Kraków is routinely affected by very high air pollution levels, especially during the winter months. Although a lot of effort has been made to characterize ambient aerosol, there is a lack of online and long-term measurements of non-refractory aerosol. Our measurements at the AGH University of Science and Technology provide the online long-term chemical composition of ambient submicron particulate matter (PM₁) between January 2018 and April 2019. Here we report the chemical characterization of non-refractory submicron aerosol and source apportionment of the organic fraction by positive matrix factorization (PMF). In contrast to other long-term source apportionment studies, we let a small PMF window roll over the dataset instead of performing PMF over the full dataset or on separate seasons. In this way, the seasonal variation in the source profiles can be captured. The uncertainties in the PMF solutions are addressed by the bootstrap resampling strategy and the random a-value approach for constrained factors. We observe clear seasonal patterns in the concentration and composition of PM₁, with high concentrations during the winter months and lower concentrations during the summer months. Organics are the dominant species throughout the campaign. Five organic aerosol (OA) factors are resolved, of which three are of a primary nature (hydrocarbon-like OA (HOA), biomass burning OA (BBOA) and coal combustion OA (CCOA)) and two are of a secondary nature (more oxidized oxygenated OA (MO-OOA) and less oxidized oxygenated OA (LO-OOA)). While HOA contributes on average 8.6 % ± 2.3 % throughout the campaign, the solid-fuel-combustion-related BBOA and CCOA show a clear seasonal trend with average contributions of 10.4 % ± 2.7 % and 14.1 %, ±2.1 %, respectively. Not only BBOA but also CCOA is associated with residential heating because of the pronounced yearly cycle where the highest contributions are observed during wintertime. Throughout the campaign, the OOA can be separated into MO-OOA and LO-OOA with average contributions of 38.4 % ± 8.4 % and 28.5 % ± 11.2 %, respectively.

Particulate chloride is an important component of fine particulate matter in marine air masses. Recent field studies also report elevated concentrations of gas-phase reactive chlorine species and particulate chloride related to anthropogenic activities. This work focuses on particulate chloride detection and quantification issues observed for some quadrupole aerosol chemical speciation monitors (Q-ACSMs) which are designed for the long-term measurement of ambient aerosol composition. The ACSM reports particle concentrations based on the difference between measurements of ambient air (sample mode) and particle-free ambient air (filter mode). For our long-term campaign in Krakow, Poland, the Q-ACSM reports apparent negative total chloride concentration for most of the campaign when analyzed with the default fragmentation table. This is the result of the difference signal from m∕z 35 (³⁵Cl⁺) being negative, which dominates over the positive difference signal from m∕z 36 (H³⁵Cl⁺). Highly time-resolved experiments with NH₄Cl, NaCl and KCl particles show that the signal response of m∕z 35 is non-ideal when the signal builds up and decreases slowly for all three salts, leading to a negative difference measurement. In contrast, the m∕z 36 signal exhibits a near step-change response for NH₄Cl during the sampling and filter period, resulting in a positive difference signal. The response of m∕z 36 for NaCl and KCl is not as prompt as for NH₄Cl but still fast enough to have a positive difference signal. Furthermore, it is shown that this behavior is mostly independent of vaporizer temperature. Based on these observations, this work presents an approach to correct the chloride concentration time series by adapting the standard fragmentation table coupled with a calibration of NH₄Cl to obtain a relative ionization efficiency (RIE) based on the signal at m∕z 36 (H³⁵Cl⁺). This correction can be applied to measurements in environments where chloride is dominated by NH₄Cl. Caution should be exercised when other chloride salts dominate the ambient particulate chloride.

In large urban agglomerations, car traffic is one of the main sources of particulate matter. It consists of particulate matter directly generated in the process of incomplete liquid fuel burning in vehicle engine, secondary aerosols formed from exhaust gaseous pollutants (NO_x, SO₂) as well as products of tires, brake pads and pavement abrasion. Krakow is one of the cities in Europe with the highest concentrations of particulate matter. The article presents the results of combined elemental, chemical and isotopic analyses of particulate matter PM₁₀ at two contrasting urban environments during winter and summer seasons. Daily PM₁₀ samples were collected during the summer and winter seasons of 2018/2019 at two stations belonging to the network monitoring air quality in the city. Mean PM₁₀ concentrations at traffic-dominated stations were equal to 35 ± 7 µg/m³ and 76 ± 28 µg/m³ in summer and winter, respectively, to be compared with 25.6 ± 5.7 µg/m³ and 51 ± 25 µg/m³ in summer and winter, respectively, recorded at the urban background station. The source attribution of analyzed PM₁₀ samples was carried out using two modeling approaches: (i) The Positive Matrix Factorization (PMF) method for elemental and chemical composition (concentrations of elements, ions, as well as organic and elemental carbon in daily PM₁₀ samples), and (ii) Isotope Mass Balance (IMB) for 13C and 14C carbon isotope composition of carbonaceous fraction of PM₁₀. For PMF application, five sources of particulate matter were identified for each station: fossil fuel combustion, secondary inorganic aerosols, traffic exhaust, soil, and the fifth source which included road dust, industry, construction work. The IMB method allowed the partitioning of the total carbon reservoir of PM₁₀ into carbon originating from coal combustion, from biogenic sources (natural emissions and biomass burning) and from traffic. Both apportionment methods were applied together for the first time in the Krakow agglomeration and they gave consistent results.

It is well documented that Southern Poland is one of the most polluted areas in Europe due to the highest airborne concentrations of particulate matter (PM). Concentrations of fine particles are especially high in winter. Apart from detailed number concentrations, it is essential to accurately identify and quantify specific particulate pollution sources. Only a few Polish research centers are involved in such experiments—among them is Krakow research group. For the most part, research focuses on collecting 24-h average samples from stationary PM samplers at ambient monitoring sites and quantifying the specific elements and chemical constituents in PM. This approach includes modeling methods that can use the variability in physical and chemical PM characteristics as an input dataset to identify possible sources of the particles. The objective of this paper is to provide research results based on data collected from June 2018 to May 2019 from a single monitoring station at a central urban site. Careful comparison of data obtained prior to a 2019 law prohibiting solid fuel burning in the city of Krakow with data (2019–2020) when a regulation went into effect should indicate progress by noting lower PM levels. This work has shown that the method applied and Krakow results might be of interest to the broader community in regions of high PM concentration.

Successful mitigation of air pollution in large cities requires information about the structure of emission sources and their contribution to total atmospheric load. The presented research demonstrates a possibility of application of isotope tracers for the estimation of contribution of different sources to the carbonaceous fraction of PM_2.5 (Particulate Matter containing fraction below 2.5 μm) collected in the urban atmosphere of Krakow, Poland during the summer and winter seasons. Isotope mass balance approach was used to perform source apportionment analysis for those two seasons. The analysis showed that the dominant source of the carbonaceous fraction of PM_2.5 in Krakow is coal burning during the winter season and biogenic emissions during the summer season. Sensitivity analysis revealed that the uncertainty of the percentage contribution of different sources to the overall carbon load of the analyzed PM_2.5 fraction is in order of a few percent.

Krakow is routinely affected by very high air pollution levels, especially during the winter months. Although a lot of effort has been done on characterization of ambient aerosols, there is a lack of online and long-term measurements of non-refractory aerosols. Our measurements at AGH University provide online long-term chemical composition of ambient submicron particulate matter (PM₁) between January 2018 and April 2019. Here we report the chemical characterization of non-refractory submicron aerosols and source apportionment of the organic fraction by positive matrix factorization (PMF). In contrast to other long-term source apportionment studies, we let a small PMF window roll over the dataset instead of performing PMF over the full dataset or on separate seasons. In this way, the seasonal variation of the source profiles can be captured. The uncertainties of the PMF solutions are addressed by the bootstrap resampling strategy and the random a-value approach for constrained factors. We observe clear seasonal patterns in concentration and composition of PM₁, with high concentrations during the winter months and lower concentrations during the summer months. Organics are the dominant species throughout the campaign. Five organic aerosol (OA) factors are resolved, of which three are of primary nature (hydrocarbon-like OA (HOA), biomass burning OA (BBOA) and coal combustion OA (CCOA)) and two are of secondary nature (more oxidized oxygenated OA (MO-OOA) and less oxidized oxygenated OA (LO-OOA)). While HOA contributes on average 8.6 % ± 2.3 % throughout the campaign, the solid fuel combustion related BBOA and CCOA show a clear seasonal trend with average contributions of 10.4 % ± 2.7 % and 14.1 %, ± 2.1 % respectively. The highest contributions are observed during wintertime as a result of residential heating. Throughout the campaign, the OOA can be separated into MO-OOA and LO-OOA with average contribution of 38.4 % ± 8.4 % and 28.5 % ± 11.2 %, respectively.