Interactive comment on “ Trends analysis of PM source contributions and chemical tracers in NE Spain during 2004 – 2014 : A multi-exponential approach ” by Marco Pandolfi

the pollution control measures implemented in this period by the administrations. Two different methodologies have been used. Namely, the Mann-Kendall test (MK) and the Multi-Exponential fit (ME). In brief, I think that the present work shows some significant and novel contributions to the global scientific community in relation with trend analysis of atmospheric pollutants issues. I think that the availability of time series of PM chemical composition and of estimations of PM source contributions from receptor models, such as PMF, is nowadays a key factor for establishing reliable source-receptor relationships and obtaining robust results from trend analysis and even epidemiological analysis. However, some significant changes must be performed to clarify the usefulness of the different methodologies employed, to reduce the excess of information provided in the manuscript that makes the reading very hard, and to justify the behaviour observed of the trends of some of the time series analyzed.


Introduction
Meeting air quality (AQ) standards is one of the major environmental objectives to protect people from breathing air with high levels of pollution.Many studies have been published in these last years showing clearly that the concentrations of particulate matter (PM), and other air pollutants such as sulfur dioxide (SO 2 ) and carbon monoxide (CO), have markedly decreased during the last 15 years in many European countries (EEA, 2013;Barmpadimos et al., 2012;Cusack et al., 2012;Querol et al., 2014;Guerreiro et al., 2014 among others).Cusack et al. (2012) reported the reduction in PM 2.5 concentrations observed at regional background (RB) stations in Spain and across Europe, and, in most cases, the observed reduction was gradual and consistent over time, implying the success of cleaner anthropogenic activities.Barm-padimos et al. (2012) have also shown that PM 10 concentrations decreased at a number of urban background (UB) and rural background stations in five European countries.Henschel et al. (2013) reported the dramatic decrease in SO 2 levels in six European cities, which reflected the reduction in sulfur content in fuels, as part of European legislation, coupled with the shift towards the use of cleaner fuels.EEA (2013) also reported general decreases in NO 2 concentrations, even if they were lower compared to PM.However, Henschel et al. (2015) showed that the NO x concentrations at traffic sites in many European cities remained unchanged, underlining the need of further regulative measures to meet the air quality standards for this pollutant.In fact an important proportion of the European population lives in areas exceeding the AQ standards for the annual limit value of NO 2 , the daily limit value of PM 10 , and the health protection objective of O 3 (EEA, 2013(EEA, , 2015)).PM 10 and NO 2 are still exceeded mostly in urban areas, and especially at traffic sites (Harrison et al., 2008;Williams and Carslaw, 2011;EEA, 2013;among others).In Spain, for example, it has been reported that more than 90 % of the NO 2 exceedances can be attributed to road traffic emissions (Querol et al., 2012).Guerreiro et al. (2014) furthermore evidenced the notable reduction of ambient air concentration of SO 2 , CO, and Pb using data available in Airbase (EEA, 2013), covering 38 European countries.Querol et al. (2014) reported trends for 73 measurement sites across Spain including regional background (RB), urban background (UB), traffic stations (TS), and industrial sites (IND).They observed marked downward trends for the concentrations of PM 10 , PM 2.5 , CO, and SO 2 at most of the RB, UB, TR, and IND sites considered.Similarly, Salvador et al. (2012) detected statistically significant downward trends for the concentrations of SO 2 , NO x , CO and PM 2.5 at most of the urban and urban-background monitoring sites in the Madrid metropolitan area during 1999-2008. Cusack et al. (2012) ) and Querol et al. (2014) have also shown the statistically significant decreasing trends for many trace elements (Pb, Cu, Zn, Mn, Cd, As, Sn, V, Ni, Cr) at regional level in the northeast (NE) of Spain since 2002.
The observed reduction of air pollutants across Europe is the result of efficient emission abatement strategies, as for example those implemented in the Industrial Emission Directive (IPPC -Integrated Pollution Prevention and Control -and subsequent Industrial Emission Directive 1996/61/EC and 2008/1/EC), the Large Combustion Plant Directive (LCPD; 2001/80/EC), the EURO standards on road traffic emission (1998/69/EC, 2002/80/EC, 2007/715/EC), and the IMO (International Maritime Organization) directive on sulfur content in fuel and SO x and NO x emissions from ships (IMO, 2011; Directive 2005/33/EC).Additionally, the financial crisis, causing mainly a reduction of the primary energy consumption from 2008 to 2009, contributed to the decrease of the ambient concentration of pollutants observed in Spain (Querol et al., 2014).
Moreover, national and regional measures for AQ have been taken in many European countries.In Spain a national AQ plan was approved in 2011 and updated in 2013 by the Council of Ministers of the government of Spain.Furthermore, 45 regional and 3 local (city-scale) AQ plans have been implemented since 2004 in Spain.These AQ plans have mostly focused on improving AQ at major city centers and specific industrial areas.
Thanks to the aforementioned measures there is clear evidence that the concentrations of PM in many European countries have markedly decreased during the last decades.However, in spite of the above policy efforts, a significant proportion of the urban population in Europe lives in areas exceeding the World Health Organization (WHO) AQ standards, for example for PM 2.5 , PM 10 , and O 3 (EEA, 2013(EEA, , 2015)).
The analysis of the trends of the concentration of air pollutants helps in evaluating the effectiveness of specific AQ measures depending on the pollutant considered.Examining data over time also makes it possible to predict future frequencies and/or rates of occurrence making future projections.For the abovementioned reasons, the feasibility of studying the trends of the contributions to PM mass from specific pollutant sources, along with the trends of the chemical tracers of these sources, is especially attractive.
To the best of our knowledge, in the majority of studies dealing with trend analysis, linear fits were applied by using Mann-Kendall (MK) or Theil-Sen methods (Theil, 1950;Sen, 1968), the latter being available, for example, in the Openair software (Carslaw, 2012;Carslaw and Ropkins, 2012).However, a linear fit of data does not always properly represent the observed trends.As we will show, different abatement strategies and periods of implementation may change from one pollutant to another, thus leading to different trends for different pollutants, even over the same period.Thus, a nonlinear fit of the data may be strongly recommended at times.
The main aim of this work was to study the trends of PM 10 source contributions and of specific chemical species in both PM 10 and PM 2.5 using both the consensus methodology (Mann-Kendall) and the nonlinear approach.The data of Spanish national emissions and energy consumption were also evaluated to interpret the observed trends.Understanding past trends may be relevant for devising new strategies for air pollution abatement.PM chemical speciated data collected from 2004 to 2014 at regional (Montseny, NE Spain) and urban (Barcelona, NE Spain) sites were used with this aim.The positive matrix factorization (PMF) model was used to apportion ambient PM 10 concentrations into pollutant sources.The PMF model, as other receptor models, is widely used as a powerful tool to help policymakers to design more targeted approaches to protecting public health.Thus, the novelty of this study lies mainly (a) in the opportunity to study the trends of pollutant source contributions from the PMF model at two twin stations representative of the urban and regional environments in the western Mediter- ranean, and, (b) in the use of a novel nonlinear approach for trend studies.
2 Measurement sites and methodology

Measurement sites
The Montseny measurement station (MSY, 41 • 46 45.63 N, 02 • 21 28.92 E; 720 m a.s.l.) is a regional background site in the NE of Spain, located in a regional natural park about 50 km to the NNE of the city of Barcelona (BCN) and 25 km from the Mediterranean coast (Fig. 1).This site is representative of the typical regional background conditions of the western Mediterranean Basin (WMB), characterized by severe pollution episodes affecting not only the coastal sites closest to the emission sources, but also the more elevated rural and remote areas inland (i.e., Pérez et al., 2008;Pey et al., 2010;Pandolfi et al., 2011Pandolfi et al., , 2014)).This station is part of the ACTRIS (www.actris.net)and GAW (www.wmo.int/gaw)networks, of the EMEP Program (http://www.emep.int/)and of the measuring network of the government of Catalonia.
The Barcelona measurement station (BCN, 41 • 23 24.01 N, 02 • 06 58.06 E; 68 m a.s.l.) is an ur-ban background measurement site influenced by vehicular emissions from one of the main avenues of the city (Diagonal Avenue) located at a distance of around 300 m (cf.Fig. 1).The BCN measurement site is part of the air quality measuring network of the government of Catalonia.The metropolitan area of Barcelona (BMA), with nearly 4.5 million inhabitants, covers a strip 8 km wide between the Mediterranean Sea and the coastal mountain range.Several industrial zones, power plants, and highways are located in the area, making this region one of the most polluted in the WMB (i.e., Querol et al., 2008;Amato et al., 2009;Pandolfi et al., 2012Pandolfi et al., , 2013Pandolfi et al., , 2014)).At BCN the location of the measuring station changed in 2009 when it was moved by around 500 m (cf.Fig. 1).The effect of this change on PM measurements performed at BCN will be discussed later.

Real-time and gravimetric PM measurements
Real-time PM concentrations were continuously measured at 1h resolution by optical particle counters (OPCs) using GRIMM spectrometers (GRIMM 180 at MSY, andGRIMM 1107, 1129, and  were corrected by comparison with 24 h gravimetric mass measurements of PM x (Alastuey et al., 2011).For gravimetric measurements, 24 h PMx samples were collected at both stations every 3-4 days on 150 mm quartz micro-fiber filters (Pallflex QAT and Whatman) with highvolume samplers (DIGITEL DH80 and/or MCV CAV-A/MSb at 30 m 3 h −1 ).The mass of PM 10 and PM 2.5 samples collected on filters was determined using the EN 12341 and the EN14907 gravimetric procedures, respectively.

PM chemical speciated data
Once the gravimetric mass was determined from filters, the samples were analyzed with different techniques, including acidic digestion (1/2 of each filter; HNO 3 : HF : HClO 4 ), water extraction of soluble anions (1/2 of each filter), and thermal-optical analysis (1.5 cm 2 sections).Inductively coupled atomic emission spectrometry, ICP-AES, (IRIS Advantage TJA Solutions, THERMO) was used for the determination of the major elements (Al, Ca, Fe, K, Na, Mg, S, Ti, P), and inductively coupled plasma mass spectrometry, ICP-MS, (X Series II, THERMO) for the trace elements (Li, Ti, V, Cr, Mn, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Cd, Sn, Sb, Ba, rare earths, Pb, Bi, Th, U).Ionic chromatography was used for the concentrations of NO − 3 , SO 2− 4 , and Cl − , whereas NH + 4 was determined using a specific electrode MODEL 710 A+, THERMO Orion.The levels of organic carbon (OC) and elemental carbon (EC) were determined using a thermal-optical carbon analyzer (SUNSET), using protocol EUSAAR_2 (Cavalli et al., 2010).Other analytical details may be found in Querol et al. (2008).
Following the above procedures, a total of 1093 PM 10 and 794 PM 2.5 filters were collected and analyzed at MSY during the period 2004-2014.At BCN the collected filters were 1037 and 1063 in PM 10 and PM 2.5 , respectively.

Positive matrix factorization (PMF) model
The PMF model (PMFv5.0,EPA) was applied to the collected daily PM 10 speciated data for source identification and apportionment at both sites.Detailed information about the PMF model can be found in literature (Paatero and Tapper, 1994;Paatero, 1997;Paatero and Hopke, 2003;Paatero et al., 2005).The PMF model is a factor analytical tool, reducing the dimension of the input matrix to a limited number of factors (or sources), and it is based on the weighted leastsquares method.Thus, most important in PMF applications is the estimation of uncertainties of the chemical species included in the input matrix.In the present study, individual uncertainties and detection limits were calculated as in Escrig et al. (2009) and Amato et al. (2009).Thus, both the analytical uncertainties and the standard deviations of species concentrations in the blank filters were considered in the uncertainties calculations.The signal-to-noise ratio (S/N) was estimated starting from the calculated uncertainties and used as a criterion (S/N > 2) for selecting the species used within the PMF model.In order to avoid any bias in the PMF results, the data matrix was uncensored (Paatero, 2004).The PMF was run in robust mode (Paatero, 1997), and rotational ambiguity was handled by means of the F PEAK parameter (Paatero et al., 2005).The optimal number of sources was selected by inspecting the variation of the objective function Q (defined as the ratio between residuals and errors in each data value) with a varying number of sources (i.e., Paatero et al., 2002) and by studying the physical meaningfulness of the calculated factors.

Mann-Kendall (MK) fit
The purpose of the Mann-Kendall (MK) test (Mann, 1945;Kendall, 1975;Gilbert, 1987) is to statistically assess whether there is a monotonic upward or downward trend of the variable of interest with time.A monotonic upward (downward) trend means that the variable consistently increases (decreases) through time.The Mann-Kendall test tests the null hypothesis H 0 of no trend, i.e., the observations are randomly ordered in time, against the alternative hypothesis, H 1 , where there is an increasing or decreasing monotonic trend.The main advantage of the Mann-Kendall test is that data need not conform to any particular distribution and missing data are allowed.To estimate the slope of the trend, Sen's method was used (Salmi et al., 2002).

Multi-exponential (ME) fit
A program aiming at studying trends by means of multiexponential fit of the data was developed within the Task Force on Measurements and Modelling (TFMM) by the Meteorological Synthesizing Centre -East (MSC-E; http: //www.msceast.org/)group (Shatalov et al., 2015).The TFMM together with the Task Force on Emission Inventories and Projections (TFEIP), the Task Force on Integrated Assessment Modelling (TFIAM), and Task Force on Hemispheric Transport of Air Pollution (TFHTAP) provide fora for discussion and scientific exchange in support of the EMEP (European Monitoring and Evaluation Programme; http://www.emep.int/)work plan, which is a scientifically based and policy-driven program under the Convention on Long-range Transboundary Air Pollution (CLRTAP; http: //www.unece.org/env/lrtap/lrtap_h1.html) to promote international co-operation and to solve transboundary air pollution problems.The TFMM was established in 2000 to evaluate measurements and modeling and to further develop working methods and tools.In this context, five EMEP centers are undertaking efforts in support of the EMEP work plan, namely the MSC-E, the Centre on Emission Inventories and Projections (CEIP; http://www.ceip.at/), the Chemical Coordinating Centre (CCC; http://www.nilu.no/projects/ccc/), the Meteorological Synthesizing Centre -West (MSC-W; http: //emep.int/mscw/index_mscw.html), and the Centre for In-tegrated Assessment Modelling (CIAM; http://www.iiasa.ac.at/~rains/ciam.html).In 2014 the TFMM initiated an exercise dedicated to assessing the efficiency of CLRTAP air pollution mitigation strategies over the past 20 years, evaluating the benefit of its main policy instruments.Within this exercise a piece of software was made available by EMEP/MSC-E, with the aim of studying nonlinear trends.Annual, monthly, and daily resolution data can be analyzed with the help of this program.Since in this paper we will apply the program to annual averages of species concentrations and source contributions, we restrict the description of the multi-exponential approximations to this case.In particular, seasonal variations are not taken into consideration.The basic equations solved by the program for this particular case (annual averages) are reported below: where C t are the values of the considered time series, with t = 1, . .., N, N being the length of the series (years), τ n are the characteristic times of the considered exponential, a n are constants, and ω t are the residue values.In the case of single exponential decay (n = 1) the characteristic time τ is the time at which the pollutant concentration is reduced to 1/e (i.e., 0.3678) times its initial value.The main difference between linear and exponential fit is that in the latter case, the trend is not gradual and constant over time.For an exponential trend the absolute (µg m −3 ) reduction per year decreases with time, being the highest at the beginning of the period.
Conversely, for a linear fit, the absolute reduction is constant over time.For an exponential fit, the lower the characteristic time τ , the more rapidly the considered quantity vanishes.Deviations from single exponential fit can be taken into account introducing more exponential terms.In this work, for example, two exponential terms were sometimes used.In this case two characteristic times are calculated by the software.
If the decrease of the considered quantity is very sharp at the beginning of the period (more than single exponential), then both τ 1 and τ 2 are positive.Conversely, one exponential term with negative τ takes possible increase of the quantity at the end of the period into account.Both τ n and a n are calculated by the program by means of the least-squares method, minimizing the residue ω, and the statistically significance of the exponential fit is provided by means of the p value.The number of exponential terms that should be included into the approximation can be evaluated using F-statistics (i.e., Smith, 2002).For example, the F-statistics for the evaluation of the statistical significance of the second term in equation ( 1) for n = 2 can be calculated as where SS 1 and SS 2 are sums of squares of residual component for approximations with one and two exponential terms, respectively, and s is the estimate of standard deviation of residual component.This statistic approximately follows the Fisher distribution, with 2 and N −2 • of freedom.The second exponential is considered to be significant if F exceeds the corresponding threshold value at the chosen significance level.
The following parameters can be calculated from Eq. ( 1): annual reduction for year i : where C beg and C end are the first and the last points, respectively, of the exponential fit.The formula for the calculation of the average annual reduction takes into account that the ratio C i + 1/C i is a multiplicative quantity, so that geometrical mean of ratios should be used.The relative contribution of residues (residual component: RC) is calculated as the standard deviation of the ratios between the residue values of the fit ω t (cf.Eq. 1) and the main component of the fit.
The MSC-E also proposed a statistic that measures the deviation of the obtained trend from the linear one (nonlinearity parameter: NL).A trend is defined as linear if the NL parameter is lower than 10 %, indicating a small difference between ME and MK fits (Shatalov et al., 2015).In the following, the reported trends were analyzed using the MK test for NL < 10 % and the ME test for NL > 10 %.A more detailed description of the multi-exponential approach is available in the TFMM wiki and in the MSC-E technical report 2015 (Shatalov et al., 2015).

Results
Results are presented and discussed in the following order.In Sect.3.1, we compare the trends at both stations of PM x concentrations from optical particle counters (OPCs; annual data coverage around 90 %) and from 24 h gravimetric samples (filters; annual data coverage around 20-30 %).This comparison will demonstrate the feasibility of studying trends of chemical species concentrations from filters despite the relatively low annual data coverage.In Sect.3.2, we compare the magnitude of the trend of PM 2.5 concentrations at MSY during 2004-2014 (period selected for this study) with the magnitude of trends calculated at the same station over different periods, namely 2002-2010(the period used in Cusack et al., 2012) ) and 2002-2014 (representing the largest period of gravimetric PM 2.5 measurements available at MSY station at the time of writing).This comparison was performed in order to study the differences in the trends over short periods (9 www.atmos-chem-phys.net/16/11787/2016/ to 13 years).The gravimetric concentrations of PM 2.5 measured at MSY were used with this aim.Then, in Sect.3.3, we present and discuss the trends at both stations of chemical species from filters in both PM 10 and PM 2.5 .In Sect.4.0 we discuss the sources of pollutants identified by the PMF model in PM 10 at both sites.Finally, we present and discuss the trends of PM 10 source contributions at BCN and MSY (Sect.4.1), providing possible explanations for the observed trends.Conclusions are reported in Sect. 5.

Trends of PM: comparison between gravimetric and real-time optical measurements
Annual data coverage is an important factor to take into account in order to study trends of a given parameter.The gravimetric PM measurements, from which chemical speciated data are obtained, are typically performed with rather low frequency over 1 year.In our case, the annual data coverage of gravimetric measurements was around 20-30 % at both Barcelona and Montseny.In this section, we compare the trends of PM concentrations from gravimetric and realtime optical measurements (Table 1).Given that the trends of the considered PM x fractions were linear at both sites (NL < 10 %), only results from MK test were reported in Table 1.However, we will show later (Sect.4.1) that the contributions from specific PM 10 pollutant sources, mainly those related with anthropogenic activities, showed nonlinear (i.e., exponential) decreasing trends, thus mirroring the different effectiveness of the mitigation strategies depending on the source of pollutants considered.
As reported in Table 1, statistically significant decreasing trends were observed for the considered PM size fractions at BCN (−2.20 µg m −3 yr −1 with p < 0.001 for PM 10 and −1.55 µg m −3 yr −1 with p < 0.01 for PM 2.5 from OPC measurements), whereas at Montseny, only the PM 2.5 fraction showed a slightly significant decreasing trend (−0.26 µg m −3 yr −1 ; p < 0.1 from OPC measurements).Total reduction (TR) ranged between 50.4 % (OPC PM 10 at BCN) and 7.8 % (OPC PM 10 at MSY), and the residual component (RC) was lower than 18 %, reflecting the goodness of the linear (MK) fit used.It must be noted that the higher p-values, magnitude of the trends, and TR observed at BCN compared to MSY was likely due to the change of the measuring station in 2009 in BCN (cf.Fig. 1).Based on the comparison between simultaneous PM x chemical speciated data collected at both BCN measurement sites during 1 month (not shown), we concluded that after 2009, the BCN measuring site was less affected by mineral matter and, to a lesser extent, by road traffic emissions, both being important sources of PM in Barcelona.In Fig. 1 we highlight the proximity of the BCN measuring station before 2009 to an unpaved parking area and different construction works.The effect of the change of the station in BCN in 2009 on PM 10 gravimetric measurements is reported in the Supplement (Fig. S1).However, despite the change of the station, the comparison between BCN and MSY for specific chemical species and pollutant sources not linked with mineral matter and road traffic emissions was possible.
Table 1 shows that the p-values calculated using gravimetric and OPC measurements were the same despite the different annual data coverage.The differences in the magnitude of the trends were 22 and 24 % between gravimetric and OPC PM 10 and PM 2.5 measurements, respectively, at BCN and 24 and 21 %, respectively, at MSY. Relative differences of total reduction (TR) ranged between 24 % for gravimetric and OPC PM 10 measurements at MSY and 15 % for PM 10 at BCN.Thus, despite the different data coverage, the magnitude of the trends and TR calculated from OPCs and gravimetric measurements were rather similar.Other PM mass fractions (PM 1−10 and PM 2.5−10 ) and PM ratios (PM 1 / PM 10 and PM 2.5 / PM 10 ) at MSY showed nonstatistically significant trends.

Trends of PM: comparison among different periods
In this study we used the period 2004-2014 for trends analysis, given that gravimetric PM 2.5 measurements at BCN have been available since 2004.Conversely, at MSY PM 2.5 , gravimetric measurements started in 2002.Figure 2 shows the trends of PM 2.5 concentrations at MSY, calculated using the MK test for the three different periods.The ME test was not used here given that the observed trends were linear (NL < 10 %).The period 2002-2010 was the period considered in the paper from Cusack et al. (2012) presenting the trends of PM 2.5 gravimetric mass and chemical species at MSY.The period 2002-2014 is the largest period, with PM 2.5 filter measurements available at the time of writing.The trend observed at MSY for the PM 2. mass concentration in 2012 (cf.Fig. 2).Chemical PM 2.5 speciated data revealed that this increase was partly driven by organic matter showing a mean annual concentration in 2012 higher by around 20 % compared to the 2004-2014 average.

Trends of chemical species
The trends of the annual mean concentrations of chemical species at BCN and MSY are reported in Table 2 (for PM 10 ) and Table 3 (for PM 2.5 ). Figure 3 (for BCN) and Fig. 4 (for MSY) show the trends of chemical species in PM 10 .In Tables 2 and 3 and Figs. 3 and 4, only the species that have statistically significant trends were reported.
As already noted we assume that the change of the station in BCN in 2009 affected the trends of the concentrations of OC, EC, Cu, Sn, Sb, and Zn (mainly traffic tracers), Al 2 O 3 , Ca, Mg, Ti, Rb, Sr (crustal elements related with both natural and anthropogenic sources), and Fe (traffic and crustal tracer).These chemical species at BCN were removed from Tables 2 and 3 and from Fig. 3.
Other species measured in BCN were not affected by the change of the station.These are SO 2− 4 , NH + 4 , V, Ni (related with heavy oil combustion in the study area according to source apportionment results, cf.Par.4), Pb, Cd, and As (related with industrial/metallurgy activities), Na and Cl (sea spray), and NO − 3 .Although nitrate particles in Barcelona were mainly from traffic, the concentrations of these particles were not strongly affected by the change of the station due to their secondary origin.The MSY station will be considered as reference station given that no location change occurred at this monitoring site during the study period.
Statistically significant exponential trends (p < 0.01 or 0.001) were mainly observed for the industrial tracers (Pb, Cd, As) in both PM 10 and PM 2.5 .For these elements TR was high and around 50-80 % in PM 10 and 67-81 % in PM 2.5 .The RCs were lower than 20 %, thus suggesting the goodness of the exponential fits used to study the trends of these species.Exponential fits were needed on average, indicating that the trends were not gradual and consistent over time and that the effectiveness of the control measures for these pollutants was stronger at the beginning of the period under study (2004-2009 approximately) compared to the end of the period (Figs. 3 and 4).This is also evident by comparing the linear MK fit (dashed black line) with the ME fit (red line) in Figs. 3 and 4. In PM 10 the magnitudes of the trends ranged from −0.00222 (Pb; p < 0.001) to −3.10 × 10 −5 µg m −3 yr −1 (Cd; p < 0.001) at BCN and from −0.00031 (Pb; p < 0.01) to −1.12 × 10 −5 µg m −3 yr −1 (Cd; p < 0.01) at MSY.In PM 2.5 the magnitudes of the trends were similar and ranged between −0.00163 µg m −3 yr −1 (Pb; p < 0.001) and −3.11 × 10 −5 µg m −3 yr −1 (Cd; p < 0.001) at BCN and between −0.00049 µg m −3 yr −1 (Pb; p < 0.001) and −1.35 × 10 −5 µg m −3 yr −1 (Cd; p < 0.001) at MSY.Similar magnitude of the trends for these species in both PM fractions at both sites confirmed the common origin of these elements and the impact at regional scale of industrial sources.For Pb and Cd, the characteristic time (τ ) of the exponential trends was similar at both sites, whereas for As it was higher due to the slightly less intense exponential downward trend observed for As compared to Cd and Pb.Note that the PMF analysis (cf.Sect.4) revealed that the concentrations of As were explained by multiple sources (especially at BCN), whereas the industrial/metallurgy source alone explained more than around 70 % of Pb and Cd concentrations (not shown).The implementation of the IPPC Directive in 2008 in Spain is the most probable cause for this downward trend.The decrease observed for Pb, Cd, and As may also be attributed to a decrease in the emissions from industrial production (smelters; Querol et al., 2007) at a regional scale around Barcelona.
The concentrations of V and Ni in Barcelona in both PM 10 and PM 2.5 fractions showed very similar exponential decreasing trends.Similar characteristic times (around 10-11 years), TR (around 59-63 %), and RC (15-17 %) in both fractions suggested the common and mainly fine origin of these two elements.At MSY, V and Ni showed linear trends likely because of the higher distance of the MSY station to the sources of V and Ni (shipping and, before 2008, energy production) compared to BCN.Note also that the NL parameter for BCN V and Ni was around 10-12 %, indicating that in this case the exponential fit did not differ very much from the linear one.Total reduction for V and Ni at MSY was around 59-64 and 42-43 %, respectively, and RCs were lower than 24 %.
Sn and Cu in PM 10 at MSY showed very similar behavior by decreasing linearly with time, with TR around 36-39 % and RC around 16-20 %.Decreasing rates of −3.65 × 10 −5 (p < 0.05) and −0.00014 µg m −3 yr −1 (p < 0.05) were ob-Table 3. Mann-Kendall and multi-exponential trends of different chemical species in PM 2.5 at BCN (italic) and MSY.Type of trend: linear (L), single exponential (SE), double exponential (DE); a (µgm −3 ) and τ (yr) are the constants and the characteristic times, respectively, of the exponential data fittings; NL (%) is the nonlinearity; TR (%) is the total reduction; RC (%) is the residual component; ns denotes not statistically significant; ni denotes not included.Significance of the trends: served in PM 10 for Sn and Cu, respectively.In PM 2.5 , the concentrations of Sn and Cu decreased markedly compared to PM 10 at the rate of −0.00084 (p < 0.001) and −0.00026 µg m −3 yr −1 (p < 0.01), respectively.This difference could be explained by possible sources of coarser Sn and Cu, which reduced the magnitude of the trends in PM 10 mass fraction.Sb showed marked decreasing trends in both PM mass fractions compared to Sn and Cu, with TR around 62-70 %.The magnitude of the trend for Sb was similar in both fractions and around −3.57 ÷ −3.86 × 10 −5 µg m −3 yr −1 .The concentrations of Sb were better fitted with exponential curves (SE with p < 0.01 in PM 10 and DE with p < 0.01 in PM 2.5 ).The DE fit for Sb in PM 2.5 had one positive and one negative characteristic time; the latter was needed to explain the slight increase in Sb concentrations at the end of the considered period.The marked decreasing trend observed for Sb compared to other traffic tracers could be explained by a progressive reduction of Sb contained in the vehicle brakes.Cr did not show a statistically significant trend in both PM fractions.Sulfate (SO 2− 4 ) and ammonium (NH + 4 ) particles' concentrations showed very similar behavior in PM 2.5 and PM 10 size fractions due to their fine nature.In BCN, the magnitudes of the trends were −0.37868 (p < 0.001) and −0.11095 µg m −3 yr −1 (p < 0.001) for SO 2− 4 and NH + 4 , respectively, in PM 10 and −0.32778 (p < 0.001) and −0.12701 µg m −3 yr −1 (p < 0.001), respectively, in PM 2.5 .The trends were SE with very similar characteristic times (9.64-9.81years in PM 10 and 9.69-10.53years in PM 2.5 ), TR (64-65 % in PM 10 and 61-64 % in PM 2.5 ), and RC (12-14 % in PM 10 and 9-15 % in PM 2.5 ).At MSY, the magnitudes of the trends of SO 2− 4 and NH + 4 and their statistical significance were lower compared to BCN in both fractions.Moreover, at MSY, the trends were linear for SO 2− 4 in both fractions (as for V and Ni).These differences could be explained by the distance of MSY to direct specific sources of sulfate, such as shipping, compared to BCN, thus slightly reducing the magnitude and the statistical significance of the trend of SO 2− 4 at regional level.It is also interesting to note the similitude between the characteristic times of the exponential fits for V and Ni and SO 2− 4 in both PM fractions at BCN, suggesting the main common origin of these chemical species.Possible reasons for the observed reduction in the concentrations of ambient sulfate in and around Barcelona will be discussed later.
Fine NO − 3 (Table 3) showed statistically significant SE trends similar at both sites with p < 0.001, TR around 73-82 %, RC around 16-21 %, and characteristic times around 5.8-7.6 years.In PM 10 the TRs were lower and around 54-64 % and the fits were linear at MSY and SE at BCN.The SE fit in PM 10 at BCN provided a characteristic time around 9.8 years, which was higher compared to the τ obtained for the fine mode (7.6 years), thus indicating that fine NO − 3 had a more pronounced downward trend compared to PM 10 NO − 3 .For the mineral species (Al 2 O 3 , Ca, Fe) linear (with the exception of Al 2 O 3 in PM 2.5 which was SE) and statistically significant decreasing trends were detected at MSY.On average the TR was higher in the fine fraction, ranging from 50 for Ca to 66 % for Al 2 O 3 , compared to PM 10 (6-38 % cf.Table 2).A downward decreasing trend for crustal material in PM 2.5 at MSY was also reported by Cusack et al. (2012) for the period 2002-2010 and by Querol et al. (2014) for the period 2001-2012.These trends were probably driven by weather conditions associated with negative North Atlantic Oscillation (NAO) index (iNAO) that could be the cause for this slight reduction observed in crustal material.Pey et al. (2013) found a correlation between iNAO (calculated between June and September) and the contribution of Saharan  dust to PM 10 mass in the NE of Spain, and showed that the more negative the iNAO is, the lower the dust contribution to PM.The iNAO was unusually negative during the period 2008-2012 (http://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/norm.nao.monthly.b5001.current.ascii),thus likely contributing to the explanation of the observed trends of crustal elements.Moreover, negative NAO can favor the presence of fronts that can sweep the Iberian Peninsula from west to east, causing stronger winds and less stagnant conditions, thus favoring the dispersion of pollutants.In addition, as suggested by Cusack et al. (2012), it could also be hypothesized that some part of the crustal material measured at MSY is a product of the construction industry.The construction industry in Spain has been especially affected by the current economic recession, and crustal material produced by this industry may have contributed to the crustal load in PM 2.5 .For example the number of home construction works in Barcelona during 2008-2014 (from the beginning of the economic crisis; mean number of works of 1281) reduced by around 75 % compared to the period 2000-2007 (mean number of works of 5187; http://www.bcn.cat/estadistica/castella/dades/timm/construccio/index.htm).The fact that the total reduction calculated for mineral elements reported in Tables 2 and 3 was higher in PM 2.5 compared to PM 10 could corroborate the hypothesis of an anthropogenic contribution to the mineral matter measured at MSY.
Finally, Na concentrations showed linear decreasing trends at both sites, with the exception of PM 10 Na at MSY.Other species at MSY such as OC and EC did not show statistically significant trends.Consider that the concentrations of EC at MSY are very low and around an annual mean of 0.2-0.3µg m −3 .Both anthropogenic activity and biomass burning were expected to contribute to this chemical species.Concerning OC, the lack of trend was probably due to the contribution from biogenic sources to the concentration of this species at regional level.

PMF source profiles and contributions
Eight and seven sources were detected at BCN and MSY, respectively, in PM 10 from PMF model.The absolute and relative contributions of these sources to the measured PM 10 mass were reported in Fig. 5, whereas the chemical profiles of the detected sources were reported in the Supplement (Fig. S2).
Some sources were common at both BCN and MSY.These are secondary sulfate (secondary inorganic source traced by SO 2− 4 and NH + 4 and contributing 3.95 (23.7 %) and 4.67 µg m −3 (13.7 %) at MSY and BCN, respectively), secondary nitrate (secondary inorganic source traced by NO − 3 and NH + 4 and contributing 1.31(7.9%) and 4.45 µg m −3 (13.1 %) at MSY and BCN, respectively), V-Ni-bearing source (traced mainly by V, Ni, and SO 2− 4 , it represents the direct emissions from heavy oil combustion and contributed 0.71 (4.3 %) and 3.32 µg m −3 (9.8 %) at MSY and BCN, respectively), mineral (traced by typical crustal elements such as Al, Ca, Ti, Rb, and Sr and contributing 2.70 (16.2 %) and 4.61 µg m −3 (13.6 %) at MSY and BCN, respectively), and aged marine (traced by Na and Cl mainly with contributions from SO 2− 4 and NO − 3 and contributing 1.76 (10.6 %) and 5.73 µg m −3 (16.9 %) at MSY and BCN, respectively).Sources detected at MSY but not at BCN were the industrial/traffic source (traced by EC, OC, Cr, Cu, Zn, As, Cd, Sn, Sb, and Pb), which included contributions from anthropogenic sources such as road traffic and metallurgic industries and contributed 1.43 µg m −3 (8.6 %), and aged organics (traced mainly by OC and EC), with maxima in summer indicating mainly a biogenic origin and contributing 3.78 µg m −3 (22.7 %).The ratio OC : EC in the industrial/traffic and aged organic source profiles at MSY were 4.2 and 11.7, respectively, thus indicating a strong influence of aged particles in the latter source, with the former source being more fresh.The statistic of the OC : EC ratio based on chemical data at MSY is reported in the Supplement (Fig. S3).Mean and median values of OC : EC ratio at MSY were 9.1 and 7.8, respectively.
A sensitivity study was performed in order to better interpret the PMF sources at BCN.In fact, for the period 2007-2014, separate OC and EC measurements were available, and PMF was performed over this period.The comparison between the PMF source contributions obtained using the period 2007-2014 (separate OC and EC measurements) and the whole period (2004-2014; C nm (non-mineral carbon) available) is reported in the Supplement (Fig. S4).As reported Table 4. Mann-Kendall and multi-exponential trends of source contributions in PM 10 from PMF at BCN (italic) and MSY.Type: linear (L), single exponential (SE), double exponential (DE); a (µgm −3 ) and τ (yr) are the constants and the characteristic times, respectively, of the exponential data fittings; NL (%) is the nonlinearity; TR (%) is the total reduction; RC (%) is the residual component; ni denotes not included.Significance of the trends following the Mann-Kendall test: * * * (p value < 0.001), * * (p value < 0.01), * (p value < 0.05), + (p value < 0.1).mostly from power generation (MAGRAMA, 2013;Querol et al., 2014).In BCN the two characteristic times (one low and the other high, cf.Table 4) of the DE fit indicated a strong decrease of the secondary sulfate source contribution at the beginning of the period.This decrease was sharper compared to MSY where the SE fit was used.This difference was mostly due to the ban of heavy oils and petroleum coke for power generation around Barcelona from 2007.The effects of this AQ regional plan were likely more visible in BCN compared to MSY, thus explaining the two different exponential fits used.Overall, for the secondary sulfate source contributions, the TRs were rather high around 53 at MSY and 67 % at BCN, with RC ranging from 16 (BCN) to 21 % (MSY).The fact that the trend of the secondary sulfate source contribution was exponential likely suggested the attainment of a lower limit, and indicated a limited scope for further reduction of SO 2 emissions in our region.In fact, it has been estimated that the maximum in EU will be a further 20 % reduction through measures in industry, residential and commercial heating, and reduced agricultural waste burning (UNECE, 2016).Conversely, in eastern European countries the scope for reduction is much greater and around 60 % (UNECE, 2016).The trends of the secondary nitrate source contributions were SE at both stations with very similar τ (8.96-8.59years), TR (67-69 %), and RC (13-17 %).The decrease observed for the contribution from the secondary nitrate source was related to the reduction in ambient NO x concentrations (Figs. 8 and 9). Figure 9 shows the levels of tropospheric NO 2 column from 2005 to 2014 in southern Europe from NASA NO 2 Ozone Monitoring Instrument (OMI) Level 3 plotted using the Giovanni online data system (Acker and Leptoukh, 2007).In Spain, a general decrease of the concentrations of columnar NO 2 at regional level can be observed.Overall, the implementation of European directives affecting industrial and power generation emissions as well as the increase of the proportion of energy produced from renewable sources (cf.Fig. 10 for Spain), among others, produced a significant reduction of SO 2 and NO x emissions.Around Barcelona the observed decreases were also attributed to the decrease of NO x emissions, mainly from the five power generation plants around the city.Moreover, the implementation of the regional AQ Plan for SCRT (continuously regenerating PM traps with selective catalytic reduction for NO 2 ) and the hybridization and shift to natural gas engines of Barcelona's bus fleet may have had an influence in the observed reductions.
The decreasing trends (p < 0.01) of the V-Ni-bearing source contributions were SE and L at BCN and MSY, respectively, reflecting the trends observed at both stations for the concentrations of V and Ni (cf.Table 2).At BCN the characteristic times (τ ) were very similar to the characteristic times calculated for PM 10 V and Ni (cf.Table 2), which were the main tracers of this source.TRs were around 61 % at BCN and 64 % at MSY, and RCs were similar (19-25 %).The observed decrease in the V-Ni-bearing source contribution was mainly attributed to the ban of the use of heavy oils and petroleum coke for power generation in Spain from 2008.The industrial/metallurgy source contribution at BCN decreased exponentially (SE) at the rate of −0.10 µg m −3 yr −1 (p < 0.001), reflecting the SE decreasing trends observed for the main tracers of this pollutant source (Pb, Cd, and As; cf.Table 2).The decrease of industrial emissions was mainly attributed to the implementation of the IPPC (Integrated Pollution Prevention and Control) Directive.Moreover, the observed decrease may be attributed to a decrease in the emissions from industrial production (smelters; Querol et al., 2007) at a regional scale around Barcelona.Also, the financial crisis, whose impact on industrial production and use of fuels has been evident since October 2008 also contributed to the observed trend.TR and RC for the industrial source contributions at BCN were 65 and 16 %, respectively.As for the contributions from secondary sulfate and nitrate sources, the exponential trend observed for the industrial/metallurgy source contribution suggested the attainment of a lower limit.As evidenced in Fig. 6, the contribution from this source from 2010 was quite low and rather constant.
The contribution of the industrial/traffic source at MSY showed similar magnitude of the trend (−0.11 µg m −3 yr −1 with p < 0.01) compared to the BCN industrial/metallurgy contribution, both sources being traced mostly by the same industrial tracers.However, this source at MSY was also traced by traffic tracers (i.e., Cu and Sn), which decreased linearly with time (cf.Table 2), thus likely explaining the linear trend observed for the contribution of this source at MSY. TR and RC were 56 and 13 %, respectively, similar to those calculated for the industrial source contribution at BCN.
Finally, the mineral source contribution at MSY showed linear slightly significant decreasing trend (p < 0.1) in agreement with that observed at the same station by Cusack et al. (2012).As already noted in Sect.3.3, this negative trend could be due to both a possible decrease of the emissions of finer anthropogenic mineral species from specific sources, such as cement and concrete production and construction works, and unusual weather conditions, reducing the Saharan dust contribution to PM and resuspension of dust.
In order to further interpret the observed trends, annual data on the annual National Energy Consumption (NECo) from different energy sources (MINETUR, 2013) were also evaluated (Fig. 10).Overall, the primary energy consumption in Spain (NECo statistical data for Spain-MINETUR, This trend was probably governed by the fuel consumption for traffic road.Coal consumption remained constantly high from 2004 to 2007, whereas, as for the emissions of SO 2 (Fig. 8), a sharp decrease occurred from 2007.However, in the period 2011-2014, there was an important increase of coal consumption leading to an average consumption similar to the year 2008.However, the implementation of FGD systems contributed to maintain SO 2 at low concentrations, even in the coal production regions in Spain (cf.Querol et al., 2014).The hydroelectric generation was rather specular to coal consumption.For example, the increase in 2010 of hydroelectric consumption, due to high rainfall rate, mirrored the decrease in the coal consumption observed the same year.Finally, renewable energy consumption increased by 440 % from 2004 to 2014, with a gradual growth in the NECo.

Conclusions
PM chemical speciated data collected at two twin stations in the NE of Spain (Barcelona: urban background station and Montseny: regional background station) during 2004-2014 were used to study the trends of the contributions of pollutant sources from PMF model and of their chemical tracers.Despite the fact the trends of different PM fractions (PM 2.5 and PM 10 ) were linear during the period under study, the trends of specific chemical elements and source contributions were exponential, demonstrating the different effectiveness and/or time of implementation of the different mitigation strategies.Statistically significant exponential trends (p < 0.01 or 0.001) were mainly observed for the industrial tracers (Pb, Cd, As) in both PM 10 and PM 2.5 and at both sites.The concentrations of V and Ni showed exponential trends in BCN and linear trends at MSY, likely because of the higher distance of the MSY station to the sources of V and Ni (shipping and, before 2008, energy production) compared to BCN.Traffic tracers at MSY (Sn, Cu) showed very similar linear decreasing trends, with higher magnitude of the trends in the fine (PM 2.5 ) fractions compared to PM 10 , likely because of possible sources of coarser Sn and Cu reducing the magnitude of the trends in the PM 10 mass fraction.The concentrations of Sb at MSY showed a marked exponential decreasing trend compared to other traffic tracers (Cu and Sn), which could be explained by a possible progressive reduction of Sb content  in vehicle brakes.Secondary inorganic aerosols (SO 2− 4 , NO − 3 and NH + 4 ) also showed marked decreasing trends (both linear and exponential) in both fractions and at both sites.However, in general the magnitude of the trends for these species and their statistical significance were higher at BCN compared to MSY.
The PM 10 source contributions that showed statistically significant downward trends at both Barcelona (BCN; UB) and Montseny (MSY; RB) were from secondary sulfate,secondary nitrate, and V-Ni-bearing sources.For these source contributions, the decreasing trends were exponential, indicating that the trends were not gradual and consistent over time and that the effectiveness of the control measures for these pollutants was stronger at the beginning of the period under study (2004-2009 approximately) compared to the end of the period considered.Statistically significant decreasing trends were observed for the contributions from industrial/traffic and mineral sources at MSY and from the industrial/metallurgy source at BCN.These sources were mostly linked with anthropogenic activities, and the observed decreasing trends confirmed the effectiveness of pollution control measures implemented at European or regional/local levels.The economic crisis which started in 2008 in Spain also contributed to the observed trends.Conversely, the contributions from sources mostly linked with natural processes such as aged marine (at both BCN and MSY) and aged organic (at MSY) did not show statistically significant trends.The general trends observed for the calculated PMF source contributions reflected the trends observed for the chemical tracers of these pollutant sources well.The decrease in the secondary sulfate source contribution was mainly attributed to the EC Directive on Large Combustion Plants implemented in Spain from 2008, resulting in the application of flue gas desulfurization (FGD) systems in a number of large facilities.Moreover, according to the 2008 regional AQ plan, the use of heavy oils and petroleum coke for power generation was forbidden around Barcelona from 2008 in favor of natural gas.As a consequence, a decrease of the contributions from the V-Ni-bearing source at both sites was also observed.The decrease observed for the contribution of the secondary nitrate source was mainly due to the reduction in ambient NO x concentrations.In Spain a general decrease of the concentrations of NO 2 at regional level was observed, and it was mainly related with the lower energy consumption related with the financial crisis.The decrease of nitrates concentrations and secondary nitrate source contributions around Barcelona was also attributed to the decrease of NO x emissions from the five power generation plants around the city.Moreover, a regional AQ plan implementing the SCRT (continuously regenerating PM traps with selective catalytic reduction for NO 2 ) and the hybridization and shift to natural gas engines of Barcelona's bus fleet may have also had an influence on NO x ambient concentrations.The industrial/metallurgy source contribution at BCN decreased exponentially, reflecting the exponential trends observed for the main tracers of this pollutant source (Pb, Cd and As).The implementation of the IPPC (Integrated Pollution Prevention and Control) Directive, together with a decrease in the emissions from industrial production (smelters) at a regional scale around Barcelona, explained the observed trends.Overall, the magnitudes of the decreasing trends of the contributions of the pollutant sources were higher at BCN compared to MSY, likely because of the proximity of the BCN measurement site to anthropogenic pollutant sources compared to the MSY site.The results presented in this work clearly confirm the beneficial effect of the AQ measures taken in recent years in Europe.However, the WHO limit values of specific pollutants, such as PM 10 and PM 2.5 , are still exceeded, especially at urban level and industrial hotspots.To meet the WHO guide levels, important action is still required for the next decade and the interpretation of past air quality trends may yield relevant outcomes for planning further cost-effective actions.We would like to highlight that a nonlinear approach to trend studies is very attractive, given that some air pollutants reported in this work showed reductions that are not gradual with time.Conversely, for specific pollutant source-contribution/concentration in our region, the decreasing trends were less steep at the end of the period compared to the beginning, thus likely indicating the attainment of a lower limit.This was the case, for example, for the sec-ondary sulfate source contribution decreasing exponentially from 2004 to 2014, thus likely indicating a limited scope for further reduction of SO 2 emissions in our region.

Data availability
The Montseny data sets used for this publication are accessible online on the following web page: www.http://ebas.nilu.no/.The Barcelona data sets were collected within different national/regional projects and/or agreements and are available upon request.
The Supplement related to this article is available online at doi:10.5194/acp-16-11787-2016-supplement.

Figure 8 %Figure 8 .
Figure 8 2013) increased from 2004 to 2007 and decreased from 2007, with a marked decrease in 2009.From 2009, the energy consumption indicator remained rather low and constant until 2012, when an additional decrease in 2013 and 2014 was observed.Oil consumption was fairly constant during 2004-2007, showing an important decrease during 2008-2014.