A study of summer and winter highly time-resolved submicron aerosol composition measured at a suburban site in Prague

Atmospheric Environment 118 (2015) 45e57

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A study of summer and winter highly time-resolved submicron aerosol composition measured at a suburban site in Prague
a, b, *, Petr Vodi Lucie Kubelova cka a, Jaroslav Schwarz a, Michael Cusack a, a 
 cek a, Vladimír Zdímal Otakar Makes a, b, Jakub Ondra a b


135, Prague 6, Czech Republic Institute of Chemical Process Fundamentals, CAS, v.v.i., Rozvojova
tska
2, 12801 Prague 2, Czech Republic Institute for Environmental Studies, Faculty of Science, Charles University in Prague, Bena

h i g h l i g h t s
Atmospheric aerosol NR-PM1 was measured by c-ToF-AMS at a suburban site in Prague.
High time-resolution enabled detailed description of daily cycles of pollutants.
Size distribution of fine mode was described in both summer and winter.
Analysis of organic fragments proved influence of wood combustion in winter.
Atmospheric aerosol NR-PM1 was less oxygenated in winter than in summer.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 22 April 2015 Received in revised form 13 July 2015 Accepted 21 July 2015 Available online 23 July 2015

The variability of aerosol chemical composition and the impact of the origin of respective air masses were studied in high time resolution for selected periods of high and low levels of aerosol burden at a suburban station in Prague-Suchdol, Czech Republic in summer and winter. Ambient aerosol measurements were performed using the compact-Time of Flight-Aerosol Mass Spectrometer (c-ToF-AMS) and variations in concentration of the main species are discussed. The average mass concentrations for the main species were (summer; winter): organic matter (4.2 mg/m3; 8.4 mg/m3), SO4 2 (2.0 mg/m3; 4.4 mg/ m3), NH4 þ (1.2 mg/m3; 2.8 mg/m3), NO3  (0.8 mg/m3; 5.4 mg/m3) and Cl (0.1 mg/m3; 0.23 mg/m3). We found an inverse relationship between non-refractory submicron particulate matter (NR-PM1) levels and the boundary layer height, mainly in winter. Furthermore, levels of pollution were influenced by the air mass origin, where cleaner maritime air masses resulted in lower aerosol levels compared to those of continental origin. Analysis of the diurnal variation of NR-PM1 showed minimum concentrations in the afternoon caused by dilution as a result of an increase in the boundary layer height. Most maximum concentrations of the main species occurred in the morning or night except sulphate which had a midday maximum, probably due to downdraft from upper boundary layer air and photochemical formation in the afternoon. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Atmospheric aerosol Chemical composition Size distribution Aerosol mass spectrometer Seasonal differences

1. Introduction Atmospheric aerosols play a significant role in many environmental processes such as climate change (IPCC, 2013) and visibility reduction (Horvath, 1993; Hyslop, 2009; Tang, 1997). Furthermore, it is widely accepted that long-term exposure to increased

* Corresponding author. Institute of Chemical Process Fundamentals, CAS, v.v.i.,
135, Prague 6, Czech Republic. Rozvojova
). E-mail address: [email protected] (L. Kubelova http://dx.doi.org/10.1016/j.atmosenv.2015.07.030 1352-2310/© 2015 Elsevier Ltd. All rights reserved.

concentration of particles of atmospheric aerosol significantly increases the risk of serious health problems (Bascom et al., 1996; Dockery and Pope, 1994; Oberdorster, 2001; Pope et al., 1995; Pope III, 2002). To gain better understanding of the aerosol processes such as aerosol formation, coagulation, chemical reaction and removal, it is desirable to measure the chemical composition of aerosol particles at a high time resolution, in the order of minutes (Jayne et al., 2000; Laskin et al., 2012). Until recently, chemical composition of atmospheric aerosols has been determined mainly by chemical analysis of samples collected offline, typically using filter samplers or cascade

46


et al. / Atmospheric Environment 118 (2015) 45e57 L. Kubelova

impactors. These analyses are arduous, do not allow for high time resolution, and are susceptible to inaccurate results due to evaporation or absorption of semi-volatile and volatile compounds and chemical reactions during and after sampling (Jayne et al., 2000; Pratt and Prather, 2012; Sullivan and Prather, 2005; Wittmaack and Keck, 2004). The Aerosol Mass Spectrometer (AMS) overcomes this difficulty and enables detailed description of the dynamics of chemical composition of the ambient aerosol particles (Jayne et al., 2000). The high time resolution allows the investigation of diurnal cycles including atmospheric mixing effects (Emmenegger et al., 2005; Solomon and Sioutas, 2008; Sorooshian et al., 2007). In the last ten years there has been a notable increase in published AMS papers from European measurement sites. The studies are summarized in several review articles (Crippa et al., 2014; Lanz et al., 2010; Ng et al., 2010; Zhang et al., 2007). The most widely studied characteristics of aerosol particles other than particulate mass are number concentration, size distribution, and chemical composition. The characteristics of fine aerosol particles at the Prague suburban site Suchdol have been already described in previous studies, which can be divided into three categories according to the applied methods and instruments. The first group focuses on time-resolved number size distribution obtained from the Scanning Mobility Particle Sizer (SMPS) and 
cova
et al., 2011). Aerodynamic Particle Spectrometer (APS) (Rimn a The second group presents size-resolved chemical composition from measurements with cascade impactors with short time res
et al., 2010). The third olution (Schwarz et al., 2012; Stefancov a group describes time evolution of carbonaceous aerosol resulting from measurements with the Organic Carbon/Elemental Carbon (OC/EC) field analyzers (Vodi cka et al., 2013) and also OC thermal subfractions (Vodi cka et al., 2015). Other short term studies combined the SMPS, APS, and cascade impactors (Smolík et al., 2008), or the SMPS, APS, cascade impactor, and OC/EC measurements (Ondr
a cek et al., 2011). These studies concluded that winter mass concentrations were 
et al., higher than in summer (Schwarz et al., 2008; Stefancov a 2010), and the major inorganic components of the fine mode were nitrate, sulphate, and ammonium (Smolík et al., 2008). Furthermore, during the non-heating season secondary organic aerosol (SOA) originated mainly from long-range transport whereas during the heating season, SOA originated mainly from residential heating and the condensation of organic matter on aged aerosol. The main source of organic carbon in winter was probably wood burning emissions (Schwarz et al., 2008). The properties of aerosols were also found to be strongly influenced by air mass origin. Sea-influenced aerosol had maxima of the size distribution of the fine fraction around 0.21e0.33 mm, whereas the maxima of continental aerosol were around 0.5 mm (Schwarz et al., 2012). Continental air masses were also connected with the most intense air pollution episodes, mostly of a regional origin (Schwarz et al., 2008). These air masses originated mainly over Northeastern Europe, having recirculated over Central Europe over time. In this study, we describe the time evolution and variation of the mass concentration of chemical species measured by the AMS at a suburban station in Prague. We compare the AMS data with results from other instruments and meteorological conditions, and also with air mass back trajectories calculated using the HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory model) transport and dispersion model (Draxler and Rolph, 2015). Furthermore, we discuss the results from the Prague Suchdol measurement site in light of results of measurements across Europe, mainly from suburban sites in Zürich (Lanz et al., 2010) and Paris (Petit et al., 2015) and also from the rural site Melpitz (Poulain et al., 2011). Melpitz was chosen as it represents a station that is

frequently affected by air mass trajectories that also affect Prague Suchdol. The majority of air masses arrive to Prague Suchdol from the northwest and having previously passed through Melpitz. 2. Methods 2.1. Measurement site The measurement site Prague-Suchdol (50 70 35.5000 N; 14 230 4.7000 E, altitude 277 m a.s.l.) is located in the campus of the Institute of Chemical Process Fundamental (ICPF) of the Czech Academy of Sciences (CAS). The campus is approximately 6 km (NW) from Prague city center and 9 km (E) from Vaclav Havel Airport Prague. In the vicinity of 200 m from the measurement site there is a road with a traffic volume of around 15 000 cars per day. The whole campus is surrounded by houses that may present a significant source of pollution, especially in winter, from domestic heating using mainly gas but also coal and wood burning. Therefore, we assume that air quality in the region in winter is heavily influenced by traditional heating methods. The meteorological data used in this study were collected by the Automated Immission Monitoring (AIM) station run by the Czech Hydrometeorological Institute (CHMI). The AIM station is located about 20 m from the measurement site which is classified as a suburban background site located in a residential area. Within the network, the Prague-Suchdol measurement site is classified as a suburban background site located in a residential area. It provides meteorological data (wind speed and direction, relative humidity, temperature, pressure, solar radiation), concentration of gaseous pollutants (SO2, CO, NO, NO2, O3, toluene, benzene), and concentration of particulate matter (PM10 and PM2.5). SMPS data obtained in accordance with ACTRIS (Aerosols, Clouds, and Trace gases Research InfraStructure network) project (Wiedensohler et al., 2012) standards are also available. 2.2. Instrumentation During the measurement campaigns, we deployed the following instrumentation: compact-Time of Flight-Aerosol Mass Spectrometer (c-ToF-AMS, Aerodyne, 1 min resolution) (Drewnick et al., 2005), OC/EC field analyzer (Sunset Laboratories, 2-h resolution for thermal analysis e more about the method in Vodi cka et al. (2013) e and 1 min resolution for optical EC measurement), and PM1 filter measurement (23-h and 24-h resolution, every day and every other day) that were subsequently analyzed by Ion Chromatography (IC, details about the method are in supplementary material). The SMPS data (TSI 3034 model upgraded by IFT Leipzig to follow ACTRIS project standards, CPC 3010, 5-min resolution) were inverted into 36 size bins format covering sizes from 10 nm to 
cova
et al., 2011; Zíkova
510 nm e more about the method in Rimn a  and Zdímal, 2013. The c-ToF-AMS (hereafter referred to as AMS) provides real-time measurements of chemical composition and size distribution of non-refractory (NR) submicron aerosol particles within the vacuum aerodynamic diameter (dva) range of 30e600 nm (Allan, 2003). Particles of other sizes are transmitted into the detection region with lower efficiencies (total fraction approximately PM1). We used a sampling head PM10 with flow of 16.7 l/min. Isokinetic subsamplings were used to split the flow to AMS (0.1 l/min) from the main flow. The flow to AMS was dried prior to sampling using a nafion dryer (Perma Pure MD-110-24P-4). The flow, size, and ionization efficiency (IE) calibrations of the instrument were performed at the beginning of each campaign. In addition to that, the IE calibration was done in regular intervals during incident-free operation of the instrument. The IE is given by


et al. / Atmospheric Environment 118 (2015) 45e57 L. Kubelova

the slope determined by linear regression analysis, derived from the average number of ions detected per particle in relation to the number of molecules per particle (Jayne et al., 2000). In our study, the IE was assessed by calibration in the brute-force single-particle mode (BFSP) (Decarlo et al., 2006; Drewnick et al., 2005). During the campaigns, the IE calibration was performed on a weekly basis. Zero calibration measurements using a HEPA filter applied to the inlet were also performed to account for zero values and for adjustment of the fragmentation table (Allan et al., 2004). The microchannel plate (MCP) detector was replaced in the period between the summer and winter campaigns. In order to ensure accurate results, several corrections of the AMS data (baseline setting, m/z correction, and airbeam correction) were applied. The ionization efficiency was set as an average value of the BFSP weekly calibrations (winter 3.01$107 ± 1.16$108, summer 1.45$107 ± 4.01$109). In order to correctly quantify the mass concentration of the aerosol sample, it was necessary to take into account particle losses during measurement. The ratio between the mass of particles detected by AMS to the mass of particles sampled by AMS is referred to as the collection efficiency (CE). The CE depends mainly on losses caused by particles bouncing off the vaporizer (Huffman et al., 2009). The bounce depends largely on particulate phase that is connected with particle composition, acidity, and humidity (Middlebrook et al., 2012). The frequently used value of CE for atmospheric aerosol is 0.5. It has been proven in numerous studies that the application of such a CE is reasonable for most environments (Alfarra et al., 2004; Takegawa et al., 2005; Timonen et al., 2010). However, it has also been shown that a CE of 0.5 is more appropriate for dry conditions such as when the inlet temperature is 10  C higher than the dew point of ambient air (Takegawa et al., 2005). As has been shown in the literature, the CE may vary significantly depending on the conditions (Matthew et al., 2008) and particle composition (Middlebrook et al., 2012). To obtain a CE related to our instrument and conditions we correlated the AMS data with data from other instruments (SMPS, PM1 filter measurement analyzed by Ion Chromatography, field OC/EC analyzer) and obtained a slope that we assumed to be related to CE. However, uncertainties exist and they can, among others, occur from: the density estimation and cut-off point for the SMPS, a presumed value of OM to OC ratio in the case of OC/EC analyzer, the influence of ambient RH on PM1 sampling in the case of filter sampling and IC analysis. To determine CEs used for particular campaigns, we selected results obtained from the correlation of sulphate mass concentrations measured by the AMS with concentrations of sulphate measured by PM1 filter measurement analyzed by Ion Chromatography as sulphate does not undergo significant changes such as evaporation between sampling and IC analysis. Nevertheless, similar values of CE were obtained from SMPS data using density 1.5 g/cm3 (section 3.1). Another means to determine the CE is through the Chemical Dependent Collection Efficiency (CDCE) approach (Middlebrook et al., 2012). The CDCE takes into consideration the ratio of NH4 þ concentration measured and NH4 þ concentration necessary to neutralize the measured amount of ions NO3  , SO4 2 , and Cl and the mass fraction of NH4NO3 in the sample. These two parameters determine how much the default CE (usually 0.45) increases for each measurement. To process the data from the AMS we used the analysis software Squirrel v1.55 (Sueper, 2014) running on the Igor platform (Wavemetrics Inc.). The Squirrel software assigns a signal of a particular mass peak to the main chemical groups measured by AMS (organic matter, nitrate, sulphate, ammonium, chloride) using the fragmentation table described by Allan et al. (2004) and Aiken et al. (2008).

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2.3. Campaigns and data coverage In order to identify the characteristics of aerosol during the warmest and coldest seasons, summer (20 Junee31 July 2012) and winter (8 Januarye19 February 2013) measurement campaigns were conducted. The measurements were done in coordination with the ACTRIS project but the campaign periods also coincided with an intensive European Monitoring and Evaluation Program (EMEP) campaign. The AMS data coverage was over 98% for the winter and over 97% for summer. The interruptions in measurements were caused mainly by instrument calibration and maintenance. In the case of the meteorological data collected at the AIM station described above, data were collected in 10 min intervals. The data coverage was 87% for the winter campaign and 97% for the summer campaign. 3. Results and discussion 3.1. Determination of collection efficiency The CE was determined by comparison of sulphate concentrations measured by the AMS and by chemical analysis by IC of filter samples taken in parallel (Fig. 1). In summer (winter), we took 26 (25) samples in total, from which 13 (9) samples were taken every day covering 23 h and the remaining 13 (16) samples were taken every other day covering 24 h. The estimated CEs were 0.29 (±0.01) for summer and 0.35 (±0.01) for winter. Even though similarly low values of collection efficiencies have been estimated by the same technique at a residential site Roverendo in Switzerland (CE of 0.33) (Lanz et al., 2010), such low values are still rather exceptional as the CE is usually between 0.5 and 1. Figs. S1, S2 show a similar comparison of AMS and IC measurements for nitrates and ammonia. Using the CE stated above, nitrates gave a slope of 2.5 (±0.29) in summer and 1.31 (±0.07) in winter. The summer slope is higher as nitrate evaporated from the filters due to higher temperatures. The same influence of higher temperatures in summer was observed by ammonium. The slope was 1.38 (±0.08) in summer and 0.9 (±0.04) in winter. This shows that high time resolution measurements can provide in some cases more accurate values of mass concentration of aerosol particles as it eliminates losses on filters between sampling and analysis. A similar comparison was done for AMS total concentration and total mass concentration measured by SMPS (with estimated density of 1.5 g/cm3). The concentration of elemental carbon measured optically by the OC/EC field analyzer was subtracted from the SMPS results as elemental carbon is not detected by the AMS. The calculations were done with 5 min averages of 1 min optical EC data. The results are shown in the supplementary material (Fig. S3). The CEs calculated from SMPS vs AMS comparison were 0.26 for summer and 0.35 for winter, which is in close agreement with the AMS vs IC comparison. We also applied the corrections of CE according to the chemical composition (CDCE) of a sample. The CDCE results were compared with calculations done with a default CE of 0.5 and with a CE from IC comparison explained above. The CDCE correction did not show any significant improvement. The results are summarized in Table S1 in the supplementary material. For the above-mentioned reasons, the following results have been calculated with a CE of 0.29 for the summer campaign and a CE of 0.35 for the winter campaign. These values are close to results for dry solid ammonium nitrate particles of 0.24 ± 0.03 described in literature (Matthew et al., 2008). On the other hand, CE for liquid ambient particles was proven to be 1 (Matthew et al., 2008). The fact that the CE is higher in winter than in summer might be due to


et al. / Atmospheric Environment 118 (2015) 45e57 L. Kubelova

48

Fig. 1. Comparison of sulphate concentration measured by the AMS and by filter measurement analyzed by IC (a) summer campaign (b) winter campaign.

the higher relative contribution of nitrate particles in winter (Table 1), as a higher content of ammonium nitrate leads to a higher CE (Middlebrook et al., 2012). The collection efficiency for Prague differs from a similar measurement done in Melpitz (Poulain et al., 2011), where it was set as 0.5 for winter by comparison with filter measurements of SO4 2 . Unfortunately, such a comparison for summer was not performed as well, but in autumn the collection efficiency was 0.38. The aerosol was dried during both measurements performed in Melpitz. In Zürich, the CE was estimated as 1 in both summer and winter. However, in summer this was done based on literature whereas in winter CE was estimated according to comparison sulphate mass concentration measured by AMS and IC (Lanz et al., 2010). In Paris (Petit et al., 2015), the collection efficiency was estimated using the CDCE approach. 3.2. Average composition of submicron aerosol particles The average mass concentration of NR-PM1 and its main chemical components are shown in Table 1, together with their relative contributions. The results are in qualitative agreement with previous measurements done at the Suchdol site using IC analysis 
et al., of cascade impactor (CI) samples (Tables 3 and 4 in Stefancov a 2010). We compared the NR-PM1 measured by the AMS and the cumulative sample collected on the last three impactor stages and back-up filter where the largest upper cut diameter was 0.89 mm (the reference values for the fractions was the sum of mass concentration measured at the last three impactor stages and back-up filter). Regarding sulphate, in both AMS and impactor measurements sulphate dominated over nitrate in summer (percentage share of total mass concentration: AMS: SO4 2 24.4%, NO3  7.7%; CI: SO4 2 12.0%, NO3  6.8%). Conversely, in winter nitrate had a higher contribution than sulphate (share on total mass concentration: AMS: SO4 2 20.9%, NO3  25.4%; CI: SO4 2 10.2%, NO3  15.6%). The similar change of sulphate and nitrate shares between summer

and winter were observed in Zürich (summer: SO4 2 15%, NO3  8%; winter: SO4 2 17%, NO3  31%), Paris, and Melpitz (summer: SO4 2 21.5%, NO3  5.4%; winter:SO4 2 17.6%, NO3  34.4%). Similarly to 
et al. (2010), and as can be expected results presented by Stefancov a if most of the ammonia is present as ammonium nitrate and sulphate, the contribution of ammonia in winter at Suchdol was lower than the concentration of sulphate and nitrate (share on total mass concentration: AMS: NH4 þ 13.1% IC: NH4 þ 8.0%). The same was true for Melpitz (NH4 þ 17.2%) and Zürich (NH4 þ 15%), but in Paris ammonia prevailed over sulphate and was lower than nitrate. Sulphates originated mainly from oxidation of SO2 to H2SO4 and its neutralization by NH3 (Seinfeld and Pandis, 2006). A probable significant source of SO2 for this region is domestic heating by coal and by coal power plants with the closest one located ca 25 km north of the measurement site. Atmospheric ammonium is present mainly as particulate ammonium sulphate and ammonium nitrate (Asman et al., 1998). The main sources of ammonia are in general agriculture including animal husbandry and fertilizer usage, traffic, industrial processes, and volatilization from soil (Behera et al., 2013). Nitrate particles exist primarily as ammonium nitrate, which originates from NOx being oxidized to nitric acid that is neutralized by ammonia (Seinfeld and Pandis, 2006), with the main source of NOx being mainly from traffic and other combustion sources. The measured chloride concentrations at the site were negligible. Their origin is mainly associated with domestic heating (in winter) and sea salt aerosol (Schwarz et al., 2012). In absolute concentrations nitrate had a higher increase from summer to winter in Prague (0.8 mg/m3 in summer; 5.4 mg/m3 in winter) compared to Zürich (0.8 mg/m3 in summer; 4.0 mg/m3 in winter) and Melpitz (0.66 mg/m3 in summer; 3.62 mg/m3 in winter), which is most likely related to the stronger influence of traffic and domestic heating emissions in Prague. In both Prague and Zürich the concentration of sulphate increased from summer to winter (Prague (Zürich): 2.0 (1.4) mg/m3

Table 1 Average values of mass concentration measured by AMS for both summer and winter campaign calculated with CE corrections. The values were calculated from one-minuteresolution data. Summer

Winter

Compound

Median (0,1; 0,9) (mg/m3)

Average ± St. dev. (mg/m3)

Org NH4 þ SO4 2 NO3  Cl Total

3.2 0.85 1.5 0.5 0.04 6.1

4.2 1.2 2.0 0.8 0.1 8.3

(1.4; 9.5) (0.4; 2.5) (0.6; 4.5) (0.2; 2.1) (0.01; 0.1) (2.8; 17.6)

± ± ± ± ± ±

3.2 0.9 1.6 0.9 0.1 6.0

Max (mg/m3)

Average share

Compound

Median (0,1; 0,9) (mg/m3)

Average ± St. dev. (mg/m3)

38.5 6.6 11.9 7.7 3.0 52.4

51.2% 14.0% 24.4% 9.7% 0.7% x

Org NH4 þ SO4 2 NO3  Cl Total

7.1 2.4 4.0 4.0 0.1 19.0

8.4 2.8 4.4 5.4 0.23 21.2

(0.9; 18.3) (0.3; 5.8) (0.4; 10.3) (0.5; 11.8) (0.03; 0.5) (2.1; 45.3)

± ± ± ± ± ±

6.9 2.1 3.7 4.6 0.26 16.4

Max (mg/m3)

Average share

70.6 10.9 18.9 27.1 2.2 103.2

39.5% 13.1% 20.9% 25.4% 1.1% x


et al. / Atmospheric Environment 118 (2015) 45e57 L. Kubelova

in summer; 4.4 (2.2) mg/m3 in winter). The increase was higher in Prague probably as a result of a strong influence of coal combustion used for domestic heating at Suchdol and generally in the central Bohemian district as it has the highest coal consumption of any region in the Czech Republic (Mach
alek and Machart, 2003). On the contrary, in Melpitz sulphate concentrations was lower in winter than in summer (Melpitz: 2.44 mg/m3 in summer; 1.66 mg/m3 in winter) owing to less intense photochemical production of secondary sulphate in winter. Similarly as in Zürich and Paris the main contributor in Prague was organic matter in both summer and winter whereas in Melpitz, it was organic matter in summer and nitrate in winter. The average organic concentration increased from summer to winter in Prague (4.2 mg/m3 in summer to 8.4 mg/m3 in winter) whereas in Zürich it dropped (6.5 mg/m3 in summer to 4.6 mg/m3 in winter). This difference in the behavior of organic matter could be explained by a very low difference between total summer and winter mass concentrations in Zürich (9.6 mg/m3 in summer; 12.8 mg/m3 in winter) as in both cities the mass fraction of organic matter dropped between the seasons (Prague: 51.3% in summer to 39.5% in winter; Zürich 68% in summer to 36% in winter). The higher share of organic matter in Zürich in summer is probably due to the higher influence of biogenic activity. The drop in both absolute and relative organic matter mass concentration was even larger in Melpitz (from 6.89 mg/m3 (51.3%) in summer to 2.08 mg/m3 (22.6%) in winter), which could be explained by different site classification and prevailing sources (Melpitz: rural background, biogenic activity, animal husbandry; Prague, Zürich: urban background, domestic heating in winter, traffic).

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S5). Episodes of high pollution were associated with air mass trajectories originating above the continent, to the east of the measurement site. On the other hand, episodes of low pollution were associated with air mass trajectories originating from a western direction to the measurement site, presumably of a maritime origin. The average compositions for specific episodes are shown in Table S2. The composition does not show any significant trends; however, during polluted episodes there was a slightly higher share of sulphate whereas during clean episodes, there was a slightly higher share of ammonium. A similar influence of air masses for Central Europe has been previously observed in Dresden and Melpitz (Brüggemann et al., 2009; Spindler et al., 2010). In Prague, air masses recirculating over Central Europe, and therefore also being continental, were connected with most of the pollution episodes (Schwarz et al., 2008), whereby the mass concentration during continental air masses were found to be 2.5 times higher than during the presence of maritime air masses (Schwarz et al., 2012). In Melpitz, Atlantic and continental air masses were connected with low and high particulate mass concentrations, respectively (Poulain et al., 2011). The time evolution of mass concentrations measured by the AMS was also compared with meteorological measurements (Table S3, Figs. S4eS7). In winter, the clean episodes were also linked with higher temperatures and wind speed. Therefore, low levels of pollution seem to be influenced mainly by dilution (boundary layer height), dispersion (higher wind speeds) and less intense domestic heating (high temperatures). In summer, the wind speed during the clean episode was higher compared to the polluted episode but there was no significant difference in temperatures.

3.3. Influence of air mass origin and boundary layer height 3.4. Size distribution In order to understand the fluctuations between low and high aerosol concentrations at Prague Suchdol, we selected periods of high and low aerosol concentrations and studied the aerosol concentration dependence on air mass origin and boundary layer height using data obtained from the HYSPLIT model. The backward air mass trajectories were calculated every six hours (at 0, 6, 12, and 18 h, arrival height 100, 500, and 1000 m a.g.l.) during the campaign and also the heights of the boundary layer for the same time periods were calculated using HYSPLIT. The vertical motion was calculated using the data fields from the meteorological model. Each trajectory covered 96 h before the air mass arrival to the sampling site. GDAS meteorological data were used. The time series of concentrations of the main chemical species measured by the AMS is shown in Figs. 2 and 3 for the summer and winter campaigns, respectively. During the summer campaign, we identified three episodes of increased pollution and one episode of low pollution (Fig. 2), while two clean and three polluted episodes were found in winter (Fig. 3). Table 1 shows the median and average values for each campaign. The average values of individual species are two to eight times higher in winter compared to summer. To explain the high levels of pollution in winter, the measured concentrations were compared with the boundary layer height. A clear inverse relationship between the boundary layer thickness and the total mass concentration of aerosol particles was observed in winter, as a result of the accumulation of pollutants within the condensed mixing layer and decreased dispersion as a result of calm conditions (Fig. 3). This is in accordance with our expectation as an increased height of the mixing layer leads to dilution of pollution (Raatikainen et al., 2014). The influence of boundary layer thickness in summer was much less pronounced. The episodes of high and low pollution were compared with calculated backward air mass trajectories. We calculated the clusters of backward air mass trajectories for each episode (Figs. S4 and

Figs. 4 and 5 show the mass size distributions of the main species during the selected episodes described in the previous section for summer and winter. The maxima of the size distribution occurred at larger sizes of the accumulation mode in winter compared to summer. In winter, higher relative humidity might also cause an increased influence of liquid phase reactions (cloud processing (Hoose et al., 2008)) on individual particle growth. A lower temperature also promotes the condensation of species with higher volatility (Hinds, 1999) and the Kelvin effect in general causes the transport of semi volatile species from smaller particles to larger ones (Riipinen et al., 2011). Fig. 4 shows that in summer, organic matter tended to have a lower modal diameter in comparison with inorganic matter. It is suggested that in such cases organic matter is emitted or formed to a higher extent from fresher (local) sources (Sun et al., 2009) or the formed compounds condense on the surface of preexisting particles. A similar effect is seen for nitrates in winter as a result of the gas-to-particle partitioning due to its semi volatile character (Seinfeld and Pandis, 2006). This has been already observed in Pittsburgh (Zhang et al., 2005). Moreover, in both seasons the maxima of mass size distributions during polluted episodes were found at larger particle sizes compared to the clean episodes. It is probable that the aged aerosol brought by the prevailing continental air mass trajectories caused a shift in the accumulation mode to larger particle sizes. Moreover, winter pollution episodes, such as those which occur during thermal inversions which are commonplace for this region of Europe, were characterized by lower temperatures promoting condensation and enhanced heating sources. This may also cause an increase in smaller particles found for all winter polluted episodes. In general, the results are in qualitative agreement with data obtained by cascade impactors at the same site (Schwarz et al., 2012).

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et al. / Atmospheric Environment 118 (2015) 45e57 L. Kubelova

Fig. 2. Time evolution of mass concentrations of compounds measured by the AMS for the summer campaign. Selected episodes of high and low pollution are shown by red and blue rectangles, respectively. Right side axis shows the boundary layer thickness. Dates of particular episodes are shown above the graph. The clusters of backward air mass trajectories for particular episodes are shown in Fig. S4. The dates of the selected episodes are: EC1 9.7.-18.7.2012; EP1 28.6.-1.7.2012; EP2 3.7.-8.7.2012; EP3 23.7.-28.7.2012. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3. Time evolution of mass concentrations of compounds measured by the AMS for the winter campaign. Selected episodes of high and low pollution are shown by red and blue rectangles, respectively. Right side axis shows the boundary layer thickness. Dates of particular episodes are shown above the graph. The clusters of backward air mass trajectories for particular episodes are shown in Fig. S5. The dates of the selected episodes are: EC1 9.1.-12.1.2013; EC2 29.1.-5.2.2013; EP1 13.1.-17.1.2013; EP2 18.1.-28.1.2013; EP3 12.2.-19.2.2013. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

3.5. Comparison of organic aerosol and organic carbon measurements The comparison of non-refractory organic mass concentration measured by the AMS and OC/EC field analyzer is shown in Fig. 6. Turpin (Turpin and Lim, 2001) recommends the ratio of 2.1 and 1.6 OM/OC (Organic Matter/Organic Carbon) for nonurban (aged) and urban aerosol particles, respectively. Takegawa et al. (2005) observed an OM/OC ratio of 1.8 in summer and 1.6 in autumn at an urban measurement site in Tokyo. In our measurements, the mean OM/OC ratio was 1.89 (±0.49) in summer and 1.31 (±0.19) in winter, which is close to the recommended values. We explain the higher summer OM/OC ratio by the presence of more oxidized organic compounds as the products of photochemical reactions increasing the average organic molecular weight per carbon weight (Turpin and Lim, 2001). Other influencing factors include increased boundary layer height, enabling mixing from higher altitudes and

therefore entrainment of aged (more oxidized) aerosol from long range transport (Querol et al., 1998). On the contrary, the OM/OC ratio in winter was even lower than the values recommended by Turpin and Lim (2001). The extremely low value may be a result of the large scale usage of coal for domestic heating from the surrounding houses (Xing et al., 2013) and by traffic. Moreover, the winter season is generally more influenced by fresh emissions of hydrocarbons owing to the lower boundary layer height in winter which does not support transport of oxidized pollutants within the mixing layer (Schwarz et al., 2008). The same tendency of OM/OC ratio being higher in summer than in winter was observed in Melpitz (Poulain et al., 2011). However, the seasonal average ratios in Melpitz (summer 1.73 (±0.06); winter 1.64 (±0.14)) did not differ as significantly as in Prague as the rural measurement site in Melpitz is not exposed to as many primary sources as the Prague suburban site. The difference may be also partially caused by the difference in the experimental technique


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Fig. 4. Size distribution during clean (EC1) and polluted (EP1, EP2, EP3) episodes in summer 2012 campaign.

(OC determined by field OC/EC analyzer deployed at Suchdol site vs. OC from HR-ToF-AMS with higher resolution deployed at Melpitz) used here in comparison with the Melpitz site. The aging of aerosol was also examined by comparison of f44 and f43 parameters (ratio between organic m/z 44 and m/z 43 respectively and total organic mass) (Fig. 7). The triangular region shows the range where ambient OOA (oxygenated organic aerosol) usually falls (Ng et al., 2010). The upper part of the triangular region (higher f44 and lower f43 parameter) suggest less volatile, highly oxygenated OA, and more photochemically aged aerosol, whereas the bottom part of the triangular region (lower f44 and higher f43 factor) is connected with semi-volatile and less oxygenated OA (Ng et al., 2010; Poulain et al., 2011). Typically biogenic OA lie towards the left arm of the triangle and anthropogenic OA fall within the triangle arms to the right. Fig. 7 shows that winter aerosol was less oxidized than the summer aerosol. This indicates that local sources of pollution dominate, such as domestic heating in winter. However, both summer and winter markers include outlying points. In winter, the polluted episode EP3 was significantly more photochemically aged than the rest of the episodes. This is probably due to higher solar radiation during this episode compared to the other pollution episodes, resulting in higher photochemical oxidation (Fig. S9). In summer, the most outlying episode was EP1 that is connected with a significantly lower value of f44 than the rest of the summer episodes, which points to a local source of pollution, characterized by a smaller mode in the size distribution in comparison with other polluted events. As the wind direction during EP 1 was mainly south easterly (Fig. S8) i.e. from Prague, we assume that the local source was traffic. The summer markers lie outside the triangular area as the triangular area was empirically designed for atmospheric oxygenated organic aerosol (OOA) whereas in this work general organic aerosol (OA) was evaluated. The organic aerosol was examined by comparison of f44 and f60 parameters (ratio between organic m/z 44 (CO2 þ ) and m/z 60 (levoglucosan marker, but also produced by other anhydrous sugars

such as mannosan and galactosan) respectively and total organic mass) (Cubison et al., 2011) (Fig. 8). Higher values of f44 point to photochemically aged aerosol whereas higher f60 values are more influenced by fresh emissions from biomass burning. The comparison of average values of f44 and f60 for the entire summer and winter campaigns showed that winter aerosol was more influenced by biomass burning and less oxidized than summer aerosol, which was anticipated due to the influence of local domestic heating in winter. Winter data fall within the triangular area and are more widespread than the summer data. The most oxygenated winter episode was EP3 which is also the episode with the lowest f60 parameter. This is in compliance with the fact that an aged biomass burning plume is characterized by increasing f44 and decreasing f60 (Zhao et al., 2014). Similarly, the winter episode with the highest f60 parameter was EC2, which was rather less oxidized. The source of the biomass burning plumes was most probably local domestic heating.

3.6. Diurnal trends The Open Air Software (Ropkins and Carslaw, 2012) was used to calculate the daily cycles and wind roses for the most important compounds (Fig. 9). The high time resolution of the AMS data enabled us to detect even short variations in mass concentration. We observed that the total concentration measured by the AMS (marked as PM1) drops in the afternoon in both summer and winter. We explain this by the expansion of the boundary layer in the afternoon (marked by the temperature increase shown in Fig. S7) leading to dilution of pollutants. The afternoon temperature increase also causes evaporation of volatile and semi volatile compounds and it is connected with a shift in the gas to particle equilibrium (Finlayson-Pitts and Pitts, 2000). In the case of organic matter, the winter daily cycle and wind rose suggest pollution is of a more local origin with a probable important influence of household heating, whereas in summer the

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Fig. 5. Size distribution during clean (EC1, EC2) and polluted (EP1, EP2, EP3) episodes in winter 2013 campaign.

main source is rather of a more regional origin. This is in compliance with previous results from Suchdol (Vodi cka et al., 2013), where the main source of organic aerosol was household heating in winter and secondary organic aerosol in summer. In summer, organic matter had its maxima around 6 am when there is a relatively low boundary layer height and it is also near minimum (maximum) daily temperature (relative humidity). Low temperatures may cause higher partitioning of semi-volatile OA to the particulate phase and higher RH can cause transition of solid particles to liquid phase due to presence of hygroscopic salts. This may enable higher influence of liquid phase reactions and this way increasing of organic aerosol mass concentration. Moreover, the lower boundary layer height amplifies the influence of early morning traffic. During the day, concentration of organic matter decreased with small peaks occurring around 11 am. This small peak occurs simultaneously with the first peak in the sulphate diurnal cycle, indicating that down-mixing of an upper layer of aerosol as a result of the expansion of the mixed boundary layer may be the cause of these peaks. Cooking aerosol is an unlikely candidate for causing this peak as a similar increase was not observed in the diurnal cycle for cooking m/z tracers (f55 and f57). Another maximum of organic matter around 5 pm is probably a

result of photochemical oxidation of both biogenic and anthropogenic precursors leading to SOA formation. The main indicator of this maxima is the afternoon increase in diurnal cycle of m/z 44 (see also f44/f43 ratio diurnal cycle in Fig. 10a). From 6 pm onwards rise in concentrations is due to a decrease in boundary layer height and a decrease in temperatures causing condensation of semi-volatile species. This also leads to a decrease in the f44/f43 ratio as can be seen in Fig. 10a. In winter, the daily cycle of organic matter had its minima around 1 pm as a result of the dilution effect of increasing boundary layer and probably also of a decrease in domestic heating at this time of day. This can be observed in the f44/f43 diurnal cycle shown in Fig. 10b, showing maximum influence of aged oxidized aerosol (maximum of f44/f43 ratio) around the same time. Similar behavior was observed at the same site in winter using thermal OC fractions from semi-online thermal/optical transmittance analysis of OC and EC by Vodicka et al. (2015). From 1 pm to midnight the organic matter concentration increases due to a combination of early evening traffic and evening domestic heating. The decrease in concentration from midnight to the morning was caused by a lack of significant anthropogenic sources during the night. In Paris and Zürich, organic matter showed a different pattern. In Paris, the summer daily cycle showed poor temporal variations,


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Fig. 6. Comparison of organic mass concentration measured by the AMS and by OC/EC. AMS data were adjusted using the CE from comparison with IC measurements. The fitting method was linear regression.

Fig. 7. Comparison of organic fragments f44 and f43 for the summer and winter campaign and for specific episodes. The bars show the standard deviation of the data. Polluted episodes are marked by EP sign and clean episodes by EC sign. The dashed lines delimit a triangular area where ambient OOA usually falls (Ng et al., 2010).

which was probably connected with a rapid formation of SOA in the afternoon from diverse biogenic and anthropogenic sources that compensated for the expected drop. On the other hand, in winter the evening maxima were observed (Petit et al., 2015). In Zürich, the daily cycle for hydrocarbon-like (HOA) and oxygenated (OOA) organic aerosol was observed. The traffic related HOA showed two daily maxima e first in the morning and second in the evening. The OOA showed an approximately inverse pattern as it is related to photochemically induced aerosol (Lanz et al., 2010). The daily cycle of nitrate exhibits a peak in the morning and a drop in the afternoon in both seasons. However, there are

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Fig. 8. Comparison of organic fragments f44 and f60 for summer and winter campaign. The dashed line shows the background limit of f60, which was set based on measurements without the influence of biomass burning. The triangular area delimits the occurrence of atmospheric aerosol of biomass burning origin (Cubison et al., 2011). The bars show the standard deviation of the data. Polluted episodes are marked by EP sign and clean episodes by EC sign.

differences in driving processes between summer and winter due to the thermal instability of ammonium nitrate. In summer, the morning nitrate maximum happened before rush hour traffic and before associated traffic tracers (CO, NO, and NOx) began to increase. This shows that the morning temperature minimum caused formation of ammonium nitrate in this period from its precursors present in the atmosphere. The concentration of nitrates subsequently decreased to very low values until the temperature decreased again during late evening. During the winter the comparison of the daily cycles of nitrate and NO and NO2 (Fig. S6) showed that the NO emitted during morning rush hour by traffic was rapidly oxidized to NO2. Subsequently, the NO2 was converted to nitric acid which afterwards reacts with ammonia to form particulate ammonium nitrate (similar as in Carbone et al., 2013). In both summer and winter, the dilution effect contributes to the afternoon minima. In compliance with literature (Poulain et al., 2011), the absolute difference between morning maxima and afternoon minima was larger (0.8 mg/ m3) in winter than in summer (0.3 mg/m3). This is connected with higher total concentrations in winter than in summer. Similarly to Prague, in Paris nitrate showed maxima at night (winter) and early morning. Nitrate and ammonia daily cycles closely resembled which was connected with enhanced formation of ammonium nitrate during periods of high humidity and low temperature (Petit et al., 2015). In Prague, the daily cycles of nitrate and ammonia also resemble each other, to a greater extent in winter than in summer. However, there are similarities in the shape of summer daily cycles of ammonia and sulphate. This confirms that ammonia exists mostly as ammonium nitrate and ammonium sulphate. Sulphate in summer shows a reverse daily cycle compared to other species, i.e. maximum in the afternoon and minimum in morning (Carbone et al., 2013). In summer, we observed two daily maxima. The first maximum around 10 am followed the daily maximum of SO2 (Fig. S6) and we therefore presumed sulphate originated from photochemical oxidation of SO2 (Querol et al., 1998), together with downward mixing of polluted strata of

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Fig. 9. Daily cycles and wind roses of the main compounds measured by the AMS. The 95% confidence interval for the mean of the daily cycle is shown.

air containing both species at higher altitudes. The maximum in sulphate around 4 pm was most likely caused by the sulphate reservoir effect as it coincides with low values of SO2 and maximum in temperature, i.e. increased boundary layer height (Kvietkus et al., 2013; Querol et al., 1998). In winter, the daily cycle of sulphate did not show any clear trend. This might be connected with generally higher levels of pollutants due to domestic heating and a more regional source of secondary sulphate. Such relatively stable diurnal characteristics have already been observed (Docherty et al., 2011; Kvietkus et al., 2011; Petit et al., 2015), governed at least partially by less intense photochemistry, lower mixing layer height, and mid-to long range transport origin.

4. Conclusion This is the first study to present results from AMS measurements performed in the Czech Republic. The summer (20 June 2012e31 July 2012) and winter (8 January 2013e19 February 2013) measurement campaigns were carried out at the suburban site of Suchdol, Prague. The CE was determined through comparison of sulphate concentrations measured by AMS and IC, and were set at 0.29 and 0.35 for the summer and winter, respectively. The average composition was (summer, winter): organic matter (51.3%, 39.5%), sulphate (24.4%, 20.9%), ammonium (14%, 13.1%), nitrate (9.7%, 25.4%), and chloride (0.6%, 1.1%). Average submicron mass concentrations were 8.3 mg/m3 in summer and 21.2 mg/m3 in winter.


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Fig. 10. The mean daily cycle of ratio of f44/f43 organic fragments for (a) summer and (b) winter. The 95% confidence interval for the mean is shown.

The different composition in summer and winter is caused by different seasonal sources (e.g. domestic heating by coal in winter, biogenic sources in summer) and by variable properties of individual species (e.g. volatility of nitrate, photochemical origin of sulphate) connected to meteorological differences between summer and winter. In winter, we found a clear inverse relationship between the boundary layer height and the overall level of pollution. Based on the analysis of the selected clean and polluted episodes, we assume that low pollution levels were caused not only by the dilution effect of increased boundary layer but were also influenced by higher wind speed enhancing mixing and higher temperature resulting in a decrease in domestic heating. In both seasons, the periods with low concentrations were connected with marine air masses and periods with high concentrations were found when continental trajectories prevailed. In winter, the aerosol fine mode maxima was shifted to larger sizes in comparison with summer. Higher relative humidity increasing the influence of liquid phase reactions together with lower temperature enhancing condensation may lead to this effect. In summer, fresh sources of organic aerosol probably led to its lower maximum modal diameter compared to inorganic matter. In both seasons the fine mode maxima under polluted conditions were shifted to larger sizes in comparison with low pollution episodes. The ratio between organic mass measured by the AMS and organic carbon measured by field OC/EC analyser was 1.89 (±0.49) in summer and 1.31 (±0.19) in winter. The very low winter ratio points to an influence of domestic heating by coal. On the contrary, the relatively high summer value is probably caused by enhanced photochemical reactions leading to more oxidized organic matter. The analysis of organic fragments f43 and f44 revealed that photochemically aged aerosol was observed during days with higher solar radiation, as indicated by higher f44. In winter, the influence of local sources such as domestic heating was observed. This was further confirmed by the f44 and f60 comparison which revealed a strong influence of biomass burning in winter. The calculated daily cycles revealed a significant drop in total mass concentrations in the afternoon. This was caused by an afternoon increase in boundary layer height leading to a dilution effect. Also, an increase in temperature leading to evaporation of less volatile particles might have contributed to this phenomenon. Especially for winter, the influence of boundary layer mixing with maximum around 1 pm was confirmed by a strong maxima on diurnal cycle of f44/f43 ratio showing also the presence of aged

aerosol in the upper boundary layer. An afternoon decrease in mass concentration was observed for all measured species except sulphate. The daily trend of sulphate was governed by photochemical reactions and by entrainment of pollution from higher atmospheric layers accompanying the increase in boundary layer height. In general, highly time-resolved measurement of NR-PM1 aerosol composition enabled to identify additional mechanisms of changes in aerosol burden in comparison with previous work at Prague Suchdol site. Acknowledgments The authors of this work gratefully appreciate financial support by the Czech Science Foundation under project No. CSF P209/11/ 1342. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.atmosenv.2015.07.030. References Aiken, A.C., Decarlo, P.F., Kroll, J., Worsnop, D.R., Huffman, J.A., Docherty, K.S., 2008. O/C and OM/OC ratios of primary, secondary, and ambient organic aerosols with high-resolution time-of-flight aerosol mass spectrometry. Environ. Sci. Technol. 42, 4478e4485. Alfarra, M.R., Coe, H., Allan, J.D., Bower, K.N., Boudries, H., Canagaratna, M.R., Jimenez, J.L., Jayne, J.T., Garforth, A. a., Li, S.M., Worsnop, D.R., 2004. Characterization of urban and rural organic particulate in the Lower Fraser Valley using two Aerodyne Aerosol Mass Spectrometers. Atmos. Environ. 38, 5745e5758. http://dx.doi.org/10.1016/j.atmosenv.2004.01.054. Allan, J.D., 2003. Quantitative sampling using an Aerodyne aerosol mass spectrometer 1. Techniques of data interpretation and error analysis. J. Geophys. Res. 108, 4090. http://dx.doi.org/10.1029/2002JD002358. Allan, J.D., Delia, A.E., Coe, H., Bower, K.N., Alfarra, M.R., Jimenez, J.L., Middlebrook, A.M., Drewnick, F., Onasch, T.B., Canagaratna, M.R., Jayne, J.T., Worsnop, D.R., 2004. A generalised method for the extraction of chemically resolved mass spectra from Aerodyne aerosol mass spectrometer data. J. Aerosol Sci. 35, 909e922. http://dx.doi.org/10.1016/j.jaerosci.2004.02.007. Asman, W.A.H., Sutton, M.A., Schjorring, J.K., Asmanl, B.Y.W.A.H., Schjorring, J.A.N.K., 1998. Ammonia: emission, atmospheric transport and deposition. New Phytol. 139, 27e48. Bascom, R., Bromberg, P., Costa, D., Devlin, R., Dockery, D., Frampton, M., Lambert, W., Samet, J., Speizer, F., Utell, M., 1996. Health effects of outdoor air pollution. Am. J. Respir. Crit. Care Med. 153, 3e50. Behera, S.N., Sharma, M., Aneja, V.P., Balasubramanian, R., 2013. Ammonia in the atmosphere: a review on emission sources, atmospheric chemistry and deposition on terrestrial bodies. Environ. Sci. Pollut. Res. 20, 8092e8131. http:// dx.doi.org/10.1007/s11356-013-2051-9. Brüggemann, E., Gerwig, H., Gnauk, T., Müller, K., Herrmann, H., 2009. Influence of

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