Long-term monitoring at sites with relatively low
particulate pollution could provide an opportunity to identify changes in
pollutant concentration and potential effects of current air quality
policies. In this study, 9-year sampling of PM10 (particles with an aerodynamic diameter below 10 µm) was performed in a rural background site in France (Observatoire Pérenne de l'Environnement or OPE) from 28 February 2012 to 22 December 2020. The positive matrix factorization
(PMF) method was used to apportion sources of PM10 based on quantified
chemical constituents and specific chemical tracers analysed on collected
filters. Oxidative potential (OP), an emerging health metric that measures
PM capability to potentially cause anti-oxidant imbalance in the lung, was
also measured using two acellular assays: dithiothreitol (DTT) and ascorbic
acid (AA). The sources of OP were also estimated using multiple linear
regression (MLR) analysis. In terms of mass contribution, the dominant
sources are secondary aerosols (nitrate- and sulfate-rich) associated with
long-range transport (LRT). However, in terms of OP contributions, the main
drivers are traffic, mineral dust, and biomass burning factors. There is
also some OP contribution apportioned to the sulfate- and nitrate-rich
sources influenced by processes and ageing during LRT that could have
encouraged mixing with other anthropogenic sources. The study indicates much lower OP values than in urban areas. A substantial decrease (58 %
reduction from the year 2012 to 2020) in the mass contributions from the traffic factor was found, even though this is not clearly reflected in its OP contribution. Nevertheless, the findings in this long-term study at the OPE site could indicate effectiveness of implemented emission control policies, as also seen in other long-term studies conducted in Europe, mainly for urban areas.
Introduction
Particulate matter (PM) pollution causes various environmental concerns
affecting public health and climate. An overwhelming part of the scientific
literature on PM chemical characterization and sources focuses on urban
and populated areas, where most emissions originate from and where
populations are impacted. Further work has also been carried out in more
specific areas to understand particular processes of aerosol formation and
transport, as well as specific sources such as in the boreal forest
(Yan et al., 2016), polar environments (Barrie and Hoff, 1985; Moroni et al., 2016), high altitude (Rinaldi et al., 2015), or marine sites (Scerri et al.,
2016). Rural sites are of great interest as well because they can represent
the regional background of the atmosphere and potential influence from
long-range transport (LRT) of pollutants. Studies at such sites provide more understanding of large-scale and mesoscale processes (Anenberg et al.,
2010; Mues et al., 2013; Konovalov et al., 2009), which can be useful in
chemical transport models. The continuation of these studies could lead to the identification of
long-term trends and the effect of recent changes in the source emissions.
Indeed, several programmes have been set to monitor atmospheric composition
in a harmonized way for background areas throughout Europe and North
America. Among these are ACTRIS (Aerosol, Clouds and Trace Gases Research
Infrastructure) (Pappalardo, 2018), EMEP (European Monitoring and Evaluation Programme) (Aas et al., 2012; Alastuey et al., 2016), IMPROVE (Interagency Monitoring of Protected Visual Environments) (Hand et al., 2012), and
CAPMoN (Canadian Air and Precipitation Monitoring Network)
(Nejedlý et al., 1998). However, only few
sites provide long-term in-depth series of PM chemical speciation data.
Further, although current air quality standards are based on ambient mass
concentration of PM, there is also a growing interest in new types of
characterization that take into account not only particle composition, but
also its capability to generate health impacts (Park et al., 2018; Crobeddu
et al., 2017; Møller et al., 2010; Bates et al., 2019). This is the case
of PM oxidative potential (OP) (Nel,
2005; Conte et al., 2017; Yang et al., 2014; Verma et al., 2014), the
ability of PM to generate reactive oxygen species (ROS) leading to
PM-induced oxidative stress in the lungs. In France, several studies have
reported about the OP of ambient PM in different urban environments (Weber
et al., 2018, 2021; Borlaza et al., 2021b; Calas et al., 2019; Daellenbach et
al., 2020), but there are still limited studies performed in rural areas.
The characterization of PM sources and OP in a rural site will enable us to
investigate the large-scale effects of mitigation policies that target
reduction of PM mass concentrations. This will also provide knowledge of the
efficiency of current air quality guidelines in terms of other emerging
health-based metrics of PM exposure.
The understanding of trends of PM is essential to evaluate the effects of
mitigation policies on air pollution levels. A reference background site
offers a good opportunity to gauge the broad effects of certain improvements
in the transportation fleet and other regulations aimed at reducing
vehicular emissions in large cities. Thus, in this study, an extensive
dataset of PM over a 9-year period (n=434), obtained from a French
national background site, was investigated to (1) provide insights on the
long-term trends of PM sources and other emerging health-based metrics of PM
exposure, such as OP of PM, and (2) quantify the temporal evolution of the
contributions of these sources, particularly focusing on vehicular emissions
that have already been shown to decrease in urban environments in Europe
during the last decades.
MethodologySite description and sampling parameters
The OPE (Observatoire Pérenne de l'Environnement) sampling site is
managed by the French national radioactive waste management agency (ANDRA).
It is located in a remote area in the north-eastern part of France
(48.5∘ N, 5.5∘ E) at an altitude of 390 m above sea
level, in a large agricultural area without any residential areas within
several kilometres (Fig. 1). The mean annual
temperature between 2011 and 2018 in the area was 10.5 ∘C
[minimum, maximum: -15.2 ∘C, 36.4 ∘C], average cumulated
yearly precipitation was 829 mm, and the predominant local wind regimes are
south-westerly and east-north-easterly winds (Conil et al., 2019).
This site, being far from any local anthropogenic sources, is considered
representative of the rural atmospheric background of north-eastern France.
Golly et al. (2019) also demonstrated that the PM chemistry at this site is
very close to that in several other background rural sites in France.
The PM10 samples in this study were collected from 28 February 2012 to
22 December 2020. Particularly, from 28 February 2012 to 28 December
2015, the samples (n= 181) were collected on a weekly basis (from Tuesday 09:00 CET to Tuesday 09:00 CET) using a low volume sampler (Partisol, 1 m3 h-1) onto 47 mm diameter quartz fibre filters (Tissuquartz PALL QAT-UP 2500 diameter 47 mm). From 12 January 2016 to 22 December 2020, the samples (n= 253) were collected on a daily (24 h) basis in a 6 d sampling interval using a high-volume sampler (Digitel DA80, 30 m3 h-1) onto 150 mm diameter quartz fibre filters (Tissuquartz PALL QAT-UP 2500 diameter 150 mm).
All filters were preheated at 500 ∘C for 12 h before use to
avoid organic contamination. Blank filters (about 10 % by number of the
actual filters) were also collected to quantify detection limits and to
secure the absence of contamination during sample transport, setup, and
recovery. After collection, all filter samples were wrapped in aluminium
foil, sealed in zipper plastic bags, and stored at < 4 ∘C
until further chemical analysis.
Chemical analyses
After collection, samples were subject to various chemical analyses to
perform the quantification of the major constituents by mass and specific
chemical tracers of sources needed for the positive matrix factorization (PMF) model. These analyses were
performed in the same laboratory for all samples (n=434) during the entire
sampling duration (28 February 2012 to 22 December 2020).
The carbonaceous components (organic carbon (OC) and elemental carbon (EC))
were analysed using a thermo-optical method on a Sunset Lab analyser
(Birch and Cary, 1996), using the EUSAAR2 temperature
programme. Total organic matter (OM) in daily ambient PM10 was estimated
by multiplying the OC mass concentration by a factor of 1.8. Yazdani et al. (2021) showed that this is consistent with the range estimated for rural
samples from the IMPROVE network that is generally higher than for urban
samples.
A set of other chemical analyses was performed on a single water extraction
of each filter. This extraction was performed in 10 mL of ultra-pure water
under vortex agitation for 20 min. The extract was then filtered with a
0.22 µm porosity Nuclepore filter. The major ionic components
(Na+, NH4+, K+, Mg2+, Ca2+, Cl-,
NO3-, SO42-) and methane sulfonic acid (MSA) were
measured by ion chromatography (IC, Thermo Fisher ICS 3000) following a
standard protocol described in Jaffrezo et al. (1998) and Waked et al. (2014). An ICS300 (Thermo-Fisher) with AS11 HC
column for the anions and CS16 for the cations was used.
The analyses of anhydro-sugars and primary saccharides were achieved using
high-performance liquid chromatography with pulsed amperometric detection
(HPLC-PAD). The samples collected before the year 2017 were analysed using a set
of Metrohm columns (Metrosep A Supp 15 and Metrosep Carb1) on Dionex DX500
HPLC. The samples collected after the year 2017 were analysed with a
Thermo-Fisher ICS 5000+ HPLC equipped with 4 mm diameter Metrosep Carb
2 × 150 mm column and 50 mm pre-column. The analytical run is
isocratic, with 15 % of an eluent of sodium hydroxide (200 mM) and sodium
acetate (4 mM) and 85 % water, at 1 mL min-1. These methods allowed for the quantification of anhydrous saccharides (levoglucosan and mannosan) and polyols (sum of arabitol and mannitol), as described in detail
in Waked et al. (2014) and Samaké et al. (2019).
Trace elements were analysed after mineralization, using 5 mL of HNO3
(70 %) and 1.25 mL of H2O2 during 30 min at 180 ∘C in a microwave oven (microwave MARS 6, CEM). The elemental analysis (Al,
As, Ba, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, K, La, Li, Mg, Mn, Mo, Ni, Pb, Pd,
Pt, Rb, Sb, Se, Sn, Sr, Ti, Tl, V, Zn, Zr) was performed on this extract
using inductively coupled plasma mass spectroscopy (ICP-MS) (ELAN 6100 DRC II PerkinElmer or NEXION PerkinElmer), as described by Alleman et al. (2010).
All procedures have been performed following the related EN standards (i.e.
EN 12341, EN 14902, EN 16909, EN 16913). A quality control of the chemical
analyses, including a mass closure test, is available in the Supplement (see Sect. S1). A summary of the quantification limits (QLs) on
each chemical species measured at the OPE site is also provided in Table S1.
Finally, our group successfully and regularly participates in
inter-laboratory comparison exercises for OC and EC within ACTRIS and in
EMEP (European Monitoring and Evaluation Programme) for ion analysis. The
PM10 measurements from the tapered element oscillating microbalance
(TEOM) are all in a daily (24 h, 09:00 to 09:00 CET) resolution, while the
reconstructed PM10 were obtained from chemical analysis performed on filters
collected on a weekly (7 d, 09:00 to 09:00 CET) or daily (24 h, 09:00 to
09:00 CET) basis. A total of 299 out of 434 (69 %) TEOM measurements were
paired with reconstructed PM10 data, due to many interruptions in the TEOM functioning, in order to evaluate the semi-volatile mass missing in the mass reconstruction with filter chemistry.
Oxidative potential (OP) analysis
The OP analysis was performed on PM10 extracts from collected filter
samples using a simulated lung fluid (SLF) solution composed of a Gamble +
DPPC (dipalmitoylphosphatidylcholine) at 25 µg mL-1 iso-mass
concentration (Calas et al., 2017). This methodology
facilitates particle extraction in conditions closer to lung physiology. The
OP analysis only started on samples collected from 13 June 2017 to 22 December 2020, amounting to a total of 191 samples.
Two assays were used to characterize OP activity: (1) dithiothreitol (DTT)
and (2) ascorbic acid (AA) assays, as briefly described in the following
sections. The volume-normalized OP activity (OPv) is the OP consumption (nmol min-1) normalized by the sampled air volume
(m3), representing the OP exposure in each sample. All samples analysed
were subjected to triplicate analysis, and each sample results in the mean of
such a triplicate. The common coefficient of variation (CV) is between 0 % and 10 % for each assay.
DTT is used as a chemical surrogate to mimic in vivo interaction of PM with
biological reducing agents, such as adenine dinucleotide (NADH) and
nicotinamide adenine dinucleotide phosphate (NADPH), in the DTT assay. The
consumption of DTT in the assay represents the ability of PM to generate ROS
(i.e. superoxide radical formation) (Cho et al.,
2005). The PM10 extract is mixed with the DTT solution. Afterwards, the
remaining DTT that did not react with PM10 is reacted with
5,50-dithiobis-(2-nitrobenzoic acid) (DTNB). This reaction produces
5-mercapto-2-nitrobenzoic acid or TNB. The TNB is measured by absorbance at
412 nm wavelength using a plate reader (TECAN spectrophotometer Infinite
M200 Pro) with 96-well plates (CELLSTAR, Greiner-Bio) in a 10 min time
step interval for a total of 30 min of analysis time.
AA is a known antioxidant used in AA assays using a respiratory tract lining
fluid (RTFL) (Kelly and Mudway, 2003). This antioxidant
prevents the oxidation of lipids and proteins in the lung lining fluid
(Valko et al., 2005). The consumption of AA also represents
PM-induced depletion of a chemical proxy (i.e. cellular AA antioxidant). The
mixture (PM10 extracts reacted with AA) is injected into a 96-well
multiwall UV-transparent plate (CELLSTAR, Greiner-Bio) and measured at 265 nm absorbance using a plate reader (TECAN spectrophotometer Infinite M200 Pro) in a 4 min time step interval for a total of 30 min of analysis
time.
Both DTT and AA assays measure OP by depletion of specific chemical proxies,
cellular reductants (for DTT), and antioxidants (for AA). Studies have
well identified a large number of PM constituents that influence OP
concentrations. At least, OP assays are known to be associated with some
metals (Cu, Fe, and Mn, among others) and some organic species (especially
photochemically sensitive species such as quinones) (Calas et al., 2017,
2019; Charrier et al., 2014; Pietrogrande et al., 2019). However, in ambient
air, each assay reports its own associations that may vary according to the
local context (emission sources, local transport leading to various ageing
processes and spatio-temporal variations) (Gao et al., 2020a). Hence, a
synergetic approach using multiple OP assays, to capture the most complete
information regarding PM reactivity, is commonly suggested
(Bates et al., 2019; Calas et al., 2017; Borlaza et al., 2021b).
In every experiment, a positive control test is performed to ensure the
accuracy and precision of measurements. A 1,4-naphthoquinone (1,4-NQ)
solution was used for both DTT (40 µL of 24.7 µM stock solution) and AA (80 µL of 24.7 µM 1,4-NQ solution) assays. The CV of the
positive controls was < 3 % for the two assays. Additionally, an
ambient filter collected from the lab roof (with an expected constant OP
value) was added on each microplate to ensure precision of OP measurements.
Source apportionmentPMF model and input variables
The United States Environmental Protection Agency Positive Matrix
Factorization (EPA PMF 5.0) software (Norris et al., 2014) was
used to identify and quantify the major sources of PM10. PMF is a
receptor model fully described by Paatero and Tapper (1994) and
is now widely used for source apportionment around the world. Additional
information about the model description is provided in the Supplement (Sect. S2).
In this study, 23 chemical species were used as input variables, namely OC,
EC, ions (Na+, NH4+, Mg2+, Ca2+, NO3-,
SO42-), trace metals (Al, Cu, Fe, Rb, Sb, Se, Sn, Ti, Zn), and
organic markers (MSA, levoglucosan, polyols (sum of arabitol and mannitol)).
We assumed that arabitol and mannitol came from a similar source and, hence,
combined them into one component named “polyols”
(Samaké et al., 2019). The uncertainties of the input variables were calculated based on Gianini et al. (2012). Finally, the species displaying a signal-to-noise ratio (S/N) lower than 0.2
were discarded, and those with S/N between 0.2 and 2 were classified as
“weak”, consequently multiplying the uncertainties by a factor 3.
Criteria for a valid solution
Solutions with a total number of factors from 6 to 11 were tested for the
baseline models. Following the recommendations of the European guide on air
pollution source apportionment with receptor models (Belis,
2019), the Q/Qexp ratio (< 1.5), the geochemical interpretation
of the factors, the weighted residual distribution, and the total
reconstructed mass were evaluated during factor selection.
Moreover, the bootstrapping method (BS) was used on the final solution to
estimate errors and ensure the stability and accuracy of the solutions. The
BS method was applied with 100 iterations of the model, and contribution
uncertainties are presented in the Supplement (Sect. S3) as mean ± SD of the 100 BS
runs. The contribution uncertainties were estimated based on the method
presented in Weber et al. (2019) and presented in Figs. S2 to S10. The daily species contributions are estimated using
XBSi=Gref×FBSi,
where FBSi is the profile of the bootstrap i, and XBSi is the time series of each species according the reference contribution Gref and the bootstrap run FBSi.
Finally, the factor chemical profiles obtained during this study were
compared with those from previous studies in France, using the PD-SID (Pearson distance–similarity identity distance) method
(Belis, 2019; Weber et al., 2019), in
order to validate their proper similarity.
Appropriate constraints in the PMF model
A set of constraints were applied on a basic model solution, in order to
refine the results of the mathematical model by providing sound geochemical
knowledge. Hence, the usual constraints as discussed in
Weber et al. (2019), and some constraints corresponding to the traffic source following Charron et al. (2019) were also
tested on the model (Table S3). However, these set of constraints were
tested with caution as most of them have been previously applied only on
sites with different typologies (i.e. urban or roadside sites), questioning
their applicability in a rural site such as the OPE site. Finally, only a
limited set was applied to generate the final solution (Sect. S1). After
application of the constraints, a BS method was re-applied to verify the
stability of the model.
Similarity assessment of chemical profiles
To investigate further any differences in the chemical profiles at the OPE
site compared to those obtained at other French sites, a test of similarity
was performed using the Pearson distance (PD) and standardized identity
distance (SID) metric. This is calculated using Eqs. (S5) and (S6) in the Supplement (Sect. S2) (Belis et al., 2015), closely following a
previous work by our group (Weber et al., 2019).
This comparison is based on the source relative mass composition, which
allows for the evaluation of the variability of PMF solutions across different
sites. In this case, the chemical profiles obtained for the OPE site were
compared against 15 different other sites over France. A “homogenous
source” tends to have a similar profile over different site types and
should have PD < 0.4 and SID < 1.0 (Pernigotti and Belis, 2018). Conversely, the sources with PD and SD values outside of this range are considered “heterogeneous sources”.
OP source contribution estimation
The OP contribution of each PM10 source was determined by performing an
OP deconvolution method using multiple linear regression (MLR) analysis.
This methodology is based on the procedure proposed in
Weber et al. (2018). Briefly, the OP activity (in nmol min-1 m-3) was used as the dependent variable, while the PMF-resolved source PM10 mass contributions (in µg m-3) are the independent variables, as shown in Eq. (1):
OPobs=Gn×βn+ε,
where OPobs is the observed daily OPv (nmol min-1 m-3) with matrix size d×1, G is the PMF-resolved source contribution (µg m-3) of size d×n, and β is the regression coefficient representative of the intrinsic OP (OPm) (nmol min-1µg-1) of each n source. Finally, ε is the residual between the observed and modelled OP (nmol min-1 m-3). The source-specific OP
contribution is calculated by multiplying the regression coefficient of each
source by the mass contribution of the source to PM10 (Gk×βk). This methodology is essentially based on that in
Weber et al. (2018).
Season-trend (STL) deconvolution method
The STL (season-trend deconvolution using locally estimated scatterplot
smoothing) model is a versatile and robust statistical method allowing for the
decomposition of a time-series dataset into three components including
trend, seasonality, and residual. The trend provides a general direction of
the overall data; the seasonality is a repeating pattern that recurs over a
fixed period of time; finally, the residual is the random fluctuation or
unpredictable change in the dataset. The seasonal component allows us to
eliminate seasonal variation from the time series, resulting in a smoothed
trend line that shows the tendency of the time-series dataset. This method
somehow takes into account the changes in seasonal cycles from year to year,
which could also delineate part of the effect of meteorology on the
long-term trend of PM10.
To investigate the long-term trends of sources or species concentrations,
the STL model (Cleveland et al., 1990) was applied on the monthly
mean concentrations, as described by Eq. (2):
Y(t)=T(t)+S(t)+r(t),
where Y(t) is the time series observed monthly on average,
T(t) is the trend component of the signal, S(t) is the seasonal component, and r(t) is the residual
part not explained by the trend and seasonal part. The frequency was set to
13 (i.e. 6 months before and after the current month) to account for yearly
seasonality. This model uses an iterative algorithm that constantly
minimizes the residual r(t) by successively adjusting the
trend and seasonal components. It has to be noted that the resulting
T(t) and S(t) do not represent concentrations
but a statistical deconvolution of the input signal Y(t).
S(t) could then be negative, and the trend T(t)
should be interpreted as an elaborated “moving average” of the
concentrations. To account for extreme events or outliers in the data, the
impact of data points with very high residuals was given less weight in the
estimation of the trend and seasonal components, using the “robust” option
of the algorithm. The presence and strength of tendency were evaluated thanks
to the ordinary least-squares (OLS) linear fit of the T(t)
component against time, removing the first and last 6 months of the
time series to avoid edge effects. Note that due to the lack of PM10
measurements in July 2019, the concentrations for that month were
arbitrarily set to the August 2019 values. This model was implemented in
Python 3.8, making use of the “statsmodels” module (Seabold and
Perktold, 2010).
Results and discussion
Section 3.1 to 3.5 below discuss the concentrations, sources, and
trends of PM10, while Sect. 3.6 and 3.7 discuss the OP
measurements and sources.
PM10 and its major chemical components
The reconstructed mass of PM10 at the OPE site was calculated following
Eq. (S1) in the Supplement and is presented in Fig. 2. The
mass concentration of the reconstructed daily PM10 samples ranged from
2 to 51 µg m-3, with an overall average of 9 ± 7 µg m-3 (median: 8 µg m-3). This reconstructed PM10 mass
concentration only exceeded the PM10 European limit value of 40 µg m-3 a few times (n= 3) in the entire measurement period. These values are in the lower range of the concentrations reported for rural areas in Europe, ranging from 3 to 35 µg m-3
(Putaud et al.,
2004), and are relatively close to the values found at a remote site in
Revin (France, located 165 km away), as described in the SOURCES programme
(average of 13 µg m-3) (Weber et al.,
2019). Some changes in the concentration can be observed in the PM10
mass concentration, but there are no drastic changes in the major chemical
components at the OPE, even with the lockdown restrictions during the year 2020.
The yearly average volatile mass (i.e. unaccounted by chemical analysis),
deduced from the difference between TEOM-FDMS (filter dynamics measurement system) measurements and reconstructed
PM10, ranges from 9 % to 44 % with an average of 22 % (of the yearly median) and is well within range generally found in a rural
environment (Pey et al., 2009).
The annual average of PM10 composition at the OPE site.
Accounting for 37 % to 45 % (based on year) of the reconstructed
PM10 mass concentrations, organic matter (OM) is the largest
contributor. The other main contributors are inorganic secondary species
(NO3-, NH4+, non-sea-salt sulfate), suggesting a strong
influence from LRT of pollutants. There are also contributions coming from
dust and sea salt. Although all of these components are often dominated by
specific emissions, they can be derived from a wide range of sources. For
example, vehicular emissions are usually composed of both carbonaceous and
metals species, while road dust is usually minerals and some metal species.
Understanding the sources (as with the PMF methodology) and transformation
processes of PM proves to be an essential step for efficient air quality
policies.
Statistical stability of the PMF solution
The final retained solution includes nine factors as described in Sect. 3.3.
Only 71 out of 100 baseline runs (without constraints) converged for this
solution, but most factors were 100 % correctly mapped, except for the
traffic factor (93 %, 66 out of 71 converged solutions) and sulfate-rich
factor (99 %, 70 out of 71 converged solutions). Applying the constraints
greatly improved the BS mapping to 100 % on all factors. Adding
constraints in the base model allowed for refining of the model through addition
of expert knowledge on the profiles, which led to the increased model
stability. In previous source apportionment studies, specifically by our
group, there are common constraints used depending on the site type such as
presented in Borlaza et al. (2021a).
Particularly, the constraints for traffic-related factors reported in
Charron et al. (2019) have been
optimized for traffic and urban background sites in our previous works.
However, these constraints appeared restrictive when applied in a rural
typology such as the OPE site. In fact, the Cu-to-Sb ratios appeared
unsuitable as this ratio was 6.3 in our final solution compared to 12.6 in
Charron et al. (2019). Based on literature, the Cu-to-Sb ratio can range from 1.6 (Handler et al., 2008) to 12.6 (Charron et al., 2019)
depending on site typology. The addition of this constraint resulted in or
led to a non-convergent solution at the OPE site. To avoid inconsistencies,
the Cu / Sb constraint was excluded in the optimal solution. The OC-to-EC
ratio in the traffic profile was also too restrictive for the model, as this
ratio was 3.9 in our baseline solution against 0.44 in Charron et al. (2019). The OC / EC levels calculated in this profile also suggest a strong influence of secondary organic aerosols (SOAs) (Johnson
et al., 2006; Pio et al., 2011; Rodríguez González et al., 2003;
Viana et al., 2006), instead of primary traffic emissions. As OC in a rural
site can undergo multiple re-transformations in the atmosphere from the
emissions sources, this has led to a wide range of OC-to-EC ratios as
similarly found in Weber et al. (2019); hence
this constraint was excluded.
In the final model, some constraints were used as summarized in Table S3,
which resulted in all factors being correctly mapped and all BS runs
converging, suggesting a good improvement in the traffic (from 93 % to
100 %) and sulfate-rich (from 99 % to 100 %) factors as well as the overall statistical robustness of the model. The other constraints either
resulted in a non-convergent constrained model and/or less robust BS
results. This implies that in sites with strong influence of LRT, the
appropriate constraints tend to vary, and an optimal PMF solution can be more
difficult to achieve.
The challenge in adding the constraints may also be linked to the inherent
nature of the PMF algorithm since it assumes chemical profiles are identical
for the whole period of analysis. However, during the 9 years of this study,
some chemical source profiles may have changed, notably the traffic factor.
Indeed, an evolution of the car fleet in France and Europe could lead to the
changes in the OC-to-EC ratio emitted by the vehicle, so this profile may
also have changed during this period. For this specific case, a rolling PMF
approach (Canonaco
et al., 2021) with a statistically mapped PMF profile could be useful to
investigate the time variability of a given profile, slightly evolving with
time.
PMF solution description and PM10 contributions
The nine resolved sources of PM10 at the OPE site include nitrate-rich
(25 % of average contribution to PM10 for the full period),
sulfate-rich (15 %), traffic (12 %), mineral dust (16 %), biomass burning (9 %), fresh sea salt (4 %), aged sea salt (6 %), primary biogenic (7 %), and MSA-rich (7 %). These factors were identified based
on their chemical profiles and the mass loading of specific tracers, as
summarized in Table S2 in the Supplement. The error estimations, chemical profiles, and temporal evolutions of the PMF-resolved sources are available in the Supplement (Sect. S3). Figure 3 represents the repartition of the
chemical species in the different factors. The summed PM10
contributions from all sources showed very good mass closure (r=0.95) with
PM10 mass reconstructed with Eq. (S1), indicating very good model
results.
Species repartition by PMF-resolved sources at the OPE site.
The factors with highest average contribution to the PM10 mass are the
two inorganic secondary aerosol sources, nitrate-rich (25 %, 2.3 ± 4.3 µg m-3) and sulfate-rich (15 %, 1.4 ± 1.5 µg m-3), and mineral dust (16 %, 1.6 ± 1.7 µg m-3).
Sulfates and nitrates are mainly formed through secondary processes in the
atmosphere with long atmospheric lifetimes and can, therefore, originate
from regional sources or LRT. Considering the agriculture and natural
emissions of ammonia, especially expected in a rural site, secondary
aerosols could also be formed locally at the OPE site. The less dominant
sources are traffic, biomass burning, biogenic (MSA-rich, primary biogenic),
and sea salts (fresh and aged). The contributions of the different factors
are quite similar to those observed at other rural sites in France
(Weber et al., 2019).
Seasonal and annual contribution of the PMF-resolved sources at
the OPE site.
The OPE site has a Northern Hemisphere mid-latitude climate with four
seasons, (1) a winter season corresponding to the months of December, January, and February; (2) a spring season corresponding to March, April, and May; (3) a summer
season corresponding to June, July, and August; and (4) a fall season
corresponding to September, October, and November. Seasonality in some
factors can be apparent, such as for the biomass burning and nitrate-rich
factors, which are more prominent in winter and spring, respectively, and
the primary biogenic and MSA-rich factors, increasing in summer due to
greater photochemical and biological activities.
Figure 4 depicts the seasonal average contributions
of the PM10 sources at the OPE site from the year 2012 to 2020.
The nitrate-rich factor, identified by high loadings of
NO3- and NH4+, has a strong seasonal pattern with
a maximum contribution to PM10 mass concentration, especially in the
months of March and April.
The sulfate-rich factor is identified by high loadings of
SO42- and NH4+. There are also contributions from some
metal species (Se, Zn, Cu, and Sb) in this factor, suggesting potential
influence from road dust and/or non-tailpipe vehicular emissions. A small
portion of OC (5 % of OC mass) is also observed in this factor. The
presence of these metals remained, even when the number of factors was
increased (up to 11 factors) during the PMF optimization process.
The aged sea salt factor is characterized by high loadings of
Na+ and Mg2+, with a certain number of species originating from
potentially anthropogenic sources such as nitrates (6 % of NO3-
mass) and sulfates (19 % of SO42- mass) that can be attributed
to mixing and transformation processes in the atmosphere. Interestingly,
there are some contributions from EC (8 % of EC mass), Cu (11 % of Cu
mass), Sb (13 % of Sb mass), and Se (19 % of Se mass). This could imply potential mixing of aged sea salt with other anthropogenic source linked to these species (e.g. traffic, shipping). The minimal loadings observed in the contributions of Cl- in this factor could likely be the result of ageing processes occurring between sea salt and acidic particulate compounds such as nitric and sulfuric acid (Seinfeld and Pandis, 2016). This
factor could also be associated with road salting in the winter; however there
is no clear seasonality in the contributions to support this hypothesis.
There was no added constraint in this factor as our solution shows a
Mg2+ / Na+ ratio at 0.06, while this ratio is usually found to be around
0.12 in sea salt emissions (Henderson and Henderson, 2010).
The fresh sea salt factor is characterized by high loadings of
Cl- (91 % of Cl- mass) and some contributions from Na+
(35 % of Na+ mass) and Mg2+ (25 % of Mg2+ mass). This
factor contributes 4 % to total PM10 mass, and, unlike the aged sea
salt factor, it is less likely influenced by anthropogenic sources with
extremely low contributions from carbonaceous and metal species.
The MSA-rich factor is identified by high loadings of MSA
(methanesulfonic acid), a known product of oxidation of dimethylsulfide
(DMS), commonly from marine phytoplankton emissions (Chen
et al., 2018; Li et al., 1993). A small mass fraction of SO42-
(7 % of SO42- mass) is also found in this factor that may be
due to the co-emission of DMS and non-sea-salt sulfates but also results
from the production of biogenic sulfate from DMS oxidation. Hence, MSA-rich sources could potentially be mixed with secondary inorganic aerosols as well. The
measured MSA mass concentration showed weak correlations with specific ionic
species from marine aerosols such as Na+ (r< 0.01) and Mg2+
(r< 0.01). This could indicate that marine biogenic emissions may not
be the only source of this factor. Instead, this factor could be influenced
by sources with terrestrial origins and/or from forest biota, as previously
reported in other studies (Bozzetti
et al., 2017; Golly et al., 2019; Jardine et al., 2015; Miyazaki et al.,
2012). This factor also presents a clear seasonal pattern with maximum
contribution from May to July due to higher photochemical activity and algae/microbial activity. Golly et al. (2019) reported a very coherent seasonal cycle for MSA concentrations over a large portion of the French territory, including at the OPE site.
The primary biogenic factor is characterized entirely by polyols.
These species are emitted by fungal spores which partly explains the high
loadings of OC found in this factor. This factor has a higher contribution
to PM10 during the summer season, consistent with the observations at
other rural and urban sites (Samaké et al., 2019; Weber et al., 2019; Borlaza et al., 2021a). More details about
the characteristics of primary biogenic aerosols can be found in Samaké
et al. (2019).
Briefly, meteorological conditions, such as high temperature and relative
humidity, could facilitate the increase in their formation. This factor can
also include some fraction of plant debris, identified by cellulose
measurements, as discussed in Samaké
et al. (2019), Borlaza et al. (2021a), and Brighty et al. (2022).
The biomass burning factor, a major contributor to PM10 during
the winter season, includes mostly levoglucosan and mannosan. This factor
contains around 25 % of the total EC mass, consistent with a combustion
chemical profile. Trace elements like Rb and Sn are also found in this
factor, rubidium being the major trace element with 21 % of its mass being
reconstructed in this factor. Due to the distance of any residential areas
from the OPE site, the contributions of this factor to PM10 at 20 %
on average in winter are much less than the contributions generally observed in
most sites in France in winter (e.g. urban, suburban, or countryside
sites; Zhang et al., 2020), which are mainly in the range of 25 %–70 %, with an average of about 35 % (Weber et al., 2019). The
contribution at the OPE site most probably represents the average winter
loading of the French national background of the atmosphere.
The traffic factor is the second factor where EC is a
major contributor (52 % of EC mass). The major trace elements found in the traffic factor are Cu, Sb, Sn, and Zn, and most of their masses are
reconstructed in this factor. There is no seasonality associated with this
factor. However, its contribution to PM10 presents a decreasing trend
over the sampling period from 2012 to 2020. This is also consistent with the
decreasing trend found in EC mass concentrations over the same period, as
presented in Fig. S11. These findings present an opportunity to explore
the potential decrease in traffic emissions observed at the OPE site, taken
as a good proxy of the national background burden of the rural atmosphere.
This is further discussed in Sect. 3.5.
The mineral dust factor is mainly composed of Ca2+ (78 % of
Ca2+ mass), Al (84 % of Al mass), and Ti (86 % of Ti mass). There are also contributions from other trace elements that could originate from re-suspended road dust or non-tailpipe emissions such as Fe (65 % of Fe mass), Cu (27 % of Cu mass), Rb (51 % of Rb mass), and Zn (16 % of Zn mass). A fraction of it could therefore be of anthropogenic origin.
Comparison of the source chemical profiles with previous results in France
Figure 5 presents the similarity plot (PD-SID distances) obtained for the nine factors found at the OPE site compared to the French sites included in the SOURCES programme (Weber et al., 2019). It is striking that most
factors remained homogeneous within France, including both rural and urban
sites. The most stable factors are nitrate-rich (lowest PD) and mineral dust
(lowest SID). The traffic factor also appears relatively stable but
presents some dissimilarities according to the high variation in the PD
metric. A high PD variation generally indicates a difference in the chemical
species that identify the main mass contribution of the profile. In fact, in
the traffic factor, this variation between the OPE and other sites can be
attributed to the variations in the OC to EC levels, similar to the findings
in Weber et al. (2019). Compared to the other
French sites, the OC to EC levels of this factor at the OPE site are much
higher, which highlights a strong influence from LRT processes with SOA
formation.
Similarity plot of the OPE site against all the French sites in
the SOURCES programme. The shaded area (in green) shows the acceptable range
of the PD-SID metric. For each point, the error bars represent the standard
deviation in the comparisons of all pairs of sites. The number in the legend
indicates the number of sites over France where the given profile is
available.
The aged sea salt and MSA-rich factors are the only ones positioned outside
of the shaded box in Fig. 5, indicative of
heterogeneous profiles between the OPE and the other sites. The
heterogeneity found in the aged sea salt profile can be attributed to the
contributions of EC and some metals in this factor. These were not typically
found in other sites in France and could also be due to the mixing of this
sea salt profile with other anthropogenic contributions as a result of LRT
processes, as well as different ageing processes. Similarly, the MSA-rich
factor has previously shown site-to-site variations and a wide variation in
the PD-SID metric (Weber et al., 2019), mostly
attributed to the variability of the contribution of OC in some sites. This
is also the case at the OPE site, with a large contribution of about 11 %
of OC in this MSA-rich factor. Despite these few differences, the very large
similarity of the chemical profiles at OPE compared to those at all other
sites in France will be essential to the comparison of the intrinsic OP of
sources in Sect. 3.7.
Long-term trends of PM10 sources
Figure S10 in the Supplement presents the long-term trend of the observed PM10 at the OPE site. The PM10 levels appear to be consistent from 2012 to 2020, and there is no clear increasing or decreasing trend found in PM10 (r2= 0.2, Table 1). However, there is a
clear decline found in EC mass concentrations (r2= 0.9), with a
reduction of 22 ng m-3 yr-1 (p≤0.01) (Fig. S11 in the
Supplement). This could indicate that the mass contribution of one or more sources contributing to EC should also be decreasing. Following Germany and Italy, France is placed third on highly impacted countries in Europe from vehicular exhaust emissions (Anenberg et al., 2010). Through the
years, a variety of vehicular regulations have been adopted to reduce
traffic-related emissions, not only in France (Bernard et al.,
2020), but also across Europe (Wappelhorst and Muncrief, 2019).
The data obtained at the OPE site present an interesting opportunity as they
cover 9 years of sampling in a rural area, making it possible to
investigate emission trends over a long period of time in a site
representing a background atmosphere.
STL tendencies of the observed PM10 and each PMF-resolved
source contributions to PM10 from the year 2012 to 2020 at the OPE site.
The season-trend (STL) deconvolution of contributions (in µg m-3) from the traffic factor to PM10 from the year 2012 to 2020.
Using the model described in Sect. 2.6 (Eq. 2), the STL deconvolution of
the PMF-resolved sources at the OPE site was also investigated. It is
extremely significant that the contributions from the traffic factor at the OPE site also decreased substantially, as presented in
Fig. 6. A very large reduction of 58 % from the year 2012 to 2020 based on average mass contribution and an overall yearly average reduction of 104 ng m-3 yr-1 (p≤0.01) were found. In
parallel, there is also a reduction observed in the sulfate-rich factor,
proposed as a highly anthropogenic-derived factor (see Fig. S12 in the
Supplement), with 66 % reduction of average mass contribution and an overall yearly average reduction of 72 ng m-3 yr-1 (p≤0.01). Indeed, several other sources have shown lower but significant decrease of their mass contribution over the years (Table 1) at the OPE site, except for the fresh sea salt, nitrate-rich, and MSA-rich factors.
These findings allowed for the unravelling of the decreasing trend in terms of
source contributions by the STL model. The STL deconvolution was applied on
all the identified sources, which clearly showed that the traffic source has
the highest tendency with a decreasing trend. The other major sources of PM,
such as biomass burning, mineral dust, and nitrate-rich sources, do not have as
much decreasing tendency as the traffic factor. The internal annual
variabilities of weather/climate conditions might not be the leading factors
explaining these trends, as they would have affected PM sources in the same
way.
The downward trends found in our study are very consistent with other
existing studies in Europe (Li
et al., 2018; Sun et al., 2020; Salvador et al., 2012; Pandolfi et al.,
2016; Gama et al., 2018; Amato et al., 2014), with nearly all of them conducted
in urban areas. Pandolfi et al. (2016) found a significant long-term decrease of the contributions from anthropogenic emissions (specifically a mixed industrial/traffic factor, -0.11 µg m-3 yr-1, 56 % total reduction) in a
regional background site in altitude in the northeast of Spain (Montseny, Spain)
from 2004 to 2014. This is also consistent with a similar study in the
metropolitan area of Madrid, Spain (Salvador et al.,
2012), which showed a reduction of 32.7 % attributed to traffic emissions,
alongside the decrease of the carbonaceous and SO42- in PM. In a
southern Spain area (Andalusia), the same group also found a consistent
decreasing trend of PM at some traffic and urban sites in the region
(Amato et al., 2014).
Another long-term study in central Europe
(Sun et al., 2020) focusing on equivalent black carbon (eBC) concentrations found decreasing trends in high-altitude Alpine sites located in Germany (-3.88 % yr-1, [-10.15 %, 0.56 %]) and Switzerland (-3.36 % yr-1, [-8.71 %, -0.28 %]). These findings are also consistent with results from other parts of Europe, with the largest decrease found in OC up to -48 % (Cusack et al., 2012), and the decrease in PM has been associated with non-meteorological factors (Barmpadimos
et al., 2012). Other studies with pluri-annual series of data on PM
chemistry in rural environments in Europe include
Spindler et al. (2013) (Melpitz, Germany, including EC
measurement for 2003–2011) and Grange et al. (2021)
(Payerne, Switzerland, comparison of three periods every 10 years since 1998,
including EC and trace elements). Both show a decrease in EC
concentrations over time during the study. Finally, while these studies did
not target specific chemical species solely linked to vehicular emissions,
most of them attributed the decline to the efforts to reduce vehicular
emissions and other mitigation policies in their respective areas.
It should be noted that the role of meteorology in the observed decrease in
PM in these studies (including ours) cannot be totally ruled out
(Hou and Wu, 2016; Czernecki et al., 2017; Kim, 2019) and is generally not fully considered. In most cases, there
is a complex interplay between PM and meteorological conditions that could
increase or decrease PM mass concentration (Chen et al.,
2020). Indeed, there are some studies at high-altitude or regional
background sites that highlighted a concurrent role of changing meteorology
and changes in frequency of Saharan dust advection to Europe
(Brattich et al., 2020) in modulating the dust concentrations in the atmosphere. The study at Melpitz (Spindler et al., 2013), despite an in-depth work on the wind sector classification, does not address the impact of possible changing in the air mass origin on long-term changing origins.
Comparison of the evolution of the traffic factor source
contribution (in µg m-3) at the OPE site and the black carbon
(BC) emissions (in kilotonnes) by the transport sector (source: CITEPA,
https://www.citepa.org/fr/2021-bc/) for overall France.
The evolution of the absolute concentration of the traffic factor (in µg m-3) at the OPE site was also compared to an evaluation of black carbon (BC) emissions (in kilotonnes) by the transport sector for overall
France, provided by the CITEPA, the official agency in charge of the
emissions inventory in France (https://www.citepa.org/fr/2021-bc/, last access: 5 July 2022). Both series were converted to an
arbitrary level starting from 100, using 2012 as the base year
(Fig. 7). This figure shows an excellent agreement in the trend and in the total percentage decrease (%) for estimated BC emissions from traffic (-64 %) and the traffic source contributions observed at the OPE site (-52 %), between the years 2012 to
2020. While local or regional changes in meteorology may be a factor in the
evolution of the concentrations observed, this is unlikely to be the
dominant one in the evolution of the concentrations of chemical species
related to traffic emissions, in light of the strong correlation observed
with the national emissions inventory in France.
The interesting point of our study is that it pertains directly to a
specific source, identified with a long-term and robust PMF study. Further,
as there are minimal local anthropogenic sources expected at the OPE site,
it may be safe to assume that these contributions to PM mass in this area
are influenced by LRT of pollutants. Our results indicate that the
implementation of emission control policies is also playing a role in the
consistent decrease in traffic emissions in rural sites far away from direct
emissions.
Temporal trends of observed OP of PM10
Figure 8 presents the observed average values of OP of PM10 at the OPE site compared to the other sites in France (Calas
et al., 2018, 2019; Weber et al., 2021, 2018). All series cover at least 1
year of sampling. As expected, the OP level in a rural background is much
lower (about 2 to 8 times) than other typologies including traffic, urban,
and urban alpine sites, for both AA and DTT assays. Further, the average
ratio between urban sites and the rural OPE site is generally much higher
for OP than for PM mass, an indication that the nature of the particles at
the rural site makes them less oxidant than in urban areas, as already
pointed out in Daellenbach et al. (2020).
The comparison of observed OP activity
(OPvDTT and
OPvAA) between the OPE site and other
sites in France. Bar plots depict the mean value with standard deviation.
Temporal distributions of observed PM10 and OP activity
(OPvDTT and
OPvAA) from the year 2017 to 2020 at the OPE site in terms of daily and monthly mean.
Figure 9 presents the daily and monthly mean distributions of observed PM10 and OP activity (OPvDTT and
OPvAA) from 13 June 2017 to 22 December 2020. There is an observed seasonality, where PM10 and OPvDTT appear to be
similar with relatively higher levels during warmer months. On the contrary,
OPvAA has slightly higher levels during colder months.
In many European studies, the seasonality in PM10 mass concentration
can be usually explained by higher contributions from biomass burning during
winter (Bessagnet et al., 2020; Tomaz et al., 2017), especially in alpine valleys (Calas
et al., 2019; Favez et al., 2010; Herich et al., 2014; Srivastava et al.,
2018; Tomaz et al., 2016, 2017; Weber et al., 2018, 2019; Borlaza et al.,
2021a). Similarly, this seasonal pattern has been observed in OP as well (Borlaza
et al., 2021b; Weber et al., 2018; Calas et al., 2019; Weber et al., 2021).
However, the typology (i.e. rural) of the OPE site could be associated with a
different type of OP temporal profile as it is far from direct anthropogenic
emission sources (but not from vegetation and soil biogenic emissions).
With the large influence of LRT on the sources of PM at the OPE site, this
also poses an opportunity to investigate the impact of LRT on OP. In
fact, few studies have looked into the effects of aerosol ageing on OP
properties (Guascito et al., 2021; Bates et al., 2019). Pietrogrande et al. (2019) reported association of OP with redox-active organics linked to
photo-oxidative ageing. Using backward trajectory analysis,
Wang et al. (2020) found strong effects of LRT on OP of
fine PM. This is also consistent with the findings in
Paraskevopoulou et al. (2019), which revealed
highly oxygenated secondary aerosols as one of the main drivers of OP in
fine PM, further highlighting the importance of combustion and ageing
processes in OP. In a shipborne measurement study in South Korea, a higher
intrinsic OP has also been found in samples where secondary aerosol
formation is more dominant, also suggesting the strong impacts of long-range
transported PM (Oh et al., 2021). Cesari et al. (2019) found
negligible contribution from secondary sulfates but have found relevant OP
contributions from a factor identified as a combination of vehicular traffic
and secondary nitrates. All these studies used DTT assays to measure the OP
of PM.
The OP assay sensitivity to specific species and/or sources can also explain
the difference in seasonality found in OPvDTT and OPvAA in our study. OPvDTT appears sensitive towards organics, metals, and, possibly, a synergistic effect between the two (Bates
et al., 2019; Dou et al., 2015; Fang et al., 2017; Gao et al., 2020b, a;
Jiang et al., 2019; Weber et al., 2021; Yu et al., 2018; Borlaza et al., 2021b), while OPvAA shows sensitivity mostly towards metal species (Bates et al., 2019; Crobeddu et al., 2017; Visentin et al., 2016; Weber et al., 2021; Borlaza et al., 2021b).
Generally, current literature shows importance of the role of secondary/aged
aerosols in the OP of PM, especially those with influence from anthropogenic
sources. However, to our knowledge, this is the first long-term OP study that
specifically deals with ambient samples far from direct emissions at a rural
background site.
Sources of OP in PM10
The sources of OP in PM10 were apportioned following an OP deconvolution
method proposed by Weber et al. (2018) using the source contributions (µg m-3) obtained in the PMF and the measured OP (nmol min-1 m-3) at the OPE site. Generally, the modelled OP (OPm) is within range of the observed OP,
with a reasonable reconstruction (OPvDTT (r=0.76) and OPvAA (r=0.76)). The OPm of each PM source is given in
Table 2, where OPmDTT can range from
-0.01 ± 0.02 to 0.10 ± 0.03 nmol min-1µg-1, and
OPmAA can range from -0.001 ± 0.02 to 0.16 ± 0.03 nmol min-1µg-1. Generally, higher OPm indicates higher
redox activity associated with the factor. There are some differences in the
OPm based on the type of assay used, and this can be attributed to the sensitivity of the assay towards certain redox-active species in PM (Borlaza et al., 2021b; Xiong et al., 2017; Charrier and Anastasio, 2012).
Regression coefficients (i.e. intrinsic OP or
OPm, expressed in nmol min-1µg-1) at the OPE site for the DTT and AA assays. The values are the mean ± standard deviation, and the p value is in the parentheses.
Overall daily mean OPv contribution of
the sources to PM10 and OP activity (OPvDTT and
OPvAA) using MLR analysis in the form
of mean and 95 % confidence interval of the mean (error bar) (n=168
samples).
In terms of overall daily mean contribution, as presented in
Fig. 10, the main contributors to PM10 mass are the nitrate-rich, mineral dust, and sulfate-rich factors at the OPE site. However, in terms of OPvDTT, the mineral dust factor showed
the highest average contribution (0.15 nmol min-1 m-3),
followed by the sulfate-rich (0.11 nmol min-1 m-3) and
traffic (0.07 nmol min-1 m-3) factors. For
OPvAA, the biomass burning factor showed the highest contribution (0.12 nmol min-1 m-3), followed by the traffic (0.07 nmol min-1 m-3) and nitrate-rich (0.06 nmol min-1 m-3) factors.
Although lower in magnitude, the OP contribution of mineral dust, traffic,
and biomass burning (only in OPvAA) is also prominent at the OPE site, similar to other sites in France
(Weber et al., 2021). These sources
are commonly composed of species that are highly redox-active; hence it is
not surprising that they are one of the main drivers of OP, even in a rural
site. Both mineral dust and biomass burning are also sources that can be
associated with LRT and ageing, respectively, which are atmospheric processes
linked to increased OP (Pietrogrande
et al., 2019; Wang et al., 2020; Paraskevopoulou et al., 2019; Oh et al.,
2021). Further, while the mineral dust profile in OPE is considered
homogeneous with those determined in other parts of France, as discussed
above, its chemical composition includes slightly larger fractions of some
metals, particularly Fe and Cu, possibly making it more redox-active.
As observed in other studies (Daellenbach
et al., 2020; Borlaza et al., 2021b; Weber et al., 2021), there is also a
clear difference in source ranking when considering the PM mass or OP,
highlighting that the sources driving PM mass are not the same as the ones
driving OP activity. The mass contributions of the nitrate-rich factor can
be twice those of the traffic factor. However, in terms of OP (both
OPvDTT and OPvAA), the traffic factor contribution can
be twice as much as that of the nitrate-rich factor. The biomass burning
factor, with only < 1 µg m-3 mass contribution on annual
average, appears to have the highest contribution in OPvAA.
Some previous studies associated secondary inorganic aerosol (SIA) sources with
minimal contributions on PM toxicity (Cassee et al., 2013; Daellenbach
et al., 2020; Park et al., 2018). However, the nitrate-rich factor
apportioned at the OPE site showed contributions to both OPvDTT
and OPvAA and the sulfate-rich factor to OPvDTT. In the sulfate-rich factor, a fraction of OC (5 %) and metals (Se (42 %), Zn (44 %), Cu (27 %), and Sb (25 %)) were apportioned, while in the
nitrate-rich factor there are contributions from OC (6 %), EC (4 %), and metals (Sb (6 %) and Sn (11 %)). These species are commonly
anthropogenic-derived, signalling that the sulfate- and nitrate-rich
factors could be influenced by these types of emissions as well. In fact, a
similar study considered that both SIA factors can be associated with
anthropogenic SOA sources (Borlaza et al., 2021a).
Although both DTT and AA assays represent potential PM-induced oxidative
stress, through in vivo interactions between redox-active components in
PM10 and biological oxidants, it can be observed that they differ in
terms of source impacts. This can be attributed to the sensitivity of each
assay to specific species and/or emission sources of PM. Nevertheless, most
of the sources of PM suggested to be anthropogenic-derived or impacted, such
as traffic, mineral dust, nitrate-rich, sulfate-rich (only in
OPvDTT), and biomass burning (only in OPvAA), were all usually on the upper half of the scale (Fig. 8) in terms of OPv contributions. The knowledge of source-specific OPv contributions provides useful information on the main drivers of
OPv, even in a rural area such as the OPE site.
Monthly contribution of sources to OP activity in nmol min-1 m-3 (OPvDTT and
OPvAA) from the year 2017 to 2020 at the OPE site. Note that the months are labelled from January (1) to December (12).
There is an interesting seasonality observed in the OP of PM, as previously
shown in Fig. 9. With the OP source deconvolution method, this seasonality has been revealed in terms of PM sources, as presented in Fig. 11. During colder months, the biomass burning factor clearly dominated the OPvAA contributions. During warmer months, the OPvDTT contributions were dominated by
the mineral dust factor. However, there is a relatively consistent monthly
contribution for both assays coming from the traffic factor.
It is also interesting to note the negative contributions from some sources.
This negative contribution is brought by a negative intrinsic OP (OPm obtained in the OP deconvolution method; Table 2).
This can be broadly interpreted as follows: for every 1 µg m-3 increase in the MSA-rich factor, there is an associated decrease in OPvAA contributions (OPm=-0.06 ± 0.03, p= 0.06). A
similar interpretation can be done for the primary biogenic factor and its
OPvDTT contributions (OPm=-0.01 ± 0.02, p= 0.68).
However, it is important to note that both MSA-rich and primary biogenic
factors do not always present a negative OPm at every site investigated in our group. One cannot completely assume that these two
factors always act as suppressors of OP of PM. In fact, these two factors
have shown high OPmvariabilities across different sites in France (Weber et al., 2021). With the use of fit-for-purpose organic
tracers, possible mixing issues in these factors can be minimized
(Borlaza et al., 2021a). However, these supplementary tracers were not available at the OPE site, making it difficult to eliminate potential influence from other factors or species in PM.
Yearly average contributions of sources to OP activity in nmol min-1 m-3 (OPvDTT and
OPvAA) from the year 2017 to 2020 at the OPE site. Note that total OP is the sum of OP contributions of all sources as
modelled by the MLR analysis.
Figure 12 presents the yearly contribution of each
factor to the OP of PM10 for the 3 years investigated in this study.
There is no clear decreasing trend in the total OP reconstructed by MLR
analysis. Although there is a decreasing trend found in the mass
contributions of the traffic factor, this was not clearly reflected in its
OP contributions. The other sources seem to have comparatively consistent OP
contributions from 2017 to 2020, and no notable tendency can be found in the
total OP contribution as opposed to the contributions to PM10 mass
showing decreasing trends. This may be explained by the limited subset of
samples for the OP assay (OP data spanning 4 years against 9 years for the
PMF data), the shorter time range being insufficient to reach significance
and robustness in the trend assessment of OP levels.
Limitations of the study
In spite of the advantages offered by the long-term monitoring at the OPE site, there were a few limitations encountered during the investigation of
the dataset. Each limitation is discussed as follows:
There was a change in sampling duration between the collection performed in year 2012 to 2016 (7 d sampling) and 2016 to 2020 (24 h sampling). A
7 d filter sample includes both weekdays and weekends, whereas a 24 h
sample will either be a weekday or weekend, depending on the sampling
interval. This implies that the weekly collected samples may contain
particles that are not fully captured in a daily sample. However, since the
OPE site is quite distant from direct emissions, the expected difference in
the weekday and weekend levels should be relatively small. Further, PMF
source apportionment was conducted separately (i.e. 7 d samples versus
24 h samples), leading to very similar results for the chemical profiles
and source contributions, justifying the coupled analysis (see Supplement, Table S4 and Figs. S13 to S21).
There is a lack of fit-for-purpose tracers to fully elucidate the influence of SOA in a site with relevant distance from direct emissions (or rural typology) such as OPE. In Borlaza
et al. (2021a), a secondary biogenic oxidation source was additionally
identified using organic tracers (3-MBTCA and pinic acid), while
anthropogenic influence was supported by contributions of phthalic acid,
even in secondary aerosol sources. With the typology at the OPE site, this
would have been useful.
The use of a single chemical profile for long-term PM source apportionment
could be limiting for the PMF model. As we have found consistent decrease in
some species, particularly EC, perhaps a rolling PMF (e.g. yearly PMF)
could better capture possible changes in the source profiles.
The absence of samples for OP analysis from the years prior to 2017 has limited
the investigation of long-term OP at the OPE site. Consequently, it was not
easy to capture the decrease in OP contributions from the traffic factor as
similarly captured in the mass contributions. Perhaps a hindcasting method
on the years without OP data could have been performed; however that would
heavily rely on the OPm modelled from the year 2017 to 2020, which can lead to bias in the results.
In the year 2020, a series of lockdown restrictions were placed nationally
as a response to the coronavirus disease (COVID-19) pandemic. In the OPE
site, there is no clear decrease in average PM10 mass concentration in
2020 (Fig. 12) that could have greatly affected the results of this study. In fact, excluding all samples from the year 2020, the traffic factor contributions to PM10 still has a reduction of 39 % from the year 2012 to 2019 and an overall yearly average reduction of 135 ng m-3 yr-1 (p≤ 0.01).
Conclusions
Over the 9-year analysis in a rural background site in France (OPE), the
observed PM10 mass concentration and OP were found to be much lower
than other sites in France. The sources of PM10 mass and OP were
apportioned using PMF and MLR analysis, respectively. The nine identified
factors relevant for PM10 include secondary inorganics (nitrate- and
sulfate-rich), traffic, mineral dust, biomass burning, sea salts (fresh and
aged), primary biogenic, and MSA-rich.
A redistribution of the factor impacts between mass and OP contributions was
observed, underlining the importance of taking into account the redox
activity of PM when considering their potential health effects. Based on PM
mass, the major contributors are nitrate- and sulfate-rich factors, both
factors being associated with secondary inorganics formed during long range
transport (LRT). On the other hand, based on OP activity, the main
contributors are mostly anthropogenic-derived sources such as traffic,
mineral dust, and biomass burning factors.
As the OPE site is located far from direct anthropogenic emissions, the
influence of LRT processes was noted in some sources. Sources such as
sulfate- and nitrate-rich, MSA-rich, and aged sea salt factors have shown
potential mixing with other anthropogenic sources, most probably due to the
transit time. These potential mixing and ageing processes were reflected in
the chemical mass profile of each factor as well as in their OP
contributions.
Thanks to the long-term dataset at the OPE site, it was observed that the
traffic factor contribution to total PM10 has decreased over the years
for this site that may well represent the French national background PM.
This decrease is much larger than any change observed for the other PM
sources and is in excellent agreement with estimations in the decrease in BC
emissions from the transport sector all over France from the national
inventory. This effect may be attributed to improvement of the exhaust
emission of terrestrial transportation fleet, and/or to regulations
restricting vehicular emissions in bigger cities and/or other
regional-scale. However, persistent changes in meteorological conditions
influencing the transport of air masses to OPE or formation of PM during
this transport cannot be totally ruled out.
Code availability
The software code can be made available upon request by contacting the corresponding author.
Data availability
The chemical, PMF, and OP datasets can be made available upon request by contacting the corresponding author. The data used
for the comparison in Fig. 8 are obtained from many different programmes,
including the CARA programme coordinated by Olivier Favez (Favez et al., 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-8701-2022-supplement.
Author contributions
SC manages the overall observatory at OPE, including the supervision of the PM sampling. JLJ designed the project, in collaboration with SC. LJB, SW, and AM did the curation of the database. LJB performed the data analysis and wrote the paper. GU managed the OP analytical procedures at IGE. VJ designed part of the analytical method on the Air O Sol plateau. MC is the representation of Atmo GE, who helped maintain the sampling equipment. JLB was in charge of the coordination of different research programs and funding acquisitions. All authors read and commented on the paper.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The authors wish to thank all the many people from the different
laboratories (OPE, IGE, Air O Sol analytical platform, LCME) and from the
regional air quality monitoring network Atmos Grand Est, who have actively
contributed to filter sampling and/or analysis over the years.
Review statement
This paper was edited by Leiming Zhang and reviewed by three anonymous referees.
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