Aerosol and Air Quality Research, 18: 636–654, 2018 Copyright © Taiwan Association for Aerosol Research ISSN: 1680-8584 print / 2071-1409 online doi: 10.4209/aaqr.2017.06.0221

Concentration, Chemical Composition and Origin of PM1: Results from the First Long-term Measurement Campaign in Warsaw (Poland)

Grzegorz Majewski1*, Wioletta Rogula-Kozłowska2,3, Katarzyna Rozbicka1, Patrycja Rogula-Kopiec3, Barbara Mathews3, Andrzej Brandyk1

1 Warsaw University of Life Sciences, Faculty of Civil and Environmental Engineering, 02-776 Warsaw, Poland 2 The Main School of Fire Service, Faculty of Fire Safety Engineering, 01-629 Warsaw, Poland 3 Institute of Environmental Engineering, Polish Academy of Sciences, 41-819 Zabrze, Poland

ABSTRACT

This paper presents a 120-day-long variability of chemical composition of submicron particulate matter (PM1) over Warsaw. The content of the following components was examined in the PM1 mass: primary (POM) and secondary (SOM) organic matter, secondary inorganic matter (SIM), elemental carbon (EC) as well as Na and Cl ions (primary inorganic matter). The 24-hour concentrations of PM1 were subject to seasonal fluctuations which are typical of urban areas in Poland; –3 –3 their values averaged 11 µg m in summer and 17 µg m in winter. Most of the PM1 components and gaseous pollutants (SO2, NO2 and NOx) revealed higher mean concentrations in winter than in summer. A statistical analysis of meteorological parameters and 24-h concentrations of PM1, PM10, SO2, NO2 and NOx confirmed a significant influence of air temperature and precipitation on the concentration patterns of these pollutants over Warsaw. The highest concentrations of PM1 occurred in winter for the following wind directions: S, SE, N and NE; in summer for NE, E and S. The analysis of back trajectories demonstrated that on days with the highest 24-h concentration of PM1 polluted air masses arrived from S and SE and affected the concentration of PM1 over Warsaw. The submicron particulate matter, in as much as 62%, comprises secondary matter (SOM and SIM). The primary sources of its precursors and – to a smaller extent – of the primary matter as well – are traffic and combustion of various fuels for the purpose of heat and power generation. Their average contribution to the development of PM1 was 15% and 51%, respectively, for the entire period of observations.

Keywords: Secondary aerosol; Carbonaceous matter; Submicron particles; Air mass inflow; Fossil fuels combustion.

INTRODUCTION and precipitation contribute to its substantial decrease (Rogula-Kozłowska et al., 2014; Majewski and Rogula- Synoptic and meteorological conditions have a major Kozłowska, 2016; Widziewicz et al., 2017). It has also role in the development of atmospheric particulate matter been demonstrated, that over Polish areas, at the times of (PM) concentration. The most important parameters affecting strong insolation, the mass fraction of organic and inorganic PM concentration are: the wind speed - influencing the rate secondary aerosols in PM is greater than when insolation is and scale of dispersion of PM, the humidity of air and the smaller. This is particularly noticeable in areas located solar radiation leading to the formation of secondary PM, outside the city centre, i.e., in suburban and rural areas and precipitation influencing the rate of washing PM out of (Rogula-Kozłowska and Klejnowski, 2013; Błaszczak et the atmosphere (Pueschel, 1996; Mues et al., 2012; Seinfeld al., 2016; Pyta et al., 2017). The reason behind very high and Pandis, 2012; Olszowski, 2016; He et al., 2017; concentrations of airborne PM, mainly observed in Poland Olszowski, 2017). Our earlier research shows that on wind- in winter, is also air temperature inversion which favours calm days and without rain, the concentration of PM in the concentration of PM in the atmospheric boundary layer Polish cities is the highest, whereas considerable wind speeds (Majewski and Przewoźniczuk, 2009; Pastuszka et al., 2010; Rogula-Kozłowska et al., 2014; Czarnecka et al., 2016). Atmospheric stability can have a strong influence on PM concentration, also at a local scale (Perrino et al., 2008; *Corresponding author. Pastuszka et al., 2010). Tel.: +48-22-59-353-25 However, the primary factor which determines the E-mail address: [email protected] presence of PM or gaseous precursors of PM in the

Majewski et al., Aerosol and Air Quality Research, 18: 636–654, 2018 637 atmosphere (and thus deciding on what and in what was determined for each day of the measurement period. quantities is introduced into the atmosphere and subject to the above-mentioned phenomena), is the emission. In other METHODOLOGY words, the chemical composition of PM at a given location, regardless of the prevailing synoptic and meteorological Collection and Preparation of Samples conditions, depends on the PM origin. (Chow, 1995; Rogula- Observations were conducted in Warsaw (λE = 21° 02′, Kozłowska, 2014; Chow et al., 2015; Błaszczak et al., 2016; φN = 52° 09′) at a measurement site located in the district Hong et al., 2017). of Ursynów, selected to satisfy the criteria of urban In Polish urban areas, main sources of PM and its background location as specified in the Directive 2008/50/EC gaseous precursor emissions are: the so-called low or (Fig. 1). Topographical and meteorological conditions, the communal emissions, which involves dust and gases from structure and amount of emissions from PM and gas the combustion of coal and its derivatives in household pollution sources (the fraction of local emissions from stoves and local boiler plants. They tend to be occasionally boiler plants and rooms, stoves, industrial facilities and fed with plastics, solid and organic waste. The main sources traffic at the sampling point) were typical of the whole also involve energy sector and industries (such as coking conurbation (Majewski et al., 2014; Majewski and Rogula- plants, foundries, steel mills and cement plants), and – first Kozłowska, 2016). of all – road traffic emissions in a broad sense (Rogula- From 24 June to 22 August 2014 (60 samples; summer) Kozłowska et al., 2015). A dense network of traffic routes, and from 8 January to 8 March 2015 (60 samples; winter), increasing traffic volumes, growing numbers of vehicles, 120 diurnal samples of PM1 were collected by means of an often with faulty catalytic converters or diesel particulate MVS6D PM sampler (provided by ATMOSERVICE; filters (DPFs), largely contribute to the increase of PM Poznań, Poland). The sampler was equipped with a sampling concentrations in urban areas in Poland (Rogula-Kozłowska head (produced by TSI; PM inlet with PM1 jet impactor) 3 –1 et al., 2013; Rogula-Kozłowska, 2015). which aspired PM1 at a flow of 2.3 m h (according to The Warsaw conurbation is the largest metropolitan area EN14907). The PM1 was collected using quartz fibre filters in Poland with 1,735,442 inhabitants on a surface of 517 km2. (Quartz Air Sampling Filter, Grade QMA, 47 mm circle; Data on PM pollution over Warsaw conurbation comes GE Healthcare Life Sciences Corp.; Piscataway, NJ, USA). directly from measurements carried out at six air quality The mass of PM1 was determined by weighing the filters monitoring stations, being part of the national network. before and after exposure; MYA 5.3Y.F micro balance Concentrations of fine particles (PM2.5) are analysed at (RADWAG; Radom, Poland) was used (1-µg resolution). only four of these stations. As a matter of fact, there is no Before each weighing, the filters were conditioned for 48-h data on chemical composition of PM in that part of the in the weighing room (relative air humidity 45 ± 5%, air country. At the same time, in order to establish the source temperature 20 ± 2°C). of PM and implement measures to control PM concentrations The adopted method of PM sampling and the subsequent in urban areas, comprehensive research has been conducted gravimetric analysis of PM were consistent with EN around the world already for years, regarding chemical 14907/2005 - Standard gravimetric measurement method composition of PM (Chow et al., 2015). for the determination of the PM2.5 mass fraction of suspended Submicron particulate matter (PM1) is a significant particulate matter and EN 12341/1998: Air quality element of PM-related research due to typically anthropogenic Determination of the PM10 fraction of suspended particulate origin and prevalent mass fraction in PM, as well as matter reference method and field test procedure to insufficiently studied chemical composition (in Polish and demonstrate reference equivalence of measurement methods. other European conurbations). Correlation between exposure The weighing accuracy, determined as three standard to submicron particles and health effects, as well as their deviations of the mean from ten weightings of a blank filter influence on other environmental components, and even (conditioning performed every 48-h), was equal to 16 µg. climate change, have been proven and are widely discussed When the concentration of PM1 was established, a 1 × 1 in literature (Massolo et al., 2002; Pope and Dockery, 2006; cm square was cut out of each diurnal sample in which the Karlsson et al., 2009; Paasonen et al., 2013; Atkinson et organic (OC) and elemental carbon (EC) content were al., 2015; Badyda et al., 2016; Majewski et al., 2018). determined. The remaining portion of each sample was In this paper, concentrations and chemical composition then used to determine the content of ions from water- of PM1 were analysed on the basis of 120-day-long soluble compounds. measurement campaign conducted at one measurement site within the Warsaw conurbation. We consider it to be the first Chemical Analysis long-term study where day-by-day differences in chemical The OC and EC contents of PM1 were determined using composition of PM1 are shown in typical urban area in a Lab OC-EC Aerosol Analyzer (Sunet Laboratories Inc.; Central-Eastern Europe. Correlations between PM1 and its Portland, OR, USA) and the EUSAAR protocol. The main components with meteorological parameters were performance was controlled by systematic calibration of examined and the sources of PM1 in different measurement the analyser within the range proper for the determined periods were identified. The PM1 mass, which came either concentrations, and by analysing standards with certified directly from emissions (primary PM) or from the carbon content (RM 8785 and RM 8786, NIST, Gaithersburg, transformations of PM gaseous precursors (secondary PM), MD, USA) and the blank samples.

638 Majewski et al., Aerosol and Air Quality Research, 18: 636–654, 2018

Fig. 1. Location of the sampling point in Warsaw (Poland).

The detection limit for total carbon (TC), computed after University of Life Sciences (λE = 21°02’; φN = 52°09’) analysing the 10 blanks, was 0.91 µgC cm–2 (0.79 and according to instructions for the network of state weather 0.12 µgC cm–2 for OC and EC, respectively). The standard stations operated by the Institute of Meteorology and recovery was from 98% to 122% of the certified value for Water Management (IMWM). Following meteorological OC and from 95% to 116% for EC (the certified values parameters were used: air temperature, solar radiation, were taken from the IMPROVE program). relative humidity of air, atmospheric pressure, precipitation Water extracts of PM1 were made by ultrasonizing the totals, speed and direction of wind. Hourly results of the 3 filters containing PM1 in 25 cm of de-ionized water for 60 measurements were averaged for 24-h intervals from 12:00 min at the temperature of 15°C and then shaking the a.m. to 12:00 a.m.; diurnal samples of PM1 were taken at extracts for about 12 h (18°C, 60 r min–1). The ion content the same time. of the extracts was determined by the ion chromatograph Data regarding air pollution came from the (Metrohm AG; Herisau, Switzerland). The method was MzWarszUrsynów station (λE = 21° 02’; φN = 52° 09’) validated against the CRM Fluka product nos. 89316 and operated by the Provincial Inspectorate of Environmental 89886; and standard recoveries were from 92% (Na+) to Protection in Warsaw. 109% (Cl–) of the certified values, and detection limits Hourly data was used herein (averaged for 24-h intervals –3 + –3 – were as follows: 10 ng cm for NH4 , 18ng cm for Cl from 12:00 to 12:00) of the following air pollutants, 2– –3 – + and SO4 , and 27 ng cm for NO3 and Na . measured by means of appropriate analysers: sulphur dioxide (SO2) – MLU 100A, carbon monoxide, ozone (O3) Meteorological Parameters and Air Pollution Data – MLU 400, nitrogen oxides (NO2) – MLU 200A, and In the course of observations, values of basic PM10 – TEOM1400a. They were monitored with pulsed meteorological parameters were measured at Ursynów fluorescence, ultraviolet light absorption, chemiluminescence, Meteorological Station (Ursynów-SGGW) of the Warsaw and a β -gauge automated particle sampler, respectively.

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The methodology to evaluate the fraction of primary and [OC]/[EC])pri = ([OC]/[EC])min for the estimation of SOM secondary matter in the mass of PM1 was based on and POM. Indeed, the achieved values of ([OC]/[EC])min following assumptions: for Warsaw were very low, close to 1. Minimum values of – Primary matter comprises: elemental carbon (EC), OC/EC for both seasons appeared on those days, when the primary organic matter (POM) and a sum of sodium conditions were unfavorable for SOM formation (e.g., in and chloride (Na_Cl), comparison to consecutive days): low solar radiation, high – Secondary matter comprises: secondary inorganic wind speed and low ozone concentration. The prevalence of matter (SIM) and secondary organic matter (SOM). traffic emission, that justifies the ([OC]/[EC])min assumption, The mass of EC was assumed to be the analytically is also supported by the fact of high OC/EC values determined mass [EC] A of elemental carbon: occurring more rarely than lower ones in both seasons over the research area. This also results in a low average value [EC] = [EC]A (1) of 24-h OC/EC ratios for both seasons (about 3) and the standard deviation reaching 1. In other regions of Poland, PM-bound organic compounds can be divided into where the emission from biomass and coal combustion secondary organic matter (SOM) and primary organic matter dominates, ([OC]/[EC])min is usually not lower than 3 (POM) (Turpin and Lim, 2001). Their masses, [SOM] and (Rogula-Kozłowska et al., 2014; Błaszczak et al., 2016), [POM], in the sampled PM are computed from the mass and for this reason the estimation of organic matter [OC]A of the analytically determined OC. At first, by fractioning into SOM and POM for Warsaw air seems to be utilizing the known [OC]A, the mass of secondary [SOC] more accurate with the use of OC/EC ratio than for the and primary organic carbon [POC] was calculated. The above mentioned regions. For the ratios of ([OC]/[EC])min division of OC into secondary and primary was based on higher than 2 it would be difficult to prove, that their values the following assumptions: were influenced either by organic matter from fossil fuels and biomass combustion (reaching OC/EC ratios considerably [POC] = [EC]A·([OC]/[EC])pri (2) higher than 1) or by photochemical reactions of SOM gaseous precursors (Schauer, 2003; Boogaard et al., 2011). [SOC] = [OC]A – [POC] (3) Conversion factor for the [POM] and for the [SOM] is taken from (Turpin and Lim, 2001) — Eqs. (4) and (5). where ([OC]/[EC])pri is the ratio of primary OC to EC, assumed to be relatively constant for a specific location, [POM] = 1.4 [POC] (4) season and local meteorology (Castro et al., 1999; Wu and Yu, 2016). It was assumed that ([OC]/[EC])pri = [SOM] = x [SOC] (5) ([OC]/[EC])min; ([OC]/[EC])min is the minimum among all 24-h proportions of [OC]A/[EC]A. where: x = 1.4 for winter season and x = 1.8 for summer Minimum value of 24-hour OC/EC ratios for Warsaw, season. out of 60 achieved values, was equal to 1.3 for the summer As a ratio for POM estimation, based on POC, the value season and 0.6 for winter one. Maximum values for both of 1.4 was adopted for the whole observation period. seasons amounted to about 6. However, the average ones Because the ratio of 1.4 can describe primary and less for 24-h OC/EC ratios for Warsaw reached about 3 for both oxidized organic mass, typically observed in winter, the seasons. Previous research of the authors revealed that the same conversion factor (1.4) was assumed to estimate traffic emission in Warsaw significantly contributed to fine SOM (basing on SOC) for the winter period. Higher values PM concentration (Majewski and Rogula-Kozłowska, 2016). of the conversion factor are generally reported for summer However, in the winter period, both the emission from season (up to 1.8 (Aiken et al., 2008)). Therefore, we assumed energy sector and pollutants' inflow from highly contaminated the summer conversion factor for SOM (based on SOC) to region of southern Poland (Rogula-Kozłowska et al., 2013, be equal to 1.8. The above methodological approach could 2014; Widziewicz et al., 2017a) may cause the increase of guarantee to achieve in Warsaw similar results to those carbonaceous matter concentration in the air, including observed for other European countries (Crippa et al., 2014). volatile organic compounds - the precursors of SOM Except for that, it would enable to minimize the (Błaszczak et al., 2016). The OC/EC ratio close to 1 is underestimation of SOM in PM1 mass, resulting from typical for traffic emission as proved by the measurements overestimation of POM in summer mass of PM1 (the either in a traffic tunnel or in urbanized areas influenced problem of a proper ratio of POC/EC). However it should mainly by traffic emission, showing also lower values for be stressed, that in Poland there exists different structure of fumes from Diesel engines than for gasoline ones (Hildemann PM sources and precursors than in other European areas. et al., 1991; Lee et al., 2006; Zhu et al., 2010; Ancelet et The main energy source in Poland is hard and brown coal al., 2011; Khan et al., 2011). For the exhaust gases coming combustion. The emission from energy production is thought from coal and biomass combustion, that ratio is several to be the prevailing one and can veil the influence of other times higher (Watson et al., 1994; Cao et al., 2005; Lonati emission sources of PM, depending on the country region. et al., 2007; Keywood et al., 2011). Assuming that traffic- Besides, there has been no knowledge available to authors related emission is the main source of PM1-bound OC and so far, neither on seasonal changes of SOM main precursors EC in the research area, it seems reasonable to use concentration, nor qualitative and quantitative SOM

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2– + + composition in Warsaw. That is why the above assumptions, SO4 , Na , NH4 . Subsequently, the values of new variables basing only on available literature data, had to be made for – principal components (only principal components with both ([OC]/[EC])pri and the detailed composition of OM, eigenvalues > 1.0 were considered according to Kaiser and finally yield a conversion factor proper for a certain criterion) were used in MLRA. All the computations were location, aerosol origin and research period. performed using Statistica 12.0. 2- - + SIA consisted of SO4 , NO3 , and NH4 and RESULTS AND DISCUSSION 2– – + [SIA] = [SO4 ]A + [NO3 ]A + [NH4 ]A (6) Relationship between 24-h Concentrations of PM1 and [Na_Cl] = [Na+]A + [Cl–]A (7) 24-h Concentrations of Other Pollutants and Meteorological Factors Further analysis of the data utilized the methodology Twenty-four-hour (24-h) concentrations of PM1 show a described by Thurston and Spengler (Thurston and Spengler, typical pattern of the study area: they are lower in warm 1985), which combines a factor analysis (FA) to identify months (July–August) and higher in the cold period, the possible sources and a multi-linear regression analysis January–March (averaging 11.1 ± 3.3 µg m–3 and 17.4 ± –3 (MLRA) to quantify their contribution to PM concentrations. 8.4 µg m , respectively; Table 1). During summer the PM1 The first step in this stage was the principal component mass is a major contributor to PM10, averaging about 60% analysis (PCA) to the 8 × 120 data matrix of the PM1- of PM10 mass. On the contrary, the coarse fraction prevails components concentrations representing the 24-h in winter when the PM1 contribution to PM10 tends to be – – concentrations of PM1-bound EC, SOM, POM, Cl , NO3 , lower (40% on average; Table 1).

Table 1. Basic statistics of the series of 24-h concentrations of PM1, PM10, gaseous pollutants and meteorological conditions for each season and the whole sampling period. All Wintera Summerb Mean ± S.D Mean ± S.D. Mean ± S.D. Valid N Valid N Valid N (Min–Max) (Min–Max) (Min–Max) –3 PM1 µg m 120 14.2 ± 7.1 60 17.4 ± 8.4 60 11.1 ± 3.3 (4.7–39.0) (4.7–39.0) (5.7–22.6) –3 PM10 µg m 120 30.7 ± 16.8 60 41.6 ± 17.3 60 19.9 ± 5.7 (10.9–78.4) (11.7–78.4) (10.9–39.9) –3 NO2 µg m 120 23.6 ± 10.6 60 28.7 ± 10.9 60 18.5 ± 7.4 (5.9–54.3) (7.4–54.3) (5.9–45.0) –3 NOx µg m 120 28.8 ± 15.9 60 35.1 ± 17.1 60 22.5 ± 11.8 (6.8–93.1) (8.4–93.1) (6.8–86.5) –3 O3 µg m 120 44.7 ± 23.2 60 27.2 ± 13.7 60 62.2 ± 16.5 (6.9–98.4) (6.9–59.5) (24.2–98.4) –3 SO2 µg m 120 5.6 ± 2.7 60 7.1 ± 2.7 60 4.0 ± 1.6 (1.3–13.6) (2.1–13.6) (1.3–8.4) Temperature °C 120 11.2 ± 9.7 60 2.0 ± 2.8 60 20.3 ± 3.4 (–4.9–25.7) (–4.9–9.9) (13.6–25.7) Humidity % 120 71.1 ± 12.7 60 77.4 ± 12.1 60 64.9 ± 10.0 (38.6–95.0) (38.6–95.0) (48.0–85.1) Precc mm 120 234.8 60 62.2 60 172.6 (0.0–26.6) (0.0–9.2) (0.0–26.6) Wind speed m s–1 120 2.8 ± 1.2 60 3.3 ± 1.4 60 2.2 ± 0.6 (1.2–7.4) (1.4–7.4) (1.2–4.0) Solar radiation W m–2 120 109.1 ± 88.7 60 33.1 ± 21.8 60 185.1 ± 60.5 (2.0–301.5) (5.3–102.7) (2.0–301.5) Pressure hPa 120 1001.9 ±8.9 60 1002.4 ±12.2 60 1001.4 ±3.5 (966.6–1025.2) (966.6–1025.2) (994.6–1009.3) Notes: a Winter: 8 January 2015–8 March 2015. b Summer: 24 June 2014–22 August 2014. c Precipitation presented as the total precipitation during the reporting period. S.D. = standard deviation. Valid N = daily data. Min. - minimum value. Max. - maximum value.

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Mean annual and seasonal (winter and summer) higher air temperatures and a greater solar radiation in concentrations of PM1 in Warsaw were definitely lower than summer, which increases the rate of ozone formation in the those observed over cities in the south of Poland (Katowice, atmospheric boundary layer (Rozbicka et al., 2014). Racibórz, Wrocław) but higher than observed in the north Evident seasonal variability of pollutant concentrations of the country (Gdynia) (Table 2). The concentrations of is mainly correlated with the annual course of air temperature. PM1 in Warsaw did not differ much from the values given Mean air temperature in Warsaw for a long-term period of for other European cities and thus were considerably lower 1960–2016 was 8.5°C with the lowest mean observed in than the values obtained in Asia (Table 2). During Warsaw January (–2.3°C) and the highest in July (19.1°C) (archive measurement campaign, a similar seasonal variation to that data of the Division of Meteorology and Climatology of of the 24-h concentrations of PM1 was also observed for the Warsaw University of Life Sciences). 2015 was one of gaseous pollutants subject to continuous monitoring: SO2, the warmest in years 1960–2015 when mean annual air NO2, NOx and O3. An evident increase in the 24-h temperature reached 10.6°C, the mean air temperature in concentrations of SO2, NO2 and NOx was observed in summer (Jul–Aug) was 20.3°C, and 2.0°C in winter (Jun– winter months (Jan–Mar) as compared with summer months Mar). (Jul–Aug). In the case of O3 the trend was the opposite – in Air temperature affects the concentration of airborne warm months the concentrations of O3 were approx. 44% pollutants in two ways: by participating in the development higher on average than in cold months. This results from of conditions favouring their dispersion (which is connected

Table 2. Comparison of mean concentrations of PM1 in Warsaw with PM1 concentrations in previous studies available in –3 the literature for similar sampling sites. PM1 in µg m .

Reference PM1 Location/Site (characterization) Sampling year This work 14.2 ± 7.1 Poland, Warsaw (Urban) 2014–2015 17.4 ± 8.4 2014–2015 winter 11.1 ± 3.3 2014–2015 summer Witkowska and Lewandowska, 2.6± 1.3 Poland, Gdynia (Urban) 2013–2014 2016 3.5 ± 1.7 2013, winter 1.3 ± 0.3 2013, summer Widziewicz et al., 2016 49.2 ± 20.5 Poland, Katowice (Urban traffic) 2010, winter 19.7 ± 4.6 Poland, Katowice (Urban traffic) 2010, summer Squizzato et al., 2016 21 ± 22 Italy, Venice-Mestre (Urban background) 2013–2014 34.0 ± 24.0 Italy, Venice-Mestre (Urban background) 2013–2014 winter 6.4 ± 2.0 Italy, Venice-Mestre (Urban background) 2014, summer Qiao et al., 2015 49.8 ± 31.8 China, Shanghai (Urban) 2013–2014 Rogula-Kozłowska et al., 2014 26.1 ± 13.21 Poland, Racibórz (Urban) 2011 Zwoździak et al., 2013 25.5 ± 7.8 Poland, Wrocław (Urban) 2009–2010, winter 8.9 ± 3.4 Poland, Wrocław (Urban) 2009–2010 summer Lyu et al., 2015 81.7 China, Wuhan (Suburban) 2012–2013 Tao et al., 2012 52.4 ± 27.3 China, Guangzhou (Urban) 2009–2010 29.9 ± 13.3 2009, summer 63.4 ± 30.2 2010, winter Křůmal and Mikuška, 2013 19.9 Czech Republic , Brno (Urban) 2009, winter 13.2 2009, summer 33.4 2010, winter 12.9 2010, summer Peteraki et al., 2013 19.3 ± 6.51 Grece, Votanikos (Urban industrial)) 2008 14.2 ± 5.06 Grece, Tourlos (Rural background) Harrrison and Yin, 2008 9.2 England, Birmingham (Urban background) 2005, summer 12.6 2006 winter Pateraki et al., 2008 16.8 Grece, Agia Paraskevi (Suburban background) 2005 13.9 Grece, Marousi (Urban traffic) Theodosi et al., 2011 18.6 Grece, Lykovrisi (Suburban) 2005–2006 20.2 Grece, Goudi (Urban) 13.9 Grece, Marousi (Urban traffic) Spindler et al., 2010 13.0 Germany, Melpitz (Rural background) 2004 13.0 2005 12.0 2006 Pey et al., 2010 12.0 Spain, (Regional background) 2003, 2004, 2005 8.0 Spain, (Urban background)

642 Majewski et al., Aerosol and Air Quality Research, 18: 636–654, 2018 with the height of the mixing layer) (Schäfer et al., 2006), and by influencing the scale of household heating practices (Rogula-Kozłowska et al., 2014). The high mean temperature in both measurement seasons resulted in rather low mean concentrations of PM1 over Warsaw in 2015, which were similar to those observed in previous years over cities of Northern and Western Europe, rather than to the values measured in Southern Polish cities (Table 2). This conclusion applies to the winter season, when mean concentration of PM1 observed over Warsaw in 2015 was a few times lower than the values determined for Zabrze, Katowice or Wrocław in the winter of that year. Besides air temperature, precipitation also influences the concentration of pollutants in the air, especially of particulate matter (Olszowski, 2016; Widziewicz et al., 2016; Olszowski, 2017). During the above - mentioned period of measurements, which lasted 120 days altogether, on 60 days precipitation Fig. 2. Distribution of the mean concentrations (µg m–3) of was greater than 0.1 mm. A clear decrease in pollutant PM1 with regard to eight wind directions. concentrations was observed when daily precipitation exceeded 5 mm: the 24-h concentrations of PM1 and PM10 were on average 30% and 40% lower, respectively, than on of PM1, PM10, SO2, NO2 and NOx were generally closely dry days. The washing effect of precipitation in the period correlated with a majority of meteorological parameters of measurements was also observed for NO2, NOx and SO2 (the correlation was statistically significant), with Pearson – their mean concentrations on days with precipitation correlation coefficients ranging from 0.35 to 0.74 (Table 3). were respectively 24%, 30% and 30% lower than on days The 24-h concentrations of the pollutants were also without precipitation. In the case of ozone, its concentration significantly correlated with one another, so that the greatest on such days was 23% higher on average. correlation was noted between PM1 and PM10 (r = 0.74), In the assumed 120 day period of observations, the 24-h followed by PM10 and SO2 (r = 0.65) (Table 3). concentrations of PM1, PM10, NOx and NO2 decreased by An analysis of the correlation between ambient particles 43%, 25%, 26% and 24%, respectively when a mean daily and temperature revealed different seasonal patterns for PM10 –1 wind speed exceeded 5 m s . However no significant and PM1. A positive correlation with temperature of higher difference was observed in the daily concentrations of coefficients appeared in summer for both PM10 and PM1, other pollutants when the wind speed increased. At the which is associated with stronger convection and unstable time, the prevailing wind direction in Warsaw was SW atmospheric conditions. In summer, when temperatures and (28.6%) and E (21%), followed by S and (13.4% in each solar radiation are high, and when ozone concentration case). The respective fractions of other wind directions: increases, reactions of gaseous precursors of PM occur in NE, SE, W and NW were 9.2%, 7.6%, 5.9% and 0.8%. the atmosphere more intensively (Seinfeld and Pandis, The highest concentrations of PM1 in Warsaw coincided 2012). A negative correlation between PM1 and PM10 in with the following wind directions: S, SE, N and NE in winter suggests an inverse relation with the temperature winter, and NE, E and S in summer (Fig. 2). To the SE and and seems to support the hypothesis of a reduced dispersion S of the measurement site there was a dense network of and stable atmospheric conditions, however the correlations streets with a rather heavy road traffic. The minimum distance are small (not statistically significant). However, the influence of such streets in those directions from the measurement site of temperature on PM concentrations is rather complicated. was about 500 m (SE) and 1000 m (S). In those directions, On one hand, vertical mixing, which causes dilution of there were also small local house boilers and individual particles in the boundary layer, is affected by the change in household furnaces and further to the south there was a temperature and wind with height. Generally, vertical mixing polluted region of Silesia. In the east and south west, there becomes stronger in hot seasons as the temperature is also were mainly residential buildings with individual furnaces higher. On the other hand, temperature impacts the chemical and oil and gas boiler plants. The very centre of Warsaw, formation of particles. Higher temperature accelerates the along with its developed traffic system, was north and reactions to form more products that can partition into north-east of the measurement site. Moreover, in the NE particulate phase, but meanwhile the products tend to remain sector the largest Polish (and Europe’s second in size) heat in their gaseous phase when temperature is too high (Hu et and power generating plant, Siekierki, was situated. al., 2008; Miao et al., 2015). A statistical analysis of the relationship between The concentrations of particulate matter correlated meteorological parameters and the 24-h concentrations of negatively with relative humidity, irrespective of the PM1, PM10, SO2, NO2 and NOx (for the whole period of season of the year, but with a relatively greater coefficients measurements) confirmed the significant influence of air in winter. Pollutants may be scavenged by fog or cloud temperature and precipitation on the patterns of occurrence of droplets and deposited onto surfaces, leading to lower those concentrations over Warsaw. The 24-h concentrations ambient concentrations (Olszowski, 2016, 2017). However, a

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Table 3. Pearson correlation coefficients between 24-h concentrations of PM1 and PM10, gaseous pollutant and 24-h averaged meteorological factors in Warsaw.

PM1 PM10 NO2 NOx O3 SO2 Temp Hum. Prec. Wind.S. Rad. Press. PM1 All 1 0.74 0.46 0.38 –0.35 0.40 –0.38 0.15 –0.24 –0.26 –0.27 0.10 Summer 1 0.94 –0.01 –0.01 0.55 0.11 0.67 –0.35 –0.18 –0.22 0.30 0.04 Winter 1 0.62 0.41 0.32 –0.29 0.23 –0.03 –0.02 –0.37 –0.64 0.45 0.09 PM10 All 0.74 1 0.71 0.63 –0.60 0.65 –0.57 0.23 –0.26 –0.03 –0.47 0.09 Summer 0.94 1 0.05 0.05 0.51 0.13 0.68 –0.34 –0.19 –0.23 0.31 0.11 Winter 0.62 1 0.75 0.67 –0.56 0.52 –0.01 –0.10 –0.38 –0.51 0.44 0.07 NO2 All 0.46 0.71 1 0.92 –0.66 0.48 –0.50 0.20 –0.19 –0.08 –0.38 0.22 Summer –0.01 0.05 1 0.94 –0.42 –0.06 –0.24 –0.01 –0.03 –0.41 –0.03 0.56 Winter 0.41 0.75 1 0.90 –0.62 0.43 –0.04 –0.08 –0.36 –0.38 0.32 0.17 NOx All 0.38 0.63 0.92 1 –0.65 0.39 –0.43 0.23 –0.18 –0.10 –0.33 0.20 Summer –0.01 0.05 0.94 1 –0.46 –0.01 –0.30 –0.05 –0.08 –0.33 –0.04 0.44 Winter 0.32 0.67 0.90 1 –0.71 0.31 –0.12 0.10 –0.26 –0.34 0.13 0.15 O3 All –0.35 –0.60 –0.66 –0.65 1 –0.44 0.84 –0.62 0.12 –0.15 0.76 –0.10 Summer 0.55 0.51 –0.42 –0.46 1 0.15 0.77 –0.50 –0.15 0.06 0.41 –0.01 Winter –0.29 –0.56 –0.62 –0.71 1 –0.14 0.27 –0.39 0.24 0.52 0.13 –0.14 SO2 All 0.40 0.65 0.48 0.39 –0.44 1 –0.51 0.10 –0.20 0.20 –0.43 –0.22 Summer 0.11 0.13 –0.06 –0.01 0.15 1 0.28 –0.19 –0.15 –0.12 0.12 –0.06 Winter 0.23 0.52 0.43 0.31 –0.14 1 –0.01 –0.28 –0.10 –0.06 0.23 –0.36 In red: statistically significant at the 0.05 level. far more effective process of pollutants’ removal, contrary period, the wind speed was also negatively correlated with to precipitation, is condensation and cloud formation PM concentration. Neither did the solar radiation. through forced and ascent airflow in a frontal zone In addition, the influx of fresh and clean air can be observed Analysis of Meteorological Conditions on Days with High in such circumstances. Therefore, the concentration of PM and Low 24-h Concentrations of PM1 in Warsaw - Air does not change even if it rains, since there is no advection Mass Trajectories of cleaner air. For this reason, the observed decline in Days of the Highest Concentrations of PM1 in Winter and concentration cannot be attributed solely to precipitation, Summer considering also the fact that PM’s are pushed away by the For two days: one in February 2015 (winter season) and rush of air before falling droplets. one in August 2014 (summer season) 48-hour back Lastly, PM and wind speed are also negatively correlated, trajectories were computed using the HYSPLIT_4 model in both seasons. Higher correlation coefficients were (Hybrid Single Particle Lagrangian Integrated Trajectory) observed in winter (Table 3). (Draxler and Rolph, 2012) (Fig. 3). They began at the The search for direct relationship between PM sampling points, 50 m, 200 m and 500 m above the ground concentration and meteorological parameters has a complex level, at 00:00 UTC. The two days were selected because character as suggested by the above mentioned results. For of their highest 24-h concentrations of PM1 in Warsaw this reason, multivariate regression models were built at which amounted to 39.0 µg m–3 in winter (5 February) and the next stage of research. Such models, in which all 22.6 µg m–3 (3 August). On 5 February 2015, Poland was predictors are simultaneously applied, show much better being released from the influence of the Atlantic high agreement. Table 4 presents optimal empirical models for pressure wedge: the maximum pressure was 1032 hPa, the both the winter and the summer period as well as for the average air temperature was –0.4°C and the wind speed did whole research period. not exceed 1.5 m s–1. The mixing layer height (MLH) ranged The model obtained for the whole research period proves, from 30 m to 350 m and a slight temperature inversion was that air temperature and wind speed had the greatest effect on noticeable [http://ready.arl.noaa.gov/HYSPLIT_traj.php]. th PM1 concentration. It was also found that PM1 concentration Back trajectories, generated for 5 February, indicated a was negatively correlated with both air temperature and period of dynamic weather conditions, with the flow of wind speed. The multivariate regression coefficient for that clean air from the west or north, the high pressure system model was equal to 0.601 indicating a moderate correlation. is gradually extended. The wind is weakening and in the However, the model built for the summer period showed evening when low inversion is formed, impurities slowly the PM1 concentration to be positively correlated with air undergo dispersion and the smog in a regional scale is temperature only, while the multivariate R reached 0.675. created. The centre of the high pressure system is moving Considerably higher value of that coefficient was found for over Atlantic. Above the inversion level, the warmer air the winter period (0.748) which incorporated two parameters: from the sector SW–W is flowing, which further strengthens the wind speed and radiation as exerting the highest the inversion. The inversion thickness is of major importance influence on PM concentrations. Like for the whole analysed for the formation of PM concentrations, lower wind speed

644 Majewski et al., Aerosol and Air Quality Research, 18: 636–654, 2018

Table 4. Regression result for the optimal empirical model of PM1 in Warsaw. Estimated Estimated Regression regression standard t* p value cofficient cofficient deviation All R = 0.601 PM1 = –0.441Temp – 3.089Wind.S. + 27.690 β1 [Temp] –0.441 0.060 –7.329 < 0.001 β2 [Wind.S.] –3.089 0.486 –6.350 < 0.001 β3 27.690 1.819 15.226 < 0.001

Summer R = 0.675 PM1 = 0.657Temp – 2.270 β1 [Temp] 0.657 0.094 6.960 < 0.001 β2 –2.270 1.942 –1.169 0.247

Winter R = 0.748 PM1 = –3.681WindS. + 0.152Prom + β1 [Wind.S.] –3.681 0.538 –6.838 < 0.001 24.508 β2 [Rad] 0.152 0.034 4.443 < 0.001 β3 24.508 2.307 10.623 < 0.001 *- for a level of significance of 0.05, tcritical (0.975; 120) = 1.980.

Fig. 3. Forty-eight-hour back-trajectories calculated using the HYSPLIT4 model for Warsaw at an altitude of 50 (green, circles), 200 (blue, squares), and 500 (red, triangles) m a.g.l., ending at 00 UTC on 3 August 2014 and 5 February 2015. and the precipitation are rather marginal. This is classic from the territory of SE Romania and Ukraine, that smog situation with the influx of contaminated air from contributes to the occurrence of high temperatures (25.6°C), Upper Silesia and Malopolska. These impurities can reach a small wind speed and lack of cloudiness. At the same Warsaw within twenty hours. time, an inversion mixing layer of a considerable thickness Similar synoptic situation influences high concentrations is developed (up to 3000) during the day, but rapidly in the summer period on 3th August 2014. The high pressure decreasing in the afternoon hours, reaching only 130 m. at system from NW Russia is gradually extended over the night. The above synoptic situations contribute to high analysed area and the Eastern Poland, which form an pollutants’ concentration especially at night, which is the obstacle for a formerly existing and weakening low pressure result of weak dispersion and finally pollutants cumulation system over the Western Poland. A dry and hot air inflows in the ground layer of the air.

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Days of the Lowest 24-h Concentrations of PM1 in Winter matter in the PM1 mass can be observed in winter (heating and Summer season), when average ambient concentrations of SIM and –3 –3 The days on which 24-h concentrations of PM1 were the SOM were 6.4 µg m and 5.6 µg m , respectively (Table 5), lowest were 12 January 2015 (winter) and 26 June 2014 thus making up 36.8% and 32.3% of the total mass of PM1. (summer) (Fig. 4). On 12th January, Poland was under the During the summer period, SIM and SOM constituted influence of a deep, low –pressure mass as a result of a 27.8% and 23.2% of PM1 mass respectively, which is 10% dynamically developing western circulation from the lower in comparison to winter. There are few reasons for Atlantic. This was related with high pressure gradients, this situation, one of which is a probable underestimation stronger wind and precipitation, mainly rainfall. Through of SOM mass for the summer season or its overestimation the inflow of a mild and humid air as well as increasing for winter, resulting from the assumptions made for SOC mixing layer height up to 3000 m. during the day, followed and SOM estimation (Eqs. (2)–(5)). It should be stressed by only slight inversion at night, very good dispersion here, that the conversion factor should be considerably conditions were created, resulting in a low pollutants’ higher for very warm seasons in comparison to the other concentration, amounting to 4.7 µg m–3 in the case of 24-h ones (e.g., winter), even exceeding the value of 1.8, owing PM1 concentration. to photochemical reactions (Turpin and Lim, 2001; Aiken On 26th June 2014 there was a stagnant character of two et al., 2008). In winter period, however, SOM formation pressure systems: the high pressure system from the becomes more difficult because of meteorological conditions, Norwegian Sea that blocked the zonal, western circulation and the resultant conversion factor might be lower than 1.4 and weakly developing low pressure system over Russia. It (Turpin and Lim, 2001; Chou et al., 2010).On the other caused the inflow of a clean and cold air from the territory hand, the estimation of SIM assumed that the whole mass 2– – + of Finland and the Norhtern Baltic Sea. The presence of of SO4 , NO3 and NH4 originated from secondary – 2– inversion during the day, and the increase in the mixing inorganic compounds (mainly NH4NO3 and (NH4)2SO4 ). layer height to 2000 m. in the afternoon, contributed to good Such an assumption would be valid for one season only, conditions for pollutants’ dispersion which was manifested neither for a second one (Hu et al., 2016; Li et al., 2016). through low PM concentration, reaching (7.0 µg m–3) on However, the same nature of relationship, meaning higher that day. concentrations in the air and higher fractions in PM1 mass at the same time, referred to both: SOM and SIM. Chemical Composition and Origin of PM1 Moreover, the previous research realized for other regions On average, 62.2% of the mass of PM1 in Warsaw of Poland made evident, that the organic matter fraction in comprises secondary matter: inorganic (SIM) and organic the mass of fine PM and/or ambient concentration of PM- (SOM) (Fig. 5). Particularly high contribution of secondary bound SOC in winter could be higher than in summer

Fig. 4. Forty-eight-hour back-trajectories calculated using the HYSPLIT4 model for Warsaw at an altitude of 50 (green, circles), 200 (blue, squares), and 500 (red, triangles) m a.g.l., ending at 00 UTC on 12 January 2015 and 26 June 2014.

646 Majewski et al., Aerosol and Air Quality Research, 18: 636–654, 2018

35

30

25 3 ‐ 20 m

µg 15

10

5

0 20 07 17 04 14 01 11 26 08 22 05 19 02 16 30 13 27 10 24 07 21 04 18 01 15 29 12 26 09 23 06 20 03 17 30 14 27 11 24 08 ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ 08 03 08 03 08 03 08 02 08 02 08 02 08 02 07 02 07 02 07 02 07 02 07 02 07 01 07 01 07 01 07 01 07 01 06 01 06 01 06 01 ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ 2015 2014 2015 2014 2015 2014 2015 2014 2015 2014 2015 2014 2015 2014 2015 2014 2015 2014 2015 2014 2015 2014 2015 2014 2015 2014 2015 2014 2015 2014 2015 2014 2015 2014 2015 2014 2015 2014 2015 2014 EC POM Na_Cl SIM SOM Other Fig. 5. 120-day-long course of the concentrations (µg m–3) of the main primary components (EC – elemental carbon, POM – primary organic matter, Na_Cl – sodium and chlorine concentration total), secondary components (SOM – secondary organic matter and SIM – secondary inorganic matter) and unidentified matter (Other) in Warsaw.

Table 5. Basic statistics of 24-h concentrations (µg m–3) of the main primary components (EC – elemental carbon, POM – primary organic matter, Na_Cl – sodium and chlorine concentration total), secondary components (SOM – secondary organic matter and SIM – secondary inorganic matter) and unidentified matter (Other) in Warsaw; three averaging periods. Period N Mean Minimum Maximum St. deviation EC All 120 1.15 0.16 3.15 0.55 *Summer 60 0.88 0.16 2.37 0.33 *Winter 60 1.42 0,43 3.15 0.59 POM All 120 1.24 0.25 3.75 0.51 *Summer 60 1.39 0.25 3.75 0.52 *Winter 60 1.10 0.33 2.44 0.46 Na_Cl All 120 0.83 0.12 2.39 0.55 *Summer 60 0.40 0.12 2.39 0.28 *Winter 60 1.26 0.62 2.01 0.39 SOM All 120 4.08 0.00 16.32 3.36 *Summer 60 2.57 0.00 6.35 1.36 *Winter 60 5.58 0.00 16.32 4.04 SIM All 120 4.75 1.38 14.47 3.02 *Summer 60 3.08 1.38 11.31 1.42 *Winter 60 6.43 1.56 14.48 3.27 Other All 120 2.19 0.01 7.40 1.56 *Summer 60 2.75 0.01 7.40 1.44 *Winter 60 1.63 0.19 6.91 1.47 1Σprimary All 120 3.22 1.46 7.33 1.22 matter Summer 60 2.66 1.51 6.51 0.81 Winter 60 3.77 1.46 7.33 1.31 2Σsecondary All 120 8.83 1.56 30.77 6.10 matter Summer 60 5.66 2.32 12.54 2.25 Winter 60 12.01 1.56 30.77 7.04 1 Σprimary matter = [EC] + [POM] + [Na_Cl]. 2 Σsecondary matter = [SOM] + [SIM]. * The mean values are statistically different in both seasons, p < 0.05 (U Mann-Whitney test at passump = 0.05).

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(Błaszczak et al., 2016; Witkowska et al., 2016; Pyta et al., hypothesis of the equality of 24-h concentrations of EC, 2017). Then it is possible to claim the achieved results to SOM, POM, SIM and Na_Cl; using the U Mann-Whitney be valid, and attributable to the study site, despite being test. The hypothesis was rejected (p < 0.05 at passump = different from those reported for other parts of the world. 0.05), and thus statistically significant differences between More intensive energy production in Poland during winter 24-h concentrations of PM1 components in summer and is almost all based on hard and brown coal combustion, winter were confirmed (Table 5). Their presence is connected and becomes a major source of winter SOM and SIM with different origins of PM1 in both seasons. Therefore, gaseous precursors. Even if their changes could be less the identification of PM1 sources using principal components intensive in winter than in summer, it is likely the difference analysis (PCA) (Majewski and Rogula-Kozłowska, 2016) in the emission between those seasons, that caused average was carried out for both seasons separately. In either case secondary matter fraction in fine PM over Warsaw to be (summer and winter), the PCA was performed for eight considerably higher in winter . variables (PM1 components) and 60 cases (60 daily The fraction of primary matter (a sum of EC, POM and measurements). For the analysis, in order to identify the Na_Cl) in PM1 over Warsaw was more or less stable sources of PM1, one of the principal components analysed throughout the year, averaging 22.6%. In summer POM – SIA – was divided into three water-soluble ions making 2– – + prevails in the mass of primary matter related to PM1, it, i.e., SO4 , NO3 and NH4 , considering, that although whereas in winter the principal components are EC and these compounds (or rather their precursors – sulphur and Na_Cl (Fig. 5, Table 5). Similar conclusions were drawn in nitrogen oxides and ammonia) react with one another in 2010 from a research conducted in cities in the south and atmospheric air and form parts of the main portion of SIM north of Poland, where the considerable share of EC and (Sainfield and Pandis, 2006; Chow et al., 2015), they come Na_Cl in the mass of fine particulate matter in winter was from different sources. The presence of nitrogen oxides is attributed to the combustion of coal for heating, mainly in related to emissions from road traffic (exhaust emission) local boiler plants or rooms and household stoves (Rogula- and, to a lesser extent, from the combustion of natural gas, Kozłowska et al., 2012, 2014). POM prevails in the mass whereas sulphur oxides in Poland’s urban areas mainly of PM1-bound primary matter during the non-heating period come from the combustion of hard coal and lignite, and probably due to significant contribution of road traffic less often from industrial processes (Dębski et al., 2015). emission to PM concentration and its components (Rogula- In general, the concentration of ammonia over the research Kozłowska et al., 2012, 2014). POM in summer is prevailing area, as well as other European regions, tends to be related in term of relative composition, but it is higher also in terms to agriculture and to a lesser extent to road transport of absolute concentrations, with respect to winter. Because (Werner et al., 2015; EEA, 2017). the traffic emission of POM in terms of absolute mass is Instead of 24-h concentrations of Na_Cl, for the PCA probably not higher in summer than in winter, it seems that the concentrations of Na+ and Cl– were used (Table 6). The higher summer POM concentration over the research area obtained PCA models for summer and winter were is attributable to primary biological aerosol particles (PBAP) evidently different. The PCA model for summer identified – bacteria and archaea, fungal spores and fragments, pollen, three main components with the following fractions in the – 2– + viruses, algae and cyanobacteria, biological crust sand lichens total variance: 39%, 26% and 21%. Cl , SO4 and Na and others like plant or animal fragments and detritus, etc. demonstrated an evident and the strongest correlation with Although plant spores and pollen are usually large the first component PC1. These are the components of fine particulates (> 20 µm), the mold spores, fungal spores, particles, occurring in the air over Poland as a result of the bacteria, viruses and some allergens have their aerodynamic combustion of coal and lignite (Rogula-Kozłowska et al., diameter lower than 1 µm (Després et al., 2012; Clauß, 2015; 2016; Majewski and Rogula-Kozłowska, 2016). In 2015; Wolf et al., 2017). That fact may be supported by summer there is also considerable increase of Na+, Cl– and 2– our observations, because during the summer period SO4 concentrations, attributable to inflow of air masses measurements the sampled PM1 on filter surfaces was from northern directions (Fig. 6). It should be mentioned, noticed to take a yellow or yellow- brown color, that was that except for the relationship of those directions with the entirely different from the one of PM1 sampled in winter. inflow of oceanic air masses, those directions are also The fraction of unidentified (‘other’) matter in the mass supposed to coincide with air masses polluted by coal of PM1 was very noticeable in summer but negligible in combustion products. Both at the northern and the southern winter. This also supports previous conclusions from direction from the research area large heat and power earlier research, where it was demonstrated, that in summer plants are located, using coal and lignite (Siekierki combined in Poland’s urban areas considerable amounts of road dust heat and power plant in the NE and major plants of the (containing mainly certain metal oxides, not identified in Upper Silesia in the SE). In the south and south-west, on the study; Rogula-Kozłowska, 2016) were present in both the other hand, residential estates are situated, where some coarse and fine particles. On average, up to 19% of the houses may be heated using coal as well. Also the increasing + – mass of PM1 over Warsaw, irrespective of the season, may share of Na and Cl in the mass of PM1 (Fig. 5) and the comprise water built in the structure of other compounds greater average concentrations of Na+ and Cl– in winter vs. (Rogula-Kozłowska et al., 2017). Therefore, the differences summer (Fig. 6) suggest that the concentrations of Na+ and – in the chemical composition of PM1 in summer and winter Cl are influenced by coal combustion emissions. The same are evident and have been ascertained by checking the follows from negative, statistically significant correlations

648 Majewski et al., Aerosol and Air Quality Research, 18: 636–654, 2018

Table 6. Results of principal components analysis (PCA) and a multi-linear regression analysis (MLRA) performed for the concentrations of PM1 and PM1-bound elements. Average source contributions (%) to PM concentrations in Period Component Element factor loading Source/ %variance 1 sampling period (results from MLRA) + – – All PC1 NH4 -0.96, NO3 -0.93, SOM-0.90, Cl -0.86, 62 50.8 + 2– EC-0.84, Na 0.67, SO4 -0.54 + 2– PC2 POM0.89, Na 0,57, EC0.48, SO4 -0.30 19 15.0 Total variance 81% 65.8% 2– – + Summer PC1 SO4 0.91, Cl 0.88, Na 0,87, POM-0.51, 39 + EC-0.51, NH4 0.40 + PC2 NH4 -0.74, POM-0.71, EC-0.71, SOM-0.59, 26 2– SO4 -0.37 – PC3 SOM-0.71, NO3 0.70, EC0.45, POM0.45, 21 – + Cl 0.37, NH4 -0.33 Total variance 86% + – Winter PC1 SOM-0.94, NH4 -0.94, NO3 0.91, EC-0.90, 71 2– – + POM-0.90, SO4 -0.82, Cl -0.79, Na -0,41 + 2– PC2 Na 0,80, EC-0.35, POM-0.35, SO4 0.31 13 Total variance 84% Elements with factor loadings < 0.30 are not included. Elements presented in descending order of their factor loads, with factor loadings indicated as subscript. * The sets of measured PM1 daily/24-h concentrations and the concentrations computed for each day from MLRA- determined contributions were substantially correlated (R2 = 0.93). between 24-h concentrations of Na+ and Cl– and the daily used as the ice- removing agent on the roads in cities air temperature in the analysed period (Table 7). Therefore, during winter, traffic emissions could be attributed to PC2 it can be assumed that PC1 reflects emissions from energy (in the PCA model for winter), whereas a single source of production and in summer it has a huge influence on PM1 PM is difficult to find for PC1. Undoubtedly, there are a concentrations in Warsaw. few sources of PM1 connected with the combustion of The second principal component, PC2, is correlated the various fuels in PC1, however it is hard to define and + strongest with EC, POM and NH4 . The distribution of 24-h identify them. Therefore, further analysis of the data – with concentrations of these components follows distinctly the purpose to attribute percentage fractions for 24-h different patterns in summer and winter (Fig. 6). In summer, concentrations of PM1 to sources identified as the principal their highest concentrations are observed for S, SW and E components – used a PCA model developed for all 24-h wind directions, whereas in winter for SE and northern data together, that is for the entire period of measurements + directions (in the case of NH4 ). Considering that busy (Table 6). arterial roads are situated in SW and E (the very centre of Fig. 7 shows 120-day-long course of fractions of PC1 Warsaw), in summer these components must be connected and PC2 in PM1 concentrations throughout the period of with traffic emissions and in winter with additional energy measurements in Warsaw. What follows from this pattern, emissions from commercial sites and households, as it is and from the interpretation of the variability and correlations + – 2– with Na , Cl and SO4 . This is also evident in the share of of the concentrations of PM1 components given above, is EC in PM1 mass (Fig. 5), which increases slightly in winter, that PC1 can be described as energy emissions (commercial indicating the presence of an extra source of EC. Moreover, and municipal together) and PC2 as traffic emissions. there is a positive correlation of 24-h concentrations of Therefore, the average share of emissions from heat and POM with temperature, which suggests that POM content power generation in PM1 concentrations in Warsaw can be in the air increases with temperature, as the fraction of estimated at more than 50%, whereas traffic emissions traffic emissions increases; this kind of correlation is not account for 15% (Table 6). In summer, the share of traffic observed for EC (Table 7). The third component, PC3, is emissions is greater than in winter: 18.3% (summer) vs. – the strongest correlated with NO3 and SOM, representing 13.3% (winter), along with a smaller share of energy the fraction of PM1 precursors in the air as the main source emissions: 43.6% (summer) vs. 57.9% (winter). It should of PM1. be noted that the shares determined in this article result not In the PCA model for winter, only two principal only from primary particles of PM but also from its components were identified (Table 6). PC1 was strongly precursors, which are included in the compositions of SOM correlated with all analysed components, except for Na+, and SIA. Therefore, they are most probably estimated more whereas PC2 was correlated the strongest with Na+ and accurately than what usually results from the analysis, for then with EC and POM. Taking into account, that salt is example, PM elemental compositions. Still, the results

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–3 Fig. 6. Distribution of the concentration of selected components of PM1 (µg m ) with reference to the eight wind directions.

obtained from this research are consistent with those of PM1 was approx. 35%, include industrial emissions, soil previously obtained during an analysis of a monthly PM2.5 and traffic dust, as well as foreign emissions. data series for winter (Majewski and Rogula-Kozłowska, 2016). CONCLUSIONS All the other – unidentified here – sources of PM1 in Warsaw, which total average fraction in the concentration For the first time a diurnal course of principal components

650 Majewski et al., Aerosol and Air Quality Research, 18: 636–654, 2018

Table 7. Pearson correlation coefficients between 24-h concentrations of PM1 components and daily averaged meteorological factors in Warsaw. Temp Hum. Prec. Wind.S. Rad. Press. EC –0.50 0.19 –0.20 –0.21 –0.35 0.18 SOM –0.38 0.07 –0.20 –0.21 –0.26 0.05 POM 0.24 –0.25 –0.05 –0.53 0.34 0.14 Cl– –0.79 0.48 –0.21 0.15 –0.67 0.10 – NO3 –0.65 0.30 –0.17 0.01 –0.54 0.06 2– SO4 0.03 –0.04 –0.18 –0.24 0.07 0.07 Na+ –0.50 0.13 –0.23 0.22 –0.47 0.20 + NH4 –0.57 0.28 –0.24 –0.09 –0.45 0.07 In red: statistically significant at the 0.05 level.

A B Undefined 100%

90%

80%

70% [%]

60%

50% contribution

40% Source 30%

20%

10%

0%

Fig. 7. 120-day-long course of share of A – energy emissions (commercial and municipal sources: boiler plants and household heating furnaces) and B – traffic emissions, in the 24-h concentrations of PM1 in Warsaw.

fraction in PM1 has been presented in a long-term data metallic particles from industrial plants, bacteria, spores series (120 days) for an urban area in Poland. The fraction and plant litter, soil matter etc.) will not result in a of primary and secondary matter in the mass of PM1 was significant reduction of PM1 concentrations over Warsaw. analysed for each day of the observation period. It was However, curbing emissions from some of the above- found, that secondary aerosol accounts for approx. 62% of mentioned sources will definitely limit PM1 concentrations. the PM1 mass over Warsaw. In summer, the average is 51% On average, 15% of PM1 in the air comes from traffic and in winter it is 69%. Secondary inorganic matter (SIM) emissions, that is mainly from transformations of NOx and constitutes nearly 57% of the secondary aerosol in winter, VOCs. Almost 51% comprises particulate matter connected and 56% in summer. The respective fractions of secondary with emissions from the combustion of various fuels to organic matter (SOM) are 43% and 44%. This means that generate power and heat, both in households and in almost 6 µg m–3 in summer and 12 µg m–3 in winter are combined heat and power plants. Such a reduction will submicron particles originating from transformations of target gaseous precursors of PM and not the PM itself. So volatile organic compounds, sulphur and nitrogen oxides, far, the attempts to determine the balance of emissions and ammonia. Therefore, limiting the emissions of primary from various sources and to translate drops and rises in PM PM (soot from diesel powered engines and household emissions into falling or growing concentrations of PM in furnaces, dust from road surfaces wearing off, primary the air over Poland, have assumed, that all the particulate

Majewski et al., Aerosol and Air Quality Research, 18: 636–654, 2018 651 matter is primary. Yet, this paper indicates, primary particles Boogaard, H., Kos, G.P.A., Weijers, E.P., Janssen, N.A. have a much smaller share in the concentration of H., Fischer, P.H., van der Zee, S.C., de Hartog, J.J. and submicron particulate matter than particles coming from Hoek, G. (2011). Contrast in air pollution components transformations of gases, which have been overlooked in between major streets and background locations: such balancing. Moreover, it should be also stressed that Particulate matter mass, black carbon, elemental seasonality (winter and summer period) plays a significant composition, nitrogen oxide and ultrafine particle number. role in the intensity of PM formation from its gaseous Atmos. Environ. 45: 650–658. precursors. As a matter of fact, it determines the final share Cao, J.J., Wu, F., Chow, J.C., Lee, S.C., Li, Y., Chen, S.W., of SIM and SOM in PM. An, Z.S., Fung, K.K., Watson, J.G., Zhu, C.S. and Liu, S.X. (2005). Characterization and source apportionment ACKNOWLEDGEMENTS of atmospheric organic and elemental carbon during fall and winter of 2003 in Xi'an, China. Atmos. Chem. Phys. The work was carried out within the project No. 5: 3127–3137. 2012/07/D/ST10/02895 (ID 202319) financed by the Castro, L.M., Pio, C.A., Harrison, R.M. and Smith, D.J.T. National Science Centre Poland (NCN). (1999). Carbonaceous aerosol in urban and rural European This research was supported by The Faculty of Civil and atmospheres: Estimation of secondary organic carbon Environmental Engineering basic (statutory) research concentrations. Atmos. Environ. 33: 2771–2781. projects. Chou, C.C.K., Lee, C.T., Cheng, M.T., Yuan, C.S., Chen, The authors are willing to thank very much the S.J., Wu, Y.L., Hsu, W.C., Lung, S.C., Hsu, S.C., Lin, reviewers and the editor-Ms. Stefania Gilardoni Ph.D. for C.Y. and Liu, S.C. (2010). Seasonal variation and spatial all relevant remarks that enabled us to refine the distribution of carbonaceous aerosols in Taiwan. Atmos. presentation and discussion of results and finally improve Chem. Phys. 10: 9563–9578. the quality of the paper. Chow, J.C (1995). Measurement methods to determine The authors grate fully acknowledge the NOAA Air compliance with ambient air quality standards for Resources Laboratory (ARL) for the provision of the suspended particles. J. Air Waste Manage. Assoc. 45: HYSPLIT transport and dispersion model and/or READY 320–382. website (http://www.arl.noaa.gov/ready.php) used in this Chow, J.C., Lowenthal, D.H., Chen, L.W.A., Wang, X. publication. and Watson, J.G. (2015). Mass reconstruction methods for PM2.5: A review. Air Qual. Atmos. Health 8: 243– REFERENCES 263. Clauß, M. (2015). Particle size distribution of airborne Aiken, A.C., DeCarlo, P.F., Kroll, J.H., Worsnop, D.R., micro-organisms in the environment-A review. Appl. 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