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# A model for simultaneous evaluation of NO_{2}, O_{3}, and PM_{10} pollution in urban and rural areas: handling incomplete data sets with multivariate curve resolution analysis

_{2}, O

_{3}, and PM

_{10}pollution in urban and rural areas: handling incomplete data sets with multivariate curve resolution analysis A model for simultaneous evaluation of NO

_{2}, O

_{3}, and PM

_{10}pollution in urban and rural areas:... Eva Gorrochategui et al.

### Eva Gorrochategui

### Isabel Hernandez

### Romà Tauler

A powerful methodology, based on the multivariate curve resolution
alternating least squares (MCR-ALS) method with quadrilinearity constraints, is
proposed to handle complex and incomplete four-way atmospheric data sets,
providing concise results that are easy to interpret. Changes in air quality by
nitrogen dioxide (NO_{2}), ozone (O_{3}), and particulate matter
(PM_{10}) in eight sampling stations located in the Barcelona metropolitan
area and other parts of Catalonia during the COVID-19 lockdown period (2020) with
respect to previous years (2018 and 2019), are investigated using such
methodology. The MCR-ALS simultaneous analysis of the three contaminants among the eight
stations and for the 3 years allows the evaluation of potential correlations
among the pollutants, even when having missing data blocks. Correlated profiles are shown by NO_{2} and
PM_{10} due to similar pollution sources (traffic
and industry), evidencing a decrease in 2019 and 2020 due to traffic
restriction policies and the COVID-19 lockdown period, especially noticeable in the
most transited urban areas (i.e., Vall d'Hebron, Granollers and Gràcia).
The O_{3} evidences an opposed interannual trend, showing higher amounts in
2019 and 2020 with respect to 2018 due to the decreased titration effect, more
significant in rural areas (Begur) and in the control site (Obserbatori
Fabra).

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_{2}, O

_{3}, and PM

_{10}pollution in urban and rural areas: handling incomplete data sets with multivariate curve resolution analysis, Atmos. Chem. Phys., 22, 9111–9127, https://doi.org/10.5194/acp-22-9111-2022, 2022.

Monitoring studies of air quality have always been indispensable to assess
the impact of air pollutants on human health and the environment. Most
evaluated air pollutants include the ones linked to industrial and traffic
emissions, such as tropospheric ozone (O_{3}), nitrogen dioxide (NO_{2})
and particulate matter (PM_{10}), due to its potential effects on human health
(Zúñiga,
et al., 2016; Khaniabadi et al., 2017), and are the chemicals evaluated in
the present study.

The chemistry of nitrogen oxides (NO_{x}) and O_{3} is highly complex
because NO_{x} is the responsible for tropospheric O_{3} production but
also for its elimination (Lerdau et al., 2000;
Crutzen, 1979). On the one hand, the formation of tropospheric O_{3} is a
consequence of the photochemical reaction of the sunlight with NO_{x} and
volatile organic compounds (VOCs) released by car exhausts and industries,
according to the following equation: NO_{x} + VOC + *h**v* → O_{3}. Thus, (NO_{x}) behave as catalysts in the photochemical
production of O_{3}, especially at higher solar radiation and during hours
of high traffic. However, at hours of low solar radiation and during
nighttime, NO_{x} are responsible for the O_{3} destruction in a process
called titration: NO_{x} + O_{3} → NO_{x} + O_{2}.
In inner rural areas with low anthropogenic activities, the latter titration
effect produced by NO_{x} emissions is generally not observed, resulting
in higher average O_{3} concentrations than in urban areas. Overall, the
complex equilibrium among O_{3} and NO_{x} species results in continuous
concentration changes of O_{3}, which is difficult to attribute to a unique source.

Conversely, the chemistry of PM_{10} is not directly correlated
to NO_{x} and O_{3}, but it is also complex due to its multiple and
diverse emission sources. Different PM_{10} sources exist, including city
background (background levels of emissions such as construction, demolition
and domestic heating), traffic (motor emissions and tire, pavement and
brakes abrasion products), industry (high levels of sulfate, nitrate, and
other burning products), and natural sources (i.e., marine aerosols and air masses, especially African dust) (Querol et al., 2004; Saud et al., 2011).

Different approaches exist to assess air quality by evaluating concentration changes of these chemical pollutants. In classical air quality monitoring studies, the data treatment strategy generally involves data arrangement and analysis using traditional statistics. However, these methods require extensive computer calculations and their results are often limited and restricted. Instead, chemometric methods are powerful data analysis tools used to investigate the sources of data variance in experimentally measured environmental monitoring big data sets, such as air quality data sets that often contain some missing blocks. These methods can be used to extract and summarize the information often hidden in these environmental big data sets. Among these methods, the Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) method (Tauler, 1995), originally used in the spectrochemical analysis of chemical mixtures, has also been proved to be a competitive method in air pollution studies (Malik and Tauler, 2013; Alier et al., 2011). The MCR-ALS method is a flexible, soft-modeling factor analysis method that allows for the introduction of natural constraints, like non-negativity of the factor solutions. Although it only requires the fulfillment of a bilinear model for the factor decomposition, it can be easily adjusted to the analysis of more complex multiway data structures and multilinear models, such as three-way and four-way environmental data sets (Tauler, 2021), which can be analyzed using trilinear and quadrilinear MCR-ALS models, as shown in this study. The results of the application of the MCR-ALS method can be used for the discovery of the main driving factors (latent variables) responsible for the observed data variance, in this case, the observed changes in the measured chemical pollutants.

The present study is focused on promoting and extending the use of
the MCR-ALS method, including
trilinear and quadrilinear constraints, for the investigation of NO_{2},
O_{3}, and PM_{10} air pollution. In addition, this study is aimed at
providing different strategies to deal with and estimate missing data, also using the MCR-ALS methodology (Alier and Tauler, 2022). The
selected chemometric strategy is ultimately used to evaluate the temporal
patterns of the three pollutants during 2018, 2019, and 2020 in eight
monitoring stations located in Catalonia (Spain), including three urban, one
control site, one semi-urban, and three rural stations. The different stations were specifically selected to evaluate the influence of the geographical location
on air pollution. The period of time evaluated (i.e., 1 January to
31 December 2018, 2019, and 2020) was chosen to cover the
COVID-19 lockdown period in Catalonia, and to enable a comparison with respect to the same time period in the previous 2 years. Considering that the strictest COVID-19 lockdown period in Catalonia occurred in the month of April 2020, a specific evaluation of air quality changes produced during this
time period with respect to the previous 2 years is provided in this study.

## 2.1 Air quality data

The experimental data used in this work consisted of O_{3}, NO_{2}, and
PM_{10} concentrations recorded from eight air quality monitoring stations,
operated by the Department of Environment of the Catalan Autonomous
Government. The selected air quality monitoring stations consisted of three
urban stations (Gràcia, Vall d'Hebron and Granollers), one semi-urban stations (Manlleu),
and one control site (Observatori Fabra), all located in the
province of Barcelona, and three rural stations, i.e., Juneda and Bellver de Cerdanya in
the province of Lleida, and Begur (Costa Brava, NE Catalonia) in the
province of Girona. More detailed information about the characteristics of
the stations is provided in a previous air quality monitoring study by the
authors (Gorrochategui et
al., 2021). The NO_{2} concentrations were measured by means of
chemiluminescence according to the UNE method 77212:1993, using
automatically operated MCV 30QL analyzers. The O_{3} concentrations were
measured by means of UV photometry according to ISO FDIS 139464:1998,
automatically operated with MCV 48 AUV analyzers. The PM_{10} concentrations were measured by means of gravimetric determination, using manually operated high volume samplers (MCV CAV-A/MS). The generated databases with all the concentrations measured were compiled by the Department of Air Monitoring and Control Service of the Generalitat de Catalunya (Xarxa de Vigilància i Previsió de la
Contaminació Atmosfèrica, XVPCA. Departament de Territori i
Sostenibilitat, 2020).

## 2.2 Experimental data multisets

In this study, two experimental data multisets were analyzed (see Fig. 1).
Both of them contained hourly concentrations of NO_{2}, O_{3}, and
PM_{10} measured in the eight air quality monitoring stations but for
distinct periods of time. The first data multiset contained air quality data
recorded in the month of April 2018, April 2019, and April 2020 (i.e., the latter being the time when the strictest COVID-19 lockdown occurred in
Catalonia, Gobierno de España, 2020b) in the different
stations. The second data multiset contained air quality data recorded in the
same eight stations, but during a longer period of time: from 1 January to
31 December 2018, 2019, and 2020. The latter multiset was built in
order to evaluate annual trends of air pollution; especially interesting in
2020, an extraordinary year due to the COVID-19 pandemic.

As observed in Fig. 1, both data multisets contained some missing data blocks, which were not included in the MCR-ALS analyses of individual contaminants, apart from some spot values, which were further estimated to undergo chemometric analysis.

In the data set of the month of April, no missing data existed for NO_{2}
and O_{3}. However, for PM_{10}, data for 3 months of April were
missing (i.e., Begur 2018, Begur 2020, and Observatori Fabra 2018), as observed
in Fig. 1a. In the data set for the entire 3 years (Fig. 1b), for
NO_{2} and O_{3}, the months of January and February 2018 in the Observatori Fabra station were missing, respectively. For PM_{10}, data from three air
quality monitoring stations were missing: Gràcia (September and October,
2018), Begur (months from January to October, 2018, and months from January
to July 2020) and Observatori Fabra (months from January to September 2018).

## 2.3 Data sets arrangement

In this study, the two data multisets were separately arranged to further undergo individual MCR-ALS analyses of the complete experimental data sets (Fig. 1).

To conduct the analysis of the month of April, data matrices for NO_{2},
O_{3}, and PM_{10} were separately arranged in a first step. For each
contaminant, a total of 24 data matrices, one per year (3 years) and per
monitoring station (eight stations), of size 30×24 (month days × hourly
measurements), were obtained. As observed in Fig. 1a, these 24 data matrices
were labeled as **D**_{station–year}; with the name of the corresponding air quality station (V: Vall d'Hebron, Gn: Ganollers, M: Manlleu, J: Juneda, Bl:
Bellver, Ga: Gràcia, Bg: Begur, and O: Observatori Fabra) and the 2
last digits of the year (2018, 2019, and 2020). These 24 data matrices were
then arranged using a column-wise augmentation, obtaining 3 augmented
data matrices: ${\mathbf{D}}_{{\text{caug-April-NO}}_{\mathrm{2}}}$, ${\mathbf{D}}_{{\text{caug-April-O}}_{\mathrm{3}}}$ and
${\mathbf{D}}_{{\text{caug-April-PM}}_{\mathrm{10}}}$. The first two augmented matrices (NO_{2} and
O_{3}) contained concentration measures for the month of April for each
station and each year folded one on top of the other, first performing the
augmentation for the 3 years (30×3) and then for the eight stations ($\mathrm{30}\times \mathrm{3}\times \mathrm{8}$), as shown in Fig. 1a. The resulting dimensions of these two
column-wise augmented data matrices for further MCR-ALS analysis were 720×24. However, as previously stated, for PM_{10}, data of 3 months were
missing and thus, the final column-wise augmented matrix was built only with
the six stations containing no missing data ($\mathrm{30}\times \mathrm{3}\times \mathrm{6}$), resulting in a (540×24) matrix (yellow-shaded area in Fig. 1a).

To conduct the analysis of the entire 3 years, data matrices for
NO_{2}, O_{3}, and PM_{10} were also separately arranged in a second
step. For each contaminant, a total of 24 data matrices, one per year and
per monitoring station, of size 365×24 (year days × hourly measurements), were obtained.

These 24 data matrices were then arranged using a column-wise arrangement,
obtaining three augmented data matrices: ${\mathbf{D}}_{{\text{caug-allyear-NO}}_{\mathrm{2}}}$,
${\mathbf{D}}_{{\text{caug-allyear-O}}_{\mathrm{3}}}$, and ${\mathbf{D}}_{{\text{caug-allyear-PM}}_{\mathrm{10}}}$ (Fig. 1b). In this
case, for the three contaminants, some data were missing (white gaps in the
figure). For NO_{2} and O_{3}, some data from the Observatori Fabra station
was missing. Thus, the resulting augmented matrices, ${\mathbf{D}}_{{\text{caug-allyear-NO}}_{\mathrm{2}}}$ and
${\mathbf{D}}_{{\text{caug-allyear-O}}_{\mathrm{3}}}$, contained information of the whole year, seven
stations and the 3 years ($\mathrm{365}\times \mathrm{3}\times \mathrm{7}$), resulting in 7655×24 matrices, as shown in the figure. For PM_{10}, data of three stations were missing (white gaps in the figure) corresponding to Gràcia, Begur and
Observatori Fabra. Thus, in order to perform the MCR-ALS analysis, the
resulting PM_{10} column-wise augmented matrix only contained information
of five stations with no missing data ($\mathrm{365}\times \mathrm{3}\times \mathrm{5}$), resulting in a 5475×24 matrix (yellow-shaded area in Fig. 1b).

Data arrangement for the simultaneous study of the three pollutants considering the whole incomplete multiblock experimental data sets is further described in Sect. 2.7.

## 2.4 Estimation of missing data

Estimation of missing data was used for the case when failures of stations
and/or their malfunction caused the absence of measurements for a few hours
or a few days. In order to estimate such missing data, the nearest-neighbor
method (Peterson, 2009) (i.e., knn imputation) was used. In this
study, the function *mdcheck* (i.e., missing data checker and infiller) of PLS Toolbox
version 8.9.1 (Eigenvector Inc., WA) was utilized to perform the imputation.
This function checks for missing data and infills them using a PCA model
imputation from distinct algorithms. In our case, three algorithms were
tested consisting of “SVD” (Singular Value Decomposition), “NIPALS”
(Nonlinear Iterative Partial Least Squares) and “knn”, the latter providing
the better estimation results in our case, and thus, the one that was
finally used in this study.

It is important to mention that estimation of missing data was not performed in cases where the entire month was missing. For those cases, the station was not included in the MCR-ALS analysis of the complete data set. For the analysis of incomplete multiblock data sets, an especial arrangement was performed using a particular data fusion strategy, as further explained in Sect. 2.7.

## 2.5 MCR-ALS analysis of the experimental data

Different chemometric methods have been proposed in the literature for the analysis of environmental monitoring data. The MCR-ALS method is frequently used in spectrochemical mixture data analysis, which can also be easily extended to the analysis of environmental source apportionment data sets (Alier et al., 2011). The MCR-ALS is a flexible, soft-modeling factor analysis tool which allows for the application of natural constraints (see below), and it can be easily adapted to the analysis of complex multiway (multimode) data structures, such as three- and four-way environmental data sets using trilinear and quadrilinear model constraints (De Juan et al., 1998; Smilde et al., 2004; Malik and Tauler, 2013).

The simplest application of the MCR-ALS method is based on a bilinear model that performs the factor decomposition of a two-way data set (i.e., a data table or a data matrix). Equation (1) summarizes this bilinear model in its element-wise way, while Eq. (2) presents the same model in a matrix linear algebra format:

In Eq. (1), the individual data values, **d**_{i, j} elements (in this case the
O_{3}, NO_{2} or PM_{10} concentration values measured on 1 d (*i*) at
a particular hour (*j*)) are decomposed as the sum of a reduced number of
contributions (components), *n*=1, … *N*. Each one of them are defined by the product of two factors, **x**_{i, n} (scores) and **y**_{j, n} (loadings). In
addition, the term **e**_{i, j} is the residual part of **d**_{i, j}, which cannot
be explained by these *N* components and accounts as experimental noise and
uncertainties. In Eq. (2), the data matrix, **D**, of dimensions *I*×*J* is
decomposed into the scores factor matrix **X** (*I*×*N*) and the loadings factor
matrix, **Y**^{T} (*N*×*J*). The number of components, *N*, is selected to explain the data variance as much as possible, while the unexplained small
contributions of data variance and experimental noise are in **E**. Multivariate
Curve Resolution (MCR; Tauler, 1995) performs the
bilinear model factor decomposition shown in Eqs. (1) and (2) using an
alternating least squares (ALS) algorithm under a set of constraints
which reduce the extent of the bilinear model rotation
ambiguities (Abdollahi and Tauler, 2011) and allow
the physical identification and interpretation of the factor matrices
**X** and **Y**^{T}, for example, the application of non-negativity constraints to
the elements of the factor matrices **X** and
**Y**^{T} (Bro and De Jong, 1997; De Juan and
Tauler, 2003). Models with a different number of components can be tested and
a final decision is taken considering the data fit and the shapes and
reliability of the resolved profiles. The ALS algorithm also needs initial
estimates of either **X** or **Y**^{T} factor matrices. These initial estimates
can be obtained from the more “dissimilar” rows or columns of the original
data matrix (Winding and Guilment, 2002). Equation (2) for **D** is
solved iteratively, which updates the solutions (vector profiles in **X** and **Y**^{T} matrices) until they fit the data optimally and fulfill the proposed constraints.

In this work the MCR-ALS method has been applied, either to the individual
data matrices **D**_{station, year} described in the previous section and in Fig. 1
for every pollutant (O_{3}, NO_{2} or PM_{10}), at one period of time
(April or full year), and at one monitoring station, or to the augmented
matrices of the same three pollutants for the 3 years
($k=\mathrm{1},\phantom{\rule{0.125em}{0ex}}$… 3) simultaneously and for the different stations
(*l*=1, … ,8), concatenated vertically in **D**_{caug-April} or
**D**_{caug-allyear} (see Fig. 1). In the case of the individual data matrices
described above for the period of time (April or full year),
**D**_{station,april} or **D**_{station, year}, the factor (scores) matrix **X**
will have the April or full year day profiles of the components, respectively, and the factor (loadings) matrix **Y**^{T} will have the corresponding hour
profiles of these components. In the case of the column-wise augmented data
matrices **D**_{caug-April} or **D**_{caug-allyear}, bilinear model Eq. (2) was
extended as

where **X**_{caug} is now the augmented factor (scores) matrix with the
augmented day profiles concatenated vertically for the different years and
stations, and **Y**^{T} is the matrix of the hour profiles again, which are
common for all the concatenated matrices in **X**_{caug}. During the ALS
optimization of the bilinear model in Eq. (3), constraints can be also
applied, and the same aspects relating to the number of components and
convergence as for solving Eq. (2), are considered.

## 2.6 MCR-ALS analysis of the complete experimental data sets using trilinear and quadrilinear constraints

Solving Eq. (3) using bilinear MCR-ALS does not take into account the temporal and spatial structure of the data in the vertical concatenated mode, which includes the information of the day, year and station. This data structure can be considered in the trilinear and especially in the quadrilinear extensions of the bilinear models described in Eqs. (1)–(3).

The factor decomposition model given before can be extended to a three-way dataset, $\underset{\mathrm{\xaf}}{\mathbf{D}}$, or to a four-way data set, $\underset{=}{\mathbf{D}}$, expressed individually for every data value as given by Eqs. (4) and (5).

where ${d}_{i,\phantom{\rule{0.125em}{0ex}}j,\phantom{\rule{0.125em}{0ex}}kl}$ are the individual data values (concentrations of
O_{3}, NO_{2} or PM_{10}) in the four experimental data modes: the day
of April or of the full year $i=\mathrm{1},\phantom{\rule{0.125em}{0ex}}\mathrm{\dots}\phantom{\rule{0.125em}{0ex}}\mathrm{30}$ or $i=\mathrm{1},\phantom{\rule{0.125em}{0ex}}\mathrm{\dots}\phantom{\rule{0.125em}{0ex}},\mathrm{365}$, the
hour of the day $j=\mathrm{1},\phantom{\rule{0.125em}{0ex}}\mathrm{\dots},\mathrm{24}$, and the year–station $kl=\mathrm{1},\phantom{\rule{0.125em}{0ex}}\mathrm{\dots}\phantom{\rule{0.125em}{0ex}}\mathrm{24}$
in the case of the trilinear model, and ${d}_{i,\phantom{\rule{0.125em}{0ex}}j,\phantom{\rule{0.125em}{0ex}}k,\phantom{\rule{0.125em}{0ex}}l}$ has the
year–station third mode separated in year $k=\mathrm{1},\mathrm{2},\mathrm{3}$, and station,
$l=\mathrm{1},\phantom{\rule{0.125em}{0ex}}\mathrm{\dots}\phantom{\rule{0.125em}{0ex}},\mathrm{8}$ in the quadrilinear model. These data values are
modeled as the sum of a number of components (contributions), $n=\mathrm{1},\phantom{\rule{0.125em}{0ex}}\mathrm{\dots}\phantom{\rule{0.125em}{0ex}}N$,
defined by the product of three factors **x**_{i, n}, **y**_{j, n}, and
**z**_{kl, n} in the case of the trilinear model and in four factors in the
case of the quadrilinear model,= **x**_{i, n}, **y**_{j, n}, **z**_{k, n} and
**w**_{l, n}. These factors are related to the three and four data modes,
respectively (day, hour and year–station or day, hour, year and station).
${\mathbf{e}}_{i,\phantom{\rule{0.125em}{0ex}}j,\phantom{\rule{0.125em}{0ex}}kl}$ and ${\mathbf{e}}_{i,\phantom{\rule{0.125em}{0ex}}j,\phantom{\rule{0.125em}{0ex}}k,l}$ are the part of ${\mathbf{d}}_{i,\phantom{\rule{0.125em}{0ex}}j,\phantom{\rule{0.125em}{0ex}}kl}$ and
${\mathbf{d}}_{i,\phantom{\rule{0.125em}{0ex}}j,\phantom{\rule{0.125em}{0ex}}k,l}$ not explained by the contribution of these *N* components. These
trilinear and quadrilinear models can be written in a matrix form using the
decomposition of every individual **D**_{kl} data slice (every individual
matrix **D**_{kl}), as shown in Eqs. (6) and (7).

Under the trilinear model, all individual data matrices, **D**_{kl}, (*I*, *J*) are
simultaneously decomposed with the same number of components *N* and the same
daily **X** (*I*, *N*) and hourly **Y**^{T} (*N*, *J*) profiles. Thus, they differ only in
a diagonal matrix **Z**_{kl} (*N*, *N*), different for every one of the
$kl=\mathrm{1},\phantom{\rule{0.125em}{0ex}}\mathrm{\dots}\phantom{\rule{0.125em}{0ex}},\mathrm{24}$ year–stations (year–station profiles), which gives
the relative amounts of the *N* components in every data matrix
(year–station), **D**_{kl}. These *N* diagonal elements of **Z**_{kl} can also
be grouped in the third factor matrix **Z** ($K\times L,\phantom{\rule{0.125em}{0ex}}N$). Under the quadrilinear
model, all individual data matrices, **D**_{kl}, (*I*, *J*) are simultaneously
decomposed with the same number of components *N* and the same daily **X** (*I*, *N*)
and hourly **Y**^{T} (*N*, *J*) profiles. Thus, they differ in the diagonal
matrices **Z**_{k} (*N*, *N*) and **W**_{l} (*N*, *N*), which are different for every
year (*k*) and station (*l*), which give the relative amounts of the *N*
components in every data matrix **D**_{kl}, respectively. These *N* diagonal
elements of the **Z**_{k} and **W**_{l} matrices can also be grouped in the
third- and four-factor matrices **Z** (*K*, *N*) and **W** (*L*, *N*). Therefore, the proposed
trilinear and quadrilinear models take advantage of the natural structure of
the analyzed data sets, especially in relation to their different temporal
modes (i.e., hourly, daily, yearly) and the different type of monitoring
stations analyzed simultaneously. The implementation of trilinear and
quadrilinear models as a constraint in the MCR-ALS method has been described
in previous works (Tauler, 2021; Malik and Tauler, 2013; Alier et al., 2011). Here only a brief explanation of the implementation of the quadrilinear model constraint for the case of study is shown.

Figure 2 shows the practical implementation of the quadrilinearity constraint
in the MCR-ALS analysis of the four-way data set obtained in the two types of
data, when the April data of the three parameters (O_{3}, NO_{2} and
PM_{10}) were studied over the 3 years (2018–2019 and 2020), and over
the different monitoring stations described above, and also for the
analogous four-way data set when instead of April data, the full-year data
were considered for the same years and stations.

The individual data sets with the concentrations of the three parameters
(one per year and station), were arranged in the column-wise augmented data
matrix **D**_{caug} of dimensions 30 (April) or 365 d × 3 years × 8 stations, giving a total number of 720 rows for April data or of 8760 rows for the full-year data, and 24 hourly measures in columns. These number of row elements is for the case of no-missing data, however they will be lowered for the cases of missing data, especially in the case of the full-year data (see previous section in missing data). The application of the quadrilinearity constraint implies that the augmented profiles of every component *n*, ${\mathbf{x}}_{\mathrm{aug}}^{n}$, having the vertically concatenated information
of days × years × stations is first refolded in the data matrix
${\mathbf{X}}_{\mathrm{aug}}^{n}$ of dimensions 30 (April) or 365 (all year) × 3 (years)
rows by 8 (stations). This augmented factor matrix is decomposed by SVD
considering only the first singular component into the product of two vector
profiles, one long vector profile ${\mathbf{x}}_{\mathrm{caug}}^{n}$ (90 or 1095×1) of the
combined day–year profile by a vector profile **w**^{n} (8×1) describing
differences among the different stations for the component *n*.
The ${\mathbf{x}}_{\mathrm{caug}}^{n}$ long vector day–year profile can be further refolded in a matrix and decomposed by SVD into the product of two new vector profiles, one related to the year profile, **z**^{n}, and another to the day profile **x**^{n}, for the considered component *n*. In this way, for every component (contribution), the concentration of any one of the three parameters (O_{3}, NO_{2}, and PM_{10}) is decomposed in the product of four profiles, one related to the day (of April or of the whole year), **x**^{n}, another related to the hour of the day, **y**^{n}, another to the considered year, **z**^{n}, and another to the monitoring station, **w**^{n}.
This factor decomposition allows a detangled interpretation of the temporal
and spatial sources of variation for the observed concentrations of the three
pollutants. Therefore, the application of this quadrilinearity constraint
implies that for every component, the daily changes are described by the
same single **x**^{n} vector profile which changes over the years and station
by station by the corresponding scalar values in **z**^{n} and **w**^{n}. Once
the three profiles in the three augmented modes, **x**_{n}, **z**_{n} and
**w**_{n}, are obtained, they can be multiplied using the Kronecker
product (Soloveychik and Trushin, 2016) to
reconstruct the long vector profile, ${\mathbf{x}}_{\mathrm{aug}}^{n}$, (see Fig. 2) and
rebuild the bilinear model in the next iteration of the general ALS
optimization. Finally, the vector profiles for every component *n* in the
different modes, can be grouped in the corresponding factor matrices **X**, **Z**,
and **W**, which together with **Y**^{T} give the full quadrilinear decomposition of
the four-way data set, $\underset{=}{\mathbf{D}}$. See previous works for a more detailed description of the algorithm used for the practical implementation of the quadrilinearity constraint in MCR-ALS (De Juan and Tauler, 2001; De Juan et al., 1998).

## 2.7 MCR-ALS simultaneous analysis of incomplete multiblock experimental data

The simultaneous analysis of the NO_{2}, O_{3}, and PM_{10}
experimental data can be done one step forward using a data fusion
multiblock strategy. This would imply building a single MCR model for the
whole multiset data obtained for the three pollutants, NO_{2}, O_{3}, and
PM_{10} in April or in the whole year, for the 3 years, 2018, 2019, and
2020, and for the different monitoring stations. This is expressed in the
following data matrix equation (see also Fig. 3):

In this Equation the column-wise augmented data matrices, ${\mathbf{D}}_{{\mathrm{caugNO}}_{\mathrm{2}}}$,
${\mathbf{D}}_{{\mathrm{caugO}}_{\mathrm{3}}}$, and ${\mathbf{D}}_{{\mathrm{caugPM}}_{\mathrm{10}}}$ described previously (and analyzed
separately by MCR-ALS with different factor decomposition models, bilinear,
trilinear and quadrilinear), are now concatenated horizontally giving the
new single row and column-wise super-augmented data matrix **D**_{craug}, which
is decomposed in the two new augmented factor matrices, **X**_{caug} and
${\mathbf{Y}}_{\mathrm{raug}}^{T}$, using the MCR bilinear model and constraints, like it was
described in Sect. 2.4. The resolved hour profiles for the three contaminants
${\mathbf{Y}}_{{\mathrm{NO}}_{\mathrm{2}}}^{T}$, ${\mathbf{Y}}_{{\mathrm{O}}_{\mathrm{3}}}^{T}$, and ${\mathbf{Y}}_{\mathrm{PM}}^{T}$ will be in the augmented rows of the new ${\mathbf{Y}}_{\mathrm{raug}}^{T}$. In addition, if the
trilinearity/quadrilinearity constraints are applied to the columns of the
resolved factor matrix **X**_{caug} as described above in Sect. 2.6 using
matrix decompositions of Eqs. (6) and (7), the common day, year and station
profiles will be separately recovered and analyzed.

However as previously described, April and the whole-year individual data
sets were not obtained for all stations, years, and pollutants. Therefore,
they could not be fitted together in a rectangular super-augmented data
matrix containing all the data for all the years and stations as shown in
Eq. (8) for **D**_{craug}. Some of the individual data sets were missing (see Sect. 2.3 and 2.4). In particular, in the case of April, two different data blocks could be arranged. First, the NO_{2}, O_{3} and PM_{10} concentrations data for 3 years and 6 stations were arranged in the complete
row- and column-wise augmented April data block, **DA****1**_{craug}, with 540
rows (30 d × 3 years × 6 stations) and 72 columns (24 h for NO_{2} + 24 h for O_{3} + 24 h for PM_{10}). Secondly, the
additional NO_{2} and O_{3} concentration data for 3 years and 2
stations were arranged in the complete row- and column-wise augmented April data
block **DA****2**_{craug} with 180 rows (30 d × 3 years × 2 stations) and 48
columns (24 h for NO_{2} + 24 h for O_{3}). These two April
data blocks can be analyzed independently, but a new data set can be built
concatenating the two data blocks as shown in Fig. S1, which will be
reformulated and analyzed as shown in the next Equation.

This new incomplete data set **DA****12**_{craug} is built using the two data
blocks previously defined, **DA****1**_{craug} and **DA****2**_{craug}, both
concatenated vertically and filling the empty data block, corresponding to
the unknown concentrations of PM_{10} for two missing stations with the
NaN notation. The application of MCR-ALS to this incomplete data set is
decomposed using a bilinear model (see in Fig. 3), giving the two factor
matrices **XA****12**_{caug} and $\mathbf{YA}{\mathbf{12}}_{\mathrm{raug}}^{T}$. The factor matrix **XA****12**_{caug} has the column-wise augmented day × year × station profiles in its columns
and the $\mathbf{YA}{\mathbf{12}}_{\mathrm{raug}}^{T}$ factor matrix has the row-wise augmented hour profiles for NO_{2} (${\mathbf{Y}}_{{\mathrm{NO}}_{\mathrm{2}}}$), O_{3} (${\mathbf{Y}}_{{\mathrm{O}}_{\mathrm{3}}}$), and PM_{10}
(${\mathbf{Y}}_{{\mathrm{PM}}_{\mathrm{10}}}$) in its rows. As previously stated, the trilinearity/quadrilinearity constraints can be applied during the ALS factor decomposition to the
**XA****12**_{caug} factor matrix and allow the separate recovery of the day, year
and station profiles, a part of the hour profiles for NO_{2}, O_{3} and
PM_{10 }obtained in $\mathbf{YA}{\mathbf{12}}_{\mathrm{raug}}^{T}$.

Analogous equations can be described for the NO_{2}, O_{3}, and PM_{10}
experimental data measured not only in April but during the whole year. In
this case however, the two data blocks, **DY****1**_{craug} and **DY****2**_{craug},
will have different sizes than for the only April month data because they
are for all the days of the whole year. Different data sets were missing in
this case. The data for 5 stations with 5475 rows (365 d × 3 years × 5 stations) and 72 columns (24 h for NO_{2} + 24 h for O_{3} + 24 h for PM) is contained in **DY****1**_{craug}, and **DY****2**_{craug} has the
additional data for 2 stations, but only for NO_{2} and O_{3}
concentrations, with 2190 rows (365 d × 3 years × 2 stations) and 48
columns (24 h for NO_{2} + 24 h for O_{3}) (see Fig. S2). For
the whole year data, the bilinear factor decomposition can be described by
the new Eq. (10):

where **DY****12**_{craug} is now the new incomplete data set built with the two data blocks **DY****1**_{craug} and **DY****2**_{craug} concatenated vertically, and **NaN**
(2190,24) is for the missing PM_{10} concentrations during 3 years in the
missing 2 stations (see Fig. S2 in the Supplement). The two factor matrices, **XY****12**_{caug} and
$\mathbf{YY}{\mathbf{12}}_{\mathrm{raug}}^{T}$, are now obtained in the
bilinear decomposition of **DY****12**_{craug}. The first factor matrix **XY****12**_{caug} has the column-wise augmented day × year × station profiles
in its columns and the second factor matrix $\mathbf{YY}{\mathbf{12}}_{\mathrm{raug}}^{T}$ has the hour profiles for NO_{2}, O_{3}, and PM_{10} in its rows. Similarly, as previously stated, the trilinearity/quadrilinearity constraints applied during the ALS factor decomposition to the **XY****12**_{caug} factor matrix will allow the
separate recovery of the day, year and station profiles, apart from the hour
profiles for NO_{2} (${\mathbf{Y}}_{{\mathrm{NO}}_{\mathrm{2}}}$), O_{3} (${\mathbf{Y}}_{{\mathrm{O}}_{\mathrm{3}}}$), and
PM_{10} (${\mathbf{Y}}_{{\mathrm{PM}}_{\mathrm{10}}}$) obtained in $\mathbf{YY}{\mathbf{12}}_{\mathrm{raug}}^{T}$. The difference with
the results of April data is that now the column-wise augmented profiles in
**XY****12**_{caug} will have information about the 365 d of the whole year and
not only for the 30 d of April. Figure S3 in the Supplement is given to graphically illustrate the bilinear model applied to the incomplete two-block data set.

In the proposed approach, missing data blocks were not included in the least
squares estimations of the factor solutions
(**XY****12**_{caug} and [${\mathbf{Y}}_{{\mathrm{NO}}_{\mathrm{2}}}$, ${\mathbf{Y}}_{{\mathrm{O}}_{\mathrm{3}}}$, ${\mathbf{Y}}_{{\mathrm{PM}}_{\mathrm{10}}}$] in Eq. 10). On the one hand, this is an advantage of the proposed method since linear
equations are only solved for the known data blocks; but on the other hand,
some data regions of the factor solutions (those corresponding to the
missing data blocks, NaN block in Eq. 10) will not give so much
overdetermined linear equations from a least squares point of view as the
other data blocks without missing values. Therefore, this can be
reflected in the reliability of some parts of the factor estimations. This
is an important aspect that needs further investigation and some research is
pursued in this direction.

## 2.8 Evaluation of MCR-ALS results

The final evaluation of the MCR-ALS fitting results is performed calculating
the explained data variances (*R*^{2}) using Eq. (11):

where **d**_{ij} are the experimental predicted O_{3}, NO_{2}, or
PM_{10} concentrations, and ${\widehat{d}}_{ij}$ are the corresponding
calculated values by MCR-ALS using either the bilinear (Eqs. 1–3),
trilinear (Eqs. 4 and 6), or quadrilinear (Eqs. 5 and 7) models. Apart
from the global fitting with the full model (all components), the explained
variances can also be calculated individually for every MCR-ALS component,
where now the calculated values, ${\widehat{d}}_{ij}$, only take one
of the n components of the model into account. In this way, the relative importance of the different contributions can be evaluated, as well as their overlapping degree with the other contributions or components.

## 2.9 Software

The development platform MATLAB 9.10.0 R2021a (The MathWorks, Inc., Natick, MA, USA) was used for data analysis and visualization. The new graphical
interface MCR-ALS GUI 2.0 (Malik and Tauler,
2013), freely available as a toolbox at the web address
http://www.mcrals.info/ (last access: 6 July 2022), was used for bilinear and trilinear data sets.
Statistics Toolbox^{TM} for MATLAB and PLS Toolbox 8.9.1 (Eigenvector
Research Inc., Wenatchee, WA, USA) were also used in this work. A new specific MCR-ALS command line code for incomplete multiblock data sets is under final development and it can be requested from one of the authors (RT,
email:roma.tauler@idaea.csic.es).

Results of MCR-ALS will be shown separately for the analysis of the month of
April and for the analysis of the entire years. In the study of the month of
April, the individual analysis of the three contaminants per separate is
initially performed, using only data from stations with no missing blocks
(i.e., data matrices ${\mathbf{D}}_{{\text{caug-April-NO}}_{\mathrm{2}}}$, ${\mathbf{D}}_{{\text{caug-April-O}}_{\mathrm{3}}}$ and
${\mathbf{D}}_{{\text{caug-April-PM}}_{\mathrm{10}}}$, yellow-shaded area of Fig. 1a). Then, a simultaneous
analysis of the three contaminants containing incomplete data is performed
(i.e., data matrix **D**_{A12craug}, Fig. S1). In the study of the entire years,
again the individual analysis of the three contaminants per separate is
initially performed, using only data from stations with no missing blocks
(i.e., data matrices ${\mathbf{D}}_{{\text{caug-allyear-NO}}_{\mathrm{2}}}$, ${\mathbf{D}}_{{\text{caug-allyear-O}}_{\mathrm{3}}}$ and
${\mathbf{D}}_{{\text{caug-allyear-PM}}_{\mathrm{10}}}$, yellow-shaded area of Fig. 1b). Then, a
simultaneous analysis of the three contaminants containing incomplete data
is performed (i.e., data matrix **D**_{Y12craug}, Fig. S3). In all cases the
selection of the number of components and the initial estimates for MCR-ALS
were performed as described in Sect. 2.5. A summary of the explained
variances of the MCR-ALS analyses for the different data sets with
non-negativity and either bilinear, trilinear or quadrilinear modeling, and
with a different number of components is given in Table 1.

^{a} MCR-ALS for raw data with non-negativity constraint.
^{b} MCR-ALS for raw data with non-negativity and quadrilinearity constraint.
^{c} MCR-ALS for raw data with non-negativity and trilinearity constraint.
^{d} Augmented data matrices and number of components (see Fig. 1, Eqs. 3, 9 and 10, and explanation in “Data sets arrangement” section).

The MCR-ALS bilinear analysis of April data in the ${\mathbf{D}}_{{\text{caug-April-NO}}_{\mathrm{2}}}$, ${\mathbf{D}}_{{\text{caug-April-O}}_{\mathrm{3}}}$ and ${\mathbf{D}}_{{\text{caug-April-PM}}_{\mathrm{10}}}$ data matrices with non-negativity constraints
explained respectively 94.40 %, 98.4 % and 91.8 % of the total
variance when four, three, and three components were considered (Table 1).
These values indicate the higher complexity of the NO_{2} data compared to
O_{3} data, as will be shown also below. When the quadrilinearity constraint
was applied, these values decreased to 78.4 %, 92.9 % and 78. 4 %
respectively, reconfirming the less complex and more regular changes of
O_{3} concentrations in the 3 years at the different monitoring
stations. Variances explained by the individual components are given in the
figures shown below. The amount of variance overlap (also given in Table 1)
in every case can be obtained by subtracting the sum of the individual
variances from the variance obtained with all the components simultaneously.
Again, this difference is larger in the case of NO_{2}. In Table 1,
the variances obtained when the trilinearity constraint was applied, instead
of the quadrilinearity constraint, are also given, with similar results to
those obtained by both multilinear models. In the case of MCR-ALS for
all-year data of the ${\mathbf{D}}_{{\text{caug-allyear-NO}}_{\mathrm{2}}}$, ${\mathbf{D}}_{{\text{caug-allyear-O}}_{\mathrm{3}}}$ and
${\mathbf{D}}_{{\text{caug-allyear-PM}}_{\mathrm{10}}}$ data matrices, rather similar results to those from
April were obtained in terms of explained variances for all three types of
models (see Table 1), again reflecting the higher complexity of the NO_{2} data
over the years and stations compared to O_{3} data, and the intermediate
behavior of PM_{10} data, although the latter is more similar to the NO_{2} data.

Possible correlations between NO_{2}, O_{3}, and PM_{10} data sets
during the month of April of 2018, 2019, and 2020 as well as in the eight stations
were investigated using the incomplete data arrangement described in Sect. 2.7 and Fig. S1. The MCR-ALS analysis of **DA****12**_{craug} with five components and
with only negativity constraints gave a total explained variance of 96.2 %
(Table 1). When the quadrilinearity constraint was applied, the total
explained variance decreased to 90.7 % (91.2 % for the trilinear MCR-ALS model). Such a decrease of only 5 % between bilinear and quadrilinear MCR-ALS models indicated a good quadrilinear behavior of the whole system in April. The explained variances of each component individually are given
below with the corresponding figures of the resolved profiles. The case
of the simultaneous study of NO_{2}, O_{3}, and PM_{10} profiles along
all 3 years (i.e., 2018, 2019 and 2020) in the seven stations using the
incomplete data arrangement is described in Sect. 2.7 and Fig. S3. Results
using five components also indicated a rather good quadrilinear behavior of
the system. A more detailed description of the profiles, describing the
concentration changes of the three pollutants and of the behavior of the
whole systems formed by all of them in the different stations and during the
3 years, separately for April and for the entire year, is given below.

## 3.1 Study of the month of April

### 3.1.1 NO_{2} study (${\mathbf{D}}_{{\text{caug-April-NO}}_{\mathrm{2}}}$ data matrix)

In Fig. 4, from left to right, the profiles of the different modes of the four components are shown: X – day (blue), Z – year (black), W – station (green), and Y – hours (red). Component profiles in the four modes obtained by MCR-ALS when using non-negativity and quadrilinearity constraints are shown in Fig. 4.

The NO_{2} hour profile of the first component (C1) showed a narrow maximum
between 09:00–11:00 LT (Spain, throughout) coincident with the rush-hour traffic and due to
fuel combustion by vehicles. In the second component (C2) this hour profile
presented a much wider peak during daily hours (10:00–20:00 LT), again
potentially attributed to the combined effects of traffic emissions and
O_{3} formation (see below). The third component (C3) reached an hourly maximum in the late evening, approximately at 22:00 LT, whereas the fourth component
(C4) showed a maximum between 00:00 and 05:00 LT, describing the NO_{2}
nighttime behavior. As observed in the year profiles (Z-mode), for all the
components, NO_{2} contributions showed a significant decrease in 2019 and
even higher in 2020 with respect to 2018; the latter possibly attributed to the COVID-19 curfew and mobility restrictions. Moreover, as observed in the
station profiles (W-mode), such a depletion was consequently more notorious in the three urban stations (Vall d'Hebron (1), Granollers (2) and Gràcia (6)), which were the stations with higher NO_{2} concentration levels.
Considering that the principal emission source of NO_{2} is traffic, it is
reasonable that the four MCR-ALS resolved components evidenced a decline in
the year-mode, corresponding to a diminution in April 2020 (under the
strictest lockdown), compared to 2019 (under no pandemic) and 2018 (under
no pandemic and no other traffic restrictions in Barcelona, such as the low
emission zones (LEZs; LEZ – Àrea Metropolitana de Barcelona,
2020). Moreover, as stated in a previous study of the authors (Gorrochategui et al., 2021), in April 2020 a historical record of rainfall was registered in the control site of Observatory Fabra (8). Therefore, the highly rainy
conditions of April 2020 favored the cleansing of the atmosphere, including
NO_{2} gases. Finally, the day profiles (X-mode) for the different
components did not show any particular pattern for the different days of the
month.

### 3.1.2 O_{3} study (${\mathbf{D}}_{{\text{caug-April-O}}_{\mathrm{3}}}$ data matrix)

Profiles obtained by MCR-ALS for the three components using non-negativity
and quadrilinearity constraints are shown in Fig. 5. The MCR-ALS hourly (Y-mode)
resolved profiles of the first component (C1) showed a maximum between 14:00–22:00 LT, due to the cumulative solar radiation. There was practically no difference in this component among stations, years or the days
of the month. The MCR-ALS hourly resolved profile of the second component (C2) showed a different O_{3} profile, corresponding to the concentration at night. As observed in the W-mode, O_{3} concentration at night was higher in the rural station of Begur (7) and in the control site Observatori Fabra (8). The
latter is emplaced in Collserola mountain and only receives some impact from
Barcelona's city. The higher nightly O_{3} concentration observed in these
stations is due to the fact that in inner rural areas, as well as in the
control site, with low anthropogenic activities, the titration effect
(i.e., O_{3} destruction under no solar radiation) produced by NO_{2} emissions is generally not observed, resulting in higher average O_{3}
concentrations than in urban areas. Finally, the third component (C3) also showed a maximum between 16:00–21:00 LT, similar to the behavior described
by C1, but narrower and with a pattern among stations different to C1.

### 3.1.3 PM_{10} study (${\mathbf{D}}_{{\text{caug-April-PM}}_{\mathrm{10}}}$ data matrix)

Profiles obtained by MCR-ALS for these three components using non-negativity
and quadrilinearity constraints are shown in Fig. 6. The MCR-ALS hourly resolved profiles in the Y-mode for the three resolved components indicated a wide maximum between 00:00–15:00 LT (C1), between 15:00–22:00 LT (C2), and
between 10:00–20:00 LT (C3). As observed in the year profile (Z-mode), the PM_{10} contribution decreased in 2019 but most significantly in 2020,
probably due to the COVID-19 lockdown period. This same behavior was observed for NO_{2} (Fig. 4), and it is due to the fact that among the PM_{10}
sources, traffic should also be included. Moreover, such a depletion was more
evident in the urban stations profile (W-mode) of Vall d'Hebron (1), Granollers (2), and Gràcia (6).

### 3.1.4 NO_{2}, O_{3} and PM_{10} simultaneous study (**DA****12**_{craug} data matrix)

The MCR-ALS resolved profiles of the **DA****12**_{craug} data matrices (see “Materials and methods” Sect. 2.7) are given in Fig. 7. Results obtained for the hour profiles
(Y-mode, in red) of the three pollutants, NO_{2}, O_{3} and
PM_{10}, are overlaid in the same plot with the same time axis. In
this way, possible correlations among the different pollutants can be better
explored in these plots. Profiles of components 1 (C1) and 2 (C2) mostly
described the O_{3} pollution: C1 hour profile showed an O_{3} daytime
profile with a wide maximum between 12:00–22:00 LT and C2 described the
O_{3} nighttime profile, again with a large maximum between 00:00–10:00 LT. Component 3 (C3) described both PM_{10} and NO_{2} correlated
pollution sources, with PM_{10} having the highest contribution.
The correlation between NO_{2} and PM_{10} can be due to the common
sources of these contaminants (i.e., traffic and industry). Component 4 (C4)
described the nighttime profile of NO_{2} and lastly, component 5 (C5) showed the daily NO_{2} profile with two maxima, one in the morning
(10:00–15:00 LT) and another in the late evening (20:00–22:00 LT), probably
attributed to the traffic. From the year profiles in Z-mode, the evolution
of the pollution in the month of April along 2018, 2019 and 2020 could be
elucidated. Interestingly, for C1 and C2 (mostly describing O_{3}
pollution), the variation remained rather constant for the month of April
during these 3 years. Moreover, the variation among stations in the
profiles (W-mode) for the first two components was very little. Only in
C2, the stations of Begur (7) and Observatori Fabra (8) showed a higher
O_{3} contribution, probably due to the lower titration effect produced in
rural areas and in the Observatori Fabra control site (8), as previously
observed in the individual MCR-ALS analysis of O_{3} data. In contrast,
the variation among stations and among years was more significant for the
rest of the components (C3–C5), mainly describing NO_{2} and PM_{10}
contamination. As observed in Z-modes, contamination by NO_{2} and
PM_{10} was lower in 2019 and even lower in 2020. Considering that the
most important source of NO_{2} is traffic, the decrease in 2019 can be
explained by the implementation of LEZs (LEZ – Àrea Metropolitana de Barcelona, 2020) in
Barcelona, as a traffic restriction policy, first implemented in 2017 and
finally put into permanent effect on 1 January 2020. However, the decrease
observed in 2020 might be mostly associated with the COVID-19 lockdown
restrictions, being April 2020, the time when the strictest confinement was
declared in Catalonia (Gobierno de España, 2020a). Regarding the variation among stations, C3–C5 showed a higher NO_{2} and PM_{10} contribution in
three urban stations (Vall d'Hebron (1), Granollers (2), and Gràcia (6)), which is in accordance with the higher traffic density registered on these sites. The results observed for C3–C5 regarding NO_{2} and PM_{10}
pollution were in concordance with those of their respective individual models, evidencing the good performance of the MCR-ALS simultaneous analysis of the incomplete multiblock data sets and the confirmation of the reliability of the proposed approach.

## 3.2 Study of the entire years

### 3.2.1 NO_{2} study (${\mathbf{D}}_{{\text{caug-allyear-NO}}_{\mathrm{2}}}$ data matrix)

Profiles obtained by MCR-ALS using non-negativity and quadrilinearity
constraints are shown in Fig. S4. The hour profiles of the four resolved
components in the analysis of the entire years were similar to those obtained
in the analysis of the month of April: C1 hour profile in April's model was
equivalent to C3 hour profile in all years' model, and C2 and C4 hour
profiles were equivalent in both models. Also, the diminution observed in
Z-mode profile in 2019, and to a greater extent in 2020, in the month of April was also produced when analyzing all the years, but to a lesser extent. This might be due to the fact that the traffic restriction policies were mostly implemented during the strictest confinement (from 14 March to 4 May in Catalonia) and were gradually removed in the de-escalation
phases (Gorrochategui et al., 2021). Also, the extraordinary rainy conditions registered in April 2020 (Gorrochategui et al., 2021) were not registered for the rest of the months, making the
NO_{2} depletion less noticeable in the analysis of the entire year.
Regarding stations, the ones showing the higher contribution were the same
three urban stations (i.e., Vall d'Hebron (1), Granollers (2) and Gràcia (6)) observed
in the study of the month of April. In the day-of-year X-mode, some seasonal tendencies can be observed in C1 and C2, with their lower intensities in the middle of the profile corresponding to the warmer
seasons with higher sunlight radiation and higher NO_{2} depletion due to
the photochemical reaction to form O_{3}. The year profiles in C3 and C4 did not show major differences over the years.

### 3.2.2 O_{3} study (${\mathbf{D}}_{{\text{caug-allyear-O}}_{\mathrm{3}}}$ data matrix)

Only few differences between the analysis of the entire years versus that of
the month of April were observed in the inter-year Z-mode (Fig. S5).
Component 2 in all years' model, corresponding to a late evening peak of
O_{3}, suffered a slightly significant increase in 2019 and 2020 with respect to 2018, which was not observed in the analysis of the month of April. Such increments can be explained by the reduction of the titration effect, which
was a little higher when considering the entire years. The diminution of C3 in 2019 and in 2020 was also more evident when analyzing the entire years. In
this case, this component was associated with the daily maximum of O_{3},
coincident with the sunlight hours and summer and spring seasons, when the
photochemical reactions with NO_{x} take place to form O_{3}. The reason
why the changes in O_{3} were more evident when considering the all-year analysis instead of just the month of April, as opposed to what happened
with NO_{2}, might be due to the fact that despite the traffic
restrictions being gradually removed in the de-escalation phases, the curfew
policies remained, causing a potential cumulative suppression of the
titration effect.

### 3.2.3 PM_{10} study (${\mathbf{D}}_{{\text{caug-allyear-PM}}_{\mathrm{10}}}$ data matrix)

As with NO_{2} and O_{3}, the profiles of the components in the MCR-ALS analysis of PM_{10} of the all years' model were similar to those obtained in the analysis of the month of April (Fig. S6). The hour profiles of C1 and C3 in April's model were equivalent to that of C3 and C1 in all years' model, respectively, and C2 described the same PM_{10} profile in both models. Also, the diminution observed in 2019 and to a greater extent in 2020 in the month of April was
also produced when analyzing all the years, but to a lesser extent, as stated
for NO_{2}. Moreover, the meteorological stations with higher contribution
in the model were the same as in the model of April, except for Manlleu (3),
which showed a significant contribution in C2 of this model for the first
time when the entire years' PM_{10} data were investigated.

### 3.2.4 NO_{2}, O_{3} and PM_{10} simultaneous study (**DY****12**_{craug} data matrix)

The MCR-ALS resolved profiles of **DY****12**_{craug} are given in Fig. 8. As observed
in the Y-mode profiles, C1 and C2 mostly described O_{3}
pollution: C1 showed an O_{3} profile with little daily variation whereas C2 described a wide O_{3} maximum between 14:00–20:00 LT. Moreover, the
seasonal trend of C2 (X-mode) showed a wide maximum, coincident with the
solar radiation registered in summer and spring months. The C2 was higher in the urban stations and lower in the rural station of Begur (7), which could indicate that such O_{3} resulted from the photochemical reaction among NO_{x} in the presence of sunlight in highly transited areas. The nighttime profile of O_{3} was clearly showed by C3, with a wide maxim between 17:00–00:00 LT. Interestingly, C3 was the only component to clearly show an
increase in 2019 and 2020 with respect to 2018. As explained in the individual model, such an increase is due to the diminution of the titration effect. The NO_{2} profile is described in C4, with a first maximum between
09:00–12:00 LT and a second but lower maximum in the late evening
(20:00–00:00 LT). Component 5 described the simultaneous contribution of
NO_{2} and PM_{10}, with a higher contribution of PM_{10}, also having
the same two-maxima profile observed in C5 for NO_{2}.
Interestingly, both C5 and C6 presented maximums in the urban
stations (Vall d'Hebron (1), Granollers (2), and Gràcia (6)), and a decrease in 2020, due to the traffic diminution registered during the COVID-19 lockdown period.

The MCR-ALS method with quadrilinearity constraints has demonstrated to be a powerful tool to resolve the principal contamination profiles of four-way environmental data sets, even when containing missing data blocks. The main advantage provided by the use of quadrilinearity constraints is the better and easier interpretability of the profiles, which appear more condensed and concise.

In this study, resolved MCR profiles using quadrilinearity constraints have
been shown to adequately describe the different patterns and evolution of
NO_{2}, O_{3}, and PM_{10} contamination during the different hours of
the day, during the different days (hourly and daily variations) for the two
periods of time evaluated: the month of April versus the entire year for 2018, 2019 and 2020. For each period of time studied, the individual models of the contaminants together with their simultaneous analysis have been performed.

The simultaneous analysis of the incomplete multiblock data sets allowed the
exploration of the potential correlations among the three contaminants,
which was easily interpretable with the representation of overlapped hour profiles of NO_{2}, O_{3} and PM_{10 }. Interestingly, both in
the study of the month of April and the study of the entire years, the
simultaneous analysis of the three contaminants evidenced a correlation
between NO_{2} and PM_{10}, due to their common pollution sources
(i.e., traffic and industry). Moreover, the profiles of these two contaminants showed an inter-year decrease, due to the introduction of LEZs
(LEZ – Àrea Metropolitana de Barcelona, 2020) in 2019 and
due to the COVID-19 lockdown restrictions as well as the high amount of rainfall registered in April 2020 (Gobierno de España, 2020b). Such a
decrease was consistently higher in the three most transited urban stations
studied: Vall d'Hebron, Granollers and Gràcia.

On the other hand, MCR-ALS O_{3} profiles, both in individual and
simultaneous models, presented an opposite inter-year trend, especially when
analyzing the entire years. Globally, O_{3} profiles showed an increase in
2019 and in 2020 with respect to 2018, which can be attributed to the diminution of the titration effect linked to the lockdown and curfew restrictions. Such an effect was more evident in inner rural areas and in the control site
(i.e., Begur and Observatori Fabra), where the amount of NO_{x} necessary to react with O_{3} and to produce its suppression is lower compared to urban areas due to the smaller traffic density and industrial activity.

Overall, this work contributes to the better knowledge of the evolution of
NO_{2}, O_{3} and PM_{10} contamination in eight rural and urban areas
of Catalonia during the 2 years before the COVID-19 (i.e., 2018 and 2019) and
the year of the pandemic itself (i.e., 2020). The work also highlights (a) the capacity of MCR-ALS with quadrilinearity constraints to perform simultaneous analysis of different contamination sources to study potential correlations among them and (b) the good performance of this approach in the analysis of complex four-way environmental data sets containing missing data blocks,providing concise and easily interpretable results.

The MCR-ALS code is being continuously further developed, and it can be found on the public web page: https://mcrals.wordpress.com/download/mcr-als-2-0-toolbox/ (De Juan et al., 2013).

The original data set is available under https://doi.org/10.1007/s11356-021-17137-7 (Gorrochategui et al., 2021). For data requests beyond the available data, please refer to the corresponding author.

The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-9111-2022-supplement.

EG performed data curation, formal analysis and writing. IH provided air quality data. RT contributed to data curation and global supervision.

The contact author has declared that none of the authors has any competing interests.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

We thank the Spanish Ministry of Science and Innovation and Generalitat de Catalunya for providing the data and funding our research.

This research has been supported by the Ministerio de Ciencia e Innovación (grant no. PID2019-105732GB-C21).

We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).

This paper was edited by Leiming Zhang and reviewed by Vasil Simeonov and two anonymous referees.

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_{2}, O

_{3}and PM

_{10}in 8 sampling stations located in Catalonia during the COVID-19 lockdown with respect to previous years (2018 and 2019) are investigated. Simultaneous analysis of the 3 contaminants among the 8 stations and for the 3 years allows the evaluation of correlations among the pollutants, even when having missing data.