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.