Aerosol source apportionment from 1-year-measurements 1 at the CESAR tower at Cabauw, NL 2

Intensive measurements of submicron aerosol particles and their chemical composition were 2 performed with an Aerosol Chemical Speciation Monitor (ACSM) at the Cabauw 3 Experimental Site for Atmospheric Research (CESAR) in Cabauw, NL, sampling at 5 m 4 height above ground. The campaign lasted nearly one year from July 2012 to June 2013 as 5 part of the EU-FP7-ACTRIS project (Q-ACSM Network). Including equivalent black carbon 6 an average particulate mass concentration of 9.50 µ g m -3 was obtained during the whole 7 campaign with dominant contributions from ammonium nitrate (45%), organic aerosol (OA, 8 29%), and ammonium sulfate (19%). 12 exceedances of the World Health Organization 9 (WHO) PM 2.5 daily mean limit (25 µ g m -3 ) were observed at this rural site using PM 1 10 instrumentation only. Ammonium nitrate and OA represented the largest contributors to total 11 particulate matter during periods of exceedance. 16

We thank the reviewer for the careful review of our revised manuscript. In the following please find our responses to the comments one by one and the corresponding changes made to the manuscript. The original comments are shown in italics.

1) Detection limits
To me the answer by the authors "Furthermore, the ACSM Local software version used in this study could not show data acquired during the filter cycle measurements (e.g. closed mass spectra/time series)" is not convincing. Is it really the case that the "closed MS" data are not saved? Maybe contact with Aerodyne/Tofwerk/Boulder (Joel Kimmel or Mike Cubison) can help?

Response:
The closed and the open mass spectra are saved within the raw data (as itx files), from which the differential mass spectra are determined by the ACSM evaluation software. This software is for now only able to calculate and show the open, closed, and differential mass spectra and time series of the whole data set, but not distinguished for each species (by applying the frag table) or even with applied RIT and air beam correction. This was confirmed by Phil Croteau (PSI, Switzerland), who is the main responsible for the ACSM software. In the near future it is planned to add codes to investigate open and closed mass spectra for each aerosol species, similar to the AMS software bundles SQUIRREL and PIKA.
2) Particle losses I don't agree that an estimation of the particle losses in an aerosol sampling line is out of the scope of an aerosol paper, especially when the absolute mass concentrations play an important role. There are rather simple empirical formulas (e.g., in the book of W. Hinds) that allow for an estimation of particle losses based on Stokes number, settling velocity etc.

Response:
It was not the author's intention to lower down the importance of the estimation of particle losses generally in a publication where mass concentrations are in the main focus, as the reviewer emphasized correctly. We would like to emphasize though that considering the good agreement of the SMPS with the ACSM data and the low contribution of eBC to total mass concentrations, potential uncertainties resulting from the wall loss correction would not influence the conclusions of this study.
As the reviewer pointed out, the formulas provided by the book of Hinds or the recommendation of the WCCAP are based on (simple) empirical formulas. These cannot consider all potential losses of the sample inlet system of the MAAP and the SMPS, neither distinguish between different aerosol compositions. It should also be noted that the SMPS and the MAAP were sampling from the same inlet at 60 m but were located in different rooms in the basement, meaning that their quite complicated inlet lines are partly different by means of diameter, length and number and angles of bends, in addition to different flow rates.
The results given by Henzing (2011) show size dependent loss determinations from direct measurements performed at these specific inlet systems using the same instruments as used in this study. They also calculated the losses for different aerosol compositions. We therefore decided to apply the loss correction originating from comprehensive measurements evaluating them as more precise than corrections from empirical formulas.
CO and NO x time series do not exceed values above 0.47. Low correlations of POA and those tracers, e.g. HOA with CO or BC varying from far below 0.5 to slightly above 0.5 were also seen in a number of previous studies at remote and rural sites (Canonaco et  (WHO) PM 2.5 daily mean limit (25 µg m -3 ) were observed at this rural site using PM 1 10 instrumentation only. Ammonium nitrate and OA represented the largest contributors to total 11 particulate matter during periods of exceedance. 12 Source apportionment of OA was performed season-wise by Positive Matrix Factorization 13 (PMF) using the Multilinear Engine 2 (ME-2) controlled via the source finder (SoFi). Primary 14 organic aerosols were attributed mainly to traffic (8% -16% contribution to total OA, 15 averaged season-wise) and biomass burning (0% -23%). Secondary organic aerosols (SOA, 16 61% -84%) dominated the organic fraction during the whole campaign, particularly on days 17 with high mass loadings. A SOA factor which is attributed to humic-like substances (HULIS) 18 was identified as a highly oxidized background aerosol in Cabauw. This shows the 19 importance of atmospheric ageing processes for aerosol concentration at this rural site. Due to 20 the large secondary fraction, the reduction of particulate mass at this rural site is challenging 21 on a local scale. In addition, particles can impact adversely on human health by e.g. increasing the probability 7 of cardiopulmonary and lung cancer mortality (Pope et al., 2002). The World Health 8 Organization (WHO) recently estimated that globally, 3.7 million deaths were attributable to 9 ambient air pollution in both cities and rural areas in 2012 (EU, 2008). This mortality is 10 reported to be due to exposure to small particulate matter (PM 10 ), which can cause 11 cardiovascular and respiratory disease, and cancers. Particles with lower diameters such as 12 PM 2.5 or PM 1 are reported to have enhanced toxicological effects since they can deposit more 13 deeply in the respiratory system and remain suspended for longer periods of time (Pope and 14 Dockery, 2006). Therefore, a number of institutions established several air quality standards 15 for different particle sizes to limit aerosol mass. The WHO air quality guideline (global 16 update 2005, WHO (2006)) defines a PM 2.5 daily mean limit of 25 µg m -3 and a PM 2.5 annual 17 mean limit of 10 µg m -3 . The European Union Air Quality Directive 2008/50/EC provides 18 only a target value of the annual mean limit of PM 2.5 of 25 µg m -3 (EU, 2008). 19 Air quality and climate effects are not only depending on the particle number concentration 20 and size, but also on their chemical composition. This information is not only relevant to 21 investigate the nature and magnitude of each effect, but also for the identification and 22 quantification of aerosol sources and mitigation strategies for a potential reduction of aerosol 23 mass concentrations. Major inorganic components of PM 1 consist mainly of ammonium 24 nitrate (NH 4 NO 3 ) and ammonium sulfate ((NH 4 ) 2 SO 4 ), formed in the presence of ammonia 25 (NH 3 ), nitrogen oxides (NO x = NO + NO 2 ) and sulfur dioxide (SO 2 ), respectively (Seinfeld 26 and Pandis, 2006 (Paatero, 1999) via the source finder (SoFi, Canonaco et al. (2013)). This data set shows the 1 long-term variability of particle composition and is used for source apportionment of 2 atmospheric aerosols at this North Western European rural site, with the focus on periods 3 where air quality standards were violated. This information can be further used to establish 4 strategies for the reduction of particulate matter. 5 2 Methodology 1

Site description: CESAR 2
The CESAR tower is 220 m high and managed and operated by the Royal Netherlands 3 Meteorological Institute (KNMI, The Netherlands). It is located in a rural site (51.970°N, AMS, and other collocated instruments in the region of Paris, France. There, the same ACSM 20 instrument (S/N A140-145) as the one used for this study was tested. Those results indicate 21 that the ACSM can be used as a suitable and cost-effective alternative to the AMS for aerosol 22 composition measurements due to its capability of stable and reproducible operation. 23 Mass calibrations were performed approximately every month and were based on determining 24 the instrument response factor (RF) ( A site specific, time resolved particle collection efficiency (CE) correction algorithm 6 (equations are given in the supplement) was applied, which was developed by Mensah et al. 7 (2012), using SMPS data as reference. In contrast to the commonly used constant value of 0.5 8 this CE correction accounts for the high ammonium nitrate mass fraction (ANMF) found at 9 this site and is thus more suitable for the data presented here. Another algorithm for 10 composition dependent CE determination (Middlebrook et al., 2012) was also tested for its 11 validity. It uses a threshold ratio of measured to predicted NH 4 to switch between two 12 different equations to determine the CE. The threshold value of 0.75 is close to the observed 13 ratio of measured over predicted NH 4 of this data set, resulting in large discontinuities of CE 14 values and in consequence, discontinuous changes in aerosol mass concentrations. Therefore 15 the Middlebrook algorithm was not used for this data set, which showed at the same time low 16 ratios of measured to predicted NH 4

Collocated aerosol measurements 1
The following collocated aerosol instruments were used for cross-validation of the ACSM 2 data: (i) a Scanning Mobility Particle Sizer (SMPS, TSI 3034), operated by the Netherlands 3

Organization for Applied Scientific Research (TNO, The Netherlands), (ii) a Monitor for 4
Aerosol and Gases (MARGA, Applikon Analytical BV), operated by ECN,and (iii) a HR-5 ToF-AMS, which was operated by Forschungszentrum Juelich during the first 6 days of the 6 ACSM campaign. In addition, BC data obtained by a MAAP instrument (TNO, The 7 Netherlands) was included into the analysis. 8 The MAAP instrument has been introduced by Petzold  has no size selective inlet beside the PM 10 heads described below, it can be assumed that eBC-22 containing aerosol generally fall into the submicron size range (Bond et al., 2013). Thus eBC 23 mass concentrations are considered as part of the PM 1 fraction from hereon. As seen later the 24 eBC fraction is rather low throughout the campaign, meaning that the overall error of this 25 assumption is not significant. 26 The SMPS (TSI, Model 3034) is a sequential combination of several integrated components: 27 an impactor, a neutralizer, a differential mobility analyser and a condensation particle counter. 28 It determines the size distribution of particles in a range of 10 nm to 487 nm (electromobility 29 diameter). The SMPS aerosol mass concentration was calculated from the measured volume 30 distributions using the particle density determined by the aerosol composition information 31 derived from the ACSM and the MAAP. Assuming spherical particles, the total density is 32 computed by using the densities of the aerosol species, weighted by their mass fractions. Bulk 33 densities of NH 4  (2009)) were taken into account. Considering its low influence on the total particle density at 3 this site, it is acceptable to set the density for chloride to 1 g cm -3 (Mensah et al., 2012). 4 During the presented campaign, the MAAP and the SMPS were connected to the common 5 aerosol inlet which sampled at 60 m height. This inlet consisted of four PM 10 size selective 6 heads at the top, followed by a Nafion dryer to keep the relative humidity (RH) of the sample 7 air below 40%. The stainless steel pipe, ranging from the aperture at 60 m to the basement, 8 has an inner diameter of 0.5" (= 1.27 cm) and ends in a manifold, where the sampled air is 9 distributed to a variety of different instruments, including the MAAP and the SMPS, each 10 with its own sample flow. An overall sample flow of 60 L min −1 was adjusted inside the 60 m 11 pipe, assuring laminar conditions. 12 SMPS data was corrected size dependently for (diffusional) losses in the inlet system and (2014)) for ammonium, nitrate, sulfate, and chloride, respectively. 33 The MARGA inlet was equipped with a PM 10 size selective head (Rupprecht and Pataschnick, 1 R&P), placed on the roof of the tower building next to the ACSM inlet aperture at 5 m height. 2 The sample air was transferred into the instrument within a polyethylene tube with an inner 3 diameter of 0.5" (= 1.27 cm) and a sample flow of 16.7 L min -1 , which is either directed 4 through a PM 1 or a PM 2.5 size selective cyclone. A detailed description of the MARGA inlet 5 system at the Cabauw tower was previously described by Schaap et al. (2011). There, wall 6 losses were investigated and found to be less than 2% for several gaseous and particulate 7 compounds. To compare with the ACSM, only MARGA data containing PM 1 composition is 8 considered within this work. 9 An Aerodyne HR-ToF-AMS was connected to the MARGA inlet during the first 6 days of the

Statistical methods of organic aerosol data analysis 15
Source apportionment of organic aerosol components was performed using Positive Matrix 16 Factorization (PMF, Paatero (1997); Paatero and Tapper (1994)) via the ME-2 solver 17 (Paatero, 1999). PMF is a bilinear model and assumes that the original data set, containing 18 variable mass spectra over time, is a linear combination of a given number of factors, each 19 with a constant mass spectrum and its contributions over time. It has been successfully used in 20 AMS ambient studies apportioning the measured organic mass spectra in terms of 21 source/process-related components (Zhang et al., 2011). With the ME-2 solver it is possible to 22 introduce a priori mass spectral information and hence to reduce the rotational ambiguity, i.e. 23 similar PMF results with the same goodness of fit, of PMF solutions (Paatero and Hopke, 24 2003). 25 The extraction of OA data and error matrices as mass concentrations in µg m −3 over time, as 26 well as their preparation for PMF/ME-2 according to Ulbrich et al. (2009), was done within 27 the ACSM software. Only m/z's ≤ 100 were considered here since they represented nearly the 28 whole OA mass (around 98%) and did not interfere with ion fragments originating from 29 naphthalene (e.g., m/z 127, 128, and 129, see also Sec. 2.2). From these matrices, the m/z 12 30 was discarded because it showed negative signals, probably due to too short delay time of the 31 quadrupole scan (125 ms) after a valve switch (Fröhlich et al., 2015). In addition, the m/z's 37 32 and 38 were also removed from the organic matrices of the whole campaign except for winter 1 2013. This was done because the signal at these masses showed high interferences with the 2 chloride related ions 37 Cl and H 37 Cl. Including these ions lead to unreasonable PMF factors 3 which mainly contained only these two masses and represented the chloride time series, 4 whereas during Winter 2013 no such interferences were observed. 5 The interface source finder (SoFi, Canonaco  • The quality parameter Q/Q exp was minimized. 22 • Factor profiles have reasonable mass spectra, as expected for the measurement site. 23 • Factor time series have high correlations with respective external data sets such as 24 gaseous CO, CO 2 , NO x , and particulate nitrate, sulfate and black carbon. 25 • When a proper solution is found, 50 seed runs were used to find the global minimum 26 for Q/Q exp . 27 • Investigation of the rotational ambiguity of the solution space is carried out using the 28 a-value approach for the constrained factor profiles 29 30 3 Results and discussion 1

Cross-validation of particulate total mass and chemical species 2 concentrations 3
The particle density during the ACSM campaign was determined using the chemical 4 composition data from the ACSM and the MAAP and resulted in an average of 1.63 ± 5 0.12 g cm −3 . The time series of the particle density is given in the supplement. It was used to 6 calculate the SMPS total mass concentration from its measured volume concentration 7 throughout the campaign. Due to the relatively low signal-to-noise ratio of the ACSM, the 8 density shows scattering only during periods with low mass loadings. Therefore it does not 9 influence the cross-validation with the SMPS mass significantly. Figures S3 and S4 show the 10 time series of the SMPS mass and the combined mass concentrations measured by ACSM and 11 MAAP and the correlation plot of both data sets, respectively. Using 12275 common data 12 points for the linear fit, a good qualitative and quantitative agreement (Slope: 1.16 ± 0.01, 13 intercept: -1.05 ± 0.06, R 2 = 0.78) was observed. Excluding eBC data resulted in a slope of 14 1.13 ± 0.01, an intercept of -1.14 ± 0.06, and a R 2 of 0.78. The negative offset can be 15 explained by minor influences of sea salt and dust particles, which can be detected well by the 16 SMPS and MARGA but not by the ACSM with a sufficient sensitivity. But the low value of 17 the intercept shows already that the uncertainty introduced by these aerosol components is 18 rather low in general. This can also be explained by the low concentrations of Mg, Na, K and 19 Ca as measured by the MARGA (see below) and the assumption that the majority of dust 20 particles is most likely found in particles with diameters larger than 1 or even 2.5 µm 21 (Finlayson-Pitts and Pitts (2000) and references therein). 22 Since the MARGA measures routinely the water soluble inorganic aerosol compounds, data 23 from ACSM inorganic species were synchronized and compared to corresponding MARGA 24 PM 1 data for the whole measurement period. The temporal overlap with the collocated high 25 resolution AMS was between 11 and 17 July 2012. The correlation parameters of individual 26 aerosol species and respective total masses between the ACSM data and the data sets from the 27 MARGA and AMS are given in Table 1, using 1943 and 294 common data points, 28 respectively. The corresponding correlation graphs are shown in the supplement (Fig. S5 and  29 S6). Except for chloride, high correlation coefficients were achieved. Furthermore, the 30 comparison to both total mass time evolutions shows very high qualitative and quantitative 31 agreement. The quantitative difference between ACSM-and AMS-organics is also very low, 32 and the discrepancies in case of ammonium and nitrate are within the stated ± 30% accuracy 33 of the AMS and ACSM (Ng et al., 2011b) and the ± 10% for the MARGA-NO 3  originating from e.g. sea salt. For the latter, the ACSM is much less sensitive than the 24 MARGA. As described above, influences from sea salt can be considered rather low. In turn, 25 the MARGA might be less sensitive to organic chlorides, as they are likely less water soluble 26 than inorganic chlorides. These explanations would explain the low agreement between the 27 two instruments in case of chloride. 28 Overall, the comparison of the data measured by the ACSM and MAAP with collocated 29 aerosol instruments showed a good reliability, precision, and in most cases a good accuracy 30 over the whole campaign, including periods with high and low mass loadings. Therefore of total inorganics between the ACSM and the MARGA during these periods are much 7 higher, the mass loadings determined from these instruments are more reliable than the SMPS 8 data. 9 10

Aerosol chemical composition 11
A meteorological overview of this campaign, including wind direction, precipitation, Radon-12 222 measurements and ambient temperature and relative humidity (RH) is provided in Fig.  13 S7. Table S1 shows temperature and RH values averaged over selected periods (see below). If  Table S2). for the whole campaign. Corresponding plots with data averaged separately for the five 32 chosen periods can be found in the supplement (Fig. S8). Overall, NO 3 showed the largest 33 diurnal variation, with a maximum during the night/morning hours, reflecting its nighttime 1 production and a minimum during the day due to the volatility of NH 4 NO 3 . This pattern is 2 more pronounced in the warmer periods 1 and 5. Since the majority of ammonium is 3 originated from NH 4 NO 3 , NH 4 has a similar pattern to that of NO 3 . SO 4 , which is mainly 4 formed photochemically during the day from gaseous SO 2 , showed peaks during daytime, 5 although its overall variation is rather low. The maxima of BC can be attributed to direct 6 emissions from traffic (morning and evening rush hours) and biomass burning events 7 (domestic heating in the evenings/nights). Finally, OA showed peaks at the evening hours 8 during the colder periods and a daytime minimum during the summer. More detailed 9 discussion of the diurnal patterns of individual OA factors is given below. 10 An ion balance of all inorganic compounds indicates that too less NH 4 was measured to 11 neutralize all NO 3 and SO 4 to their corresponding ammonium salts. The measured NH 4 mass 12 concentration against the predicted NH 4 from the ion balance is plotted in the supplement 13 In contrast to the ion balance from the ACSM data, MARGA PM 1 measurements during the 26 whole campaign showed a nearly 1:1 correlation of measured against predicted NH 4 (slope of 27 the linear regression line: 1.03 ± 0.00, Pearson-R 2 = 0.97), but with a negative offset of ca. 28 0.30 ± 0.01 µg m -3 . This offset, which is at least 3 times higher than the detection limits of the 29 MARGA, cannot be explained by including positive metal ions to the ion balance since the 30 sum of Mg, Na, K and Ca mass concentrations had low contribution to particulate mass as 31 mentioned in section 3.1. Thus, significant influence of their nitrate salts to total nitrate can be 32 excluded. In addition, as the MARGA is measuring the water-soluble nitrate fraction, the 33 MARGA-NO 3 can be considered to be exclusively NH 4 NO 3 . This assumption is acceptable, 1 as shown by using the MARGA-NO 3 instead of the ACSM-total-NO 3 for the ion balance of 2 ACSM data (including ACSM-SO 4 , -Chl and -NH 4 ), following a procedure given by Xu et al. 3 (2015) who calculated the organic nitrate fraction by subtracting the inorganic nitrate 4 concentrations measured by a particle-into-liquid sampler (PILS, see Orsini et al. (2003)) 5 from ToF-AMS total nitrate concentrations. In the Cabauw data set, the correlation of 6 measured against predicted NH 4 resulted in a nearly 1:1 regression line without a significant 7 offset (Fig. S10). This is in agreement with the MARGA internal ion balance which also 8 indicates neutralized inorganic aerosols. Therefore, the mass concentration of nitrate groups 9 associated with organic molecules (hereafter called organic nitrate or OrgNO 3 ), can be 10 estimated by subtracting the MARGA-nitrate from the ACSM-nitrate concentration. The 11 OrgNO 3 time series using this approach is plotted in Fig. S11, the respective diurnal variation 12 averaged over for each period and for the entire campaign in

Factor analysis of organic aerosols 29
Prior to PMF analysis, the ACSM data set was subdivided into four data sets, which were corresponding graph dividing these PMF results into the five periods according to Fig. 2 is  6 shown in Fig. S13. The POA profiles were constrained within ME-2 using the HOA and 7 BBOA mass spectra found by ME-2 operated PMF analyses by Crippa et al. (2014) Fig. S14 and S15, respectively. Table S4 gives  concentrations. The SOA factors showed always higher contribution (54% -84%, averaged 23 season-wise) to total organics compared to POA (16% -46%). For all PMF factors, no 24 preferential wind direction was observed over the entire campaign. During the pollution 25 events mentioned above, OOA originated mostly from the directions between 20° and 180° in 26 respect to the tower. This is not the case for HULIS, which origins varied throughout all 27 directions, also during pollution events. 28 The seasonal average HOA contribution to total organic mass was highest in Summer 2012 29 and lowest in Spring 2013 (16% and 8%, respectively). All HOA diurnal patterns (Fig. 5b)  30 showed a maximum at 7 and 11 am (LT) and a slight increase in the evening, emphasizing 31 that its main source is related to traffic likewise to BC (see Sec. 3.1). In Winter 2013, these 32 maxima were less distinctive comparing to the other seasons. HOA Highest temporal 33 agreements with HOA were seen by the POA tracers BC, NO x and CO (R 2 = 0.38, 0.47, and 1 0.47, respectively) over the entire campaign. 2 The BBOA profile showed a very high contribution of m/z 60, which is dominated by the 3 C 2 H 4 O 2 + ion. This fragment is characteristic for anhydrosugars such as levoglucosan (Alfarra 4 et al., 2007) which are established markers of wood combustion processes (Simoneit and 5 Elias, 2001;Simoneit et al., 1999). The fractions of m/z 60 to the BBOA profile in Autumn 6 2012 (3.7%) and Winter 2013 (3.2%) are higher than in Spring 2013 (2.4%). As mentioned, 7 BBOA was not found in Summer 2012. This was verified by the fact that the contribution of 8 m/z 60 to the BBOA profile decreases for higher a-values in that season, which is an 9 indication for the non-existence of BBOA. The highest contributions of BBOA to total 10 organics were seen in the colder Autumn (23%) and Winter seasons (15%). This and the 11 diurnal maximum during the evenings and nights match the expectations for a factor linked 12 with domestic heating activities, together with the fact, that this factor was not seen during the 13 warmer summer season. Averaged over the whole campaign, the contribution to total organics 14 was 13%, including Summer 2012, where its fraction was set to zero. In Winter, the 15 correlations with eBC and CO were higher (R 2 = 0.64 and 0.57, respectively) than over the 16 whole campaign (R 2 = 0.39 and 0.49, respectively), meaning that these compounds are 17 reasonably more attributed to domestic heating during the colder periods comparing to the 18 contribution of heating to BC and CO during the other seasons in this region. 19 The OOA profile is similar to a MS pattern as expected for a low volatile OOA (LVOOA) 20 factor. The correlation coefficients (Pearson-R 2 ) with the OOA and LVOOA spectra given by 21 The HULIS factor provided the highest contribution to the total organic mass over the entire 8 campaign (41%) and was the dominant factor in Summer and Autumn 2012. Since it had no 9 distinct diurnal variation and preferential wind direction, it can be considered as regional 10 background aerosol at this rural site. Additionally, the variation between the seasonal average 11 concentrations of HULIS within the ACSM data set is less than ± 10%. Also the comparison 12 to the most important tracers (Table S4 in  low contribution of eBC to total aerosol mass, a possible partial interference with HULIS is of 20 minor importance regarding total aerosol masses. 21 The source apportionment as described here used a data set which was subdivided into the 22 four seasons prior to PMF analysis. A single PMF analysis of the whole data set with 23 constrained HOA and BBOA profiles lead to solutions with a highly overestimated BBOA 24 factor in the summer, compared to the results when the seasons were explored individually 25 (see Fig. S17 and S18 in the supplement). Furthermore, the contributions of individual factors 26 change significantly in some periods, especially for the OOA factor during pollution events. 27 This is mainly driven by the different OOA-f44 and -f43 values. This behavior is independent 28 from applied a-values for BBOA may result from the uncertainty of this statistical tool. Since 29 there was no evidence of BBOA seen in the separate analysis of the summer period (e.g. low 30 fraction of m/z 60 in the organic mass spectrum and no correlation of the constrained BBOA 31 factor with POA tracers, no matter which a-value was used), the solutions derived from the 32 single PMF analysis was reasonably rejected. 33 1

Composition and sources of aerosols during pollution events 2
The investigation of the aerosol composition during the pollution events showed that the 3 majority was contributed from secondary aerosols. As an example, Fig. 6  can be considered to represent the regional background. This regional background is adding to 5 local aerosol contributions in high populated urban sites (Pandis et al., 2013), namely the 4 6 largest cities of the Netherlands which have a distance of 40 km or less from the CESAR 7 tower. 8 Particulate mass loadings found at this rural site are dominated by secondary aerosol 9 formation through atmospheric gas phase chemistry and particle phase aging. It is shown that 10 particulate ammonium nitrate is the major aerosol component (39% on average) and 11 represents the more hygroscopic aerosol fraction Since the human respiratory system is 12 characterized by high humidity more hygroscopic aerosols have a higher deposition tendency 13 within the human lung than less water soluble particle compounds (Asgharian, 2004;Broday 14 and Georgopoulos, 2001). With special regard to adverse health effects this is very crucial 15 because Asgharian (2004) also found that especially hygroscopic submicron particles can 16 deposit in the entire lung. The high ammonium nitrate fraction also implies that inorganic SA 17 reduction in Cabauw can be most efficiently achieved through the reduction of gaseous 18 ammonia emissions in the area. found that a reduction of NH 3 emissions by 50% would have a much higher effect on 28 reducing PM 2.5 than decreasing NO x emissions by 50%. The latter scenario would even result 29 in negative side effects such as higher tropospheric ozone concentrations (especially in 30 summertime 4% over Western Europe and up to 40% in major urban areas) and higher 31 amounts of particulate sulfate and OA by 8% and 12%, respectively, in winter. 32 The local mitigation of organic aerosol mass (29% contribution on average) is more 1 challenging, as secondary organic aerosols are highly abundant at the Cabauw site (74% and 2 22% of OA and total PM 1 on average, respectively). The presented data set shows a large and 3 ubiquitous HULIS fraction (37%) which based on diurnal patterns and a lack of correlation 4 with wind direction can be considered as long-range background aerosol formed from 5 atmospheric aging processes. In turn, primary organic aerosols emitted mainly from traffic 6 and biomass burning (12% and 13% of OA on average) have only minor importance. For a 7 more detailed identification of the SOA sources compound specific measurements of OA as 8 well as routine VOC monitoring are needed. 9 Finally, the presented data set and interpretations provide an important contribution to the 10