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    Wyświetlanie 1-3 z 3
    Tytuł:
    Effect of air pollution on visibility in urban conditions. Warsaw case study
    Autorzy:
    Majewski, G.
    Czechowski, P O
    Badyda, A.
    Brandyk, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/207001.pdf
    Data publikacji:
    2014
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    visibility
    air pollution
    pollutants
    mathematical models
    nitrogen oxides
    sulfur dioxide
    urban conditions
    urban air pollution
    urban atmosphere
    particle matter (PM)
    przejrzystość powietrza
    zanieczyszczenie powietrza
    modele matematyczne
    tlenki azotu
    zanieczyszczenia
    dwutlenek siarki
    warunki miejskie
    pyły zawieszone
    Opis:
    The influence of air pollutants on visibility in Warsaw Agglomeration has been investigated. Following pollutants were considered: PM10, SO2, NO2 and O3, while meteorological parameters included: air temperatures (mean, minimum, maximum), solar radiation, relative air humidity, rainfall rates and wind speed. Initial analyses were performed with the use of principal component analysis (PCA). In next stages, the logistic regression (LR), the analysis of variance (ANOVA), one-way classification and a model path of generalized regression models (GRM) were applied. PCA analysis showed that in the cold season the visibility index depends on PM10, SO2, NO2 and the temperatures: T, Tmin, and Tmax. In the warm season, the index of visibility is mostly shaped by four elements: O3, T, Tmax and solar radiation. Logistic regression model indicated that in the warm season only two variables are significantly related to visibility: PM10 and relative humidity of air. Regularities in the cold season shown by the LR correspond with the conclusions from the PCA. Among meteorological conditions, the most important is air temperature, but only Tmax preserves the same direction of influence as the one pointed by the PCA model.
    Źródło:
    Environment Protection Engineering; 2014, 40, 2; 47-64
    0324-8828
    Pojawia się w:
    Environment Protection Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analysis of the data set from a two-year observation of the ambient water-soluble ions bound to four particulate matter fractions in an urban background site in Southern Poland
    Autorzy:
    Rogula-Kozłowska, W.
    Majewski, G.
    Czechowski, P. O.
    Rogula-Kopiec, P
    Powiązania:
    https://bibliotekanauki.pl/articles/207331.pdf
    Data publikacji:
    2017
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    chemical composition
    inorganic ions
    PM2.5
    air
    precipitation
    atmosphere
    Southern Poland
    skład chemiczny
    powietrze
    paliwa stałe
    zanieczyszczenie
    Południe Polski
    Opis:
    Ten water-soluble ions (F–, Cl–, NO3–, PO43–, SO42–, Na+, NH4+, K+, Ca2+, Mg2+), distributed among four fractions of particulate matter, PM, were investigated in an urban background site. The PM was sampled twice a week during a two-year sampling period. Mass distribution among the PM fractions and ambient concentrations of the ten PM-bound ions in the heating and non-heating periods, the seasonal effects in the PM fraction-bound ion concentrations (generalized regression model), and the principal components of all the resulting sets of ambient concentrations (principal component analysis) were determined, discussed, and interpreted in the terms of source apportionment of PM emissions. The formation of secondary inorganic aerosol in transformations of gaseous precursors (e.g., SOx, NOx, NH3) appeared to be most probable and significant source of PM2.5, especially of its sub-fraction PM1–2.5, in the non-heating period. In the heating period, PM and PM2.5 bound water-soluble ions originated mainly from combustion of coal and other solid fuels, or waste. Coarse particles (PM2.5–10 and PM10–40) and some PM2.5–40-bound ions (e.g. Na+, K+, Mg2+) may come from re-suspension of mineral matter and road dust. In some part, coarse PM may consist of mineral and salt particles containing gaseous and semi-volatile compounds.
    Źródło:
    Environment Protection Engineering; 2017, 43, 1; 137-149
    0324-8828
    Pojawia się w:
    Environment Protection Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ocena stężenia całkowitej rtęci gazowej (TGM) na terenie stacji tła regionalnego Granica-KPN (województwo mazowieckie, Polska) w latach 2010–2011
    Estimation of Total Gaseous Mercury (TGM) Concentration at the Regional Background Station in Granica-KPN (Mazovia Province, Poland), 2010–2011
    Autorzy:
    Majewski, G.
    Czechowski, P O
    Badyda, A.
    Kleniewska, M.
    Brandyk, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/1819012.pdf
    Data publikacji:
    2013
    Wydawca:
    Politechnika Koszalińska. Wydawnictwo Uczelniane
    Tematy:
    rtęć gazowa
    Granica-KPN
    powietrze atmosferyczne
    atmospheric mercury
    mercury measurement
    atmospheric air
    Opis:
    The study presents measurement results of the concentration of total gaseous mercury (TGM) in the atmospheric air of 2010–2011 coming from the only measurement station in the Mazovia Province, the Granica-KPN station (λE 20°27'20" φN 52°◦17'09.088"). A series of measurement results of mercury concentration was used to estimate the model which identifies the influence of chosen measurement results, both imission and meteorological ones, on concentrations of gaseous mercury in the atmospheric air. Due to the number of measurements limited to 2 years, the study made an attempt to perform an initial evaluation of seasonal factors. The analyses used included: the Principal Component Analysis (PCA) and a path for the Generalised Regression Model (GRM). Average concentration of TGM in 2010–2011 amounted to 1.52 ng m-3 which is very close to the background values obtained in other European countries. Seasonal dependence of TGM concentration was observed; in the cold half-year the TGM concentration was higher compared to the summer season. The obtained results of identifying the PCA and GRM models enable presenting the following synthetic, final conclusions: The employed models of PCA and GRM show that key factors which shape mercury concentration are the following: suspended dust PM10, gaseous pollutants: SO2 and NO2, and meteorological parameters: air temperature, relative humidity of air and solar radiation intensity. The index of the phenomenon, i.e. the first principal component, identifies this relationship as the strongest and most significant, but it is worth noting that there occurs inversely proportional influence of air temperature and solar radiation intensity. The GRM model shows the occurrence of seasonality in monthly periods and in total as an interaction of the year and the months, which is further confirmed in the PCA model through “distribution” of the effect of specific factors over successive principal components. Ozone, for instance, is connected with the first three components to a different degree (-0.6 with Component 1, 0.3 with Component 2 and 0.62 with Component 3) and not with the first or only one of the components. The PCA model is a linear relationship within each component separately and the relationships, being orthogonal to each other, account for successive parts of the total variance. The variables: ozone, wind velocity and atmospheric pressure are not related to the index of the phenomenon, i.e. to the first component. They are related to next principal components, which may prove a strong irregularity of the relationships or the occurrence of seasonality. To build the model, the study used data from a period of two years: 2010 and 2011. It does not give a sufficient number of observations for stable identification of seasonality (at least 5 repetitive periods) and further correlations of factors, i.e. successive principal components. Those components may indicate not so much the absence of measurement correlations with mercury, but a non-linear character or a strong dependence on various seasonal influences, such as yearly, seasonal or monthly fluctuations.
    Źródło:
    Rocznik Ochrona Środowiska; 2013, Tom 15, cz. 2; 1302-1317
    1506-218X
    Pojawia się w:
    Rocznik Ochrona Środowiska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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