**Research article**
09 Jun 2020

**Research article** | 09 Jun 2020

# Comparison of dimension reduction techniques in the analysis of mass spectrometry data

Sini Isokääntä Eetu Kari Angela Buchholz Liqing Hao Siegfried Schobesberger Annele Virtanen and Santtu Mikkonen

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**Sini Isokääntä et al.**Sini Isokääntä Eetu Kari Angela Buchholz Liqing Hao Siegfried Schobesberger Annele Virtanen and Santtu Mikkonen

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^{1}Department of Applied Physics, University of Eastern Finland, Kuopio, 70210, Finland^{2}Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio, 70210, Finland^{a}currently at: Neste Oyj, Espoo, 02150, Finland

^{1}Department of Applied Physics, University of Eastern Finland, Kuopio, 70210, Finland^{2}Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio, 70210, Finland^{a}currently at: Neste Oyj, Espoo, 02150, Finland

**Correspondence**: Sini Isokääntä (sini.isokaanta@uef.fi)

**Correspondence**: Sini Isokääntä (sini.isokaanta@uef.fi)

Received: 25 Oct 2019 – Discussion started: 15 Nov 2019 – Revised: 28 Mar 2020 – Accepted: 27 Apr 2020 – Published: 09 Jun 2020

Online analysis with mass spectrometers produces complex
data sets, consisting of mass spectra with a large number of chemical
compounds (ions). Statistical dimension reduction techniques (SDRTs) are
able to condense complex data sets into a more compact form while preserving
the information included in the original observations. The general principle
of these techniques is to investigate the underlying dependencies of the
measured variables by combining variables with similar characteristics into
distinct groups, called factors or components. Currently, positive matrix
factorization (PMF) is the most commonly exploited SDRT across a range of
atmospheric studies, in particular for source apportionment. In this study,
we used five different SDRTs in analysing mass spectral data from complex gas-
and particle-phase measurements during a laboratory experiment investigating
the interactions of gasoline car exhaust and *α*-pinene. Specifically,
we used four factor analysis techniques, namely principal component analysis (PCA),
PMF, exploratory factor analysis (EFA) and
non-negative matrix factorization (NMF), as well as one clustering
technique, partitioning around medoids (PAM).

All SDRTs were able to resolve four to five factors from the gas-phase measurements,
including an *α*-pinene precursor factor, two to three oxidation product
factors, and a background or car exhaust precursor factor. NMF and PMF provided
an additional oxidation product factor, which was not found by other SDRTs.
The results from EFA and PCA were similar after applying oblique rotations.
For the particle-phase measurements, four factors were discovered with NMF:
one primary factor, a mixed-LVOOA factor and two *α*-pinene secondary-organic-aerosol-derived (SOA-derived)
factors. PMF was able to separate two factors: semi-volatile oxygenated organic aerosol (SVOOA) and low-volatility oxygenated organic aerosol (LVOOA). PAM
was not able to resolve interpretable clusters due to general limitations of
clustering methods, as the high degree of fragmentation taking place in the
aerosol mass spectrometer (AMS) causes different compounds formed at different stages in the experiment
to be detected at the same variable. However, when preliminary analysis is
needed, or isomers and mixed sources are not expected, cluster analysis may
be a useful tool, as the results are simpler and thus easier to interpret. In
the factor analysis techniques, any single ion generally contributes to
multiple factors, although EFA and PCA try to minimize this spread.

Our analysis shows that different SDRTs put emphasis on different parts of the data, and with only one technique, some interesting data properties may still stay undiscovered. Thus, validation of the acquired results, either by comparing between different SDRTs or applying one technique multiple times (e.g. by resampling the data or giving different starting values for iterative algorithms), is important, as it may protect the user from dismissing unexpected results as “unphysical”.

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Online measurements with mass spectrometers produce highly complex data comprised of hundreds of detected ions. A high-resolution mass spectrometer enables identification of the elemental composition of these ions, revealing chemical composition information about the sample. However, even with the highest resolution, mass spectrometers are not able to resolve isomers. Instead the large number of identified ions can make data interpretation challenging due to the sheer number of variables. Different statistical dimension reduction techniques (SDRTs) were developed to compress the information from complex composition data into a small number of factors, which can be further interpreted by their physical or chemical properties. In other words, these methods are used to understand the underlying relationships of the measured variables (i.e. detected ions). Principal component analysis (PCA), which was introduced already at the beginning of the 20th century by Karl Pearson, is probably the first SDRT, even if the modern formulation of PCA was introduced decades later (Pearson, 1901; Hotelling, 1933). In atmospheric studies, the most exploited method, especially in the analysis of long time series of aerosol mass spectrometer (AMS) data, is positive matrix factorization (PMF), developed in the mid-1990s (Paatero and Tapper, 1994). Other SDRTs that are widely applied in different fields of science for the analysis of multivariate data include PCA and exploratory factor analysis (EFA), which are popular especially in medical and psychological studies (Raskin and Terry, 1988; Fabrigar et al., 1999). In atmospheric studies, the latter methods have not gained widespread popularity, but a few examples still exist. Customized PCA was applied to organic aerosol data collected from Pittsburgh in 2002 (Zhang et al., 2005), and a more traditional version of PCA was used to analyse chemical-ionization-reaction time-of-flight mass spectrometer (CIR-ToF-MS) and compact time-of-flight aerosol mass spectrometer (cToF-AMS) data acquired in smog chamber studies during several measurement campaigns (Wyche et al., 2015). Additionally, EFA and PCA have been applied in several source apportionment studied in the environmental science fields (Pekey et al., 2005; Sofowote et al., 2008), and a recent study on plant volatile organic compound (VOC) emissions applied EFA to separate effects of herbivory-induced stress from the natural diurnal cycle of the plants (Kari et al., 2019a). Very much like PMF, non-negative matrix factorization (NMF) is one of the most used methods in the analysis of DNA microarrays and metagenes in computational biology (Brunet et al., 2004; Devarajan, 2008), but NMF has also been applied in atmospheric studies (Chen et al., 2013; Malley et al., 2014).

Comparisons between the performance of some of the SDRTs presented in this
paper already exist, but due to the popularity of PMF, other methods are
not applied as widely in atmospheric studies. As EFA and PCA are rather
similar methods, and they have also existed for many decades, multiple
comparisons between them exist, especially in the medical and psychological
research fields (see e.g. Kim, 2008). The introduction of PMF has
also inspired comparison studies between PMF and EFA (Huang et
al., 1999), and PMF and PCA were already briefly compared upon publication
of PMF, as the positivity constraints were presented as an advantage over PCA
(Paatero and Tapper, 1994). Although PMF has been shown to be a very
powerful tool in the analysis of environmental AMS data from field studies
(e.g. Ulbrich et al., 2009; Zhang et al., 2011; Hao et al., 2014,
Chakraborty et al., 2015), it has not been applied as widely in
laboratory and smog chamber research (Corbin et al., 2015; Kortelainen et
al., 2015; Tiitta et al., 2016; Koss et al., 2020). The latest studies have
applied PMF also to chemical-ionization mass spectrometry (CIMS) data (Yan et
al., 2016; Massoli et al., 2018; Koss et al., 2020), which is able to resolve
more oxidized compounds. The special conditions in lab experiments (sharp
change at the beginning of experiments, e.g. switching on UV lights)
present an additional test scenario, as PMF has been mostly used for field
measurement data sets where the main focus is often on the long-term trends,
and real changes in factors are expected to be more subtle than, for example, the
variations in the noise in the data. In addition, field measurements
commonly yield very large data sets, including thousands of time points,
whereas laboratory experiments may be much shorter. Recently, scientists
from atmospheric studies have been motivated to test and adapt other
techniques and algorithms to reduce the dimensionalities of their data, in
addition to the more “traditional” version of PMF introduced in the 1990s. For
example, Rosati et al. (2019) introduced a correlation-based technique for
multivariate curve analysis (similar to NMF) in their analysis of *α*-pinene ozonolysis. Cluster analysis has been applied in a few studies.
Wyche et al. (2015) applied hierarchical cluster analysis (HCA) to
investigate the relationships between terpene and mesocosm systems. In the
study from Äijälä et al. (2017), they combined PMF and
*k*-means clustering to classify and extract the characteristics of organic
components. In addition, a very recent paper by Koss et al. (2020) also
compared the dimension reduction abilities of HCA and gamma kinetic
parametrization to PMF when studying mass-spectrometric data sets.

In our study we chose a set of SDRTs with fundamental differences. For example, PMF usually splits one ion into several factors, whereas most clustering techniques assign one ion to one cluster only. If isomers with the same chemical composition but different functionality are expected, splitting ions into several factors might be preferred. On the other hand, clustering might be more suitable for a more simplified or preliminary approach (as it is computationally less demanding) or when the chemical compounds in the data are already known or if strict division between variables is preferred. In this study, we validate the usability of the chosen SDRTs in laboratory studies for two different mass spectrometer devices, PTR-ToF-MS (proton-transfer-reaction time-of-flight mass spectrometry) and AMS (gas- and particle-phase composition), and different data sizes due to different measuring periods and time resolutions. Further, we examine the performance of the SDRTs when the data include large and rapid changes in the composition.

The data sets investigated in this study were gathered during experiments
conducted as part of the TRACA campaign at the University of Eastern
Finland. A detailed description of the experimental set-up and reaction
conditions can be found in Kari et al. (2019b).
Briefly, the measurement set-up consisted of a modern gasoline car (VW Golf,
1.2 TSI, EUR 6 classification) which was driven at a constant load of 80 km h^{−1} after a warm-up period with its front tiers in a
dynamometer. The exhaust was diluted using a two-stage dilution system and
fed into a 29 m^{3} collapsible environmental PTFE ILMARI chamber
(Leskinen et al., 2015). For the experiment investigated in
this study, *α*-pinene (∼1 µL, corresponding to
5 ppbv) was injected into the chamber to resemble biogenic VOCs in a typical
suburban area in Finland. Atmospherically relevant conditions were simulated
by adding O_{3} to convert extra NO from vehicle emissions to NO_{2}
and adding more NO_{2} to the chamber if needed. With these additions,
atmospherically relevant VOC-to-NO_{x} (∼7.4 ppbC ppb^{−1}) and
NO_{2}-to-NO ratios were achieved to resemble the typical observed level
in suburban areas (National Research Council, 1991). Chamber
temperature was held constant at ∼20 ^{∘}C, and relative
humidity was adjusted to ∼50 % before the start of the experiment.
Blacklight (UV-A) lamps with a light spectrum centred at 340 nm were used to
form OH radicals from the photolysis of H_{2}O_{2}. The start of
photo-oxidation by turning on the lamps is defined as experiment time 0 in
the following. Vertical dashed lines in the figures indicate *α*-pinene injection and the start of photo-oxidation. A short
summary of the experimental conditions and the behaviour of the injected
*α*-pinene as a time series is shown in the Supplement (Sect. S1)

VOCs in the gas phase were monitored with a
proton-transfer-reaction time-of-flight mass spectrometer (PTR-TOF-MS 8000,
IONICON Analytik, Austria, hereafter referred to as PTR-MS). Typical
concentrations for a few example VOCs midway through the experiment were 2 µm m^{−3} for toluene, 0.2 µm m^{−3} for TMB
(trimethylbenzene) and 1.7 µm m^{−3} for C_{4}H_{4}O_{3}.
The detailed set-up, calibration procedure and data analysis of the used high-resolution PTR-MS have been explicitly presented in
Kari et al. (2019b). In the campaign, the high mass resolution of the instrument (*>*5000) enabled the determination
of the elemental compositions of measured VOCs. The instrumental setting
intended to minimize the fragmentation of some compounds so that the
quantitation of the VOCs was possible. The chemical composition of the
particle phase of the formed SOA was monitored with a soot particle aerosol
mass spectrometer (SP-AMS; Aerodyne Research Inc., USA, hereafter referred
to only as AMS; Onasch et al., 2012). In brief, the
SP-AMS was operated at 5 min saving cycles, alternatively switching between
the electron ionization (EI) mode and SP mode. In EI mode, the V-mode mass
spectra were processed to determine the aerosol mass concentration and size
distribution. The mass resolution in the mode reaches ∼2000.
The SP-mode mass spectra were used to obtain the black carbon concentration. As
the used chamber was a collapsible bag, the volume of the chamber decreased
over time due to the air taken by the instruments. For the experiment
investigated in this study, both gas- and particle-phase data were analysed
with all SDRTs (Sect. 4.1 and
4.2). However, due to the small data size for the
particle phase, not all SDRTs were applicable.

In contrast to the PTR-MS data used in Kari et al. (2019b), we did not apply baseline correction to the data. Overestimation of
the baseline correction may cause some of the ions with low signal intensity
to have negative “concentration”, which is not physically interpretable.
Also, negative data values cause problems for some SDRTs, as, for example, PMF and
NMF need a positive input data matrix. In addition, SDRTs should be able to
separate background ions into their own factor, meaning that it is not mandatory
to remove them before applying SDRTs. This approach will cause some bias to
the absolute concentrations of the ions and resulting factors, but as we are
more interested in the general division of the ions to different factors,
and their behaviour as a time series when comparing the SDRTs, it does not
significantly affect our interpretation of the results. All recommended
corrections (including baseline subtraction; Ulbrich et al.,
2009) were applied to the AMS data. As the processed AMS data are always the
difference between the measured signal with and without particles, negative
values are possible if the particle-free background was elevated. In the
investigated data set, only a few data points exhibited slightly negative
values. Thus, it was possible to set these data points to a very small
positive value ($\mathrm{1}\times {\mathrm{10}}^{-\mathrm{9}}$) to enable the analysis with SDRT
methods without a significant positive bias in the data. In addition, as the
main focus of our study was to compare the performance of the different
SDRTs with different types of mass spectra, instead of detailed analysis of
the chamber experiment, we have also included the pre-mixing period during
the *α*-pinene (i.e. *t**<*0) injection into our analysis.

## 3.1 Factorization techniques

### 3.1.1 Principal component analysis (PCA)

PCA is a statistical procedure where the variables are transformed into a
new coordinate system. The first principal component accounts for the most
variance of the observed data, and each succeeding component then has the
largest possible variance, with the limitation that the component must
preserve orthogonality to the preceding component. In other words, PCA seeks
correlated variables and attempts to combine them into a set of uncorrelated
variables, i.e. principal components, which include as much of
the information that was present in the original observations as possible (Wold et
al., 1987; Morrison, 2005; Rencher and Christensen, 2012; Tabachnick and
Fidell, 2014). The principal components are often described by a group of
linear equations, where, for example, the first principal component
*c*_{1} (table of the used mathematical symbols and
notations is presented in the Appendix) can be presented as

*a*_{1j} (*j*=1, …, *m*) are normalized
characteristic vector elements assigned to the specific characteristic root
of the correlation matrix **S**, and *y*_{i}
(*i*=1, …, *m*) are the centred variables (Morrison, 2005;
Rencher and Christensen, 2012). As the responses in the first principal
component have the largest sample variance,
${s}_{{y}_{\mathrm{1}}}^{\mathrm{2}}$, for all normalized coefficient vectors, the following
applies:

where ${\mathit{a}}_{\mathrm{1}}^{\mathrm{T}}{\mathit{a}}_{\mathrm{1}}=\mathrm{1}$
(Morrison, 2005). The number of principal components is equal to the
number of variables (*m*) in the data minus 1, and *p* components are selected
to interpret the data. It should be noted, however, that Eq. (1) describes
the theory behind the PCA model, not the actual calculation process, which
is described below. Thus, for example, the centring of variables is not
required. To find the principal components, either eigenvalue decomposition
(EVD) or singular-value decomposition (SVD) can be used. Mathematical
formulation of EVD and SVD can be found from Golub and Van Loan (1996).
EVD is applied to the correlation or covariance matrix **S**, whereas
SVD can be applied also to the observed data matrix directly. Often, due to
this difference, SVD is considered to be its own method instead of being described
as a variation of PCA. Here, however, it is referred to as SVD-PCA. In
our study we applied EVD-PCA to the correlation matrix (calculated from
unscaled data matrix), and SVD-PCA was applied to the data matrix without and
with the scaling (centred and scaled by their standard deviations). In
addition, the acquired eigenvectors and vectors corresponding to the
singular values were scaled by the square root of the eigenvalues or singular
values
to produce loading values (i.e. contribution of a variable to a
component) more similar to those obtained in exploratory factor analysis
(EFA). The PCA analysis was performed in R statistical software with the
addition of the “psych” package (Revelle, 2018; R Core Team, 2019).

The acquired principal components can be rotated to enhance the interpretability of the components. Rotations can be performed in orthogonal or oblique manner, where the orthogonal methods preserve the orthogonality of the components but oblique methods allow some correlation. However, rotation of the principal components does not produce another set of principal components but merely components. By original definition (Hotelling, 1933), only presenting an unrotated solution is considered to be principal component analysis, but later formulations allow also orthogonal rotations (Wold et al., 1987). Though there are no computational restrictions for applying oblique rotation on components, the restriction is only definitional, as the original principal components were presented as orthogonal transformation. In any case, rotated solutions do not fulfil the assumption of principal ordering of components. In this study, orthogonal varimax rotation, which maximizes the squared correlations between the variables, and oblique oblimin rotation were used to increase large loading values and suppress the small ones to simplify the interpretation (Kaiser, 1958; Harman, 1976).

Multiple ways exist to calculate the PCA component scores (i.e. component time series). In general, the components scores are calculated as

where **X** includes the analysed variables (often centred and scaled
by their standard deviations), and **B** is the component coefficient
weight matrix (Comrey, 1973). One simple way to calculate the component
scores is to use the component loading values directly as weights. This
approach is often referred to as a sum-score method. Depending on the
application, the loadings can be used as they are, they can be dichotomized
(1 for loaded and 0 for not loaded), or they can be used as they are, but
suppressing the low values by some threshold limit. We applied the last
method here, as the dichotomized loadings (i.e. one ion stems only from one
source or source process) seldom describe true physical conditions in
nature. In a case when the data items are in the same unit, the data may be
used without standardization (Comrey, 1973). As this is the case in all
of our respective data sets (concentration units for PTR-MS data are in ppb and
µg m^{−3} for AMS), the scores are calculated without standardizing
the data matrix to achieve more interpretable component time series. This
applies for both EVD-PCA and SVD-PCA component scores. Very small loading
values (absolute value less than 0.3) are suppressed to zero to enhance the
separation of ions between the components. The limit of 0.3 was selected, as
this is often given as a reference value for insignificant loadings (see
e.g. Field, 2013; Izquierdo et al., 2014). The components
from PCA in the results sections are labelled as CO.

### 3.1.2 Exploratory factor analysis (EFA)

Similar to the EVD-PCA, which takes advantage of the correlations between
the original variables, EFA generates the factors, trying to explain the
correlation between the measured variables (Rencher and Christensen,
2012). For a data matrix **X** with *m* variables (ions) and *n*
observations (time points), the EFA model expresses each variable
*y*_{i} (*i*=1, 2, …, *m*) as a linear
combination of latent factors *f*_{j} (*j*=1,
2, …, *p*), where *p* is the selected number of factors. So, for
example, for the variable *y*_{1} the EFA model can be
presented as

where the residual term *ε*_{1} accounts for the unique variance,
*m* is the number of factors, ${\stackrel{\mathrm{\u203e}}{y}}_{\mathrm{1}}$ is the mean of the variable
*y*_{1}, and *λ*_{ij} is the elements from
the loading matrix ** λ** and serves as weights to show how each
factor

*f*_{j}contributes for each variable

*y*_{i}(Morrison, 2005; Rencher and Christensen, 2012). As for the PCA explained in the previous section, Eq. (4) here describes the form of an EFA model based on literature, not the direct calculation in the algorithm. Thus, no scaling (what, for example, the subtraction of the mean ${\stackrel{\mathrm{\u203e}}{y}}_{\mathrm{1}}$ from

*y*_{1}in Eq. 4 essentially is) is applied here.

Different methods exist to calculate the factorization in EFA. In this
study, principal axis factoring (hereafter pa-EFA) and maximum likelihood
factor analysis (hereafter ml-EFA) were selected due to their suitability
for our data (explained in more detail in Sect. 3.3). In pa-EFA, the
function *F*_{1} that can be minimized can be presented as

where **S**_{ij} is an element of the observed correlation matrix **S**
and **R**_{ij} is the element of the implied correlation matrix **R**.
Maximum likelihood factor analysis, on the other hand, minimizes the
function *F*_{2}:

where the variances *u*_{i} and *u*_{j} for the variables *i* and *j* are
considered. In other words, ml-EFA assigns less weight to the weaker
correlations between the variables (de Winter and Dodou, 2012; Rencher
and Christensen, 2012; Tabachnick and Fidell, 2014). In contrast to PCA,
rotations are a recommended practice before interpreting the results in EFA,
and the unrotated factor matrices are rarely useful (Osborne, 2014).
Oblique oblimin rotation was used to rotate the EFA factors. Orthogonal
varimax rotation was also tested, but as the orthogonality assumption for
the factors is rather stringent for this type of chemical data, and it
produced uninterpretable factors, those results are omitted. EFA was run in
R statistical software with an addition of the “psych” package (Revelle,
2018; R Core Team, 2019), and the factor scores were calculated as described
above for PCA. The factors from EFA in the results are labelled as FE.

### 3.1.3 Positive matrix factorization (PMF)

PMF is a bilinear model and can be presented as

where the original data matrix **X** is approximated with matrices
**G** and **F**, and **E** is the residual matrix, i.e. the
difference between observations in **X** and the approximation
**GF**. After the factorization rank is defined by the user, Eq. (7) is
solved iteratively in the least-squares sense. The values of **G** and
**F** are constrained to be positive, and the object function *Q* is
minimized (Paatero, 1997):

The term *μ*_{ij} in Eq. (8) includes the measurement
uncertainties for the observation matrix **X** at time point *i* for ion
*j*. Originally, ** μ** was calculated as the standard deviations of

**X**, but other error types have also been used (Paatero and Tapper, 1994; Paatero, 1997; Yan et al., 2016). As is apparent from Eq. (8), the measurement errors (

*μ*_{ij}) act as weighting values for the data matrix. Thus, the chosen error scheme can have a significant impact on the behaviour of

*Q*.

To test this, different error schemes were investigated. The standard deviation values alone were not used as an error, as the data include fast concentration changes due to the sudden ignition of photo-oxidation, which causes the standard deviations to be systematically too large. But as a reference, the standard error of the mean (the standard deviations of the ion traces divided by the square root of the number of observations, i.e. length of the ion time series) was used as an error for both PTR-MS and AMS data. It considers that measurements with fewer observations contain more uncertainty. These error values are constant for each ion throughout the time series and do not change with signal intensity. This type of error is labelled here as static error. In addition, a minimum error estimate was applied, as suggested by Ulbrich et al. (2009). Determination of the minimum error for PTR-MS is presented in the Sect. S2.1 and for AMS in Sect. S2.2.

Additionally, an error following the changes in the ion concentration was
constructed for PTR-MS data by applying a local polynomial regression to
smooth the ion time series (R-function loess; Cleveland et al.,
1992). From the regression fit the residuals were calculated and the running
standard deviation from the residuals was used as an error. Again, the
minimum error was applied here. This error is referred to hereafter as signal
following error. For AMS, we also applied a standard error that is
frequently used by the AMS community. The standard AMS error consists of
the minimum error-related duty cycle of the instrument and counting statistics
following the Poisson distribution (Allan et al., 2003; Ulbrich et al.,
2009). Shortly, the standard AMS error for signal *I* can be formulated as

where *α* is an empirically determined constant (here *α*=1.2, generated by the AMS analysis software PIKA; http://cires1.colorado.edu/jimenez-group/ToFAMSResources/ToFSoftware/index.html, last access: 9 September 2019), *I*_{O} and *I*_{C} are the raw signal of the ion
of particle beam (ions per second) for the chopper at the open and closed position,
respectively, and *t*_{s} is the sampling time at a particular *m*∕*z* channel
(s).

Examples of the used error values for PTR-MS and AMS data are presented in the Supplement at the end of Sect. S2.1 and S2.2, and the signal-to-noise ratios for different error matrices are reported in Sect. S2.3. In contrast to the suggested best practice (Paatero and Hopke, 2003), we did not downweight any ions in our data sets. This approach was used in order to give each SDRT an equal starting point for the analysis, as, for example, for NMF or PCA similar downweighting, this is not possible because we do not have any error estimates to calculate the signal-to-noise ratios in a similar manner. However, to avoid misguiding the reader to omit recommended data pre-processing practice for PMF, we also tested PMF with downweighting. This, as expected, did not change our results significantly, but we acknowledge that it should indeed be applied if aiming for a more detailed chemical interpretation of the PMF factors.

Often, constraining the values to be positive is not enough to produce a
unique PMF solution for Eq. (7). This can be assessed by applying rotations,
as in EFA and PCA. The rotations in PMF are controlled through the
*f*_{peak} parameter in which the changes produce new **G** and **F**
matrices by holding the *Q* value approximately constant (Paatero et
al., 2002). In this study, rotations with *f*_{peak} = (−1, −0.5, 0, 0.5, 1)
were tested. PMF analyses were conducted in Igor Pro 7 (WaveMetrics, Inc.,
Portland, Oregon) with the PMF Evaluation Tool (Ulbrich et al.,
2009). The acquired results were further processed in R statistical software
(R Core Team, 2019). The factors from PMF are labelled as FP in the
results.

### 3.1.4 Non-negative matrix factorization (NMF)

Non-negative matrix factorization was introduced to the wider public after
Lee and Seung presented their application of NMF to facial image database in
*Nature* (Lee and Seung, 1999). The method has since gained
popularity, and it has been used in various scientific fields,
e.g. in gene array analysis (Kim and Tidor, 2003; Brunet et al., 2004). As
in PMF, the NMF solution is constricted to positive values only to simplify
the interpretation of the results, and, in principle, both of these methods
attempt to solve the same bilinear equation. In contrast to PMF, the
algorithms in NMF do not require an error matrix as an input, and it makes
therefore no assumptions of the measurement error, so we present
NMF here as a separate method from PMF.

In general, the mathematical formulation of NMF is similar to the one presented for PMF in Eq. (7) and can be presented as

where **X** is the positive data matrix (*n*×*m*) and **W** and
**H** are the non-negative matrices from the factorization with sizes
*n*×*k* and *k*×*m*, respectively (Brunet et al., 2004). The value of
*k* is equivalent to the selected factorization rank *p*. Multiple algorithms to
calculate NMF exist (Lee and Seung, 2001). Here, we present results
from the method described by Lee and Seung (2001), and Brunet et al. (2004),
as this created the best fit to the data. The matrices **W** and
**H** are randomly initialized and are updated with the formula given
by Brunet et al. (2004):

and

The NMF analysis was run in R statistical software with the “NMF” package (Gaujoux and Seoighe, 2010; R Core Team, 2019). The factors from NMF are labelled as FN in the results.

### 3.1.5 Calculation of the contribution of an ion to a factor, component or cluster

From all these methods two factorization matrices (time series and factor
contribution) can be produced at the end. In PMF and NMF, both factorization
matrices are calculated simultaneously, whereas in EFA, PCA and partitioning around medoids (PAM) the
factor or component time series are calculated after the main algorithm. The
factor or component time series show the behaviour of each factor or component
during the experiment, while the contribution of the different variables to
each factor or component (factor or component scores or factor profiles) can be
interpreted as the chemical composition of each factor or component. To help
the reader visualize the similarities and differences in the results between
EFA, PCA, PMF and NMF in this paper, we calculated the “total factor
contribution” of each factor or component to each ion, i.e. how much each
factor or component contributes to the signal of a single ion. For PMF and NMF,
the values in the factorization matrices (**F** and **H**,
respectively) were extracted for each ion and scaled with the sum over all
factors for each ion. For EFA and PCA, the absolute values of the loadings
were calculated for each ion in each factor or component and then scaled by the
sum of all factor loadings. This approach allowed us to compare the division
of the ions in each factor or component between the different methods. However,
this type of approach conceals the information of the negative factor
loadings in EFA and PCA (which are included in the calculation of
factor or component time series as weights) and instead visualizes the general
contribution of an ion to a factor. Negative factor loadings may have
different interpretations. They may indicate that the compound has a
decreasing
effect on the factor; i.e. they act as a sink for the compounds with positive
loading in the same factor. In chamber experiments, negative loading may
also refer to a decreasing concentration of the compound participating in
chemical reactions if it acts as a precursor for other compounds in the same
factor. One example of this is benzene, detected (as ${\mathrm{C}}_{\mathrm{6}}{\mathrm{H}}_{\mathrm{7}}^{+}$)
by PTR-MS. When inspecting the original loading values from EFA, for
example, it has negative loading in FE1 (identified as later-forming and slowly forming
products) and positive loading in FE4 (identified as precursors from car
exhaust or background). As benzene originates from the car exhaust, it
contributes positively to FE4. However, as it oxidizes over the course of
the experiment (thus has decreasing concentration), it has a strong
correlation with oxidation products but appears negative in FE1, which
mostly includes those later-generation products.

## 3.2 Clustering methods

### Partitioning around medoids (PAM)

PAM, or *k* medoids, is a clustering algorithm in
which the data set is broken into groups in which the objects or observations
share similar properties in a way that objects in a cluster are more similar
to each other than to the objects in other clusters. The PAM algorithm is
fully described elsewhere (Kaufman and Rousseeuw, 1990). Briefly, PAM
minimizes the distances between the points and the centre of the cluster
(i.e. the medoid), which, in turn, describes the characteristics of the
cluster. The distance matrix (often also referred to as dissimilarity
matrix) from the observed data can be calculated in many ways. Here, the
data were first standardized by subtracting the mean of each ion over the
time series and scaling each ion with the standard deviations of the ions.
Then, the Euclidean distances (Rencher and Christensen, 2012) were
calculated between the ions before providing the distance matrix for PAM.
The selection of suitable distance metrics can be challenging and depends on
the application and the data. For example, Äijälä et al. (2017) tested four different metrics in their study of pollution events. In
our study, also two other distance metrics were tested: the Manhattan distance
(e.g. Pandit and Gupta, 2011) and correlation-based distance metric.
The results, however, were similar to those acquired with Euclidean
distances and therefore not shown here. The clustering was performed in R
statistical software, applying the “factoextra” and “cluster” packages
(Kassambara and Mundt, 2017; Maechler et al., 2018; R Core
Team, 2019). Clusters are labelled as CL in the results.

Often clustering is applied to the observations in the data (e.g. samples, time points). Here, we applied the clustering to the variables instead to group similarly behaving chemical compounds together. This means that our calculated distance matrix provides the distances between the variables (i.e. ions), and the centre of the cluster is the “characteristic” ion for that specific cluster. The larger the distances, the “farther apart” the ions are, and ions with shorter distances should be assigned to the same cluster. There are several clustering methods especially meant for clustering of variables (Vigneau, 2016). The time series for clusters are calculated by summing the concentrations of the compounds in the specific cluster. The interpretation of the results from cluster analysis slightly differs from the interpretation of the results of the other SDRTs. Due to the nature of cluster analysis in general (except fuzzy clustering; see e.g. Kaufman and Rousseeuw, 1990), the variables (here ions) are strictly divided between the clusters, whereas for the other SDRTs presented in this study, one ion may have different weighting parameters for different factors or components. Depending on the aim of the study and the type of the data, this property of cluster analysis may be considered to be either an advantage or disadvantage. One obvious advantage of cluster analysis (or hard division techniques in general) is computational time, especially if analysing long ambient data sets. For laboratory measurements, this most likely is not an issue. Hard division techniques have also been shown to work efficiently for VOC measurements when distinguishing between different coffee types (espresso capsules), where strict separation between clusters is needed, as shown in Sánchez-López et al. (2014). For source apportionment studies, where one variable might originate from multiple sources, cluster analysis using the hard division technique is probably not as suitable as softer division techniques, which can assign one variable to multiple sources and factors.

## 3.3 Determining the number of factors, components or clusters

One of the most difficult tasks in dimension reduction is the choice for the new dimensions of the data. For EFA and PCA, multiple different methods determining the suitable factor and component number exist. However, these are often more guidelines than strict rules when handling measurement data, as the processes creating the compounds which were measured can be somewhat unpredictable at times. Additionally, as EFA and PCA were originally developed for normally distributed data, tests for determining the number of factors may be influenced if the criterion of normality is not met. Furthermore, the existing tests to investigate multivariate normality are often oversensitive, e.g. for outliers (Korkmaz et al., 2014), which may influence the results. The analysis results from EFA and PCA, however, can be reasonably interpreted despite the data distribution, as the normality of the data mainly enhances the outcome and is not stated as a strict requirement (Tabachnick and Fidell, 2014). In addition, the two calculation methods selected for EFA were used, as they are supposed to be more suitable for non-normally distributed data. ml-EFA is rather insensitive to changes in data distribution (Fuller and Hemmerle, 1966), whereas pa-EFA is actually suggested to be more efficient if the normality condition is not met (Fabrigar et al., 1999). In this study, the multivariate normality of the data was nonetheless investigated, and results are reported in the Supplement.

For EFA and EVD-PCA, we used the scree test, first introduced by Cattel (1966), the Kaiser criterion (Kaiser, 1960) and parallel analysis (Horn, 1965) to investigate the suitable number of factors or components. In the scree test, the factor number is estimated by plotting the acquired eigenvalues (or explained variance) as a function of factor number (see e.g. Fig. 1c). A steep decrease or inflection point indicates the maximum number of usable factors. The Kaiser criterion suggests discharging all factors that have eigenvalues less than 1 (see e.g. Fig. 1d). In parallel analysis, an artificial data set is created, and the eigenvalues are compared to the eigenvalues of the real data. Here, we created the artificial data set for parallel analysis by resampling the actual measurement data by randomizing across rows, as suggested by Ruscio and Roche (2012). For SVD-PCA, the inflection point can be inspected, e.g. from a plot where the explained variance is plotted as a function of component number (see e.g. Fig. S11 in Sect. S3.1). In addition, for EFA, we calculated the standardized root-mean residuals (SRMRs; Hu and Bentler, 1998) and empirical Bayesian information criterion (BIC; Schwarz, 1978) values. These metrics measure slightly different properties of the model. The BIC is a comparative measure of the fit, balancing between increased likelihood of the model and a penalty term for number of parameters. The SRMR is an absolute measure of fit and is defined as the standardized difference between the observed correlation and the predicted correlation. See Sect. S3.2 for more details. A steep decrease in the SRMR values could indicate the number of factors similarly to the scree test with eigenvalues. From the BIC, the minimum value suggests the best-fitting model. It should be noted, however, that these methods may suggest slightly a different number of factors or components. In addition, many statistical tests are often oversensitive if the data are not completely normally distributed (Ghasemi and Zahediasl, 2012), even if large sample sizes might improve test performance, and, therefore, the final decision of the number of factors should be made after evaluating the interpretability of the results.

The suitable number of clusters for PAM was investigated with the total within sum of squares (TWSS; e.g. Syakur et al., 2018) and gap statistics (see e.g. Fig. 1e and f). Within-cluster sum of squares is a variability measure for the observations within a cluster, and for compact clusters the values are smaller, as the variability within the cluster is smaller. By calculating the TWSS, preliminary guidelines for the number of clusters can be derived by inspecting the inflection point of the graph of the TWSS versus number of clusters (often referred as the “elbow method”). In gap statistics, described in detail, e.g. by Tibshirani et al. (2001), the theoretically most suitable number of clusters is determined from either the maximum value of the statistics or in a way that the smallest number of clusters is selected where the gap statistics is within 1 standard deviation of the gap statistics of the next cluster number.

Such straightforward statistical tests are not available for PMF, but one
possible option is to inspect the relation between *Q* and *Q*_{expected}.
Ideally, the value of *Q*_{expected} corresponds to the degrees of freedom in
the data (Paatero and Tapper, 1993; Paatero et al., 2002), and when
*Q*∕*Q*_{expected} (hereafter *Q*∕*Q*_{exp}) is plotted against the factorization
rank, an inflection point may be notable and the addition of factors does not
significantly change the minimum value of *Q*∕*Q*_{exp} (Seinfeld and
Pandis, 2016). It should be noted, however, that even if the *Q*∕*Q*_{exp} summed
over all ions and time steps is low, the corresponding values of individual
ions may still either be rather large or very small, thus compensating each
other and resulting in an unreliably good overall *Q*∕*Q*_{exp} value
(interactive comment from Paatero, 2016, to Yan, 2016). In addition, the used error
scheme in PMF has a large impact on the *Q* values. If the true measurement
error was used, *Q*∕*Q*_{exp} approaches a value of 1. If the chosen error values
were larger than this, the *Q*∕*Q*_{exp} values will approach a final value
smaller than 1. Note that the shape of the curve of *Q*∕*Q*_{exp} versus number of factors
is not affected much by the chosen error scheme (see e.g. Fig. 2b).
Therefore, this method should be used as a first suggestion rather than a
strict criterion. A more empirical method for determining the number of
factors and interpretation of them exists when investigating ambient AMS
data. The acquired factor mass spectra from PMF can be compared to spectra
from known sources (Zhang et al., 2011). The time series of these
identified factors are then compared to tracer compounds for these factors
measured with other instruments (e.g. NO_{x} for traffic emissions, black
carbon for burning events). If several factors correlate with the same
tracers, it is very likely that too many factors have been chosen. An
extensive database of factor spectra exists for AMS data, and it is
maintained by the community (http://cires1.colorado.edu/jimenez-group/TDPBMSsd/, last access:
9 September 2019). The PMF evaluation tool for Igor Pro used in this study also
provides other indices, including the “explained variance/fraction of the
signal”, which is shortly discussed in Sect. 3.4.

Several approaches exist for NMF for selecting the factorization rank *p*, but
the choice of which method to use is not straightforward (Yan et al.,
2019). Brunet et al. (2004) suggested selecting the factorization
rank based on the decrease in the cophenetic correlation coefficient (CCC),
i.e. at the first value of *p* where the efficient decreases (see e.g.
Fig. 2a). In addition, we investigated the cost function that approximates
the quality factorization as a function of the factorization rank *p*. For the
Brunet algorithm that we applied in this study, this cost function is the
divergence between data matrix **X** and approximation **WH** (see Eq. 3 in Lee
and Seung, 2001).

## 3.4 Determining the “goodness of fit”

When analysing the data sets, we realized that all of the factorization methods in this study are sensitive to even small changes in the data. In order to cross-validate the calculated factorization and approximate the uncertainty in the factors, 20 resamples of the measurement data were created with bootstrap-type sampling (Efron and Tisbshirani, 1986), i.e. sampling with replacement from the original data. The resamples were formed by taking random samples (by row) from the measurement data with replicates allowed while preserving the structure of the time series. The different methods were then applied to the resamples to validate if the factorization created from the original measurement data was real and the created factorization was robust enough to maintain the achieved factor structure even if minor changes would appear in the data. Simplified, this variation in the factorization for the bootstrap-type resamples can be understood as an uncertainty for the factorization results. If we had true replicates of the data set, a similar approach could be used, as in theory the same, repeated experiments with similar chemistry should include the same factors, and the occurring variation in the factorization illustrates the uncertainties in the factorization.

In addition to the cross validation of the factorization, the results should
be evaluated in a way that we are able to justify how well the
factors, components or clusters represent the original data and the underlying
information. Often in studies where either EFA or PCA has been used,
explained variance (EV) is reported for the solution. In principle, the EV
could also be used as a guide when selecting the number of factors by
selecting factors until EV reaches an “appropriate” value or does not change
drastically when more factors are added. In PCA, the EV for each component is
calculated by dividing the eigenvalue of each component by the sum of the
eigenvalues. The sum of EVs for all *n*−1 components (*n* is the number of
variables in the data) equals 1. In EFA the EV for the factor *k* (with *p* factors
in total) can be calculated by

where *λ*_{ij} is an element from the loading
matrix, **S** is the original correlation matrix and **R** is the
reconstructed correlation matrix (**R**=*λ**λ*^{T}) (Revelle, 2018). Depending on the algorithm
used to calculate EFA, the calculation of EV may vary. In PMF, the
calculation of EV is not possible this way, as PMF factorizes the data
matrix instead of the correlation matrix. Instead, for PMF there is a
possibility to calculate, for example, the “explained fraction of the signal” for
the reconstructed factor model. This can be calculated by comparing the
original total time series (sum of the data columns, i.e. individual ion
time series) to the reconstructed one by

where ${x}_{ij}^{\ast}$ is the element from the recalculated data matrix
**X**${}^{*}=\mathbf{GF}$ (see Eq. 7), *x*_{ij} is an element from the
original data **X**, and *n* is the number of columns (variables, ions)
in the data. The disadvantage of this method is the use of the mean. If the
signal is both over- and underestimated at different parts of the data, the
explained fraction of the signal is still very good even if the fit is not. For
NMF, a similar index could be calculated. However, due to the differences
between EFA–PCA, NMF–PMF and PAM (which uses a fundamentally different approach),
the indices calculated with Eqs. (13) and (14) are not comparable between the
methods and therefore not presented here. Instead, we aim for more
universal ways to compare the SDRTs.

For NMF and PMF, it is possible to back-calculate how well the created
factorization can reproduce the information in the original data. This
method is rather straightforward, as both factorization matrices from NMF
and PMF are limited to positive values. This allows us to calculate the
reconstructed total signal for NMF–PMF, which can be compared to the
original total signal to produce residuals. For EFA and PCA, the calculation
of the total signal is not possible from the created factorization in a
similar fashion, as the acquired loading values (contribution of an ion to a
factor or component) may be negative. Therefore, for EFA and PCA the
reconstruction is possible only for the correlation matrix, as it is also
the matrix that is factorized during the calculation process. This allows us
to compare the original correlation matrix to the one produced by EFA or PCA
in a similar manner to all data in PMF and NMF. However, due to
our large data size, the visualization of the residual correlation matrix is
difficult, and instead we calculated the mean and interquartile range
(IQR: *Q*_{3}–*Q*_{1}) for the absolute values of the residuals. The
theoretical minimum value for the mean and IQR is 0, indicating perfect
reconstruction, and the theoretical maximum value, i.e. poor
reconstruction, is 1. For example, for a variable pair having a correlation
coefficient of 0.7, a mean absolute correlation residual of 0.02 and an IQR
of 0.04, this would mean that the model over- or underestimates the correlation
by 2.86 % ((0.02∕0.7) ×100). An IQR of 0.04 would mean that 50 %
of all variable pairs with correlation of 0.7 are within 5.7 % ((0.04/0.7) ×100) of the original value of 0.7.

A very important criterion for the quality of the factorization is the interpretability of the results. If the interpretation of the factors is impossible, the results are useless for the data analysis. Note that all methods presented in this paper are purely based on mathematics, and the “best” result is obtained by solving a computational problem not connected to the real processes in the chamber and instruments leading to the measured data set. Thus, the user has to apply the available external information (e.g. about possible reaction products or if ions should be split between multiple factors) to validate the feasibility of a factorization result. But there is a fine line between applying this prior knowledge about the possible chemical and physical processes in the chamber to validate a factorization result and dismissing an unexpected feature discovered by the factorization method as unphysical and thus wrong. Applying more than one factorization method may be helpful to protect the user from dismissing unexpected results.

## 4.1 Gas-phase composition from PTR-MS

### 4.1.1 EFA

Figure 1 shows results for the tests described in Sect. 3.3. The eigenvalues and parallel analysis results for EFA are not shown, as the results were very similar to those acquired for PCA. Also, the factorization results from ml-EFA and pa-EFA were so similar that only the results from ml-EFA are presented here. Figure 1a shows the empirical BIC and Fig. 1b the SRMR values for factorization ranks ranging from 1 to 10. The minimum value in the empirical BIC was achieved with four factors, and the inflection point in the SRMR also lies around four factors.

As all these tests suggest a four-factor solution for PCA and EFA, we compared the factor time series and factor contribution for the four-factor (Fig. 3) and five-factor solution (Fig. S9 in Sect. S4.1) for EFA with oblimin rotation. The additional FE5 seems to be a mixed factor with a small concentration created from FE4 and FE2 instead of a new factor with different properties. The original loading values for the four-factor solution are presented in Fig. S10 as a scatter plot.

The variation in the factors from the resamples is largest around the start of the photo-oxidation, as expected, when there are fast and large changes in the concentrations. The mean and IQR for the absolute values of residual correlations for the four-factor solutions were 0.0109 and 0.0108, indicating good reconstruction.

In the following, we interpret the factors for all SDRT methods based on
their characteristic factor time series shape and the identified compounds
in the factors. An overview of this interpretation is given in Table 1.
Based on the shapes of the factor time series, FE1 can be identified as an
oxidation reaction product factor. It starts increasing slowly when the
photo-oxidation starts, so either these are products from slow reactions or
multiple reaction steps are needed before these compounds are formed. FE2
is also an oxidation reaction product factor, but these are first-generation (or early-generation) products which rise quickly after photo-oxidation starts and are
slowly removed by consecutive reactions as the photo-oxidation continues
and/or by partitioning to the particle phase or chamber walls. FE3 is a
precursor factor which shoots up during the *α*-pinene addition
(slightly after $t=-\mathrm{50}$ min) and is stable until the start of the
photo-oxidation. Together with the factor mass spectrum which is dominated
by signals at *m*∕*z* 137 and 81, this is a clear indication that FE3 represents
*α*-pinene in the chamber. Note that although proton transfer is a
relatively soft ionization technique, a certain amount of fragmentation of
the mother molecule *α*-pinene (*m*∕*z* 137) is observed, showing fragments
at, for example, *m*∕*z* 81 (Kari et al., 2018). FE4 seems to include some car
exhaust VOCs and residue from the background. It has very low concentrations
compared to the other factors. It decreases slightly throughout the whole
experiment and seems not to be affected by the onset of photo-oxidation.

### 4.1.2 PCA

Figure 1c shows the eigenvalues as a function of the component number for
EVD-PCA with the results from parallel analysis. In Fig. 1d the eigenvalues
for the first two components are omitted to show the changes with more
components better. The blue line shows the Kaiser criterion (eigenvalue = 1). SVD-PCA (when applied to scaled data matrix) was not able to separate
*α*-pinene as its own component but instead created two factors which
were dominated by the unreacted *α*-pinene and its fragments (see Fig. S12 in Sect. S4.1). In addition, the unrotated solution included a
large number of negative loadings, which complicated the interpretation of
the components. No improvement was achieved when SVD-PCA was applied to the
data matrix without any scaling (see Fig. S13). Oblimin rotation was
applied to create factors that could be interpreted in a physically more
meaningful way, but the algorithm did not converge. So this is a case where
the result of the factorization method is very difficult to interpret or
even contrary to the available information (e.g. the *α*-pinene
precursor behaviour). As additionally the underlying algorithm struggles
with the data set (i.e. not converging), we will not discuss these results
in detail here but rather focus on the EVD-PCA.

The number of components indicated by parallel analysis is four (Fig. 1c), but the eigenvalues decrease to below one only with 10 components (Fig. 1d), indicating that nine components should be selected. However, the eigenvalues for the components five through nine are rather close to the Kaiser limit (between 1.47 and 1.04, respectively), and therefore the four-component solution was selected. In addition, the “knee” in the eigenvalues is around four or five components, but as for EFA, the addition of a fifth component did not create a new component with different properties but mixed properties of the previous components.

Figure 4 shows the component time series and total contribution from EVD-PCA
with oblimin rotation, and the original loading values for the four-component
solution are presented in Fig. S14 as a scatter plot. Oblique rotation was
used despite the orthogonality assumption of the components, as for true
physical components the assumption of orthogonality is not that realistic
either because it would indicate that the chemical processes taking place in the
chamber do not have any correlation between the different processes. Oblique
rotations allow correlation between the components, meaning that the detected
ions in different components interact with each other. For example, the
decrease in the *α*-pinene concentration is mostly caused by chemical
processes which in turn form other ions detected by PTR-MS. Additionally,
there are multiple consecutive processes (reactions) at work simultaneously,
so the correlation between the components is not a straightforward indicator
of connected processes, but it is more realistic than no correlation at all.

The mean and IQR for the absolute values of the residuals of the correlations were 0.0116 and 0.0107, respectively. Compared to the EFA solution with four factors, the residuals are slightly larger. The total contribution of compounds to each factor is very similar for EFA and EVD-PCA (Figs. 3b and 4b or Figs. S10 and S14), which agrees with the very similar factor or component time series in general in Figs. 3a and 4a. The interpretation CO1, CO2, CO3 and CO4 is therefore the same as above for the EFA factors FE1, FE2, FE3 and FE4, respectively.

### 4.1.3 PAM

The test parameters TWSS and gap statistics for PAM are shown in Fig. 1e and f. The TWSS versus number of cluster values do not show a clear inflection point, but it could be roughly assigned between three and five clusters. There is no maximum value reached with gap statistics, which indicates that the theoretical number of clusters is nine, as there the gap statistic is within 1 standard deviation of the gap value in the 10-cluster solution. However, the increase in the gap value clearly slows down after three clusters. After careful evaluation, the four-cluster solution is determined as most interpretable, and the cluster time series and distribution of the ions are shown in Fig. 5. Four clusters were selected, as the selection of only three clusters (Fig. S15 in Sect. S4.1) is not enough to explain the variation in the data because the addition of one cluster reveals new features. On the other hand, the five-cluster (Fig. S16 in Sect. S3.1) solution seems to split off an additional low-concentration cluster from CL4 (Fig. 5a) instead of showing a new distinct cluster. The distinction between the “more correct” solution with four or five clusters is not, however, straightforward because CL5 (Fig. S16) could be interpreted as a car exhaust precursor cluster, as shown by CL4 in Fig. 5. Clustering statistics are presented in Table S2 (Sect. S4.1).

When comparing the shape of the cluster time series to the EFA and PCA results in Figs. 3 and 4, the results agree well. The largest difference appears in the CL4, which has larger concentrations in PAM compared to FE4 acquired from EFA and CO4 from PCA. The shapes of FE4 and CL4 are also slightly different, as CL4 has a small decrease in the concentration at 0 min, whereas FE4 is barely affected. However, comparing the actual concentrations between these methods (EFA–PCA and PAM) may be misleading, as in EFA and PCA, the acquired loading values are used as weights when calculating the factor time series, whereas in PAM the time series of the clusters are calculated as a direct sum of the cluster compounds, as explained in Sect. 3.2.1.

Dichotomized loadings (for each ion: 1 for factor with largest loading, 0 for the other factors) for EFA were tested to see if then the results agree better with those from PAM, as in PAM there are no loading values, meaning that an ion is either in a cluster or not. With dichotomized EFA loadings we make the same assumption: one ion is classified to one factor only and therefore stems from only one source or source process. Figure S17 (Sect. S4.1) shows the results from dichotomized EFA. When compared to PAM (Fig. 5), the factor or cluster concentrations agree well, but there are clear differences in the ion distribution. EFA classifies the weak ions with a low concentration to the product factors (FE1, FE2), whereas PAM assigns them to the background or precursor cluster (CL4).

### 4.1.4 NMF

Figure 2a shows the divergence of the cost function *D*($\mathbf{X}\left|\right|\mathbf{WH}$)
and CCC for factorization ranks from 2 to 10 for NMF. The CCC has a first
decrease in the values at rank 4, and the *D*($\mathbf{X}\left|\right|\mathbf{WH}$) shows
an inflection point around ranks 4–5. Figure 6 shows the factor time
series and total contribution for the NMF with factorization rank 5. Five
factors were selected, even though CCC suggest only four factors, as the
addition of one factor to the four-factor solution (Fig. S18 in Sect. S4.2) did
add a new feature to the solution in contrast to the SDRTs presented above.
FN2 in the four-factor solution decreases drastically between $t=-\mathrm{50}$ and *t*=0, indicating that it might include background ions, but on the other hand,
it also peaks right after *t*=0, indicating that oxidation products also
contribute to that factor. These mixed properties in the factor FN2 indicate
that more factors are needed, and indeed in the five-factor solution this
contradictory behaviour no longer occurs.

Similar to the results shown above, the range in the factor time series for
the bootstrap replicates is larger when the factors exhibit fast changes in
the concentration (Fig. 6a). In addition, FN3 from the real measurement data
has a lower maximum concentration when compared to the bootstrap replicates.
This indicates that NMF is rather sensitive to the small changes in the data, and
only a few deviant observations present in the data but not in the majority of the
resamples can cause this kind of discrepancy. Factors FN1–FN4 seem to
correspond to the same factors found with EFA, PCA and PAM (Figs. 2, 3 and
4), and especially *α*-pinene is clearly assigned to the same factor
(FE3–CO3–CL3–FN3) in all the used methods. FN5 in NMF, however, has
properties that were not detected (or separated from others) with EFA, PCA
or PAM even if more factors were added. This new factor could be interpreted
also as oxidation product factor, but as it increases slower and decreases
later than the early-product factor (FN2), it mostly includes intermediate
products. These are most likely compounds which are formed through
(multiple) reactions and consumed in further oxidation reactions.

By recalculating the data matrix, **X**, with the original
factorization matrices **W** and **H**, we can inspect how well it
has been reproduced. Here, the total signal (total time series) is then
calculated by summing all ions for each time step for the original data
matrix and the reconstructed data matrix. The differences between the
original total signal and the one produced by NMF (i.e. the residuals) were
smaller than 10^{−10}, indicating a good mathematical reconstruction. The
boxplot of the residuals with four and five factors is shown in Fig. S19 (Sect. S4.2)

### 4.1.5 PMF

The acquired *Q*∕*Q*_{exp} values for different factorization ranks in PMF with the
constant error scheme are presented in Fig. 2b. The values are the minimum
values from all possible solutions, with *f*_{peak} values from −1 to 1 by a step
of 0.5. The *Q*∕*Q*_{exp} values were at the minimum at *f*_{peak}=0 with the number of
factors (1–10) tested. We notice that the values are slightly smaller in
general when using the signal following error, as the absolute values of the
errors in this error scheme are significantly larger around fast changes
than in the static error scheme and thus decreasing the observed *Q* values
(see Fig. S4 for the different error schemes). Values for the signal
following error decrease slightly below 1 (0.88) for the five-factor solution,
whereas with the static error they stay above 1.91. After careful evaluation
of the results with a different number of factors, the solution with five factors
(Fig. 7; *f*_{peak}=0) was selected to be presented and interpreted here. The
solutions with fewer factors were inconclusive, and the addition of a fifth
factor did add a new feature. The results with four factors are shown in the Supplement (Sect. S4.2, Fig. S20). In the five-factor solution, the solid lines
in the time series are the results for the measured data, and the shaded
areas show the ranges for the bootstrap resamples.

For the static error case, the factorization from resamples agrees well with that from measurement data. For the signal following error (Fig. 7c–d) the differences are significantly larger; for example, FP5 has a larger peak concentration than in any of the resamples. This is most likely caused by a few deviant values in the data which are not present in the resamples, thus creating a smaller peak concentration for FP5 for the resampled data. Resampled data include more sudden changes due to added and/or missing data rows, thus causing PMF to perform poorer. In addition, the other variations in the resampled ion time series may cause ion contributions (especially those originally assigned in-between factors) to shift slightly from FP5 to FP2, as for FP2, the values in the time series are higher in the resamples compared to the original data. This difference between the error schemes is caused by the error values themselves. For the signal following error, the factorization is more “precise” (fewer wiggly factors), but even small shifts in the data (bootstrap resamples) distort the factorization more than in the static error case.

When comparing the results for the measurement data with different error
schemes in Fig. 7, we note that the *α*-pinene precursor factor is
slightly less pronounced with the signal following error; i.e. the solving
algorithm assumes that these fast changes are not “real” but rather outliers.
This is caused by the used error scheme, where errors are larger for the
fast changes in the data (Fig. S4b). In ambient data not measured in instant
proximity of strong emission sources, for which PMF is often used, this type
of error is beneficial, as there the fast changes are more likely to be noise
or instrument malfunctions (excluding, for example, sudden primary emission
plumes), and we are more interested in the long-term changes instead. For
laboratory data, where large changes are often caused by rapid changes in
actual experimental conditions, e.g. due to injecting *α*-pinene or
turning the UV lights on, the static type of error is most likely
preferable. Usage of the static error scheme helps to avoid overcorrecting
intentional (large) changes in experimental conditions and confusing them
with real variation taking place during the experiment and typically being
much less pronounced.

Figure 8 shows how well the original data matrix can be reproduced with the
created factorization matrices. The residuals for the static error are
generally larger, as most of them are in the range 0±0.5 (for signal
following error 0±0.15), but there are much larger “outlier”
values for the signal following error. This is due to the structure of the
signal following error, which is larger during the fast changes in the data,
as shown in Fig. S4b (Sect. S1.4). For the static error the residuals
vary more throughout all the data, whereas for the signal following error
the residuals are smaller, but a few rather large values appear at the start
of the photo-oxidation, as seen in Fig. 8b. This highlights the role of the
selected error values in PMF, which act as weights for the data. A smaller
error value means that the corresponding *Q* value at this time will be much
larger, and an improvement of the model at this part of the data will have a
big impact on the optimization value. This means that the error values can
be used to emphasize certain parts of the data set which otherwise would not
be recovered very well by PMF. Note that this is a key difference to NMF,
where no error-based weighting of the data is done.

### 4.1.6 Comparing the SDRTs applied to gas-phase composition

Table 1 summarizes the acquired results from different SDRTs for the gas-phase composition data measured with PTR-MS, and Figs. S21 and S22 in Sect. S5 show separate factor contributions for each of the SDRTs. Comparison of the total factor contribution for some selected compounds for the four factors from EFA, PCA and PAM is shown in Fig. 9. We note that the differences are very minor between EFA and PCA and hardly visible in the coloured bars. When compared to PAM, we see that, for example, acetaldehyde and methyl ketene are assigned to the red cluster (CL2), which also dominates in EFA and PCA (FE2, CO2). Figure 10 shows the same compounds for NMF and PMF. There, the largest difference is between the two oxidation product factors, coloured in black (slow-oxidation products; factor 1 in Table 1) and red (fast-oxidation products; factor 2 in Table 1). For the selected compounds, NMF has more weight assigned to the fast-forming products than PMF. In addition, PMF assigns much more weight to the intermediate-oxidation product factor (pink) for some of the compounds.

The factorization acquired from PMF agrees well with the factorization from
NMF when comparing the factor time series, as expected, since the methods are
rather similar. Comparison of the concentrations of the factors between PMF
and NMF directly is not exact, as these methods have different weighting
between the produced factorization matrices due to the different solving
algorithms. The largest difference is the early-product factor, FN/FP 2. In
NMF (Fig. 6), this factor (FN2) increases from 0 to 30 ppb very fast at
*t*=0 min; then it decreases rapidly to just above 20 ppb and continues to
decrease almost linearly towards 10 ppb. In PMF, this factor (FP2; Fig. 7)
has a similar increase at *t*=0 min, but it decreases exponentially
instead of the fast drop and constant decrease present in NMF solution. The
different slope has direct implications for the interpretation of the
factor. A faster decrease is interpreted as a faster removal or destruction
process for ions classified into this factor. This is typically related to
reaction speeds or to how far along a product is in the chain of oxidation
reactions. When comparing the total contribution of FN2 in NMF and FP2 in
PMF, in NMF ions with *m*∕*z* 90–100 have a much higher contribution to FN2, whereas
in PMF these ions seem to be assigned to FP1 instead. Otherwise the factors
agree well with those acquired from NMF, and their interpretation is
therefore similar: three oxidation product factors (FP1, FP2 and FP5), one background or car exhaust precursor factor (FP4), and one *α*-pinene
injection factor (FP3).

Another important difference between NMF and PMF is the relation between the factors at the end of the experiment. In PMF, at the end everything is shifted to FP1 (later-generation oxidation products) and the other factors decrease to 0, whereas in NMF there still is a contribution from the other oxidation product factors FN2 and FN5 in addition to FN1. A more fundamental study of the algorithms for both PMF and NMF is needed to explain this behaviour.

The factorization acquired with EFA, PCA and PAM is more robust compared to NMF and PMF when inspecting the bootstrap ranges in the top panels in Figs. 3, 4, 5, 6 and 7. This may be explained with the different number of factors (four or five), as with more factors, one factor includes fewer (strongly) contributing ions, which causes factorization to vary more when the data are different. But most of the differences between these SDRTs are still explained by the methods themselves and the solving algorithms. PMF and NMF are more sensitive to small changes in the data, whereas EFA, PCA and PAM succeed more reproducibly in finding larger structures and changes in the data.

The addition of a fifth factor to EFA, PCA and PAM did not add a factor showing
a new feature, as it did in NMF and PMF, but a sub-factor. This sub-factor
has a very low concentration, but if inspected separately (not shown), it
peaks around *t*=0 min, similar to the second factor. This means that instead of
adding a factor consisting of intermediate-oxidation products (as in NMF and
PMF), the added factor is another early-product (or background) factor. This
is also caused by the difference in the methods, as these three SDRTs (EFA, PCA
and PAM) concentrate more on the fast changes (which take place here at *t*=0 min), whereas NMF and PMF focus more on slow changes. This is one
example where the chosen method (EFA–PCA or NMF–PMF) has a direct impact on
the interpretation of the data. For understanding the chemical processes in
the experiment, the existence of two or three oxidation product factors is of
great importance.

Factor 4 has different behaviour in the time series in EFA and PCA compared to NMF, PMF and PAM. In the latter SDRTs this factor starts decreasing immediately and the concentration drops throughout the whole experiment, implying that it is affected by car exhaust precursors that are oxidized, and the products are assigned to other factors later on. In EFA and PCA, this factor has a small and rather stable concentration over the time series (also in addition to a small contribution to the total signal), suggesting that it could consist of background compounds present throughout the whole experiment. Without exact identification of all the compounds present in these factors (which is out of the scope of this study), it is hard to say if this difference is real or if it is related to the different calculation of the SDRTs. We provide more details of the comparison between the factors from different SDRTs in Sect. S5 in the Supplement.

## 4.2 Particle-phase composition from AMS

### 4.2.1 EFA and PCA

As the AMS data from the experiment include only one observation about every 10 min, the data have many more variables (compounds) than observations. This causes problems for EFA and EVD-PCA, as those methods are based on the correlation matrix, which will not be positive definite due to the small number of observations (rows) compared to the number of variables (columns). In EVD-PCA, the second step is to calculate the eigenvalues, which in this case may also be negative and result in a non-interpretable outcome. With this type of data, the results of EFA are also sensitive to the used algorithm. The calculation in ml-EFA did not converge at all, but pa-EFA was able to produce results. Due to these restrictions in the calculation process, the results from EFA and PCA are only briefly discussed below, and example figures can be found in the Supplement (Sect. S6.1). In addition, due to the very small data size, the bootstrap-type resampling of the data has too drastic an effect on the data structure to validate the repeatability of the factorization and is therefore not applied for any of the SDRTs.

Figure S23a and b (Sect. S6.1) shows the results for the tests investigating the correct number of factors and components pa-EFA and EVD-PCA. For EFA in Fig. S23a, the empirical BIC reaches a minimum value with four factors, and the inflection point in SRMR is at four factors. For EVD-PCA, however, parallel analysis (Fig. S23c) suggests only one component, mainly indicating that the data are not suitable for PCA at all. The eigenvalues also do not reach 1 (Kaiser criterion) with up to 10 components tested. The eigenvalues for EFA (not shown) reached 1 for the six-factor solution, and parallel analysis results (not shown) indicated selecting only one factor. The differences in these test results are mostly caused by the computational issues mentioned above. Indeed, neither EFA nor PCA (SVD or EVD) were able to separate more than two factors or components from the data, when two to five factors or components were tested (see e.g. Figs. S24 and S25 in Sect. 6.1). While a two-factor solution could be correct in principle, it seems unlikely for the investigated system. The particle phase is constantly formed by low-volatility gaseous compounds condensing. As shown above, the gas-phase composition changes constantly as compounds are produced and consumed. Thus, it is highly unlikely that during the 4 h of chemical reactions in the chamber the same mix of low-volatility compounds is present and condenses onto the particles.

### 4.2.2 PAM

No clear inflection point is visible in the TWSS plot in Fig. S23e; the value decreases when increasing the cluster number with a small “bump” at four clusters. The gap statistics (Fig. S23f) does not reach a maximum value, and it also does not reach the other criteria explained in Sect. 3.3. The inconclusiveness of the tests' results may be caused by different reasons, and to investigate this further, PAM was conducted with two to five clusters, and the results are shown in the Supplement (Figs. S26 and S27). Increasing the number of clusters from three (Fig. S26b) upwards adds clusters with extremely small concentrations and a similar time series shape to the previously found clusters. The very similar shape of the time series of the clusters suggests that only one type of SOA particles was formed quickly after the start of photo-oxidation and that the chemical composition changed only marginally. Again, this seems unlikely for the investigated system.

The inability of PAM to identify multiple SOA particle types most likely
lies in the method itself. Each variable (ion) is assigned to one cluster
and cannot be spread over multiple clusters. However, it is well known that
AMS applies a “hard” ionization technique. Thus, a high degree of
fragmentation is expected, and indeed, most carboxylic acids, for example,
are detected as ${\mathrm{CO}}_{\mathrm{2}}^{+}$ (*m*∕*z* 44). This means that a highly oxidized
organic acid, formed late in the experiment after multiple steps of
oxidation, will be detected at the same variable (ion) as a different acid
formed much earlier. Due to the “one variable – one cluster” method, PAM is
incapable of resolving this information in the data. While EFA and PCA could
still be used if the data matrix is suitable (i.e. more rows than columns),
PAM is unsuitable for this AMS data set or generally for data sets where variables
have strong contributions from more than one source.

### 4.2.3 NMF

The *D*($\mathbf{X}\left|\right|\mathbf{WH}$) has an inflection point at factorization rank 4,
and CCC shows the first decrease in the values with four factors, as shown in
Fig. 11a. We selected the four-factor solution for the detailed interpretation.
We discuss additional reasons why the four-factor solution should be selected
in Sect. S6.2. The factor time series are shown in Fig. 12a. Figure 12b
shows the original ion-to-factor contributions from NMF without any scaling.
The total factor contribution plots are omitted, as we do not have PCA–EFA
results to compare. The delay in the time series after *t*=0 (before the
factors starts increasing or decreasing) is most likely caused by the small
time resolution (10 min) of the data. The residuals were on the same order
of magnitude as for the PTR-MS data, indicating again very good
reconstruction of the original signal.

The mass spectrum of FN1 is dominated by the C_{n}H${}_{\mathrm{2}n+\mathrm{1}}^{+}$ and
C_{n}H${}_{\mathrm{2}n-\mathrm{1}}^{+}$ ion series, conforming to the typical features of
combustion-related primary organic aerosol, and thus it is interpreted as a
hydrocarbon organic aerosol (HOA) factor. FN1 was originated from car
exhaust, as it already appears before *t*=0 min. FN1 increases slightly at
the start of the photo-oxidation. The increase is partly attributed to the
new formation of the HOA component, when the HOA-type compounds in the hot
exhaust gas were introduced into the chamber and contain marker ions
associated with HOA (e.g. *m*∕*z* 57; Zhang et al., 2005) condensed again in
a cooler chamber. Meanwhile, we cannot rule out the possibility that HOA has
been produced as a minor product after the photo-oxidation reaction was
enabled in this study. FN2 can be interpreted as *α*-pinene secondary-organic-aerosol-derived semi-volatile oxygenated organic aerosol
(*α*P-SOA-SVOOA) after we carefully compared the factor mass spectra
with pure *α*-pinene experiments conducted at similar settings
reported by Kari et al. (2019b). In addition, FN2 is
characterized by the prominent peak as *m*∕*z* 43. The mass spectra of FN2 and
FN4 are rather similar, but FN4 has a higher contribution from *m*∕*z* 44, a marker
of oxygenated organic aerosol (Zhang et al., 2005), and thus it is
identified as an *α*P-SOA-LVOOA (LVOOA – low-volatility oxygenated organic aerosol) factor. The FN3 was appointed as a
mixed LVOOA. Except for the high peak at *m*∕*z* 44 in the FN3 mass spectrum,
its time series is also consistent with the SOA formation in the mixed
*α*-pinene or car exhaust SOA experiments conducted in similar settings
(Kari et al., 2019b), and thus it is identified as a
mixed-LVOOA factor stemming from later-generation oxidation products.
A summary of the generated factors from NMF can be found in Table 2.

### 4.2.4 PMF

The *Q*∕*Q*_{exp} values for the two error schemes are shown in Fig. 11b. Neither of the
error schemes show a clear inflection point. Examples of behaviour of the
errors as a time series are shown in Fig. S7 (Sect. S2.2). With the
standard AMS error, the *Q*∕*Q*_{exp} values do not reach 1 (with 10 factors
$Q/{Q}_{\mathrm{exp}}=\mathrm{1.76}$), whereas with the static error the values decrease to below
1 for seven factors. The solutions with two to five factors were inspected, and the
two-factor solution (Fig. 13) is presented here as the most interpretable one
(summarized in Table 2). The primary OA factor, separated by NMF (Fig. 12;
FN2), was only found if using four factors and the static error scheme in PMF
(see Sect. S6.2, Fig. S31a–b). However, interpretation of the time series
for that solution was found to be very difficult due to the extreme
anticorrelation between the time series, and thus the two-factor solution was
selected. The two factors were interpreted as SVOOA and LVOOA. In addition,
the largest relative decrease in the *Q*∕*Q*_{exp} was observed with the
two-factor solution.

The residuals for the standard AMS error were smaller, as shown in Fig. 14. This agrees with the analysis of the gas-phase data set, where the residual for the signal following error (which has a similar profile in time as the standard AMS error) was generally smaller compared to the static error.

The signal following error, used for PTR-MS, was also tested for particle-phase data. However, as this type of error showed very similar behaviour as a time series to the standard AMS error and produced a very similar outcome, those results are omitted from this paper.

### 4.2.5 Comparing the SDRTs applied to particle-phase composition

Table 2 summarizes the acquired results from NMF and PMF for the particle-phase composition data measured with AMS. PAM was not able to separate
distinct clusters due to the inability of clustering techniques to classify
an ion into multiple clusters. Comparing the relative factor spectra and
fraction of signal from NMF and PMF with the static error (Figs. 12b and
13b), the distribution of ions is similar between the LVOOA in the PMF
solution and the mixed-LVOOA factor in the NMF solution and also similar
between the SVOOA in the PMF solution and the *α*P-SOA factor
(integrated *α*P-SOA-SVOOA and *α*P-SOA-LVOOA factors). When
inspecting the individual ion time series in the original AMS data, most of
them have a rather “smooth” behaviour, similar to the factors acquired from
NMF. It seems that PMF gives more weight to the background ions (with very
small concentration), which do not have that clear of a structure in their time
series, thus including more of their behaviour in the final factors, if the
number of factors in PMF is increased from two (see Sect. S6.2, Figs. S30–S32). Residuals from NMF reconstruction (with four factors) were over 10
orders of magnitudes smaller (for NMF between $-\mathrm{1.3}\times {\mathrm{10}}^{-\mathrm{13}}$ and $\mathrm{7.1}\times {\mathrm{10}}^{-\mathrm{14}})$ than those from PMF (between −0.06 and 0.13
for the two-factor solution with standard AMS error; see Fig. 14), indicating
better reconstruction of the data with NMF. Most likely, PMF struggles with
the small data set, thus not being able to recover all the factors found by
NMF and construct reasonable time series for those factors (see four-factor
solution in Fig. S31), whereas NMF does not seem to be affected by the data
size. In addition, the weighting between the factorization matrices between
NMF and PMF is different not only due to the error matrix that is given as a
weight in PMF but also because of the different solving algorithms for each
method. This, on the other hand, assigns different emphases between the
matrices, possibly causing NMF to use more effort to reconstruct the data
matrix with factor time series. However, the reader should keep in mind that
for detailed chemical analysis of such a data set, especially with PMF,
downweighting is advisable. In addition, the replacement of very small
negative numbers with very small positive numbers is not mandatory for PMF,
as it can run with a few negative values into some extent. However, we did
the replacement here, as the NMF algorithm used here does require strictly
positive input data. Acquiring a balance between statistically good results
and realistic factors might be challenging, and to achieve more robust
results, testing different error schemes may be beneficial, especially for a
data set of such a small size.

## 4.3 Computational cost

To approximate the differences in the computational time between the
different SDRTs, the methods were applied with 2–10 factors each, with nine runs in total for each method. No rotations were applied (no rotation for
EFA and PCA, *f*_{peak}=0 for PMF), as the rotational methods between EFA–PCA
and PMF are not directly comparable. Computation times include the
calculation of the correlation matrix when needed and calculation of the
factor time series for PAM, EFA and PCA (which is calculated outside the
main algorithms), as described in Sect. 3.1.1. Three
data sets with different sizes were tested, and the results are presented in
Table 3. AMS includes the particle-phase measurement data (size 26×306)
presented in Sect. 4.2. and PTR-MS the gas-phase
composition data (size 300×133), which were analysed in detail in Sect. 4.1. PTR-MS*5 is a larger data set created from the
gas-phase composition data by duplicating the data rows five times (final size
1500×133). The computational times for NMF and PMF were clearly longer when
comparing to the other SDRTs. This is not surprising, as PMF and NMF
calculate both factorization matrices at the same time, whereas for the
other SDRTs only the matrix presenting the contribution of the ion to the factor is
found at first, and the time series of the factors, components or clusters are
calculated afterwards. In addition, the PMF2 algorithm used through the PMF
evaluation tool for Igor Pro reads and writes text files for each PMF
run, thus significantly increasing the computational time.

## 4.4 Summary of the SDRTs used in this study

The methods tested in this study have many similarities and many, fundamental or computational, differences. However, in the literature, they are applied many times to similar problems. In this section we will summarize some of these properties.

EFA is fundamentally different from the other methods, as it is by definition
a measurement model of a latent variable, i.e. the factor (Osborne, 2014),
whereas the other methods basically describe the measured data with
linear combinations of measured variables. The latent variables in EFA, i.e.
the factors, cannot be directly measured, but instead, they are seen through
the relationships they initiate in a set of *Y* variables, which are measured.
In the other methods, in turn, the factors, components or clusters are
calculated directly from the measured variables *Y* (Rencher and
Christensen, 2012; Osborne, 2014).

The approach to data reduction in PCA is to create one or more summarizing variables from a larger set of measured variables by retaining as much as possible of the variation present in the original data set (e.g. Jolliffe, 2002). This is done by using a linear combination of a set of variables. The created summarizing variables are called components. The main idea of the PCA is to figure out how to optimize this process: the optimal number of components, the optimal choice of measured variables for each component and the optimal weights when calculating the component scores.

The objective of cluster analysis is to divide the observations into homogeneous and distinct groups (Rencher and Christensen, 2012). Cluster analysis is a method where the aim is to discover unknown groups in the data, which are not known in advance. The goal of the clustering algorithm is to partition the observations into homogeneous groups by using some measure of similarity (or dissimilarity) such that the within-group similarities are large compared to the between-group similarities. The choice of the similarity measure can have a large effect on the result. One property of cluster analysis is that it will always calculate clusters, even if there is no strong similarity present between the variables in the data (Wu, 2012). This should be noted when interpreting the results, especially if the user has no a priori information about the number of clusters.

NMF and PMF provide an alternative approach to the decomposition, assuming
that the data and the components are non-negative (Paatero and Tapper,
1994; Lee and Seung, 1999). Thus, all the features learned via NMF and PMF
are additive; that is, they add together strictly positive features.
PCA and EFA tend to group both positively correlated and negatively
correlated components together, as they only look for the
correlations of variables (except SVD-PCA, which can be applied to the data
matrix directly). On the other hand, NMF and PMF, by constricting **W** and **H** to
positive values, find patterns with the same direction of correlation. Thus,
NMF and PMF work well for modelling non-negative data with positive
correlations. However, if the interest is not only in the positive effects,
then PCA and EFA can provide more information for the investigated
system. Cluster analysis is suitable for classifying observations based on
certain criteria. The researcher can measure certain aspects of a group and
divide them into specific categories using cluster analysis. However, this
method is not suitable for data with variables which should show
contributions from multiple factors or components (e.g. strongly fragmented
signals in AMS data).

Factorization methods, including those used in this paper, operate on the fundamental assumption that the factor profiles (here factor mass spectra) are constant over the investigated period. Often, this has been interpreted in a way that chemical processes occurring in a chamber experiment or the atmosphere violate this assumption. However, this interpretation is based on too narrow a definition of what a factor represents. A factor can be seen as a direct (emission) source of compounds which changes its contribution to the whole signal (e.g. primary emissions from biomass burning as a fire develops and then dies). But a factor can also be interpreted as a group of compounds showing the same temporal behaviour. If this group is released together as an emission or if the compounds are formed in the same ratio by some chemical process should not matter. In the latter case, it is important how wide the group is selected, i.e. if we group products of processes together for which the contribution changes with time. This means that choosing the optimal number of factors becomes even more important when chemical processes occur. EFA and PCA account for the chemistry happening in chamber measurements with negative loadings, as described above. The same factor can contain educts and products of a chemical process (e.g. oxidation), with the difference that their loadings are negative and positive, respectively.

Taking account of everything above, the most important thing to consider when selecting the SDRT is the interpretation. What are the features the researcher wants to deduct from the data, what are the properties of the data, and how can the data answer the research questions? As we have shown in the “Results and discussion” section, the methods provide quite similar reconstructions of the time series, but the interpretations of the steps leading to these are quite different. For example, comparing reconstructions of EFA factor FE4 in Fig. 3a and PMF factor FP4 in Fig. 7a, they seem to show the same procedure, but the first one includes both positive and negative effects and the second one consists only of positive effects.

The main objectives in this study were to (a) investigate how different SDRTs
perform for gas- and particle-phase composition data measured with mass
spectrometers, (b) how the interpretation of the factors changes depending on
which SDRTs have been used, and (c) how well the SDRTs were able to resolve and
classify the factors representing chemistry behind the investigated data set
of photo-oxidation of car exhaust combined with *α*-pinene. We showed
that EFA, PCA and PAM were able to identify four factors from the gas-phase
composition data, whereas NMF and PMF succeeded in separating one additional
oxidation product factor. The behaviour of the factors as time series was
similar, when considering the differences in the calculation of the factor
time series matrix in different SDRTs. For example, the EFA and PCA factors
were nearly identical, and the differences in the interpretation lie more
in the definition; principal components are defined as linear combinations
of the variables (ions), whereas in EFA the variables are expressed as linear
combinations of the acquired factors. From the particle-phase data, NMF was
able to separate four factors, whereas PMF separated two. PAM was not able
to find more than two separate clusters, most likely due to the high degree
of fragmentation in the data and the constrain of PAM to assign one ion to
only one cluster, as discussed in Sect. 4.2.2. EFA
and PCA had computational constraints due to the small data size acquired
from the AMS and could not be applied. In addition, PMF also faced
assumedly computational issues with the small particle-phase data set, thus
not being able to reasonably separate the HOA factor.

The difference, which might be an advantage or disadvantage depending on the application, between PCA–EFA and PMF–NMF is their use of the correlations of the variables instead of the raw data. When using the raw data, ions with a high concentration may dominate and hide interesting behaviour occurring in the lower-concentration ions and instead classify those as insignificant background ions. When using correlations, the concentrations of the ions do not affect the created factorization until the factor time series are calculated, and in principle, variables with different units can be factorized simultaneously. On the other hand, it may diminish some of the more minor and subtle changes. As NMF and PMF do not rely on the correlations, they are more sensitive to the smaller changes taking place in the data. The disadvantage of signals with high intensity dominating in the analysis can be tackled in PMF by choosing an appropriate error matrix that weights the ion signals. Selection of the error matrix can also be crucial when interpreting the PMF output, as a sub-optimal choice may hide the identification of important properties of the data.

The gas-phase data resulting from PAM agreed moderately with those from EFA and PCA, when taking into account the ability of PAM to assign one ion to only one cluster instead of multiple ones. When comparing the performance of the SDRTs to the bootstrap-type resampled data, we noted that the factorizations from EFA, PCA and PAM were more robust compared to the PMF and NMF results. Results from PMF with different error schemes were similar, but the static error provided more robust solutions when applied to the bootstrap-type resamples.

The findings by Koss et al. (2020) proposed that HCA can be used to quickly
identify major patters in mass spectra data sets, which is in agreements
also with our results from PAM. Our findings for PMF partly differ, as they
suggest that PMF is not able to sort chemical species into clear generations
by their oxidation state. In our study, we found three factors (factors 1, 2 and
5; see Table 1), which can be interpreted as representatives for different
oxidation states. However, they can also present reactions taking place with
different reaction kinetics (faster and slower reactions), as discussed in
the results. In addition, Koss et al. (2020) used gas-phase data from
I^{−} CIMS and PTR3 with ${\mathrm{NH}}_{\mathrm{4}}^{+}$ as a reagent ion, which are more
sensitive to later-generation oxidation products compared to the PTR-MS
which we have used here. We have also used slightly different error types
for PMF, which we showed to have a significant impact on the resolved
factors, especially if the data size is small. Our results from PMF and NMF
agreed reasonably well, even though NMF does not use an error matrix as
input, and it solves the bilinear equation with a different algorithm,
indicating that our PMF is reasonable and correctly interpreted.

From a mathematical point of view, the selection of the most useful SDRT
depends on neither the instrument used to measure the data nor the extent
of fragmentation taking place in the instrument. Only PAM is an exception
here, as clustering techniques in general do not assign variables to
multiple clusters (i.e. “between” clusters), whereas all the other
presented SDRTs have the ability to share an ion between multiple factors.
Similarly, if a large number of isomers is to be expected, NMF or PMF may be
preferable over EFA or PCA, as the latter two try to maximize the
contribution of an ion to a single factor. Ultimately, however, the most
useful choice of SDRT also depends on what kind of chemical processes are
expected and measured, as the splitting of ions into multiple factors
generally makes the interpretation of the factors more difficult, especially
if the prevalence of possible isomerization is not known. Splitting of ions
to multiple factors is also an important topic to discuss in source
apportionment analysis, where an ion with specific *m*∕*z* may emerge from
various sources or source processes. However, it is a very subtle choice
between possibly dismissing an unexpected feature discovered by SDRT and using
prior knowledge to validate the factorization results. Therefore, applying
more than one SDRT not only may protect the user for determining surprising results to be
unphysical, and thus erroneous, but also gives a more robust outcome
for the research when the results from different techniques agree.

X, X_{ij} |
Data matrix (n×m), data matrix element |

p |
Number of factors, components or clusters |

y_{j} |
Variable/ion j (time series vector), column j from X |

c_{j} |
PCA component j |

f |
EFA factor |

, λλ_{ij} |
EFA loading matrix, loading-matrix element |

S, R |
Observed correlation matrix, implied correlation matrix |

G |
Factorization matrix (factor time series) PMF (n×p) |

F |
Factorization matrix (factor spectra or contribution) in PMF (p×m) |

μ |
PMF error matrix |

E |
Residual matrix in PMF |

W |
Factorization matrix (factor time series) in NMF (n×k) |

H |
Factorization matrix (factor spectra or contribution) in NMF (k×m) |

The data can be found in the EUROCHAMP database: https://data.eurochamp.org/data-access/chamber-experiments/bc3be07c-2209-4e46-bdcf-43b01f9ef751/ (last access: 26 May 2020, Virtanen et al., 2020).

The supplement related to this article is available online at: https://doi.org/10.5194/amt-13-2995-2020-supplement.

SI and SM designed the comparison study; EK and AV designed and organized the measurements and provided data; SI, LH, SM and AB participated in data analysis and/or interpretation; SI wrote the paper; and SM, AB, SS, LH and AV edited the paper.

The authors declare that they have no conflict of interest.

Ville Leinonen is thanked for the help he provided with the R software and constructing the error matrices for PMF for the gas-phase measurements.

This research has been supported by the Academy of Finland Centre of Excellence (grant no. 307331), the Academy of Finland Competitive funding to strengthen university research profiles (PROFI) for the University of Eastern Finland (grant no. 325022) and the Nessling Foundation. Data collection for this study has been partly funded from the European Union's Horizon 2020 research and innovation programme through the EUROCHAMP-2020 Infrastructure Activity (grant no. 730997).

This paper was edited by Mikko Sipilä and reviewed by M. Äijälä and one anonymous referee.

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- Abstract
- Introduction
- Experimental data
- Dimension reduction techniques
- Results and discussion
- Conclusions
- Appendix A: Mathematical symbols and notations used in the equations throughout the paper
- Data availability
- Author contributions
- Competing interests
- Acknowledgements
- Financial support
- Review statement
- References
- Supplement

- Abstract
- Introduction
- Experimental data
- Dimension reduction techniques
- Results and discussion
- Conclusions
- Appendix A: Mathematical symbols and notations used in the equations throughout the paper
- Data availability
- Author contributions
- Competing interests
- Acknowledgements
- Financial support
- Review statement
- References
- Supplement