
unitless and **F** carries units, an approach necessary when **X** contains
heterogeneous units. This approach, using the PMF, requires the average of each column of **G** to be
scaled to unity, by using the PMF setting mean$\left|\mathrm{G}\right|=\mathrm{1}$.

If the analysis of **X** was to be enhanced by the inclusion of data
**Z** from a
second instrument with different units, then a different approach to the
one-step method with homogeneous units would be required to analyse
the joint data matrix $[\mathbf{X},\mathbf{Z}]=\mathbf{G}[\mathbf{X},\mathbf{Z}]\cdot \mathbf{F}[\mathbf{X},\mathbf{Z}]+\mathbf{E}[\mathbf{X},\mathbf{Z}]$. If the previous method was applied where **F** was normalized, then
it would not be clear what units to assign to **G**, whether the units
from **X** or **Z**. To get around this problem, **G** is scaled to unity. This results in a
unitless time series **G** and a quantified **F** matrix. For each source profile
the sum of the species associated with either data type gives the average
total apportionment, e.g. of PM_{10} or number concentration PN. Of
course, this requires the complete mass or number closure of the elements
making up either PM_{10} or PN respectively, although inclusion of
measurements of total PM_{10} or PN can be used instead, if available.

In the ideal case, if the individually computed factors for both datasets
result in **G**(**X**) and **G**(**Z**) being identical, then a straightforward joint model
[**X**,**Z**] is successful and $\mathbf{G}[\mathbf{X},\mathbf{Z}]=\mathbf{G}\left(\mathbf{X}\right)=\mathbf{G}\left(\mathbf{Z}\right)$.
However, if **G**(**X**) and **G**(**Z**) are significantly different, then the joint model will fail, identified by
too large a *Q* value. A solution to this problem is to set the total weights of
the better dataset **X** significantly higher than the total weights of the
auxiliary dataset **Z** such that **X** will drive the model and **G**[**X**,**Z**] will be
approximately equal to **G**(**X**), and a reasonable *Q* value is
obtained for the **Z**.
However, care is required to ensure that **X** or **Z** do not contain rotational
ambiguity because such rotation for **X** may not be suitable for **Z**. For such
cases, equal total weights for both **X** and **Z** are applied in the hope that the
best rotation for both **X** and **Z** can be found.

The method proposed in this work separates the analysis of the two datasets
**X** and **Z** into two different PMF analyses. Dataset **X** is first analysed, and an
unambiguous rotation is selected, which gives computed factors **G**(**X**). These
are then carried over into a second PMF step in which **G**(**X**) are combined with
**Z** to form a joint matrix for analysis. By using **FKEY** (described below)
factors, **G**(**X**,**Z**) are forced to be equal to **G**(**X**) from step 1. So, for example,
if in the first step we analyse PM_{10} data and carry forward the output
**G**(**PM**_{10}) into a second step combined with the
NSD data, i.e. [**G**(**PM**_{10}),
**NSD**], this results in profiles
**F**[**G**(**PM**_{10}), **NSD**]. In other
words, we force out of the NSD data source profiles which have the same **G**
factors as the PM_{10} data and extend the list of components of the
sources identified in the first step, thus improving characterization of
the source. Note that this is equivalent to non-negative weighted regression
of matrix **Z** by columns of matrix **G** for which other tools exist. Furthermore,
by using a two-step method, we can continue to use the scaling method
described in Sect. 2.2.2 to apportion the sources using a quantified time
series **G**(**X**) rather than normalizing the **G**(**X**,**Z**) matrix sums to 1 and relying
on the summation of the elements in the rows of **F**(**X**,**Z**) to give the
apportionment of **X** and **Z**.

Positive matrix factorization was carried out in this work using the DOS-based executable file PMF2 v4.2 compiled by Pentti Paatero and released on 11 February 2010 (available from Dr. Pentii Paatero). This is used by the author in preference to a GUI version of PMF (e.g. US EPA PMF 5.0; Norris et al., 2014) because of the ease with which it can be incorporated into a Cran R procedure script using shell commands, thus facilitating automation of the analysis and any optimization. R script can be written to manipulate and organize input data for PMF2, run PMF2, collect the output and produce the necessary output for consideration as text, table or plot. The main strength of this approach is to improve the repeatability and transference of a method between practitioners within our group.

The two-step method is shown schematically in Fig. 2. Matrix **X** yields
factors ^{1}**G** and ^{1}**F** in the first step. The time series ^{1}**G** matrix
is carried through to the second step, where it is combined with an auxiliary
dataset **Z** to give the step 2 input matrix [^{1}**G**,**Z**]. This in turn is
analysed to produce factors ^{2}**G** and ^{2}**F**. In the current example, the
dataset of Beddows et al. (2015) is used as a starting matrix **X** and comprises the PM_{10} chemical
composition dataset. This yields time series ^{1}**G** and source profile
^{1}**F**, and the reader is referred to Beddows et al. (2015) for a
description of the analysis and output. Figure 1 shows the output from the
first step which was found to be the optimum solution after considering three- to
eight-factor solutions. The scaled time series matrix ^{1}**G** from this
analysis was combined with the NSD data – concurrently measured with the
PM_{10} data – to form the input matrix [^{1}**G**,**Z**] for step 2. The
uncertainties of the ^{1}**G**_{1} matrix, ^{1}Δ**G**,
are transferred from the
output of the first step and entered as input uncertainties for the second
step. The hourly NSD data were aggregated into daily values to match the
daily ^{1}**G** factors outputted from the PMF analysis of the daily PM_{10}
data sampled. This reduced the data matrix down to 590 rows by 57 columns
(^{1}*G*_{1}…^{1}*G*_{6}, $\mathit{N}\mathit{S}{\mathit{D}}_{\mathrm{1}}^{\mathrm{16}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}}\mathrm{\dots}\mathit{N}\mathit{S}{\mathit{D}}_{\mathrm{51}}^{\mathrm{640}\phantom{\rule{0.125em}{0ex}}\mathrm{nm}}$)
for which we have a *Q*_{theory} value of 29 748 for
a six-factor solution. For the NSD data, the uncertainties are taken as the
NSD values multiplied by the value of an arbitrary parameter *b*_{scale}
(see Fig. 2). Initially, *b*_{scale} was set to 4 to ensure that the model
was weighted such that it was driven by the PM_{10} data. However, this
operation becomes somewhat redundant by the use of the **FKEY** matrix discussed
in the next section. However, in order to find the optimal NSD uncertainties
the value of the parameter *b*_{scale} (typically 0.2) was optimized in
Cran R so that the ratio of $Q/{Q}_{\text{theory}}=\mathrm{1}\pm \mathrm{0.02}$, indicating an
relative percentage uncertainty in the region of 20 %. In retrospect – by
taking into account the decrease in reliability of the size bin counts
towards the edges of the size bin range – an improvement would be to
gradually increase the uncertainties from 5 % in the middle range of sizes
to a predefined larger value, e.g. 50 %, over the lower and upper size
bins. The uncertainties were entered directly into the model using the PMF
matrix **T**, with **U** and **V** redundant.

**GKEY** and **FKEY** are matrices with the same dimensions as
**G** and **F** respectively,
for incorporating a priori information into a PMF analysis. They are used in the
second step of the PMF analysis to “pull” elements of the source profiles
to zero. **GKEY** and **FKEY** indicate the location of suspected zeros in source
profiles ^{2}**F** or contributions ^{2}**G** (Fig. S1). Since we are
concerned with the profiles, this information is given in the form of
integer values in **FKEY**. The greater the certainty that an element of a
source profile is zero, the larger the integer value that is specified. In
this case, in the second step for the input dataset
[^{1}**G** **NSD**], it is
certain that only one unique contribution will be strong for each row of the
profile ^{2}**F**, outputted from the second PMF analysis; e.g.
only ^{1}*G*_{1} and not ^{1}*G*_{2}...^{1}*G*_{6} will
contribute to the first position in output factor ^{2}*F*_{1} (Fig. S1). All
“non-zero” elements within the output of ^{2}**F** take a *f**k**e**y* value of zero,
whereas all elements of ^{2}**F** which are pulled to zero take a non-zero
value of *fkey*_{1}. This leads to a **FKEY** matrix which can be understood in two
parts. The first part is a square matrix of dimensions equal to the number of
columns of ^{1}**G**, with all its entries equal to *fkey*_{1} except for the
leading diagonal (set to zero); this part ensures that ^{1}**G**
is the same as ^{2}**G**. The
second part of the matrix consists of all the elements as zero and represents
the NSD input data. An *fkey*_{1} value of 7 to 9 is considered a medium to
strong pull, and in this work, we used a value of 24, which in comparison is
very aggressive ensuring only one rotational solution is available ensuring
^{1}**G**≈^{2}**G**.

To extend the analysis from six factors to seven factors, an extra row was added to
**FKEY**. This was done in order to investigate any factors missed in the NSD
data which the first analysis using PM_{10} would not be sensitive to. For
example, a nucleation mode would be detected in NSD data but not in PM_{10}
data. In order to give the model freedom to factorize out a nucleation
factor, the seventh row of **FKEY** values consisted of {*fkey*${}_{\mathrm{2},\mathrm{\dots},}$*fkey*_{2},
*nsd*_{1}, *nsd*_{2…} *nsd*_{51}};
*fkey*_{2}=20. This ensured that all the ^{2}*G* contributions were
allocated to the first six factors, only leaving the seventh factor to account
for the remaining unfactorized NSD data. There is no reason why more than
seven factors could not be used to investigate possible unresolved NSD factors.
However, we constrained the scope of our investigation to reidentifying
those in Fig. 1.

As an alternative to using PMF in the second step, a regression was carried
out. Each column of data for each of the 51 size bins *j* within the NSD was
regressed against the six ^{1}**G** time series using Eq. (4):

$$\begin{array}{}\text{(4)}& {\displaystyle}\mathit{N}\mathit{S}{\mathit{D}}_{j}={\mathit{\alpha}}_{\mathrm{0},j}+{\mathit{\alpha}}_{\mathrm{1},j}\phantom{\rule{0.125em}{0ex}}{}^{\mathrm{1}}{\mathit{G}}_{\mathrm{1}}+{\mathit{\alpha}}_{\mathrm{2},j}\phantom{\rule{0.125em}{0ex}}{}^{\mathrm{1}}{\mathit{G}}_{\mathrm{2}}+\mathrm{\cdots}+{\mathit{\alpha}}_{\mathrm{6},j}\phantom{\rule{0.125em}{0ex}}{}^{\mathrm{1}}{\mathit{G}}_{\mathrm{6}},\end{array}$$

where *α*_{0} is the population intercept and *α*_{1−6} are
the population slope coefficients. This results in a 7 by 51 matrix of
values. Each column represents a size bin of the NSD data, and each row
represents the slope coefficients associated with six of the factors (giving
an indication of how each size bin scales with each of the six factors) and an
intercept. When ${\mathit{\alpha}}_{\mathrm{1}-\mathrm{6},j}$ is plotted against the size bin, six
plots showing the dependence of each size bin *j* on each of the six PM_{10}
factors are produced. It is also assumed that these (referred to here as NSD
regression source profiles) will be comparable to the actual NSD PMF source
profile. Similarly, the *α*_{0,j} values are expected to give a
background value due possibly to noise; however, it is more likely to yield
a source (such as nucleation) to which the PM_{10} mass analysis is
insensitive.

If it is assumed that the factors derived from the daily NSD data are the
same as those present in the hourly data, i.e. the factors are conserved
when averaging the data from hourly to daily data before PMF analysis, then
daily NSD profiles can be fitted to the hourly NSD spectra to recover a
diurnal cycle for the factors. However, it is worth noting that the process
of aggregating hourly data to daily NSD data may cause loss of information,
implying that minor factors (e.g. due to event episodes) might well be
averaged out of the data. For the elements of the *i*th number size distribution
NSD_{ij} (of dimensions *m*×*n*), the factors can be fitted using
Eq. (5), which is the difference across the size bins of the *i*th row of the NSD data and
the linear sum of the *p* NSD source profiles (*p*=7 in this case) scaled with
respect to the scalar values *c*_{ik}, representing the time series of each
fitted NSD source profile.

$$\begin{array}{}\text{(5)}& {\displaystyle}{d}_{i}=\begin{array}{l}\sum _{j=\mathrm{1}}^{N}\left\{{\text{NSD}}_{ij}-{\sum}_{k=\mathrm{0}}^{p}{c}_{ik}\times {f}_{kj}\right\},\phantom{\rule{0.25em}{0ex}}\phantom{\rule{0.25em}{0ex}}\phantom{\rule{0.25em}{0ex}}\phantom{\rule{0.25em}{0ex}}{c}_{ik}\ge \mathrm{0}\\ \mathrm{1}\times {\mathrm{10}}^{\mathrm{10}},\phantom{\rule{0.25em}{0ex}}\phantom{\rule{0.25em}{0ex}}\phantom{\rule{0.25em}{0ex}}\phantom{\rule{0.25em}{0ex}}{c}_{ik}<\mathrm{0}\end{array}\end{array}$$

The Cran R package non-linear minimization (nlm) (R Core Team,
2018) was used to minimize the value of *d*_{i} with respect to the scalar
value *c*_{ik} with a non-negative constraint on *c*_{ik} placed in the
function. If a negative value is returned by any of the *c*_{k} values, then
*d*_{i} returns an excessively large value. Furthermore, in order to extract
an apportionment to number concentration (cm^{−3}) the fitted values were
scaled using a scalar *β*_{k}. Seven values were derived for *β*_{k} by regressing the total particle number (total hourly SMPS) against
each of the fitted values *c*_{k} (Eq. 6).

$$\begin{array}{}\text{(6)}& {\displaystyle}\mathit{P}\mathit{N}={\mathit{\beta}}_{\mathrm{0}}+{\mathit{\beta}}_{\mathrm{1}}{\mathit{c}}_{\mathrm{1}}+{\mathit{\beta}}_{\mathrm{2}}{\mathit{c}}_{\mathrm{2}}+\mathrm{\cdots}+{\mathit{\beta}}_{\mathrm{7}}{\mathit{c}}_{\mathrm{7}}\end{array}$$

The resulting scaled-fitted values were then used to calculate the PN concentration for each of the regression source profiles (Eq. 7), allowing subsequent plotting of the seven diurnal cycles.

$$\begin{array}{}\text{(7)}& {\displaystyle}\mathit{P}{\mathit{N}}_{k}={\mathit{\beta}}_{k}{\mathit{c}}_{k}\end{array}$$

Identification of the sources responsible for the factors outputted from PMF
can be assisted by meteorological data. Time series of the *k*th factor
(or *g*_{k} values) can be plotted against wind direction and wind speed
using either the polarPlot or polarAnnulus functions provided in the Openair
package. Polar plots are simply used for plotting the factor contribution on
a polar coordinate plot with north, east, south and west axes. Mean
concentrations are calculated for wind speed direction “bins” (e.g. 0–1, 1–2 m s^{−1},...
and 0–10, 10–20^{∘}, etc.) and smoothed using a generalized
additive model. Each bin concentration is plotted as a group of pixels
(coloured according to a concentration colour scale) and positioned a
distance away from the origin according to the magnitude of wind speed and
along an angle from the north axis according to the wind direction. Such
plots are useful when identifying the nature of the source. A diffuse source
will tend to have its highest concentration showing as a “hotspot” at the origin of
the polar plot, whereas a point source will cause a hotspot both away from the
origin and in the direction pointing towards the source. On the other hand
wind blown sources tend to be recognized by their relation to wind speed and
hence do not necessarily produce hotspots. Instead, they produce a minimum to
maximum gradual gradient of colour from the origin, spreading radially out
towards the edge of the plot in the direction of the source, e.g. for a
marine source. Likewise, annulus plots plot the mean factor concentration on
a colour scale by wind direction and as a function of hour of the day as an
annulus, represented by the distance of the coloured pixels from the origin.
The function is good for visualizing how concentrations of pollutants vary
by wind direction and hour of the day. For example, for the North Kensington
site – positioned west of the city centre – we might well expect most of
the anthropogenic sources (traffic, diffuse urban, etc.) to show an easterly
direction with the appropriate diurnal cycle (e.g. rush hour traffic
patterns). Similarly, we might expect cleaner air (marine, nucleation, etc.)
to occur from a westerly direction and at times of the day when the solar
strength is highest.

3 Results and discussion

Back to toptop
The aim of this work has been to show how a given PMF result can be
complemented with concurrently measured auxiliary data. We exemplify this
using PM_{10} and NSD data collected from the North Kensington receptor
site in London and start with the premise that we are completely satisfied
with the PM_{10} analysis and are using a rotation which gives quantified
factors (quantified **G** and scaled **F**) which best represent the urban
atmosphere sampled, i.e. the output from Beddows et al. (2015). For each
PM_{10} factor we wish to assign a NSD distribution. Rather than repeat
the PMF analysis using a combined PM_{10}–NSD dataset which can be
complicated if the rotations of the individual PMF analyses of PM_{10} and
NSD data are mismatched or ambiguous, we can carry out a second PMF
analysis or a regression.

Furthermore, because of the nature of any factor analysis, we also have to make the assumption that each source chemical profile and size distribution not only remains unchanged between source and receptor but that they remain constant throughout the measurement campaign. This of course limits our capacity to fully understand the aerosol within the atmosphere we are considering. Chemical reactions during the transit of the air masses will of course modify the chemical composition. It might be assumed that a fully aged aerosol remains unchanged and is identified as a background component, but, for example, we would expect progressive chlorine depletion within a fresh marine aerosol passing over a city. Likewise, we also have to appreciate that different particle sizes will have different atmospheric transit efficiencies, with large particles settling out of the air mass before smaller ones. Similarly, particles nucleate and grow from 1 nm up to 20–30 nm over a short time period of time. It is these finer details which are missed when making an overall assessment of the chemical and physical composition of air mass measured over a long (e.g. 2 years) dataset using PMF.

Figure 3 presents the profiles ^{1}*F*_{k} and ^{2}*F*_{k} from the
first and second PMF analysis respectively. The plots of ^{1}*F*_{k} were
carried over from Beddows et al. (2015) to complete the assignment of the
source profiles.

The time series ^{1}*G*_{k} and uncertainties ^{1}Δ*G**k* from the first
PMF analysis of PM_{10} data were carried over into the second step, where
they are combined with the NSD data for PMF analysis (Fig. 2). The
uncertainties of the NSD data are taken as an optimized multiple of the NSD
values themselves (∼5 % uncertainty, yielding a *Q* value of
30 333 in the robust mode; see Table S2 for PMF settings). Also in order to
encourage ^{2}*G*_{k} to be proportional to ^{1}*G*_{k} for *k*=1–6
(see Table S4), the **FKEY** matrix is applied to pull elements in the source
matrix to zero, as described in Sect. 2.2.6. This ensured that the PMF
analysis of the NSD data was driven by the ^{1}**G** time series and resulted
in a six-factor output in which there were unique contributions from the
*k*th factor ^{1}*G*_{k} from the first analysis to the *k*th factor
^{2}*F*_{k} in the second analysis. This is mainly due to the aggressive
pulling of the factor element in ^{2}**F** applied using **FKEY**.

When inspecting Fig. 3 it is notable that the source profiles are
surprisingly similar to those calculated for the NSD-only and
PM_{10}–NSD data in Beddows et al. (2015). The diffuse urban factor has
a modal diameter just below 0.1 µm, which is comparable to the same
factor in the NSD-only analysis. The marine factor is comparable to the aged marine
factor derived from the PM_{10}–NSD analysis. The secondary factor is
again the factor with the largest modal diameter (between 0.4 and
0.5 µm), and traffic has as expected a modal diameter between 30 and 40 nm. The fuel oil factor appears to be a combination of a nucleation factor
and a mode comparable to diesel exhaust seen in the traffic factor.

Figure S2 shows the results of the linear regression of the NSD data and the PM_{10} ^{1}*G*_{k} scores, and again what is remarkable is
the similarity between these regression source profiles and both the factors
derived in Beddows et al. (2015) and those from the two-step PMF–PMF analysis.

This PMF–LR analysis was carried out using daily averaged data, and to obtain
hourly information – and thus obtain the diurnal patterns (Fig. S2) – the
resulting regression source profiles were refitted to the original NSD
data. On inspection of these source profiles and diurnal plots, the negative
values make interpretation a struggle, reinforcing one of the four conditions
(Hopke, 1991) in the analysis if it is to make sense. We
can however fit non-negative gradients using non-negative regression.
However, the surprising consequence of applying this constraint is that the
same profiles are derived, but they are clipped so that all negative values
are replaced by zero values – hence, information is lost. One
interpretation of the negative values is that these are particle sinks, but
this contradicts the PMF–PMF findings, and hence it is concluded that the
PMF–LR analysis only serves as an indication of how the PM_{10} factors
are augmented by the NSD data. If all profiles are shifted to above the zero
line, then comparisons to the PMF–PMF data can be made. However, what is
interesting to note in this result is the intercept NSD, which is comparable
in profile and diurnal pattern to the nucleation mode identified in Beddows
et al. (2015). This is a seventh regression source profile, in addition to
the six PM_{10} factors and suggests that although the PMF analysis of the
PM_{10} data alone misses a nucleation factor, this can be recovered in a
second analysis as a remainder or bias in the data. Furthermore, this result
indicates that the composition of the nucleation NSD factor has no link to
the chemical PM_{10} composition and cannot be used to infer a
composition. This is unsurprising given the very small mass contributed by
the nucleation-mode particles.

Returning to the PMF–PMF analysis and extending the analysis from six factors
to seven factors, an extra row in the **FKEY** matrix was added to pull all of the
^{1}*G*_{7} contributions to ^{2}*F*_{7} to zero in the solution (Fig. S1).
The same **FKEY** matrix of *fkey*_{1} and 0 values was used, but this time it
was augmented with a seventh row of *fkey*_{2} and zero values. In this case,
the *fkey*_{2} values were set to a value of 20.

The same six-factor solution is obtained with the additional seventh factor (Figs. 4 and S3), and, as expected, this seventh factor was a nucleation factor. It was suspected that in the six-factor solution, the nucleation factor was combined with the fuel oil factor. This does not suggest any link between the nucleation and fuel oil factor other than that there was an insufficient number of factors within the model for the two to factorize out of the data, giving the fuel oil NSD profile a more reasonable modal peak between 50 and 60 nm rather than 20, 30 and 60 nm.

Beddows et al. (2015) applied a one-step analysis to three different
datasets: PM_{10}-only; NSD-only and PM_{10}–NSD. The analyses of the
PM_{10}-only and NSD-only – both with homogeneous units – produced
quantitative time series **G**. This was unlike the analysis of the
PM_{10}–NSD with heterogeneous units, which could not apportion its
five
factors using **G** but was able to factorize out a nucleation factor from the
data, seen also in the four sources in the PMF solution for the NSD-only data.
A PM_{10}-only seven-factor solution did not reveal this factor,
presumably because the mass associated with nucleation-mode particles is too
small to affect composition significantly. Furthermore, fuel oil was not
factorized out of the PM_{10}–NSD data and was more likely divided
across all five factors.

Another interesting observation is that although only four factors were derived
from the PMF analysis of NSD-alone (diffuse urban; secondary; traffic and
nucleation), when extra information is included from the PMF analysis of the
PM_{10} data, more information can be extracted from the PMF analysis of
the NSD data in the form of the marine, fuel oil and
NET and crustal factors. The nucleation factor is only revealed when performing a regression
between the NSD size bins and the **G** scores of the PM_{10} PMF analysis,
which leads to increasing the factor number from six to seven and in turn yields the
nucleation profile. It is also reassuring that the bivariate plots for the
seven factors (discussed in the next section) correspond to the bivariate plots
given in Beddows et al. (2015). Also note that there is no reason why any
further investigation might not explore this using more than seven factors. In fact
the nucleation factor appears at first sight to be multimodal. However, we
restricted our analysis to seven factors, considering it complete in terms of
identifying the sources obtained by Beddows et al. (2015).

The original PMF was carried out on daily PM_{10} data, and in order to
make diurnal and bivariate plots, a higher time resolution is desirable. It
is assumed that the factors derived in the hourly NSD data are the same as
those derived from the daily averaged data; i.e. the factors are conserved
when averaging the data from hourly to daily data before PMF analysis. Then
the hourly NSD data can be fit with the PMF profiles derived from the daily
data (see Sect. 2.4). Figure 5 shows the resulting diurnal profiles. The
diurnal trends of the parameter *c*_{k} (Eq. 6), required to fit the seven
daily NSD factors to the hourly NSD data, are shown. These have been scaled
to PN (measured in cm^{−3}) using the integral of the NSD (Eq. 7).
The nucleation factor diurnal trend behaves as expected, rising to a maximum
during the day and then falling back down to a minimum at night. This
corresponds to the intensity of the sun during the day and the increased
likelihood of nucleation on clean days when there is sufficient precursor
material to form particles with a low particle condensation sink. The marine
factor is also high during the day, presumably due to higher wind speeds.
Diffuse urban, NET and crustal and traffic factors all follow a trend which is
synchronized to the daily cycle of anthropogenic activity and traffic as
influenced by greater atmospheric stability at night. The secondary factor
shows a small diurnal range. Fuel oil is highest during the evening and
night and may correspond to home heating rather than shipping emissions. The
particle size distributions associated with the marine and
NET and crustal sources are of limited value as these sources are dominated by coarse
particles, beyond the range of the SMPS data, although there is a sharp
increase in the volume of the particles above 0.5 µm in the marine
factor. As pointed out in Beddows et al. (2015), the marine factor is
identified by its chemical profile of sodium and chloride and is accompanied
by an aged nucleation mode at around 30 nm. This can be either viewed simply
as clean marine air being “polluted” by traffic emission and/or as the
consequence of nucleation occurring over at city in clean maritime air masses
(Brines et al., 2015). The key point here is that the factors derived in this
work are comparable to those factorized in Beddows et al. (2015) using the
combined dataset, and the advantage of the two-step approach is that now we
have quantified hourly time series **G**.

The hourly contributions are aggregated into daily values and plotted as bivariate plots in Fig. 5 to assist comparison with the daily plots in Beddows et al. (2015). In that work, the same PMF analysis of the NSD data yielded four factors which are named identically to those in the bivariate plots. The similarity of both of the polar and annular plots for each of the six factors supports our previous factor identification. The secondary and diffuse urban factors are background sources with strongest contributions in the evening and morning. Traffic is strongest for all wind speeds from the east, which makes sense since North Kensington is to the west of the city centre of London where traffic is expected to be most dense. Nucleation is also seen to be strongest for wind from the west, which is expected to be cleaner and have a lower condensation sink. NET and crustal and fuel oil factors are similar to the diffuse urban factor, suggesting a similar predominant source location in the centre of London. The marine factor is observed to be strongest for elevated wind speeds for all wind directions, which is consistent with the expected strong contribution for all high wind speeds from the south-west, as observed in the daily polar plots in Beddows et al. (2015).

The nucleation factor was extracted from the two-step PMF–PMF analysis, which
included pulling the
^{1}*G*_{1}–^{1}*G*_{6} values to zero of factor
^{2}*F*_{7}. It might be reasonable to suggest that if the
two-step PMF–PMF analysis is repeated and the order of analysis of PM_{10}
and NSD datasets reversed that it would be possible to derive the chemical
conditions within the atmosphere which were conducive to nucleation. For
this, the time series of the four NSD factors (^{1}*G*_{1}–^{1}*G*_{4})
reported in Beddows et al. (2015) were combined with the PM_{10} data. We
again assume that the first PMF step has been carried out and that we are
satisfied with how the final solution represents the urban environment of
the receptor site and that there are no rotational ambiguities. We then
carry out the second step PMF analysis on the 34×591 input matrix
([^{1}*G*_{1}…^{1}*G*_{4}], **PM**_{10}[*P*** M**,

Ideally, the chemical data would be limited to the composition of the
particles in the same size range as the SMPS data. However, since we are
using the PM_{10} composition data, we can at best describe the composition
of the aerosol which accompanied each factor (Fig. S4). For the NSD
secondary factor, with its strongest contribution (indicated by the explained
variation) ∼400 nm, we have a strong contribution to
PM_{10} and PM_{2.5} together with nitrate, sulfate and ammonium.
The diffuse urban factor, with its strongest contribution at 100 nm, is accompanied by
contributions from elemental carbon and wood smoke, indicative of traffic and
recreational wood burning. There are also contributions from barium,
chromium, iron, molybdenum, antimony and vanadium, all indicative of
non-exhaust traffic emissions and the burning of fuel oil. Similarly, the
traffic factor has a modal diameter of roughly 30 nm, which is indicative of
exhaust emissions, and this is accompanied by contributions to aluminium,
barium, calcium, copper, iron, manganese, titanium and various other metals
attributed to vehicles, albeit from tyre or brake wear or resuspension.

The nucleation factor, with its peak ∼20 nm, was associated
with marine air, as indicated by the strong contributions to Na, Cl and Mg
(Fig. S4). There are also traces of V, Cr and Ni and a high contribution to
PM_{10} mass, which are all associated with marine air. This is explained
by an association with the south-westerly wind sector, which brings strong
winds and marine aerosol rather than reflecting the composition of the
nucleation particles themselves. Marine air is considered to provide the
conditions required of an air mass conducive to nucleation, i.e. cleaner air
with particles with a low condensation sink. As these air masses pass over
the land and eventually into London, anthropogenic precursor gases are added
to this air, which then nucleate particles seen at the receptor site as a
nucleation mode. This also goes some way to explain the earlier observation
of aged nucleation particles observed in the marine factor in Fig. S3.
There are also strong contributions to vanadium, which is most likely from an
unresolved fuel oil source being mixed into the marine and diffuse urban factors.

4 Conclusions

Back to toptop
A two-step PMF analysis method is presented, whereby existing PMF profiles
can be extended to incorporate auxiliary data concurrently measured and with different units. This is exemplified using PM_{10} and NSD data.

When analysing PM_{10} composition data, the inclusion of auxiliary data such as
meteorological, gas and particle number data has proved to give a clearer
separation of factors. However, for a successful output, there must be no
rotational ambiguity in either the PM_{10} data or in the auxiliary data.
In the ideal case, the individually computed factors **G**(**X**), **G**(**Z**) and
**G**(**X**,**Z**) need to be similar if the joint model is to be successful and not produce
large residuals and hence too large a *Q* value. In the best case, the total
weight of the PM_{10} data can be set higher than the auxiliary data so
that the PM_{10} data drive the analysis. In this work, we present an
alternative method called the two-step PMF method. In the first step the
PM_{10} data are PMF-analysed using the standard approach without the
inclusion of additional data. An appropriate solution is derived using the
methods described in the literature in order to give an initial separation
of source factors. The time series **G** (and errors) of the PM_{10} solution
are then taken forward into the second step, where they are combined with the
NSD data. The PMF analysis is then repeated using the combined and mixed
unit **G** time series and NSD dataset. In order to ensure that unique factors
are obtained for the **G** scores, **FKEY** is used to pull off-diagonal values to
zero, thus driving the NSD data. This ensures that the NSD factors are
specific to the PM_{10} solution and the PM_{10} analysis is not
affected by any rotational ambiguity of the NSD data. For our demonstration
using the Beddows et al. (2015) analysis, this results in six PM_{10}
factors whose time series are not only apportioned in mass, but the source
profiles are identified for the NSD data. Comparisons of the factor
profiles, diurnal trends and bivariate plots to those of Beddows et al. (2015) show
that this technique produces one solution linking the two separate solutions
for PM_{10} and NSD datasets together. This generates confidence that
the NSD and PM_{10} factors ascribed to one source are in fact
attributable to that same source.

Hence, the process starts with a dataset which produces a solution which is
sensitive to mass, but the factors more sensitive to number can be accessed
using a second step. Furthermore, by exploring a higher number of factors,
NSD factors which are insensitive to PM_{10} mass can be identified as in
the case of the nucleation factor. This information can also be extracted
using a linear regression, PMF–LR, whereby the size bins of the NSD data are
regressed against the PM_{10} PMF time series. For this dataset, the
nucleation factor profile is identified as an intercept within the fitted
model, leading to an increase in the number of PMF factors from six to seven.

Data availability

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Data availability.

Data supporting this publication are openly available from the UBIRA eData repository at https://doi.org/10.25500/edata.bham.00000306 (Beddows and Harrison, 2019).

Supplement

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Supplement.

The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-4863-2019-supplement.

Author contributions

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Author contributions.

DCSB conceived the two-step method, ran the PMF and other data analyses and wrote the first draft of the paper. RMH provided constructive criticism, contributed to the data interpretation and wrote sections of the final paper.

Competing interests

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Competing interests.

The authors declare that they have no conflict of interest.

Acknowledgements

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Acknowledgements.

The collection of data used for this paper was funded by the Natural Environment Research Council Clean Air for London (ClearFlo) Programme through grant number NE/H003142/1. The contributions of Gary Fuller and David Green (King's College, London) are gratefully acknowledged. The National Centre for Atmospheric Science is funded by the UK Natural Environment Research Council (grant number R8/H12/83/011). Figures were produced using CRAN R and Openair (R Core Team, 2016; Carslaw and Ropkins, 2012).

The authors are grateful to three anonymous reviewers and to
Pentti Paatero for their detailed and constructive comments on the first
version of this paper.

Edited by: Ari Laaksonen

Reviewed by: Pentti Paatero and three anonymous referees

References

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Short summary

Airborne particles are a cause of illness and premature death. Cost-effective control of particles in the atmosphere depends upon a reliable knowledge of their sources. This paper proposes and tests a new method for attributing particles quantitatively to the sources responsible for their emission or atmospheric formation.

Airborne particles are a cause of illness and premature death. Cost-effective control of...

Atmospheric Chemistry and Physics

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