Ambient particulate matter (PM) was sampled in Zabrze (southern Poland) in the heating period of 2009. It was investigated for distribution of its mass and of the masses of its 18 component elements (S, Cl, K, Ca, Cr, Mn, Fe, Ni, Cu, Zn, Ge, As, Br, Sr, Cd, Sb, Ba, and Pb) among 13 PM size fractions. In the paper, the distribution modality of and the correlations between the ambient concentrations of these elements are discussed and interpreted in terms of the source apportionment of PM emissions. By weight, S, Cl, K, Ca, Cr, Mn, Fe, Ni, Cu, Zn, Ge, As, Br, Sr, Cd, Sb, Ba, and Pb were 10 % of coarse and 9 % of ultrafine particles. The collective mass of these elements was no more than 3.5 % of the mass of the particles with the aerodynamic diameter Dp between 0.4 and 1.0 μm (PM0.4–1), whose ambient mass concentration was the highest. The PM mass size distribution for the sampling period is bimodal; it has the accumulation and coarse modes. The coarse particles were probably of the mineral/soil origin (characteristic elements: Ca, Fe, Sr, and Ba), being re-suspended polluted soil or road dust (characteristic elements: Ca, Fe, Sr, Ba, S, K, Cr, Cu, Zn, Br, Sb, Pb). The maxima of the density functions (modes) of the concentration distributions with respect to particle size of PM-bound S, Cl, K, Cu, Zn, Ge, Br, Cd, Sb, and Pb within the Dp interval from 0.108 to 1.6 μm (accumulation PM particles) indicate the emissions from furnaces and road traffic. The distributions of PM-bound As, Mn, Ba, and Sr concentrations have their modes within Dp ≤ 0.108 μm (nucleation PM particles), indicating the emissions from high-temperature processes (industrial sources or car engines). In this work, principal component analysis (PCA) is applied separately to each of the 13 fraction-related sets of the concentrations of the 18 PM-bound elements, and further, the fractions are grouped by their origin using cluster analysis (CA) applied to the 13 fraction-related first principal components (PC1). Four distinct groups of the PM fractions are identified: (PM1.6–2.5, PM2.5–4.4,), (PM0.03–0.06, PM0.108–0.17), (PM0.06–0.108, PM0.17–0.26, PM0.26–0.4, PM0.4–0.65, PM0.65–1, PM1–1.6), and (PM4.4–6.8, PM6.8–10, PM>10). The PM sources attributed to these groups by using PCA followed by CA are roughly the same as the sources from the apportionment done by analyzing the modality of the mass size distributions.

Abstract:The aim of this study was to investigate how atmospheric air pollutants and meteorological conditions affected atmospheric visibility in the largest Polish agglomeration. The correlation analysis, principal component analysis (PCA) and generalized regression models (GRMs) were used to accomplish this objective. The meteorological parameters (temperature, relative humidity, precipitation, wind speed and insolation) and concentrations of the air pollutants (PM10, SO2, NO2, CO and O3) were recorded in [2004][2005][2006][2007][2008][2009][2010][2011][2012][2013]. The data came from the Ursynów-SGGW, MzWarszUrsynów and Okęcie monitoring stations, located in the south of Warsaw (Poland). It was shown that the PM10 concentration was the most important parameter affecting the visibility in Warsaw. The concentration, and indirectly the visibility, was mainly affected by the pollutant emission from the flat/building heating (combustion of various fuels). It changed intensively during the OPEN ACCESSAtmosphere 2015, 6 1155 research period. There were also periods in which this emission type did not have a great influence on the pollutant concentrations (mainly PM10) and visibility. In such seasons, the research revealed the influence of the traffic emission and secondary aerosol formation processes on the visibility.

It is essential in pulmonary disease research to take into account traffic-related air pollutant exposure among urban inhabitants. In our study, 4985 people were examined for spirometric parameters in the presented research which was conducted in the years 2008–2012. The research group was divided into urban and rural residents. Traffic density, traffic structure and velocity, as well as concentrations of selected air pollutants (CO, NO2 and PM10) were measured at selected areas. Among people who live in the city, lower percentages of predicted values of spirometric parameters were noticed in comparison to residents of rural areas. Taking into account that the difference in the five-year mean concentration of PM10 in the considered city and rural areas was over 17 μg/m3, each increase of PM10 by 10 μg/m3 is associated with the decline in FEV1 (forced expiratory volume during the first second of expiration) by 1.68%. These findings demonstrate that traffic-related air pollutants may have a significant influence on the decline of pulmonary function and the growing rate of respiratory diseases.

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Respiratory syncytial virus (RSV) contributes significantly to pediatric hospitalizations. An association between air pollution and an increased number of RSV cases has been suggested. We sought to evaluate the short-term impact of air pollutants on RSV hospitalizations in Polish children in the period 2010–2019. Daily concentrations of PM10 and PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 10 μm and 2.5 μm, respectively) and nitrogen dioxide (NO2) were analyzed in general regression models (GRM) to establish their influence and full interaction scheme. Significant seasonal and annual periodicity among 53,221 hospitalizations was observed; finally, data from the 2012–2019 RSV high-risk seasons created models for seven agglomerations. The addition of PM2.5, PM10, and NO2 to the basic model for RSV seasonality explained 23% (4.9–31%, univariate model) to 31.4% (8.4–31%, multivariate model) of the variance in RSV hospitalizations. A 10 μg/m3 increase in PM2.5, PM10, and NO2 concentrations was associated with 0.134 (0.087–0.16), 0.097 (0.031–0.087), and 0.212 (0.04–0.29) average increases in hospitalizations, respectively. In the multivariate models, PM2.5, PM10, and NO2 alone, as well as PM2.5–NO2, PM2.5–PM10, and PM10–NO2 interactions, were associated with hospitalizations in some of the locations, while the metaregression showed statistically significant interactions between each of the pollutants, and between the pollutants and the year of the study. The inclusion of PM2.5, PM10, and NO2 in GRM explains a significant number of RSV hospitalizations. The pollutants act alone and interact together in a varied manner. Reducing air contamination might decrease the costs of hospital healthcare.