The Role of PM2.5 Chemical Composition and Meteorology during High Pollution Periods at a Suburban Background Station in Southern Poland

Received: January 10, 2020
Revised: June 5, 2020
Accepted: August 5, 2020

 Copyright The Author's institutions. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited. 

Cite this article:

Barbara, B., Zioła, N., Mathews, B., Klejnowski, K. and Słaby, K. (2020). The Role of PM_2.5 Chemical Composition and Meteorology during High Pollution Periods at a Suburban Background Station in Southern Poland. Aerosol Air Qual. Res. 20: 2433–2447. https://doi.org/10.4209/aaqr.2020.01.0013

HIGHLIGHTS

• Periods of elevated PM₅ concentrations exert a critical impact on low air quality.
• High concentrations of PM₅ were caused by secondary aerosol, mainly SOC.
• Specific weather conditions favor the formation of PM₅ episodes.
• Regional and long-range transport events affect high levels of analyzed substances.

Keywords: PM2.5; High pollution days; carbonaceous aerosol; Primary and secondary organic carbon; Secondary inorganic aerosol, meteorology.

INTRODUCTION

Air pollution is one of the most serious environmental problems of all developed and developing countries (WHO, 2013). Of the various pollutants, particulate matter (PM) affects the environment in the most comprehensive way, due to the diversity of emission sources and formation mechanisms, and the resulting variation in chemical composition and particle size (Hassan and Khoder, 2017). In recent years, particular attention has been paid to PM_2.5 and PM₁ (particles with an aerodynamic diameter ≤ 2.5 µm and ≤ 1.0 µm, respectively), mainly due to the health effects that they can cause (e.g., Kim et al., 2015). What is more, fine particles are able to persist in the atmosphere for a long time, affect the climate (e.g., Fiore et al., 2015), ecosystems (e.g., Rai, 2016), materials (e.g., Rao et al., 2014) and visibility (e.g., Lin et al., 2014). The basic components of PM_2.5 include carbonaceous matter and water-soluble ions. The total carbon (TC) in the aerosol can be divided into elemental carbon (EC), organic carbon (OC) and inorganic (carbonated) carbon (IC/CC), although the latter comes mainly from geological sources and occurs almost exclusively in coarse particles (Mancilla et al., 2015). EC is a primary pollutant emitted during the incomplete combustion of fossil fuels and pyrolysis of biological material during combustion (Jones and Harrison, 2005). In contrast, OC can be emitted directly from emission sources (primary organic carbon [POC]) and/or formed in the atmosphere as a result of gas-particle transformation (secondary organic carbon [SOC]) (Khan et al., 2016). Typically, secondary organic aerosol (SOA) accounts for 20–80% of organic matter in PM_2.5 and has a large biogenic contribution. However, near urban areas, up to 90% of SOA could be anthropogenic in origin (Mancilla et al., 2015).

In addition to carbonaceous matter, inorganic ions have a dominant share in the total mass of suspended particles and exert a significant impact on the environment. These compounds are an important part of the secondary inorganic aerosol (SIA), especially sulfates (SO₄^2–), nitrates (NO₃^–) and ammonium ions (NH₄⁺) (Błaszczak et al., 2019). It is considered that SIA is almost exclusively of anthropogenic origin because its gaseous precursors—mainly sulfur dioxide (SO₂), nitrogen oxides (NO_x) and ammonia (NH₃)—are largely emitted from sources related to human activities. In addition, sources such as coal-fired power plants and residential coal combustion can emit significant amounts of primary sulfates and nitrates into the atmosphere (Dai et al., 2018). The mechanisms of SIA formation include many chemical reactions that largely depend on the characteristics of pre-existing aerosol and gaseous precursors, as well as meteorological conditions (Weijers et al., 2010). Nevertheless, this component largely affects the concentration of fine PM, both in areas far away from significant emission sources and in urban areas (e.g., Błaszczak et al., 2016; Reizer and Juda-Rezler, 2016).

Although the last two decades have brought the improvement of ambient air quality, the problem of exceeding the standards for PM_2.5 still remains unresolved in many areas within Europe (Błaszczak et al., 2019). Periods of elevated concentrations of PM and other atmospheric pollutants, also often called “haze episodes,” are of particular concern, because of its adverse effect on public health, visibility and even global climate (Hassan and Khoder, 2017; Wang et al., 2019). Extreme events of ambient PM_2.5, in which daily mass concentrations are substantially higher than annual averages, have been reported worldwide (e.g., Juda-Rezler et al., 2011; Heo et al., 2013; Ma et al., 2017). Both observational data and model simulations indicated that local meteorological features along with local source emissions may be the most direct factors resulting in formation and duration time of PM_2.5 episodes (Ma et al., 2017). Moreover, there is strong evidence that the regional and long-range transport of air pollutants can contribute significantly during heavy PM pollution (Juda-Rezler et al., 2011; Heo et al., 2013).

Bearing in mind the above considerations, an annual measurement campaign of PM_2.5 was carried out at a suburban background site in Racibórz (southern Poland). The main objectives of the present study are to: (1) assess the role of primary and secondary PM components in the occurrence of elevated concentrations of PM_2.5 and (2) explore the impacts of meteorological conditions on PM_2.5 concentration and chemical composition during high pollution periods.

 
METHODS

 
Study Area and Sampling Equipment

The study was conducted at a suburban background site in Racibórz (Fig. 1)—located on the outskirts of the town—at the meteorological station belonging to the Institute of Meteorology and Water Management–National Research Institute (IMWM–NRI). The nearest surroundings of the station (~100 m) are arable fields. National Freeway No. 45 and the nearest dispersed residential buildings are located ~100–250 m east of the station. At a distance of ~2.5 km northeast of the sampling site, the city center is located. The nearest large cities are Opava (~24 km SW) and Ostrava (~25 km S) in the Czech Republic, as well as Rybnik (~25 km E), Jastrzębie-Zdrój (~28 km SE) and cities of the Upper Silesian Industrial District (~42 km NE) in Poland. More details about the specificity of measurement station in Racibórz and the region in which it is located (Silesia Province, southern Poland) can be found in Błaszczak et al. (2016).

Fig. 1. Location of the measurement station in Racibórz.

The PM_2.5 samples were collected using a reference low volume sampler (PNS-15; AtmoService), providing the 2.3 m³ h^–1 air flow, which meet the requirements of the PN-EN 12341:2014-07 standard. Samples were accumulated on quartz microfiber filters (diameter: 47 mm, grade: QM-A; Whatman) over 24 hours starting at midnight. Research was performed during the annual measurement period (2018). The results obtained were also averaged in the heating (January–March and October–December) and non-heating (April–September) periods, to capture specific seasonal fluctuations.

Meteorological data were provided by the IMWM-NRI and are summarized in Table 1. According to division of Poland into climate regions (Romer, 1949), Racibórz lies in the climate zone “Moravian Gate,” one of the warmest zones in the country. The local climate is primarily influenced by the terrain and the land use. The relatively strong industrialization and compact development of the city center contribute to the occurrence of specific climatic features such as the “urban heat island” or an increased number of days with low precipitation. The distribution of wind directions in Racibórz is determined by general-circulation conditions with clearly visible topographical modifications. Winds blowing from the NNW, N and NW sectors dominate during the heating period, while SSW and SW sectors prevail during the non-heating one (Table 1).

 
Analytical Procedures

A total of 330 PM_2.5 samples collected over 2018—163 and 167 samples in the heating and non-heating season, respectively—were subjected to gravimetric analysis in the laboratory of the Institute of Environmental Engineering, Polish Academy of Sciences (IEE PAS), in Zabrze (Poland). PM_2.5 concentrations were determined according to the PN-EN 12341:2014‒07 standard, using Mettler Toledo AT20 microbalance (resolution: 2 µg). Conditioning, weighing and storage of both non-exposed and exposed filters were conducted in the weighing room at fixed conditions of temperature (20 ± 1°C) and relative humidity (RH; 50 ± 5%).

Every second sample (153 samples) was passed for chemical composition analyses. A 1.5 cm² fragment was cut from these samples and analyzed for OC and EC content, by means of thermal-optical carbon analyzer with a flame ionization detector (FID) (Model 4L Main Oven Assembly; Sunset Laboratory Inc., USA) using the EUSAAR_2 protocol. The measurement performance was controlled by regular analysis of blank filters and systematic calibration of the apparatus. In addition, certified reference materials (CRMs; RM 8785 and RM 8786; National Institute of Standards and Technology [NIST]) were periodically analyzed—recovery of 90–102% and 98–124% was obtained, respectively, for OC and EC. The total uncertainties in EC and OC measurements are of the order of 5%. The limit of detection (LOD) was 0.17 µg cm^–2 and 0.80 µg cm^–2 respectively for EC and OC.

The content of SOC and POC was calculated according to the methodology proposed in Castro et al. (1999)—Eqs. (1)–(2):

where OC and EC represent analytically determined daily concentrations of organic and elemental carbon, respectively, while (OC/EC)_pri is the ratio of primary organic carbon to elemental carbon. To determine the value (OC/EC)pri, a graphical approach was used, including the correlation between OC and EC concentrations. Linear regression was performed for 10% of the samples with the lowest OC/EC ratio, because the value of the determination coefficient for this range of data will represent the impact of POC emission sources (Pavuluri et al., 2011).

The remaining filter surface was subject to analysis of water-soluble ions (SO₄^2–, NO₃^–, Cl^–, NH₄⁺, Na⁺, K⁺, Ca²⁺, Mg²⁺) using the Dionex ICS-1100 ion chromatographs (Thermo Scientific, USA). The quality of analytical data was assured by using the CRM Fluka products Nos. 89316 and 89886. The method detection limits, expressed in mg L^–1, were as follows: 0.073 (NO₃^–), 0.12 (SO₄^2–), 0.111 (Cl^–), 0.027 (NH₄⁺), 0.091 (Na⁺), 0.095 (K⁺), 0.221 (Ca²⁺) and 0.17 (Mg²⁺). The total uncertainties of the measurement ranged from 4.3% (Na⁺) to 11.2% (SO₄^2–).

The assessment of the SIA contribution in PM_2.5 mass was made on the basis of analytically determined concentrations of sulfate [SO₄^2–], nitrate [NO₃^–] and ammonium ions [NH₄⁺], wherein the share of sulfates was corrected for the share of sulfates associated with sea salt (Sillanpää et al., 2006)—Eqs. (3)–(4):

More details about analytical methodologies used can be found in the earlier publications of the authors (e.g., Rogula-Kozłowska et al., 2014). Before statistical analyses, the measurement results were subjected to preliminary verification by comparing the sum of the concentrations of the analyzed components with the gravimetrically determined concentration of PM_2.5. If the total concentration of TC and ions exceeded the PM_2.5 concentration on a given measurement day, such a sample was not included in the calculations.

RESULTS AND DISCUSSION

Variability of PM_2.5 Concentrations

During the 2018 year, PM_2.5 concentrations varied from 5.11 to 129.85 µg m^–3 with an annual mean of 26.90 µg m^–3, which was slightly higher than the EU limit value (25 µg m^–3; Directive 2008/50/EC). The provisions of European law do not set limit values for average daily concentrations of PM_2.5. Such standards are provided by the World Health Organization (WHO, 2013) and the United States Environmental Protection Agency (U.S. EPA, 2016)—they are 25 and 35 µg m^–3, respectively. Considering the annual measurement period, 80 exceedances of the U.S. EPA standard were found, which constituted ~24% of the measurement time. The more stringent WHO guideline was exceeded in 113 cases (34% of measuring time).

The histogram of a series of daily PM_2.5 concentrations (Fig. 2) revealed a log-normal distribution, which is typical for the concentrations of most atmospheric pollutants (Maciejewska et al., 2015). The most common concentrations were in the range of 15–20 µg m^–3 (22.4% of cases) and 10–15 µg m^–3 (19.4% of cases); however, they were recorded mainly in the non-heating season (average: 14.74 µg m^–3). Much worse living conditions for the inhabitants of the analyzed area—in relation to exposure to PM_2.5—occurred in the heating season (average: 39.37 µg m^–3), similar to many other regions in Europe (Błaszczak et al., 2019). During this period of the year, PM_2.5 concentrations were higher than 25 µg m^–3 for 66% of the measurement time (108 days).

Fig. 2. Frequency distribution of daily PM_2.5 concentrations at suburban background station in Racibórz.

The reason for the observed seasonal differences may be due to several factors. In Poland, the production of electricity and heat is mainly based on the combustion of hard coal and lignite. However, the waste gases emitted from large point sources affect PM_2.5 concentrations throughout the whole year. The main factors shaping the levels of PM_2.5 concentrations—both in large cities and in rural areas—are municipal sources, which activity increases considerably in the cold season (Rogula-Kozłowska et al., 2014). The impact of meteorological conditions is also significant.

 
PM_2.5 vs. Meteorological Parameters: Identification of Pollution Episodes

Numerous scientific studies have proven that meteorological conditions play an important role in the results obtained at every monitoring site (Galindo et al., 2011; Reizer and Juda-Rezler, 2016; Wang et al., 2019). Their impact is particularly evident in the cold season, when low air temperatures, weak winds and the presence of a low inversion layer limit the dispersion of pollutants, while promoting their local accumulation. Previous experience to identify episodes of high concentrations of pollutants has shown that such situations are associated with a set of meteorological factors, of which the most important are: high atmospheric pressure system, average daily air temperature < 5°C, average daily wind speed < 3 m s^–1, wind direction, average daily relative humidity > 85% and no atmospheric precipitation (Rogula-Kozłowska et al., 2014).

The variations of daily PM_2.5 concentrations and selected meteorological parameters are illustrated in Fig. 3. In this study, an episode is defined as a situation with the daily PM_2.5 concentration exceeding 50 µg m^–3 for at least 2–3 consecutive days. In 2018, 38 such exceedances were recorded, but these were often only single days. During the year, a total of 7 PM_2.5 episodes were identified (shaded areas), the longest being recorded in February (06–12 Feb) and at the turn of February and March (28 Feb–06 Mar).

Fig. 3. Time series of daily PM_2.5 concentrations and selected meteorological parameters during annual measurement period (2018).

The mean values of PM_2.5 concentration and meteorological parameters for individual episodes are presented in Table 2. In general, average concentration of PM_2.5 during episodes was 85.66 µg m^–3, more than considering all days (38) with PM_2.5 > 50 µg m^–3 (average: 76.98 µg m^–3), and significantly higher than during the heating period. The highest concentrations of PM_2.5 were recorded during the long-lasting Episode 4 (average: 97.31 µg m^–3), when the lowest air temperatures were also observed (average: –6.77°C). The second long episode, Episode 2, had slightly lower concentrations of PM_2.5 (average: 89.31 µg m^–3). During the other episodes, average concentrations of PM_2.5 were within the range ~75–78 µg m^–3. Apart from the differences in observed meteorological parameters, it was found that all identified PM_2.5 episodes caused significant visibility impairment, which can pose a serious threat to transportation, economic production, and people’s living.

Concentrations and Contributions of PM_2.5 Components during High Pollution Days

Average values of the concentrations of PM_2.5 and its chemical compounds during high pollution days are shown in Table 3, against the data for other averaging periods. Since chemical composition analyses were performed every other day, all days on which PM_2.5 concentration exceeded 50 µg m^–3 were taken into account, without selection of PM_2.5 episodes.

The carbonaceous matter was the dominant component of PM_2.5 and during days with the highest PM_2.5 concentrations it represented on average more than half (~52%) of PM_2.5 mass. The concentrations of TC, EC and OC in such days were on average 36.91, 4.79 and 32.12 µg m^–3, respectively, and these values were significantly higher compared to those for normal days. The average EC share in PM_2.5 during days with PM_2.5 > 50 µg m^–3 (~7%) was slightly lower than in the entire heating season (~8%). Thus, the high share of TC in PM_2.5 in high pollution days could be attributed to the high share of OC, which was on average ~45%. Clear dominance of OC over EC was confirmed by significantly higher OC/EC ratios in such periods (average: 7.20). The reason for the observed differences was probably condensation of gas phase semi-volatile organic species due to lower temperature combined with frequent events of air stagnation (Khan et al., 2016; Ma et al., 2017). The average SOC concentration in high pollution days amounted to 14.59 µg m^–3, and it was ~5 times higher in comparison with the normal days in the heating season. Despite this, POC (average: 17.53 µg m^–3) remained dominant component of OC, suggesting the impact of local sources, which can emit significant amounts of primary organic matter (Błaszczak et al., 2016).

The periods of high PM_2.5 concentration were characterized by a large disproportion between the content of carbonaceous matter and water-soluble ions. Although the total concentration of all ions in such periods (average: 21.94 µg m^–3) was significantly higher compared to those for normal days, the share of ions in PM_2.5 decreased with the increase in the pollution level. Among all the ionic species determined in the present study, nss-SO₄^2–, NO₃^– and NH₄⁺ remained dominant ions during high pollution periods, with average concentrations of 5.54, 7.92 and 2.51 µg m^–3, respectively. Even if the SIA/PM_2.5 ratio in such days (average: ~22%) was lower than in normal days during the heating season (average: ~26%), the structure of SIA was very similar—the key component of SIA were nitrates with average share in PM_2.5 of ~11%. Interestingly, the values of SIA/ion ratio increased during high pollution days (average: ~71%), compared to those for normal days in the heating period (~64%). This could indicate that as the pollution level increased, the intensity of formation of SIA also increased, mainly thanks to enhanced heterogeneous conversion of NO_x. It should be noted that in conditions of high relative humidity (Table 2), heterogeneous reactions on pre-existing particles are more intense rather than homogeneous conversions (Song et al., 2019; Wang et al., 2019).

With regard to the other ions listed in Table 3, the chemical profile of PM_2.5 during high pollution days was enriched in Cl^– and to a lesser extent in K⁺. This suggested that the increased intensity of biomass burning and the use of coal for energy purposes could have contributed to enhanced PM_2.5 concentrations (Liu et al., 2015). The analysis of PM_2.5 ionic balance also proves the specificity of PM_2.5 chemical composition during high pollution events. At high PM_2.5 concentrations, atmosphere was ammonia-rich, as indicated by relatively low value of the SO₄^2–/NH₄⁺ ratio (average: 0.85). Despite this, the amount of NH₄⁺ ions was insufficient for complete neutralization of sulfuric acid (H₂SO₄) and nitric acid (HNO₃) in atmospheric dust. It was supported by relatively low values of the Σ_cat/Σ_an ratios (average: 0.86), indicating the acidic nature of the aerosols—in contrast to other averaging periods—due to high levels of secondary inorganic ions formed in heterogeneous reactions that are particularly intense during high pollution days. The deficit of cations (excess of anions) could probably be attributed to hydrogen ions (H⁺), since the sum of the crustal and trace elements is usually only a small percentage of PM_2.5 mass (Błaszczak et al., 2016a).

To get a further insight into the variability of PM_2.5 chemical composition, we also classified PM_2.5 pollution into different categories. Mean percentages of the main chemical components of PM_2.5—carbonaceous matter (EC, POC, SOC), secondary inorganic ions (nss-SO₄^2–, NO₃^–, NH₄⁺) and Cl^– ions—under different pollution levels are presented in Fig. 4. Other ions were omitted in the considerations, because their share in the total PM_2.5 mass during high pollution days was small and did not exceed 2% (Table 3).

Fig. 4. Contributions (%) of carbonaceous compounds (EC, SOC and POC) and inorganic ions (nss-SO₄^2–, NO₃^–, NH₄⁺ and Cl^–) in PM_2.5 for different concentration ranges.

It was generally found that the share of carbonaceous matter increased with the increase of PM_2.5 concentration. During the clean periods (PM_2.5 < 10 µg m^–3), the share of TC was low (average: ~34%), especially in the case of EC (average: ~5%) and POC (average: ~17%). The highest POC and EC shares were recorded at PM_2.5 concentrations in the range of 35–50 µg m^–3, with average values of ~32% and ~9%, respectively. The increase in the SOC share in PM_2.5 together with the increase in the pollution level was more pronounced compared to primary carbon compounds. As a result, at the highest PM_2.5 concentrations (PM_2.5 ≥ 70 µg m^–3), the SOC share (average: ~25%) was higher than the POC share (average: ~22%).

In general, the share of SIA decreased with the increase of PM_2.5 concentrations; however, differences were observed when considering the shares of individual inorganic ions. The highest share of nss-SO₄^2– in PM_2.5 (average: ~14%) was recorded at the lowest concentrations of PM_2.5 (< 10 µg m^–3), which could affect the high SIA content in PM_2.5 for the given concentration range. Also in this case the highest shares of Cl^– ions were recorded (average: ~6%). The share of NH₄⁺ ions was less variable, with the highest levels (average: ~5%) observed at PM_2.5 concentrations in the range of 10–15 µg m^–3. Nitrates were distinguished by increasing share with an increase in the pollution level, reflecting an elevated production of heterogeneous reactions. The highest share of NO₃^– (average: ~14%) was found at PM_2.5 concentrations of ≥ 70 µg m^–3, similar to the share of SOC.

The obtained results suggest that high concentration of PM_2.5 in Racibórz were mainly caused by secondary aerosol, especially secondary organic carbon and, to a lesser extent, nitrates. A high share of TC, especially OC, was also found at various measurement stations located in Poland, representing both urban backgrounds, e.g., Katowice (Rogula-Kozłowska et al., 2014) and Warsaw (Juda-Rezler et al., 2011), and rural backgrounds, e.g., Diabla Góra (Juda-Rezler et al., 2011) and Czerniawa (Reizer and Juda-Rezler, 2016). On the other hand, the importance of SIA as a factor responsible for increased concentrations of PM_2.5 has been emphasized in many scientific publications (e.g., Weijers et al., 2010; Rodelas et al., 2019; Wang et al., 2019). This has confirmed the fact that Poland is a specific country in terms of emission conditions, which also has an undeniable effect on the chemical composition of the atmospheric aerosol.

 
Correlations among PM_2.5 Chemical Components and their Relation with Meteorological Parameters

In this work, the interrelationships between the concentrations of PM_2.5 chemical components were examined using Spearman’s rank correlation (α = 0.05). A non-parametric correlation matrix was constructed separately for the data set from high pollution days and normal days in the heating season (Table 4) to capture the specifics of both periods.

PM_2.5 concentrations showed a strong positive correlation with carbonaceous aerosol, while during high pollution days the correlation was maintained only in the case of OC (r = 0.79). In such periods no statistically significant relationship was found between OC and EC, which proves the presence of mixed emission sources of these substances (Mancilla et al., 2015). For example, the significant positive correlation between Ca²⁺ and Mg²⁺ and primary carbon compounds (EC, POC) during high pollution days may indicate a common source of origin—emissions from car engines and road abrasion (Rodríguez et al., 2004). On the other hand, neither EC and POC nor Ca²⁺ and Mg²⁺ showed statistically significant associations with PM_2.5 in such periods, and therefore the role of these sources in shaping high PM_2.5 concentrations could be negligible. Conversely, the relatively high positive correlation between SOC and PM_2.5 (r = 0.61) indicated the importance of SOA formation processes (Mancilla et al., 2015).

In addition to SOC, SIA also had a significant effect on high PM_2.5 concentrations, specifically, NO₃^– (r = 0.60) and, to a lesser extent, NH₄⁺ (r = 0.46). In both averaging periods, strong positive correlation was noted between the nss- SO₄^2– and NO₃^–, suggesting a significant content of aged particles in the ambient air, which is typical for non-urban areas such as Racibórz (Heo et al., 2013; Rogula-Kozłowska et al., 2014). This is confirmed by the high correlation of NH₄⁺ with nss-SO₄^2– and NO₃^– (r = 0.74–0.85). Moreover, during days with PM_2.5 > 50 µg m^–3 there were no statistically significant relationships between SIA and other analyzed ions as well as between SIA and SOC, which could indicate that the SIA concentrations are less impacted by local emissions in such periods (Freney et al., 2011; Heo et al., 2013). On the other hand, the effect of local sources—such as coal combustion in households (Rodelas et al., 2019)—on SOC levels is confirmed by the strong correlation between SOC and Cl^– (r = 0.61).

A non-parametric (Spearman) correlation analysis (α = 0.05) between weather parameters and PM_2.5 chemical composition data was also performed in order to identify the factors affecting the variability of concentrations of the analyzed substances (Table 5). During periods of elevated PM_2.5 concentration, no statistically significant relationships were found between the analyzed PM components, and precipitation, solar radiation intensity, atmospheric pressure and wind speed. This may be due to much lower values of these parameters during high pollution days or to their small variability (see also Section 3.2). The impact of wind speed was revealed in the formal days of the heating season, with negative values of correlation coefficients. This is probably due to the cleaning function of wind—high wind speeds favor the dispersion of atmospheric pollutants, while low wind speeds allow pollutant levels to rise (Galindo et al., 2011).

During the high pollution days, the correlation with air temperature was maintained only for PM_2.5 (r = –0.48), EC and POC (r = –0.60), Cl^– (r = –0.57) and Na⁺ (r = –0.48). The lack of statistically significant relations with temperature for most of the analyzed components may indicate that other factors, such as long-range transport events, regional recirculation episodes or complex photochemical reactions, to a greater extent could affect high concentrations of these substances (Galindo et al., 2011; Heo et al., 2013). Interestingly, for most of the analyzed substances, there were no statistically significant relationships with relative humidity. In view of the above considerations (see Section 3.3), this is particularly surprising in relation to secondary PM_2.5 components. It should be remembered that the resolution of the chemical analysis data could have an effect on the results obtained—every second daily sample was analyzed. Therefore, to examine the relationships between relative humidity, and SIA and SOC levels thoroughly, further studies are needed.

Nevertheless, a significant positive correlation with visibility was noted only for SOC (r = –0.45) and secondary inorganic ions (r = –0.59 to –0.74). The results obtained in the study are consistent with literature data (e.g., Lin et al., 2014; Wang et al., 2019) and confirm that the secondary aerosol was the major contributor to atmospheric visibility deterioration during high pollution events.

 
The Role of Atmospheric Circulation during Periods of High PM_2.5 Concentration

Due to the clear weakness of the relationship of measured chemical components with meteorological parameters during the periods of elevated PM_2.5 concentrations, the variability of the share of PM_2.5 main components (SIA, EC + POC, SOC) was checked against the atmospheric circulation types in the heating season (Fig. 5). The classification of circulation types according to Lityński (1969) was used in the study. It belongs to the group of automatic (objective) classifications and has been routinely used in the IMWM-NRI measurement network. This classification contains 27 types of circulation, including 8 cyclonic, 8 anticyclonic and 8 zero-types for a given inflow of air (N, NE, E, SE, S, SW, W, NW) as well as 3 types without an identified inflow of air mass—zero cyclone (0c; center of low pressure), zero saddle (0o) and zero anticyclonic (0a; center of high pressure).

Fig. 5. Concentrations of the total carbon (TC) and secondary inorganic aerosol during heating period of 2018 with an indication of the atmospheric circulation types and high pollution days: (a) Jan–Mar and (b) Oct–Dec.

The highest PM_2.5 concentrations were associated with the inflow of air masses from the SE, and were primarily caused by the increased content of carbonaceous compounds. TC concentrations peaked on 04 and 06 Mar (Episode 4); however, these days differed in the direction of air mass inflow and the concentrations of primary and secondary compounds. On 04 Mar, with the E_c circulation type, very high concentrations of EC (11.22 µg m^–3) and POC (41.05 µg m^–3) were noted, indicating a significant impact of local emission sources—National Freeway No. 45. This statement was confirmed by the maximum EC and POC shares in PM_2.5 recorded on 28 Feb (~41% and ~11%, respectively), with the E_a circulation type. Equally high share of EC + POC was found on 26 Feb with the inflow of air masses from the NE direction, which indicated an additional impact of the city center of Racibórz (~2.5 km northeast of the station).

Generally, POC clearly dominated over SOC, although the situation is different if we look at individual days of the heating season (Fig. 5). For example, on 06 Mar, with an inflow of air masses from the SE, the SOC concentration (44.80 µg m^–3) was more than 2.5 times higher than POC, with contribution in PM_2.5 of ~35%. Equally high SOC shares in PM_2.5 were found on 27 Jan, 10 Nov and 28 Nov, with similar advection directions. Therefore, high concentrations and shares of SOC during high pollution days were probably caused by regional transport of pollutants from the Rybnicko-Jastrzębska agglomeration—an important industrial center of southern Poland—or the inflow of polluted air masses from the Czech Republic. The impact of local emission sources was also not without significance, as was evidenced by the high/moderate TC shares recorded for types with no distinct inflow direction (e.g., 19 Jan, 23 Jan, 28 Mar).

In general, the SIA concentrations were ~2 times lower compared to TC concentrations (Table 3); however, this situation changed significantly on particular days (Fig. 5). The highest concentration of SIA (33.73 µg m^–3) was found on 06 Mar (circulation type: SE_c), although the share of SIA remained at moderate level (~26%). A very high share of SIA (~44%) was recorded on 08 Feb (Episode 2), with S₀ circulation type—this indicated an inflow of polluted air masses from the Czech Republic (Ostrava). The highest share of SIA in PM_2.5 from Racibórz (~45%) was registered on 04 Feb (circulation type: NE₀), at low PM_2.5 concentration and moderate TC share. A high share of SIA in PM_2.5 (~42%) was also noted on 19 Oct, with the N_a circulation type, relatively high PM_2.5 concentration and a minimum share of TC (~28%). The results obtained may indicate a large role of regional transport of pollutants from the area of Kędzierzyn-Koźle (~30 km north of the station), where large sources of the chemical industry are located. The inflow of gaseous precursors from the Upper Silesian Industrial District could have also a potentially large impact on the high share of SIA in PM_2.5.

 
CONCLUSIONS

To better comprehend the factors leading to periods of high pollution, we analyzed daily measurements of PM_2.5 conducted during 2018 at a suburban background station in Racibórz, which is located in Silesia Province, one of the most polluted regions of Poland. The concentrations of PM_2.5 and its chemical components varied during the study period, exhibiting characteristic seasonal trends associated with variations in the emission intensity of PM and its precursors from anthropogenic sources as well as changes in the prevailing meteorological conditions. The inhabitants of the analyzed area experienced much worse living conditions during the heating season, when the PM_2.5 concentration exceeded the European standard (25 w) on ~66% of the sampled days.

We identified 7 PM_2.5 episodes, which critically affected the air quality in Racibórz. In general, the TC fraction grew (to an average of ~52%) during the polluted periods (PM_2.5 > 50 w) owing to the increased OC (average: ~45%). When the PM_2.5 concentrations reached their maximum values (≥ 70 w), the percentage of SOC (contrary to that of POC) rose dramatically. In addition, the ionic portion was dominated by secondary inorganic ions, including NO₃^–, a key component of SIA, on polluted days. Overall, the fraction of SIA decreased as the level of pollution increased; however, we observed differences in this variability between the individual species.

Although specific weather conditions during the cold season were shown to favor the occurrence of PM_2.5 episodes, obviously weak correlations between certain meteorological parameters and the PM_2.5 component concentrations reveal the potential influence of regional and long-range transport events as well as the importance of secondary aerosol formation. We found that inflowing air masses from the following areas may have significantly contributed to air pollution in Racibórz: a) the Rybnicko-Jastrzębska agglomeration (SOC), b) the Czech Republic (Ostrava and Opava; SIA and SOC); and c) Kędzierzyn-Koźle and the Upper Silesian Industrial District (SIA). Local emission sources (e.g., road transport and fossil fuel combustion) also played a role, as evidenced by the high proportions of EC and POC in the PM_2.5 mass.

These results suggest that elevated concentrations of PM_2.5 in Racibórz and at similar sites in Poland are mainly due to secondary organic carbon and, to a lesser extent, NO₃^–, which confirms the specificity of this nation’s emission conditions, namely, the heavy use of fossil fuels in energy consumption, and electricity and heat production. This study provides valuable scientific data for developing strategies and programs to protect air quality, which should particularly focus on abating emissions of PM’s gaseous precursors. Regional and long-range transport should also be considered as potential factors, especially in non-urban areas.

ACKNOWLEDGMENTS

This work was supported by the Institute of Environmental Engineering of the Polish Academy of Sciences basic (statutory) Research Project No. 1a-119/2018 (Temporal and spatial variability of the chemical composition of atmospheric aerosols as a tool for assessing the effects of implementing air protection programs in Poland, 2018–2020). The authors also gratefully acknowledge the Institute of Meteorology and Water Management–National Research Institute for the provision of the meteorological data.

Aerosol Air Qual. Res. 20 :2433 -2447 . https://doi.org/10.4209/aaqr.2020.01.0013