Coarse, fine and ultrafine particles of sub-urban continental aerosols measured using an 11-stage Berner cascade impactor

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Coarse, fine and ultrafine particles of sub-urban continental aerosols measured using an 11-stage Berner cascade impactor Dragana Đorđevića,∗, Jelena Đuričić-Milankovića,b, Ana Pantelića, Srđan Petrovića, Andrea Gambaroc,d Centre of Excellence in Environmental Chemistry and Engineering – ICTM, University of Belgrade, Njegoševa 12 (Studentski trg 14–16), Belgrade, Serbia Higher Medical and Business-Technological School of Applied Studies – Šabac, Serbia c Department of Environmental Sciences, Informatics and Statistics, University Ca’ Foscari of Venice, Dorsoduro 2137, 30123, Venice, Italy d Institute for the Dynamics of Environmental Processes – National Research Council (CNR-IDPA), Dorsoduro 2137, 30123, Venice, Italy a

b

A R T I C LE I N FO

A B S T R A C T

Keywords: Size-segregated aerosols Long-term measurements Test probability functions Correlations with meteorological parameters

The main aim of this work is characterization of atmospheric aerosol using 11 stage cascade impactor. The first investigation of size-segregated sub-urban aerosols from the continental part of the Balkan peninsula in 11 fractions in the range of 0.0085 < Dp < 16 μm was performed from March 2012 to December 2013. Aerosols were measured at the Zeleno Brdo observatory (ϕ = 44°48’; λ = 20°28′–243 m above sea level), the highest landmark on the eastern side of Belgrade. Zeleno Brdo is surrounded by wooded vegetation and comprises of both southern facing rural and north-west orientated urban areas. About 70% of total aerosols are fine particles, belonging especially to the PM0.53–1.06 fraction which is found to be more pronounced in winter period. In this work, we applied tests of probability function models for three distributions: normal, log-normal and threeparameter Weibull, by comparing expected and observed values. We found that these models offer the possibility to determine whether the dominant emission source was the vicinity or distance of the measuring point. Results of this test could be a significant supplement to existing multivariate mathematical models for source apportionment, providing accurate estimation of the origin of emission sources and offering information on their position relative to the investigated area (local, regional or remote). In addition, the dependence of particle concentrations for each fraction investigated versus meteorological parameters was determined.

1. Introduction Atmospheric lifetime as well as other specific characteristics of atmospheric aerosols has been extensively studied; still, the global distribution of atmospheric aerosols shows great differences between the regions, warranting their further region-specific analyses. Characteristics of background aerosols of the ambient atmosphere with a common contribution of natural and anthropogenic sources have been investigated (Đorđević et al., 2004, 2005; Gurjar et al., 2010; Kumar et al., 2011; Salam et al., 2003; Sun et al., 2010). A great part of research was also directed to characterising the impact of the aerosol characteristics on the air quality (McMurry, 2000; Pöschl, 2005). One of the most significant microphysical characteristics of atmospheric aerosols is number-size distribution characterized by a wide range of sizes (~15–15,000 nm). It was found to impact on the life cycle of aerosols, but also interactions between aerosols and radiation and

aerosols and clouds (Babu et al., 2016; Joshi et al., 2016). Particle concentrations and their size distributions play the most important role in direct and indirect effects of aerosols on climate change (Dusek et al., 2006; Pöschl, 2005), as well as being important parameters of air quality degradation causing adverse health effects (WHO, 2013). Aerosol size distribution determination is crucial when making associantions between their emission sources, physicochemical transformation and removal mechanisms. Based on the size of the aerodynamic particle diameter (Dp), the following modes can be distinguished: nucleation (Dp < 25 nm), Aitken (25–100 nm), accumulation (0.1–2.5 μm) and coarse mode (Mönkkönen et al., 2005), all associated to specific processes (nucleation, condensation and primary emission, respectively). Nucleation mode consists of particles formed by nucleation processes of gas-phase precursors and source condensation processes of vapours (Colbeck and Lazaridis, 2014). During coagulation and condensation processes, they are rapidly transformed into Aitken

Peer review under responsibility of Turkish National Committee for Air Pollution Research and Control. ∗ Corresponding author. E-mail addresses: [email protected], [email protected] (D. Đorđević). https://doi.org/10.1016/j.apr.2019.11.022 Received 9 August 2019; Received in revised form 29 November 2019; Accepted 29 November 2019 1309-1042/ © 2019 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V.

Please cite this article as: Dragana Đorđević, et al., Atmospheric Pollution Research, https://doi.org/10.1016/j.apr.2019.11.022

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Table 1 Descriptive statistics of size-segregated aerosol concentrations (μg m−3) for the entire period (March 2012 to December 2013) and for the seasons.

PM0.0085-0.018 PM0.018-0.035 PM0.035-0.07 PM0.07-0.138 PM0.138-0.27 PM0.27-0.53 PM0.53-1.06 PM1.06-2.09 PM2.09-4.11 PM4.11-8.11 PM8.11-16

Total (n = 101) Aver. conc. ± SD (Min. - Max.)

Spring (n = 29) Aver. conc. ± SD (Min. - Max.)

Summer (n = 30) Aver. conc. ± SD (Min. - Max.)

Autumn (n = 27) Aver. conc. ± SD (Min. - Max.)

Winter (n = 15) Aver. conc. ± SD (Min. - Max.)

0.6 ± 0.3 (0.0–1.3) 0.8 ± 0.4 (0.1–1.8) 1.0 ± 0.4 (0.0–2.3) 1.8 ± 0.7 (0.4–4.3) 3.2 ± 1.4 (0.6–7.7) 7.1 ± 4.1 (1.9–22.5) 9.2 ± 7.7 (1.5–42.9) 5.8 ± 5.3 (1.0–30.5) 3.4 ± 1.8 (0.9–13.8) 4.0 ± 1.7 (0.9–10.6) 3.5 ± 1.8 (0.9–9.0)

0.8 ± 0.3 (0.2–1.3) 0.9 ± 0.4 (0.3–1.8) 1.2 ± 0.4 (0.5–2.3) 1.8 ± 0.7 (0.5–3.2) 2.9 ± 1.1 (1.4–6.1) 5.9 ± 2.9 (1.9–14.2) 7.1 ± 4.7 (1.5–19.5) 4.3 ± 2.3 (1.0–9.8) 3.3 ± 2.3 (1.3–13.8) 3.9 ± 1.9 (1.8–10.6) 3.6 ± 2.0 (1.2–8.1)

0.5 ± 0.2 (0.0–1.2) 0.7 ± 0.3 (0.1–1.2) 1.0 ± 0.3 (0.4–1.4) 1.5 ± 0.4 (0.9–2.3) 2.6 ± 0.7 (1.3–4.5) 4.8 ± 1.4 (2.6–8.4) 4.8 ± 1.6 (2.5–8.4) 3.1 ± 1.1 (1.3–6.6) 3.2 ± 0.8 (1.7–5.4) 4.1 ± 0.9 (2.5–5.9) 3.9 ± 1.2 (1.9–7.4)

0.5 ± 0.3 (0.0–1.2) 0.8 ± 0.3 (0.3–1.4) 0.9 ± 0.4 (0.0–1.7) 1.9 ± 0.7 (0.4–3.4) 3.3 ± 1.4 (0.6–6.8) 7.6 ± 3.6 (1.9–15.5) 9.4 ± 5.7 (2.8–27.9) 5.6 ± 4.4 (1.2–20.8) 3.4 ± 1.8 (0.9–9.6) 4.5 ± 2.1 (0.9–9.2) 3.6 ± 2.1 (0.9–9.0)

0.6 ± 0.3 (0.3–1.3) 1.0 ± 0.5 (0.3–1.7) 1.3 ± 0.6 (0.3–2.2) 2.4 ± 1.0 (1.0–4.3) 5.0 ± 15 (3.0–7.7) 13.3 ± 4.5 (7.1–22.5) 21.9 ± 9.3 (8.8–42.9) 14.3 ± 7.2 (5.2–30.5) 3.7 ± 2.1 (1.4–9.6) 3.1 ± 1.6 (1.5–7.7) 2.1 ± 0.9 (1.0–4.3)

of particular interest, given their negative health effects and the toxic potential of fine particulate matter on susceptible elderly and debilitated individuals (Gietl et al., 2008). During inhalation smaller particles can penetrate deeper into the lungs and therefore have a greater adverse health effect compared to coarse particles (Jabbour et al., 2017), with the possibility of the smallest, the nano-range particles to enter the blood stream. Biomass burning as a contribution to aerosol particles has been recognized (Fine et al., 2001; Lee et al., 2008; Schauer et al., 2001; Venkataraman et al., 2005), and is substantially associated with both open and domestic fires (Cheng et al., 2013; Zhang et al., 2014). In 2016, exposure to PM2.5 caused an estimated 4.2 million premature deaths globally, including around half a million population from the European Region (WHO, 2018). The main sources of outdoor air pollution in Serbia include the energy sector (thermal power plants, district heating plants and individual household heating), the transport sector (an old vehicle fleet), waste dump sites and industrial activities (oil refineries, the chemical industry, mining and metal processing and the construction industry) (WHO Regional Office for Europe, 2019). Air pollution significantly contributes the overall burden of disease and premature death in Serbia, having higher estimates of premature death due to air pollution than most countries in the European Union (WHO Regional Office for Europe, 2019). Assessments of air quality based on data from monitoring stations managed by national authorities indicate that the concentrations of air pollutants, especially PM, regularly exceed the levels that protect human health (WHO Regional Office for Europe, 2019). The efficiency of inhalation and respiratory deposition of PM is dependent on the size of the particles (Hınds, 1999). Therefore, knowledge of particle size is vital in understanding the effects of PM on human health (Şahin et al., 2012). Several studies have investigated the mass and size distribution of urban atmospheric aerosols in Serbian region (Mihajlidi-Zelić et al., 2015; Đorđević et al., 2016). There have been no studies that evaluated the size distribution of size-segregated aerosols in the suburban area of continental part of the Balkan Peninsula. Therefore, this paper presents the results obtained by the analysis of samples, which have been collected over a period of 22 months with the aim of examining size distribution of particles, the temporal and seasonal variations of size-segregated aerosols and explored the influences of weather conditions on the concentrations of particles.

mode and further into larger particles in accumulation mode (Kulmala et al., 2004; Kulmala and Kerminen, 2008). Primary particles are directly released from sources such as carbon particles emitted from traffic, fire, combustion of fuel in households, as well as from industrial processes, windblown crustal dust released in resuspension processes, or salt nuclei originating from sea spray. Secondary particles form from gas precursors which condense to liquid droplets. For example, sulphur dioxide in the atmosphere is oxidized to sulfuric acid gas which easily condenses and forms volatile liquid droplets. Most of the particles directly emitted into the atmosphere are solid and they are usually larger in comparison to secondary particles that are liquid. During the combustion processes, both primary and secondary particles are emitted (Kumar et al., 2013). Mechanical processes generate only primary particles (Azarmi et al., 2014), but van Broekhuizen et al. (2011) pointed out that several mechanical processes involving engineering of nanomaterials can lead to the emission of ultrafine particles. Aerosol size distribution depends on the air mass type, the type of source process, weather conditions, size transformations and the effectiveness of the particle removal mechanisms. As previously mentioned, these processes show seasonality (O'Dowd et al., 2002); hence, seasonal variability of aerosol size distribution is expected. Mechanisms for removing particles (both dry and wet scavenging) are less efficient for the accumulation mode particles (Seinfeld and Pandis, 2006). In urban areas there are numerous anthropogenic sources of fine particles that contain harmful substances (Moffet et al., 2008; Suarez and Ondov, 2002). It is not well appreciated that significant amounts of ultrafine particles are emitted during the process of crushing concrete, affecting not only the local environment, but the entire urban areas. Their origin is uncertain, as the existing theories do not support the production of ultrafine particles (UFP) through mechanical processes. However, Jabbour et al. (2017) suggested a hypothesis based on material volatilization at the concrete-fracture interface confirming that UFP can be produced by mechanical methods from concrete and that these particles are volatile, providing supporting evidence that UFP could be released during mechanical processes (Jabbour et al., 2017). Moreover, during activities of the asphalt and concrete preparation (mixing, pouring, compaction) high concentration of nanoparticles was found (Asadi et al., 2012; Jabbour et al., 2017). Measurement of atmospheric aerosol particles segregated by size is 2

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The purpose of this study was to evaluate the size distribution of airborne PM in a suburban continental part of the Balkan Peninsula. Also, this study aimed to investigate temporal and seasonal characteristics of PM and to explore the influences of weather conditions on the particle concentrations. Size characteristics of PM obtained in this study are expected to provide important information for developing PM control strategies as successful characterization of aerosols in the environment can be crucial for prevention of some airborne diseases. The use of cascade impactors to characterize ambient aerosols is one of the most commonly used methods, providing data on both particle size and concentration. The main advantage of using a cascade impactor is that a single measurement gives the distribution of particle sizes by their diameters in 11 levels. This means that changes in the particle size distribution can be monitored over time, providing a much broader picture of the dynamics and origin of the particles in the range of 0.0085–16 μm. Many studies have been focused on measuring PM10 and PM2.5 concentrations in urban and rural sites worldwide. However, there have only been a few studies conducted in Serbia investigating PM10 and even fewer studies investigating PM2.5 (Rajšić et al., 2004; Mijić et al., 2009; Šerbula et al., 2010; Stojić et al., 2016). The aim of this study is to gain information on the size distribution of particles using the Cascade Impactor measurement technique and determine the effects of seasonal factors on the characteristics of PM present in the atmosphere near Belgrade, Serbia. 2. Materials and methods PM samples (as 48 h samples) were collected from March 2012 to December 2013 using an 11-stage low-pressure cascade impactors designed by Prof. Dr. Berner (Berner, 1972; Wang and John, 1988) (Model LPI 25/0.0085/2, ISAP Gerhard Schulze Automation Engineering, Germany). Total of 101 sample sets were collected during the measurement campaign. In order to include every day of the week, the samples were taken every sixth day (Đorđević et al., 2012). The sampling site is located in the sub-urban area of Belgrade, in the northern part of Serbia. Belgrade (ϕ = 44°49’; λ = 20°27′– 117 m elevation in the city centre) is the capital of Serbia and the second largest urban area in Balkan having around 2 million inhabitants. The representative sampling location is the highest landmark point near Belgrade surrounded by wooded vegetation (Zeleno Brdo site: ϕ = 44°79’; λ = 20°52’ – 243 m above sea level), located approximately 5 km to the southeast of the city centre, on the eastern edge of the urban area. Its southern areas are rural, while the north-western area is urban part of Belgrade. Individual family houses and low-rise residential buildings dominate. The sampler was placed at a height of 2.5 m above ground level, and ran at a constant flow rate of 25 l min−1. Next, ranges of Dp (in μm) were measured: PM0.0085–0.018, PM0.018–0.035, PM0.035–0.07, PM0.07–0.138, PM0.138–0.27, PM0.27–0.53, PM0.53–1.06, PM1.06–2.09, PM2.09–4.11, PM4.11–8.11 and PM8.11–16. Alumina rings and Tedlar® foil rings were used: (a) alumina for stages PM0.0085–0.018, PM0.018–0.035, PM0.035–0.07, PM0.07–0.138 and PM0.138–0.27 because of the small weights of deposits, and (b) Tedlar® foil rings for PM0.27–0.53, PM0.53–1.06, PM1.06–2.09, PM2.09–4.11, PM4.11–8.11 and PM8.11–16. The choice of two types of filters has its justification: for larger particle diameters, the Tedlar filter (polyvinyl fluoride) is more appropriate, but when submicron particles (in this case, particles less than 0.27 μm) need to be separated simultaneously, the rough surface of the Tedlar filter affects aerodynamic flow, disabling proper sampling (Buchholz et al., 2010). On the other side, the aluminium filters have smaller mass of around 0.09 g, compared to Tedlar filters with a mass of around 0.2 g. The sampling device was placed away from other objects around it in a 50 m radius; ground covered with grass was selected in order to investigate the physical properties of the collected particles. Aerosol samples were submitted to gravimetric measurements.

Fig. 1. a) Time variations of concentrations measured for all Dp intervals; b) time variations of ultrafine, fine and coarse modes; c) mass concentration distributions of particles in all Dp intervals, with medians, interquartile range – IQR (Q1: 25th percentile and Q3: 75th percentile), min, max, outliers –o (> 1.5 IQR) and extremes –* (> 3 IQR).

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Fig. 2. a) Time variations for ultrafine, fine and coarse modes for non-heating and heating seasons; b) average percentage shares of all fractions in non-heating and heating seasons; c) mass-segregated average concentrations in non-heating and heating seasons.

3. Results and discussion

Filters were measured before and after sampling, using a KERN ABT 120-5DM balance (precision 0.01 mg), in a glove-box system with a nitrogen atmosphere, and the filters were kept at a temperature of 20 ± 5 °C at 45 ± 5% humidity (Stortini et al., 2009). At the beginning of each measurement session, an internal calibration of the balance was performed on a regular basis (Smolík et al., 2003). Each filter was weighed at least three times (Pipalatkar et al., 2012). During weighing the filter before and after sampling, the control filter was also measured at least three times to check the influence of fluctuations in temperature and humidity. In order to correct the concentrations measured on the filters, blanks (which include contribution from filter handling during sampling) were also measured (Toscano et al., 2011). Uncertainty (u) determinations based on 10 replicates were made using the following equation:

u=

100

Summarized descriptive statistics for mass concentrations of aerosols size-segregated into 11 stages are presented in Table 1. Of the total aerosol mass, about 70% on average was distributed in fine mode (PM0.0085–2.09), while 30% of the total mass concentration was distributed in coarse mode (PM2.09–16). Toscano et al. (2011) reported similar percentage values for fine and coarse mode. The highest mean values of mass concentration in the fractions PM0.018–0.035, PM0.035–0.07, PM0.07–0.138, PM0.138–0.27, PM0.27–0.53, PM0.53–1.06, PM1.06–2.09 and PM2.09–4.11 were characteristic for winter periods, and can be attributed to the combustion of wood, biomass and fossil fuels, while the highest mean concentrations measured in autumn periods were in the PM4.11–8.11 fraction, and those measured during the summer were in PM8.11–16. The highest average concentrations in the PM0.0085–0.018 fraction were measured in the spring period. Meteorological conditions during winter periods can affect mass concentrations of aerosols. A lower level of the inversion layer caused by lower mixing height due to higher relative humidity, lower temperatures and lower wind speed limits the dispersion of particles, causing higher mass concentrations (Deshmukh et al., 2012). Fig. 1a shows the greatest variations in mass concentrations for fractions PM0.27–0.53, PM0.53–1.06 and PM1.06–2.09, which belong to the fine fraction of accumulation mode. The smallest variations in mass concentrations were observed for UFP in nucleation and Aitken modes, with a slight increase in the winter period when there are higher emissions of their gas precursors, while coarse mode had a larger share in the summer in relation to the winter period (Fig. 1 b). Generally, the most prevalent fraction was PM0.53–1.06, in which the largest number of outliers and extremes appeared, especially in winter time, while outliers and extremes in coarse mode appeared in spring, summer and

u v2 + up2 md2

where is uv is the uncertainty of the balance, md is the mass of deposits and up is the uncertainty of pump flow. Expanded uncertainty (2u) for masses measured in nucleation and Aitken modes was increased to 60% and 30%, respectively, while 2u for accumulation and coarse modes was increased up to 5% of measured mass concentrations. Greater uncertainty in ultrafine and fine modes is a consequence of small masses measured for these fractions. Meteorological parameters measurements (temperature, relative humidity, water vapour pressure, insolation, cloudiness, precipitation, air pressure and wind velocity) were available from the monitoring station run by Republic Hydrometeorological Service of Serbia (http:// www.hidmet.gov.rs), which is located on the sampling site.

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Fig. 3. a) Seasonal mass concentration distributions of particles in all Dp intervals, with medians, interquartile range – IQR (Q1: 25th percentile and Q3: 75th percentile), min, max, outliers –o (> 1.5 IQR) and extremes –* (> 3 IQR); b) seasonal average percentage shares of coarse and fine modes in total mass concentrations; c) seasonal size-mass distributions.

spring, farmers in Serbia openly burn the harvest residues on fields. About 70% of the territory of Serbia is under agricultural crops and significant part of them burnt in the zone of ground layer, as a low altitude emission sources, and in combination with surface temperature inversion (Đorđević and Šolević, 2008) significantly contributes to the increase in PM concentrations. Cvetković et al. (2015) reported that the average concentration of PM10 is higher during the winter season in locations with individual heating sources, rather than the urban city center of Belgrade. Variations in the concentration of coarse mode, accumulation mode and ultrafine mode in non-heating and heating seasons are shown in Fig. 2a. The largest variations were observed for accumulation mode and coarse mode. Accumulation mode dominated in the heating season, while the share of coarse mode in this season was significantly lower compared to that in the non-heating season. Average percentage share (Fig. 2b) of PM0.27–0.53, PM0.53–1.06 and PM1.06–2.09 fractions was significantly higher in the heating season, while those for coarse mode fractions PM2.09–4.11, PM4.11–8.11 and PM8.11–16, and for ultrafine and fine mode fractions PM0.018–0.035, PM0.035–0.07, PM0.07–0.138, PM0.138–0.27 and PM0.27–0.53 were lower. Mass-segregated concentrations in non-heating and heating seasons (Fig. 2c) showed higher values for particle concentrations of accumulation mode in the heating season. The seasonal distribution of mass-segregated aerosol particles is shown in Fig. 3a. Mass-segregated distribution is bimodal, with maximums in the range of 0.53 < Dp < 1.06 μm in fine mode and in the range of 4.11 < Dp < 8.11 μm in coarse mode. During spring, summer and autumn, similar distribution patterns for mass

autumn periods (Fig. 1c). Comparison with other studies, from different suburban and urban background sites in Europe, showed that the level of average total particle mass concentrations in suburban Belgrade was higher than average concentration in Lecce (Italy) (Contini et al., 2010), Boccadifalco (Italy) (Dongarra et al., 2007), Mechelen (Belgium) and Hasselt (Belgium) (Vercauteren et al., 2011), but lower than in Istambul (Turkey) (Karaca et al., 2005). The level of average fine particle mass concentrations in suburban Belgrade was higher than average PM2.5 concentration in Schiedam (Netherlands) (Mooibroek et al., 2011) and Istambul (Turkey) (Karaca et al., 2005) and similar with concentration measured in Bobadela (Portugal) (Almeida et al., 2005) and Gdańsk (Poland) (Rogula-Kozlowska et al. 2014). 3.1. Seasonal variations Total particle concentrations varied from 13.4 to 119.8 μg m−3, with average values of 40.4 ± 19.2 μg m−3. The highest average total particle concentrations were measured in the winter period, followed by those for autumn, spring and summer with lower measured concentrations of 30.2 μg m−3. These results are consistent with the results presented in the paper of Stojić et al. (2016). Ultrafine particles are emitted mainly from mobile sources (automobiles and diesel trucks) and stationary combustion sources (Cabada et al., 2004). Intensive combustion of wood, biomass and fossil fuels for domestic heating during the heating season, and meteorological conditions conditioned higher total particle concentrations in the winter period but lower concentrations in the summer period. Also, from autumn to early 5

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Fig. 4. Distribution of particle mass concentrations with corresponding expected and observed cumulative probabilities (normal, log-normal and Weibull) for nucleation and Aitken modes.

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Table 2 Varimax rotated factors. Factor

Eigenvalue % of σ2 PM0.0085-0.018 PM0.018-0.035 PM0.035-0.07 PM0.07-0.138 PM0.138-0.27 PM0.27-0.53 PM0.53-1.06 PM1.06-2.09 PM2.09-4.11 PM4.11-8.11 PM8.11-16

F1

F2

F3

4.6 41.7

2.7 24.6

1.7 15.2 0.758 0.855 0.784 0.632

0.581 0.845 0.967 0.968 0.907 0.842 0.963 0.895

concentrations distributed through the Dp intervals can be observed, while winter is characterized by a different distribution pattern. During the winter period, the major part of the aerosol mass was in fine mode (86.8%) rather than coarse mode (13.2%), which can be attributed to the intense burning of wood, harvest residues, biomass and fossil fuels for domestic, industrial and public sector heating in this period. The summer is characterized by the distribution of 37.3% coarse mode, while fine mode accounts for 62.7% of the total aerosol mass. Resuspension of road dust could have a significant effect on the concentration of coarse mode particles at the receptor site, since there are more than 500,000 registered vehicles in Belgrade. Deshmukh et al. (2012) have also reported the changes in the ratio of fine and coarse mode during the winter period due to the intensive burning of biomass during the heating season, and summer period due to the larger resuspension of road dust. Fig. 3c shows the unimodal distribution with a pronounced peak for the range size of fine mode (0.53 < Dp < 1.06 μm). Spring, summer and autumn are characterized by a trimodal distribution; two of them are in fine mode (0.27 < Dp < 0.53 and 0.53 < Dp < 1.06 μm), and the third is in coarse mode (4.11 < Dp < 8.11 μm). Two peaks in the fine mode characteristic for spring and summer can be attributed to emissions from traffic and from industry, i.e. fossil fuel combustion. Various meteorological conditions during seasons can lead to differences in mass size-segregated distributions (Deshmukh et al., 2012). Meteorological conditions characteristic in spring, summer and autumn can cause higher concentrations of aerosol particles in coarse mode compared to those in winter season. 3.2. Statistical analyses 3.2.1. Tests of probability models Tests of probability models can be applied to assess the relative distance of emission sources from the receptor (measuring points). More independent factors cause complex atmospheric processes. In datasets from atmospheric processes, there are outliers and extremes which distort the distribution and move its maximum towards lower values, extending its ‘tail’ in the area of high concentrations, forming a log-normal distribution (Đorđević et al., 2004; Reimann and Filzmoser, 2000). If the emission sources are in the vicinity of the measurement station, these datasets best describe a log-normal distribution. Wind variation is described by two- and three-parameter Weibull distributions (Đorđević et al., 2004; Wais, 2017a, 2017b), and can describe particles from remote sources carried by the wind to the investigated area. These types of distribution can be applied to databases which contain particle mass concentrations. Sub-datasets of particle mass concentrations measured for all investigated cascades were subjected to tests of expected and measured

Fig. 5. Distribution of particle mass concentrations with corresponding expected and observed cumulative probabilities (normal, log-normal and Weibull) for accumulation and coarse modes. 7

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Fig. 6. Bivariate correlations between mass concentrations of particles through Dp intervals and temperature, relative humidity (RH), water vapour pressure and insolation.

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Fig. 7. Bivariate correlations between mass concentrations of particles through Dp intervals and cloudiness, precipitation, air pressure and wind velocity.

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of re-suspension due to a drying soil surface on warmer days. A similar trend showed the dependence of particulate matter concentrations and water vapour pressure and insolation in accumulation and coarse modes (Fig. 6). Zhang et al. (2015) observed a significant negative correlation between fine mode particle concentration (PM2.5) and insolation. A day with sunshine hours often indicates the weather with a little cloudiness, or strong winds, favoring diffusion and elimination of air pollutants (Yang et al., 2009; Sanchez-Romero et al., 2014). In contrast, an increase in relative humidity (Fig. 6) and cloudiness (Fig. 7) caused increased particulate matter concentrations in accumulation mode. This can be explained by the concentration of particles from this mode in near-ground fog which is more frequent in these meteorological conditions and by decreased particle concentrations in the coarse mode which probably does not persist in fog. On the other hand, the process of re-suspension is suppressed due to a more moist ground surface. Zhang et al. (2015) observed a significant positive correlation between relative humidity and fine mode particle concentration, linking this to cloudy, windless days, which promote the accumulation and chemical reactions of pollutants (Kang et al., 2013; Csavina et al., 2014). Except for nucleation mode, increased precipitation causes a decrease in particle concentrations in all fractions, more expressed in coarse mode (Fig. 7). Rainfall can reduce the concentration of PM in ambient air through “rainout” or “washout” mechanisms (Hieu and Lee, 2010). Mbengue et al. (2014) observed the occurrence of lower concentrations of PM10 for samples taken during intense and frequent rainfall. The particles of accumulation and ultrafine modes did not show statistically significant correlation with the amount of precipitates, which is in accordance with the work of Zhang et al. (2015). Precipitation is often significant for wet deposition of atmospheric pollutants (Connan et al., 2013), but Zhang et al. (2015) did not observe any significant statistical correlation between daily precipitate amounts and daily PM2.5 concentrations. Increased wind velocity has the smallest impact on the change in particle concentrations in nucleation, Aitken and accumulation modes, while particle concentrations in coarse mode decrease with an increase of wind speed. Dependence of particle concentrations and air pressure is direct for all fractions except for nucleation mode (Fig. 7). Present study indicates a need for appropriate planning and management to prevent elevated level of fine PM concentration, thus safeguarding public health from ill-effects of fine particulate matter. The limitation of this study is lack of information regarding chemical composition of atmospheric aerosol.

probability distributions: normal, log-normal and three-parameter Weibull. Fractions PM0.0085–0.018, PM0.018–0.035, PM0.035–0.07 and PM0.07–0.138 showed agreement only with normal distribution (Fig. 4), indicating their constant and balanced presence in the atmosphere as a result of the nucleation of gas-phase precursors, condensation of vapours emitted from natural and anthropogenic sources, and their transformation to Aitken mode. On the other hand, tests of expected and observed probability distributions applied to sub-datasets of fractions PM0.138–0.27, PM0.27–0.53, PM0.53–1.06, PM1.06–2.09, PM2.09–4.11, PM4.11–8.11 and PM8.11–16 showed some agreement with all test distributions (Fig. 5). However, the least deviation was shown for lognormal distribution of sub-data sets for fractions PM0.138–0.27, PM0.27–0.53, PM0.53–1.06, PM1.06–2.09 and PM2.09–4.11, pointing to urban emission sources like heating, traffic, biomass burning, etc. as the dominant contribution to particle concentrations in these fractions, while normal distribution showed the least deviation for sub-datasets for PM4.11–8.11 and PM8.11–16 fractions, pointing to local re-suspension, firstly of road dust, as a dominant process for particles entering the air from surrounding surface soil. Nevertheless, more complex processes like coagulation and condensation processes, long-range transport, primary emissions, etc. in accumulation and coarse modes exist. 3.2.2. Principal component analysis (PCA) The database of all 11 Dp intervals of mass size-segregated concentrations of aerosols was analysed by PCA using SPSS. Before applying the PCA model, the suitability of data for factor analysis was investigated. The KMO indicator had a value of 0.708, which exceed the minimum recommended value of 0.6, and Bartlett's spherical test reached high statistical significance (p < 0.001), indicating the convenience of correlation matrices for PCA analysis. By analysing the main components, three factors were identified (Table 2) with eigenvalues above 1. The eigenvalue for the first factor F1 = 4.6, for the second factor F2 = 2.7 and for the third factor F3 = 1.7; they explain 41.7%, 24.6% and 15.2% of the variance, respectively, and are directly related to contributions of the emission sources. The three-component solution explained a total of 81.5% of the variance; the first factor with an initial eigenvalue of 4.6 was significantly higher than the others, and it contributed 41.7%, indicating the existence of a single source or a group of sources that simultaneously emit particulate matter in this size range, first of all in accumulation mode and partly in Aitken and coarse modes. The second factor (F2) could be attributed to re-suspension, and the third – the weakest factor (F3) – to nucleation processes.

4. Conclusions

3.2.3. Correlation with the meteorological parameters Correlation of mass concentrations of size-segregated aerosol particles with the meteorological parameters (Table S1) temperature, relative humidity, water vapour pressure, insolation (Fig. 6), cloudiness, precipitation, air pressure and wind velocity (Fig. 7) were made for all PM intervals. Meteorological parameters, except precipitation, air pressure, temperature and water vapour pressure, and concentrations of particles from nucleation and Aitken modes did not show statistically significant dependence. The concentration of particles in these modes in correlation with temperature, water vapour pressure and precipitation slightly decreased with increasing values of these meteorological parameters, most prominently in the PM0.07–0.138 fraction (Figs. 6 and 7), while increased air pressure had a slight influence on increasing particle concentrations, noticeable in the PM0.07–0.138 fraction (Fig. 7). Temperature, relative humidity, insolation (Fig. 6) and cloudiness (Fig. 7) had almost no influence on particle concentrations in the PM2.09–4.11 fraction. The concentration of particulate matter in the accumulation mode decreased with increasing temperature, which is related to a reduction in particle emission from primary sources due to reduced heating, but the concentration of particulate matter in coarse mode increased in PM4.11–8.11 and PM8.11–16 fractions because of a more intensive process

In this work, we have shown that in mass size-segregated sub-urban continental aerosols from the central Balkans, accumulation mode dominates; it is more intense in the heating season, pointing to combustion processes from domestic, public sector and industrial heating as its primary emission sources. The lowest concentrations were measured in summer, with the highest share of coarse mode due to a more intense re-suspension process of traffic road dust, since the ground surface is drier. There are three dominant contributions to particulate matter content in the sub-urban continental aerosols investigated: 1) wood, fossil fuel and biomass combustion, 2) re-suspension of primary emission sources and 3) nucleation of gas precursors, and gas-particle condensation as sources of the genesis of particles in nucleation and Aitken modes. Test probability models have shown that sub-datasets for nucleation and Aitken modes correspond only to the normal distribution, indicating their permanent presence in the air. Sub-datasets for accumulation and coarse modes correspond to normal, log-normal and Weibull distributions, indicating more complex atmospheric processes for the presence of aerosols in these fractions, i.e. primary emissions from local and regional sources, long-range transport, re-suspension, etc. However, these are detrended from the log-normal and from the 10

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normal distribution in accumulation mode and coarse mode, respectively, indicating that these distributions are least suitable for describing these datasets. Meteorological parameters do not have a significant influence on particle content in nucleation mode. Also, temperature, relative humidity, water vapour pressure, insolation and cloudiness have the negligible impact on particle content in the PM2.09–4.11 fraction, while all meteorological parameters have a direct or inverse influence on particle content in other fractions.

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