Characterization of PM10 sampled on the top of a former mining tower by the high-volume wind direction-dependent sampler using INNA

atmosphere Article Characterization of PM10 Sampled on the Top of a Former Mining Tower by the High-Volume Wind Direction-Dependent Sampler Using INNA Irena Pavlíková1,2,* , Daniel Hladký1,3, Oldˇrich Motyka 4, Konstantin N. Vergel 2, Ludmila P. Strelkova 2and Margarita S. Shvetsova 2   Citation: Pavlíková, I.; Hladký, D.; Motyka, O.; Vergel, K.N.; Strelkova, L.P.; Shvetsova, M.S. Characterization of PM10 Sampled on the Top of a Former Mining Tower by the High-Volume Wind Direction-Dependent Sampler Using INNA. Atmosphere 2021,12, 29. https://doi.org/10.3390/atmos12010029 Received: 3 December 2020 Accepted: 23 December 2020 Published: 28 December 2020 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2020 by the authors. LicenseeMDPI, Basel, Switzerland. This articleisanopenaccessarticledistributed under the terms and conditions of the CreativeCommonsAttribution(CCBY) license(https://creativecommons.org/ licenses/by/4.0/). 1Department of Environmental Protection in Industry, Faculty of Materials Science and Technology, VSB–Technical University of Ostrava, 708 00 Ostrava-Poruba, Czech Republic; [email protected] 2Sector of Neutron Activation Analysis and Applied Research, Frank Laboratory of Neutron Physics, Joint Institute for Nuclear Research, 141980 Dubna, Russia; [email protected]u (K.N.V.); [email protected].ru (L.P.S.); [email protected].ru (M.S.S.) 3Czech Hydrometeorological Institute, 708 00 Ostrava-Poruba, Czech Republic 4Nanotechnology Centre, VSB–Technical University of Ostrava, 708 00 Ostrava-Poruba, Czech Republic; [email protected] *Correspondence: [email protected] or [email protected]; Tel.: +420-604-636-041 Abstract: The PM 10 concentrations in the studied region (Ostravsko-karvinskáagglomeration, Czech Republic ) exceed air pollution limit values in the long-term and pose a significant problem for human health, quality of life and the environment. In order to characterize the pollution in the region and identify the pollution origin, Instrumental Neutron Activation Analysis (INAA) was employed for determination of 34 elements in PM 10 samples collected at a height of 90 m above ground level. From April 2018 to March 2019, 111 PM 10 samples from eight basic wind directions and calm and two smog situations were sampled. The elemental composition significantly varied depending on season and sampling conditions. The contribution of three important industrial sources (iron and steelworks, cement works) was identified, and the long-range cross-border transport representing the pollution from the Polish domestic boilers confirmed the most important pollution inflow during the winter season. Keywords: air pollution; PM 10 ; tower; high-volume sampler; wind-direction-dependent sampling; neutron activation analysis; elemental composition; cross-border pollution transport; AIR BORDER; Czech-Polish borderlands; Interreg 1. Introduction The studied area—Ostravsko-karvinskáagglomeration—is situated in the northeast part of the Czech Republic, in the Moravian-Silesian Region. The region, together with the adjacent cross-border Polish region of Silesia, is historically connected with the accumulation of black coal mining and heavy industry, namely energetics, coking plants and ironworks [ 1 , 2 ]. The particular industrial character of the region along with its topography (basin surrounded by fairly high mountain ranges) and local meteorological conditions [ 3 ] causes its specific air pollution problems. The strategic industrial development of the region in the 1950s [4,5] initiated intensive population growth connected with substantial emissions from households. This effect has persisted until the present, as coal is still the most widely used fuel in the Polish border area [ 6 , 7 ]. Thus, the region is one of the most polluted in Europe [ 8 ]. The air pollution significantly exceeds the limit values of particulate matter (PM 10 , PM 2.5 ), benzo[a]pyrene and ozone [ 4 , 8 , 9 ] according to European legislation [ 10 ] and World Health Organization (WHO) guidelines [ 11 , 12 ]. Both daily and long-term exposure to the substances mentioned above has a number of substantiated Atmosphere 2021,12, 29. https://doi.org/10.3390/atmos12010029 https://www.mdpi.com/journal/atmosphere Atmosphere 2021,12, 29 2 of 19 adverse biological effects [ 5 , 13 ]. Airborne particles increase mortality and morbidity, especially of the respiratory system, even at short-term exposures. The population exposed to PM shows a higher incidence of infectious diseases [ 14 – 16 ], and atmospheric pollution and PM pollution are factors classified as proven human carcinogens (category 1) [ 17 ]. Being one of the most densely populated regions in the Czech Republic (except its capital city) [18], the air pollution here poses an essential and long-term environmental problem. Substantial pollution in the region has challenged researchers to identify causes and look for solutions, starting in the 1990s with the US EPA project Silesia [ 19 , 20 ]; later on, the effort continued with projects AIR SILESIA [ 21 , 22 ] and AIR TRITIA [ 6 , 23 ]. Moreover, numerous case studies focused on the pollution origin were performed [ 24 – 26 ] together with the recently published study of the Czech Hydrometeorological Institute (CHMI) [27,28] . All the studies emphasized the role of both the industry and the transboundary pollution from Poland (caused mainly by domestic boilers). According to these studies and state air quality monitoring [ 9 , 29 ], the highest PM concentrations occur near the Czech–Polish border (characterized by more prominent growth in the colder half of the year and during smog events) and also close to important industrial sources where the limit values of PM happened to be reached not only during the winter season. The air quality in the region is significantly influenced by the rate and nature of cross-border pollution transmission along the most frequent wind directions (typically SW/NE), together with the inverse character of the weather with steady atmosphere and subsequently worsened dispersion conditions, which significantly contribute to increased air pollution during the winter. According to the available studies [ 6 , 22 , 27 , 30 ], the contribution of the cross-border PM pollution to the annual average values can vary from 20–40% depending on the location in the region, emissions and meteorological conditions in the year. The air quality monitoring presented in this study was performed within the AIR BORDER project focused on the cross-border pollution transport from Poland to the Ostravsko-karvinskáagglomeration (the Czech borderland) and vice versa. The aim of the study was to run a special monitoring campaign in order to characterize the transmission of the PM 10 particles from different groups of air pollution sources specific for the region, excluding the influence of the local sources [ 31 ]. This presumption was possible by locating a monitoring device on the top of a tower that reaches a height of over 85 m above the ground level. The device collects the PM 10 particles depending on the wind direction, which enables one to investigate from which directions and from which sources the pollution comes and to more precisely quantify its transfer within the region. 2. Experiments 2.1. Sampling The monitoring device was situated on the top of a former mining tower (90 m above ground level) located in HorníSuchávillage in the centre of the Ostravsko-karvinská agglomeration. (World Geodetic System 1984 coordinates 49.805166 N, 18.473954 E). The location of the tower in the region can be seen in Figure 1, the tower in Figure 2. Atmosphere 2021,12, 29 3 of 19 Atmosphere 2021, 12, x FOR PEER REVIEW 3 of 20 Figure 1. The location of the tower at a regional scale with plotted industrial PM10 emissions. (a) (b) Figure 2. The tower in the operation period of the 1980s (a) and presently (b). The SAM Hi 30 AUTO WIND is a fully automatic, remote-controlled sampler intended for gravimetric and chemical analyses of aerosol particles. The device samples particulate Atmosphere 2021, 12, x FOR PEER REVIEW 3 of 20 Figure 1. The location of the tower at a regional scale with plotted industrial PM10 emissions. (a) (b) Figure 2. The tower in the operation period of the 1980s (a) and presently (b). The SAM Hi 30 AUTO WIND is a fully automatic, remote-controlled sampler intended for gravimetric and chemical analyses of aerosol particles. The device samples particulate Figure 1. The location of the tower at a regional scale with plotted industrial PM10 emissions. Atmosphere 2021, 12, x FOR PEER REVIEW 3 of 20 Figure 1. The location of the tower at a regional scale with plotted industrial PM10 emissions. (a) (b) Figure 2. The tower in the operation period of the 1980s (a) and presently (b). The SAM Hi 30 AUTO WIND is a fully automatic, remote-controlled sampler intended for gravimetric and chemical analyses of aerosol particles. The device samples particulate Figure 2. The tower in the operation period of the 1980s (a) and presently (b). Atmosphere 2021,12, 29 4 of 19 For the particulate matter sampling, the high-volume sampler SAM Hi 30 AUTO WIND (Baghirra Ltd., Prague, Czech Republic) was used. The sampler operates respecting the Guidelines on high-volume sampling methods that can be found in the Compendium of Methods for the Determination of Inorganic Compounds in Ambient Air compiled by the United States Environmental Protection Agency (US EPA) [ 32 ]; a summary of the research of high-volume samplers performance can be found elsewhere [33]. The SAM Hi 30 AUTO WIND is a fully automatic, remote-controlled sampler intended for gravimetric and chemical analyses of aerosol particles. The device samples particulate matter with a <10 µ m diameter (PM 10 ) using the DIGITEL DPM10/30/00 PM 10 pre-separator for 30 m 3 /h according to EN 12341 [ 34 ]. The sampler was designed to work depending on wind conditions. It has a magazine of 15 filters (glass microfiber, Whatman GF/A, Ø 150 mm) stretched in filter holders that are automatically changed to the sampling position according to actually evaluated wind conditions. Thus the sampler was able to collect PM 10 particles from eight basic wind directions (N, NE, E, SE, S, SW, W, NW) and CALM (wind speed < 0.2 m · s −1 ). The sampler can be seen in Figure 3. Both wind speed and wind direction were measured via the WindSonic ™ SDI-12 anemometer ( Gill Instruments Ltd. , Lymington, UK). The wind direction and wind speed for selecting a particular sampling filter from the magazine were determined according to one-hour moving averages calculated from 10-min data in accordance with US EPA guidance [ 35 ]. Thus, the actual wind may be different. The wind roses representing the measured wind direction in comparison with the sampled sector can be seen in Figure 4. Moreover, a special filter was designed for the episodes with extreme air pollution, defined as three successive average hourly PM 10 concentrations exceeding 100 µ g · m −3 , using 10-min data from continuous ground monitoring. Atmosphere 2021, 12, x FOR PEER REVIEW 4 of 20 matter with a <10 μm diameter (PM10) using the DIGITEL DPM10/30/00 PM10 pre-separator for 30 m3/h according to EN 12341 [34]. The sampler was designed to work depending on wind conditions. It has a magazine of 15 filters (glass microfiber, Whatman GF/A, Ø 150 mm) stretched in filter holders that are automatically changed to the sampling position according to actually evaluated wind conditions. Thus the sampler was able to collect PM10 particles from eight basic wind directions (N, NE, E, SE, S, SW, W, NW) and CALM (wind speed < 0.2 m·s−1). The sampler can be seen in Figure 3. Both wind speed and wind direction were measured via the WindSonic™ SDI-12 anemometer (Gill Instruments Ltd., Lymington, UK). The wind direction and wind speed for selecting a particular sampling filter from the magazine were determined according to one-hour moving averages calculated from 10-min data in accordance with US EPA guidance [35]. Thus, the actual wind may be different. The wind roses representing the measured wind direction in comparison with the sampled sector can be seen in Figure 4. Moreover, a special filter was designed for the episodes with extreme air pollution, defined as three successive average hourly PM10 concentrations exceeding 100 μg·m−3, using 10-min data from continuous ground monitoring. Employing this method, 111 PM10 samples from eight wind directions and calm (and extreme air pollution if occurring) were collected for each month from April 2018 to March 2019 (total of 12 months). Figure 3. The SAM Hi 30 AUTO WIND sampler. Figure 3. The SAM Hi 30 AUTO WIND sampler. Atmosphere 2021,12, 29 5 of 19 Atmosphere 2021, 12, x FOR PEER REVIEW 5 of 20 Figure 4. Matching of sampled sectors and measured wind directions. 2.2. Determination of PM10 Mass Concentrations PM10 mass concentrations were determined following the guidelines of EN 12341 [34]. Filters were weighed using an analytical balance (Sartorius MC 210P) before and after sampling. The filters were conditioned for ≥ 48 h under controlled relative humidity (50% ± 5%) and temperature (20 °C ± 1 °C), then weighed for a first time, followed by a second weighing after additional conditioning for ≥ 12 h for filters prior to the sampling and for 24 to 72 h for filters after the sampling. In accordance with the requirements, the difference of weighing results was ≤40 μg for filters prior to the sampling and ≤60 μg for filters after the sampling. The filter weight was calculated as an average of the two results. Weights for the blank filters were also recorded. The collected PM10 mass was calculated by subtracting pre-weight from the post-weight of the filters. 2.3. Element Content Determination Using Neutron Activation Analysis One of the premises of this study was to apply NAA at the IBR-2 reactor of the Joint Institute for Nuclear Research (Russia) for the characterisation of sampled PM. Thus, the testing of filters convenient for the analyses preceded the sampling campaign. Six different filters comprising five different materials were tested (glass microfiber, quartz, PTFE membrane, cellulose and paper). Only glass microfiber filters were determined as being fully suitable; a quartz filter was more problematic for repacking and considering the price, the glass microfiber filters were chosen to be used for the sampling. Before subjecting the samples to NAA, the preparation of the subsamples of exposed and blank filters was done, as the irradiation capsules of the applied pneumatic transport system have limited volume (Ø 18 mm) and the whole filter is not able to fit in it. This also allows putting more subsamples in one capsule so the subsamples from one month and the corresponding blank filter can be irradiated all together under the same conditions. For this purpose, a special automatic punching head was designed and made (used materials: stainless steel, Teflon and surface finish synthetic rubber). Prior to cutting, filters were folded in half to avoid the loss of collected material, and then 4 circles (Ø 16 mm) were cut from the folded filter using the layering of subsamples. This way, one subsample of each filter (counting eight layers of the filter) was prepared reaching a weight of 0.06–0.07 g depending on the exposure. The preparation of the filters took place under a relative humidity of 50% Figure 4. Matching of sampled sectors and measured wind directions. Employing this method, 111 PM 10 samples from eight wind directions and calm (and extreme air pollution if occurring) were collected for each month from April 2018 to March 2019 (total of 12 months). 2.2. Determination of PM10 Mass Concentrations PM 10 mass concentrations were determined following the guidelines of EN 12341 [ 34 ]. Filters were weighed using an analytical balance (Sartorius MC 210P) before and after sampling. The filters were conditioned for ≥ 48 h under controlled relative humidity ( 50% ±5% ) and temperature (20 ◦ C ± 1 ◦ C), then weighed for a first time, followed by a second weighing after additional conditioning for ≥ 12 h for filters prior to the sampling and for 24 to 72 h for filters after the sampling. In accordance with the requirements, the difference of weighing results was ≤ 40 µ g for filters prior to the sampling and ≤ 60 µ g for filters after the sampling. The filter weight was calculated as an average of the two results. Weights for the blank filters were also recorded. The collected PM 10 mass was calculated by subtracting pre-weight from the post-weight of the filters. 2.3. Element Content Determination Using Neutron Activation Analysis One of the premises of this study was to apply NAA at the IBR-2 reactor of the Joint Institute for Nuclear Research (Russia) for the characterisation of sampled PM. Thus, the testing of filters convenient for the analyses preceded the sampling campaign. Six different filters comprising five different materials were tested (glass microfiber, quartz, PTFE membrane, cellulose and paper). Only glass microfiber filters were determined as being fully suitable; a quartz filter was more problematic for repacking and considering the price, the glass microfiber filters were chosen to be used for the sampling. Before subjecting the samples to NAA, the preparation of the subsamples of exposed and blank filters was done, as the irradiation capsules of the applied pneumatic transport system have limited volume (Ø 18 mm) and the whole filter is not able to fit in it. This also allows putting more subsamples in one capsule so the subsamples from one month and the corresponding blank filter can be irradiated all together under the same conditions. For this purpose, a special automatic punching head was designed and made (used materials: stainless steel, Teflon and surface finish synthetic rubber). Prior to cutting, filters were folded in half to avoid the loss of collected material, and then 4 circles (Ø 16 mm) were cut from the folded filter using the layering of subsamples. This way, one subsample of Atmosphere 2021,12, 29 6 of 19 each filter (counting eight layers of the filter) was prepared reaching a weight of 0.06–0.07 g depending on the exposure. The preparation of the filters took place under a relative humidity of 50% ( ± 5%) and temperature of 20 ◦ C ( ± 1 ◦ C). After the preparation, each subsample was vacuum-packed to be transported for NAA. The NAA procedure started with unpacking the subsamples and weighing them under controlled relative humidity and temperature. Then the subsamples were immediately packed in polyethylene and aluminium cups for short-term and long-term irradiation, respectively. Once packed, they were put into irradiation capsules and transported to the reactor. The employed NAA provides the activation with thermal and epithermal neutrons at low temperatures convenient for this type of samples. Complete information about samples irradiation, measurement and quality can be found elsewhere [36,37]. For short-term irradiation, Channel 2 (epithermal neutrons, flux density ϕepi = 2.0 × 1011 cm −2· s −1 ) was used with an irradiation time of about 3 min. Samples were measured with 3–5 min. decay after irradiation for 15 min. Al, Ca, Cl, I, Mg, Mn, Si, Ti and V isotopes were determined in this way. For long-term irradiation, Cd screened Channel 1 (epithermal neutrons, flux density ϕepi = 2.0 × 1011 cm −2· s −1 ) was used with an irradiation time of around 4 days . After 4-days cooling, the samples were repacked and measured twice. The first time, they were measured directly after repacking for 30 min to determine As, Br, K, La, Na, Mo, Sm, U and W, and the second time 20 days after the end of irradiation for 1.5 h to determine Ba, Ce, Co, Cr, Cs, Eu, Fe, Hf, Ni, Rb, Sb, Si, Sc, Se, Sr, Ta, Tb, Th, W, Zn and Zr. Gamma spectra of activated samples were measured on HPGe detectors (resolution of 1.9 keV for the 60 Co 1332 keV line, efficiency 40%). The gamma spectra obtained were then processed using GENIE-2K software (CANBERRA) with the verification of the peak fit in an interactive mode. The concentrations of elements were calculated using certified reference materials irradiated simultaneously with samples via “CalcConc” software developed in the Frank Laboratory of Neutron Physics, Joint Institute for Nuclear Research [ 37 ]. The element concentrations were calculated by subtracting the corresponding blank filter values from determined values of the element in the subsample and recalculating using the mass concentrations of PM 10 . In cases of values below the detection limit, half of the detection limit was used. In case of missing values (no data) caused by technical problems during the analysis, the data imputation was employed in order to retain the information in the dataset and allow the proper multivariate assessment [ 38 ]. The k-nearest neighbours algorithm (knn) [39] was applied for the missing non-sub-limit data. Recommendations of US EPA [ 40 ] were kept with respect to the workplace standard operating procedures. The quality control of the NAA results was ensured by triplicating standards per batch of unknown samples and carrying out simultaneous analysis. For filter analyses, standard reference materials were used: 2709a–San Joaquin Soil Baseline Trace Element Concentrations from the National Institute of Standards and Technology (NIST), 2710a–Montana I Soil Highly Elevated Trace Element Concentrations (NIST), 2711a– Montana II Soil Moderately Elevated Trace Element Concentrations (NIST), 1632c Trace Elements in Coal (Bituminous) (NIST), 1633c Trace Elements in Coal Fly Ash (NIST), AGV-2 Andesite from the United States Geological Survey and 433 from the Institute for Reference Materials and Measurements (IRMM). Satisfying agreement between the experimental results and certified material was obtained. The accuracy was formulated as the percentage deviation from the certified value amount up to 10%. 2.4. Statistical Analyses and Visualization Pearson correlations calculation, Principal Component Analysis (PCA) and the visualizations of the results were performed in the R environment [ 41 ], package FactoMineR [42,43] , Openair [ 44 ] and ggplot2 [ 45 ]. Prior to the PCA, the data were transformed according to the compositional data analysis (CoDa) principles [ 46 ] using the centred log-ratio (clr) transformation [ 47 ]. Only the clr-transformed elemental concentration data were used Atmosphere 2021,12, 29 7 of 19 for the construction of the model, and the information on the wind direction, calm and inversion situation were added as supplementary variables. 3. Results 3.1. PM10 Mass Concentrations The values of PM 10 concentrations corresponding to wind directions, calm and inversion situations are shown for year average, warm (from April to September) and cold (from October to March) seasons for the period of observation (04/2018–03/2019) in Table 1and Figure 5a . The values recorded during inversion events are not included in the calculation of means for respective wind directions considering the set-up function of the sampling device (see Section 2.1). For the PM 10 concentration synopsis, see Table S1 the Supplementary materials. Table 1. Average PM10 concentrations for the observed period (µg·m−3). Wind Conditions Average 1Warm Season Average Z-Score 2Cold Season Average 1Z-Score 2 CALM 23.3 19.0 −0.35 27.6 1.16 N 22.9 14.6 −1.12 31.2 1.79 NE 21.7 16.5 −0.79 27.0 1.05 E 25.0 17.8 −0.56 32.3 1.97 SE 21.1 17.5 −0.60 24.6 0.64 S 21.9 16.9 −0.71 27.0 1.05 SW 14.8 14.5 −1.14 15.1 −1.04 W 21.3 23.6 0.45 19.0 −0.35 NW 16.8 15.9 −0.89 17.8 −0.56 Inversion - - 59.0 1 Averages calculated for the concerned period (04/2018–03/2019), the inversion values not included. 2Z-score related to the average calculated for the concerned period (04/2018–03/2019). Atmosphere 2021, 12, x FOR PEER REVIEW 7 of 20 3. Results 3.1. PM10 Mass Concentrations The values of PM10 concentrations corresponding to wind directions, calm and inversion situations are shown for year average, warm (from April to September) and cold (from October to March) seasons for the period of observation (04/2018–03/2019) in Table 1 and Figure 5a. The values recorded during inversion events are not included in the calculation of means for respective wind directions considering the set-up function of the sampling device (see Section 2.1). For the PM10 concentration synopsis, see Table S1 the Supplementary materials . The lowest PM10 values were observed from the SW and NW directions regardless of the season. The concentrations were below average in the warm part of the year, with the exception of the west direction due to the peak concentration in August 2018 (Figure 6). The highest concentrations were sampled from the east and north during the cold season, though prevailing wind direction in the season was SW Figure 5b. This wind direction in the cold season was the prevailing wind direction for the whole period of observation, as Figure 5b shows (expressed as a sampled air volume). Calm was recorded for 12% of the sampling time. Table 1. Average PM10 concentrations for the observed period (μg·m−3). Wind Conditions Average 1 Warm Season Average Z-Score 2 Cold Season Average 1 Z-Score 2 CALM 23.3 19.0 −0.35 27.6 1.16 N 22.9 14.6 −1.12 31.2 1.79 NE 21.7 16.5 −0.79 27.0 1.05 E 25.0 17.8 −0.56 32.3 1.97 SE 21.1 17.5 −0.60 24.6 0.64 S 21.9 16.9 -0.71 27.0 1.05 SW 14.8 14.5 −1.14 15.1 −1.04 W 21.3 23.6 0.45 19.0 −0.35 NW 16.8 15.9 −0.89 17.8 -0.56 Inversion - - 59.0 1 Averages calculated for the concerned period (04/2018–03/2019), the inversion values not included. 2 Z-score related to the average calculated for the concerned period (04/2018–03/2019). (a) (b) Figure 5. The average PM10 concentrations (a) and wind rose for the observed period (b). Figure 5. The average PM10 concentrations (a) and wind rose for the observed period (b). The lowest PM 10 values were observed from the SW and NW directions regardless of the season. The concentrations were below average in the warm part of the year, with the exception of the west direction due to the peak concentration in August 2018 (Figure 6). The highest concentrations were sampled from the east and north during the cold season, though prevailing wind direction in the season was SW Figure 5b. This wind direction in Atmosphere 2021,12, 29 8 of 19 the cold season was the prevailing wind direction for the whole period of observation, as Figure 5b shows (expressed as a sampled air volume). Calm was recorded for 12% of the sampling time. Atmosphere 2021, 12, x FOR PEER REVIEW 8 of 20 (a) (b) Figure 6. The monthly average PM10 concentrations for warm (a) and cold (b) season. Despite the peak concentration in August 2018, there was, in general, no directionality of the pollution in the warm season, as the concentration rose in Figure 6a shows. The peak August concentration originated most likely from the metallurgical complex in the west of the sampling site (see Figure 1), as the elemental composition of this sample also suggests (see Section 3.2). According to the meteorological data, this high concentration occurred in the period of steady cyclonic airflow (wind speed from calm to 2 m/s) preceding an upcoming cold front (see Figure 7). The meteorological situation for this event is illustrated via the ICON EU model data 10 m AGL and the Skew-T diagram as the global model (ICON, NOAA) in this situation was in total disagreement with recorded wind speed and wind direction data on the sampling site. The April concentration rose has a similar course to the one for March (Figure 6). Considering the elements found in samples of these two months and the results of PCA (see Section 3.3), a similar origin of the pollution is expected. Thus, the April concentrations should be considered to appertain rather to the cold-season-related pollution sources. During the winter season, the pollution came predominantly from the north, northeast and east directions, as the monthly average PM10 concentrations show in Figure 6b. This clearly confirms the importance of the PM inflow from the Polish borderland. The increase of PM10 concentrations from these directions in the winter season average 14 μg·m−3 though the prevailing airflow in winter is from the inverse direction (Figure 5b). Figure 6. The monthly average PM10 concentrations for warm (a) and cold (b) season. Despite the peak concentration in August 2018, there was, in general, no directionality of the pollution in the warm season, as the concentration rose in Figure 6a shows. The peak August concentration originated most likely from the metallurgical complex in the west of the sampling site (see Figure 1), as the elemental composition of this sample also suggests (see Section 3.2). According to the meteorological data, this high concentration occurred in the period of steady cyclonic airflow (wind speed from calm to 2 m/s) preceding an upcoming cold front (see Figure 7). The meteorological situation for this event is illustrated via the ICON EU model data 10 m AGL and the Skew-T diagram as the global model (ICON, NOAA) in this situation was in total disagreement with recorded wind speed and wind direction data on the sampling site. Atmosphere 2021, 12, x FOR PEER REVIEW 9 of 20 (a) (b) Figure 7. The modelled wind fields (a) and the Skew-T diagram at the Prostějov sounding station (b) for August 2018 peak concentrations [48,49]. High PM 10 concentration was sampled in this season also from the south direction in February 2019. As there is no important pollution source in this direction (mountainous rural area), this peak was investigated closer. According to the meteorological data, the airflow was steady (wind speed 1–2 m/s), coming from the southwest via the Moravian Gate for more than one day, accompanied by a radiation inversion (see Figure 8). This indicates that the peak concentration originated from an important pollution source in the south part of the Moravian Gate, directing to the cement works near the city of Hranice (about 50 km from the sampling site), as the elemental composition of this sample confirms (see below in Section 3.2). (a) (b) Figure 8. The modelled wind fields (a) and backward trajectories (b) for February 2019 peak concentrations [48,50,51]. During the winter season, there were two smog events sampled, the first on 19 and 23 November 2018 (sampled on the same filter), with the PM 10 concentration reaching 60.5 Figure 7. The modelled wind fields ( a ) and the Skew-T diagram at the Prostˇejov sounding station ( b ) for August 2018 peak concentrations [48,49]. Atmosphere 2021,12, 29 9 of 19 The April concentration rose has a similar course to the one for March (Figure 6). Considering the elements found in samples of these two months and the results of PCA (see Section 3.3), a similar origin of the pollution is expected. Thus, the April concentrations should be considered to appertain rather to the cold-season-related pollution sources. During the winter season, the pollution came predominantly from the north, northeast and east directions, as the monthly average PM 10 concentrations show in Figure 6b. This clearly confirms the importance of the PM inflow from the Polish borderland. The increase of PM 10 concentrations from these directions in the winter season average 14 µ g · m −3 though the prevailing airflow in winter is from the inverse direction (Figure 5b). High PM 10 concentration was sampled in this season also from the south direction in February 2019. As there is no important pollution source in this direction (mountainous rural area), this peak was investigated closer. According to the meteorological data, the airflow was steady (wind speed 1–2 m/s), coming from the southwest via the Moravian Gate for more than one day, accompanied by a radiation inversion (see Figure 8). This indicates that the peak concentration originated from an important pollution source in the south part of the Moravian Gate, directing to the cement works near the city of Hranice (about 50 km from the sampling site), as the elemental composition of this sample confirms (see below in Section 3.2). Atmosphere 2021, 12, x FOR PEER REVIEW 9 of 20 (a) (b) Figure 7. The modelled wind fields (a) and the Skew-T diagram at the Prostějov sounding station (b) for August 2018 peak concentrations [48,49]. High PM 10 concentration was sampled in this season also from the south direction in February 2019. As there is no important pollution source in this direction (mountainous rural area), this peak was investigated closer. According to the meteorological data, the airflow was steady (wind speed 1–2 m/s), coming from the southwest via the Moravian Gate for more than one day, accompanied by a radiation inversion (see Figure 8). This indicates that the peak concentration originated from an important pollution source in the south part of the Moravian Gate, directing to the cement works near the city of Hranice (about 50 km from the sampling site), as the elemental composition of this sample confirms (see below in Section 3.2). (a) (b) Figure 8. The modelled wind fields (a) and backward trajectories (b) for February 2019 peak concentrations [48,50,51]. During the winter season, there were two smog events sampled, the first on 19 and 23 November 2018 (sampled on the same filter), with the PM 10 concentration reaching 60.5 Figure 8. The modelled wind fields (a) and backward trajectories (b) for February 2019 peak concentrations [48,50,51]. During the winter season, there were two smog events sampled, the first on 19 and 23 November 2018 (sampled on the same filter), with the PM 10 concentration reaching 60.5 µg·m−3 , and the second on 23 March 2019, with a PM 10 concentration of 57.4 µ g · m −3 . In both cases, there was a temperature inversion connected with a steady airflow (measured wind speed < 1 m/s). In November, the prevailing airflow was from the NE, E and SE directions. Most likely representing the inflow of the pollution from the metallurgical complex on the southeast of the sampling site as confirmed by models (see Figure 9). For the March smog situation, the modelled airflow indicates the direction from the NE and E, suggesting the origin of pollution in the Polish borderland (see Figure 10). The elemental composition of these samples is stated in the next chapter. Atmosphere 2021,12, 29 16 of 19 should be noted that NAA does not provide information on important elements such as Cd, Cu, Hg or Pb, the obtained information is, in the majority of cases, sufficient to identify the pollution source. To collect more data on the pollution transfer in the region during meteorologically different years and to render the assessment more precise, the monitoring on the tower continues. In addition to the sampling device used in this study, the PM continual monitoring is now operated both on the top of the tower and on the ground level. 5. Conclusions A specially designed high-volume sampler (SAM Hi 30 AUTO WIND) was used to collect PM 10 samples depending on airflow conditions. The sampler was located on the top of a former mining tower in 90 m AGL. This allowed the elimination of the influence of local sources and investigation of the regional pollution transport. The sampled particulate matter was analysed using the neutron activation analysis, which provided information on the content of 34 elements. This information—together with the PM 10 concentrations and meteorological data (measured and modelled)—was used to characterize the pollution origin in the region. A significant difference in the element composition was observed: elemental concentrations were dependent on both the season and the sampling direction. Contribution of three industrial sources, two ironworks (in the west and in the southeast) and a cement plant (southwest from the sampling site) was identified, showing that— though not detected by ground air pollution monitoring—these sources have a significant impact on the pollution transfer in the region. The measurements also confirmed that the PM 10 cross-border pollution inflow from Poland plays a crucial role during the winter season and contributes significantly to the air pollution in the whole studied region. Currently, the air quality management and decision-making in the region are performed just on a local level without considering the cross-border impacts. However, to assure that the air quality meets the air pollution limit values, international cooperation and the legitimacy of the joint interregional approach to the air quality management is imperative, as proven herein [9]. Supplementary Materials: The following are available online at https://www.mdpi.com/2073-443 3/12/1/29/s1, Figure S1: The element concentration roses for the warm season (ng · m −3 ), Figure S2: The element concentration roses for the cold season (ng · m −3 ), Figure S3: The average element concentration according to the wind direction for the warm (plain) and cold seasons (shaded) (ng · m −3 ), Figure S4: The average element concentration according to the wind direction for the cold season (plain) and smog events (shaded) (ng · m −3 ), Table S1: Synoptic table (minimum, maximum value, mean, median and standard deviation) of elements concentration (ng · m −3 ) determined in PM 10 using INAA. Table S2: Synoptic table (minimum, maximum value, mean and standard deviation) of PM10 concentration (µg·m−3). Author Contributions: Conceptualization, I.P.; validation, I.P., D.H. and O.M.; formal analysis, O.M.; investigation, D.H. and I.P.; data curation, O.M. and D.H.; resources (performing NAA), K.N.V., L.P.S. and M.S.S.; writing—original draft preparation, I.P. and O.M.; writing—review and editing, I.P., O.M. and D.H.; visualization, D.H. and O.M.; supervision, I.P.; project administration, I.P. All authors have read and agreed to the published version of the manuscript. Funding: The research was carried out within the project “AIR BORDER–Joint Czech–Polish Measurements of Cross-border Transmission of Air Pollutants” funded by the cooperation programme Interreg V-A in the Czech Republic and Poland co-financed by European Regional Development Fund (ERDF), grant number CZ.11.4.120/0.0/0.0/15_006/0000118. The NAA performance was funded within the “3+3 Project” for the cooperation with the Joint Institute for Nuclear Research by Joint Institute for Nuclear Research, grant number 29/209, theme No. 03-4-1128-2017/2019. The work was supported by European Regional Development Fund/European Social Fund—New Composite Materials for Environmental Applications (No. CZ.02.1.01/0.0/0.0/17_048/0007399). Data Availability Statement: The data presented in this study are available in article and supplementary materials. Atmosphere 2021,12, 29 17 of 19 Acknowledgments: We would like to thank Petr Janˇcík, the coordinator of the AIR BORDER project (VSB—Technical University of Ostrava, Ostrava, Czech Republic); Marina V. Frontasyeva (Joint Institute of Nuclear Research, Dubna, Russia), the coordinator of the 3 + 3 project on the side of Joint Institute of Nuclear research; Sergey S. Pavlov (Joint Institute of Nuclear Research, Dubna, Russia) for the methodological leadership of the team performing the Neutron Activation Analyses and analysis results processing. Further, we would like to thank Petra Šutarová(VSB—Technical University of Ostrava, Ostrava, Czech Republic) for financial management and administrative support of the projects. The authors also gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and/or READY website (https://www.ready.noaa.gov) used in this publication. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. References 1. Klusácek, P. Downsizing of bituminous coal mining and the restructuring of steel works and heavy machine engineering in the Ostrava region. Moriv. 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