## 3 Results and Discussion
For example, the highest average annual values of concentration in the case of grids with the resolution of 100, 250, 500, and 1000 m occurred at distances of, respectively, ca. 700, 500, 1300 and 1700 m northeast of the emission source (Figure 6). In the case of grids with the resolution of 100 and 250 m, these maximum values were obtained near the emission source, while in the case of grids which resolution is 500 and 1000 m--at a distance up to three times larger. Significant differences in spatial distribution occurred also in the case of maximum 1-h (Figure 5) and 24-h (Figure 6) concentrations. Therefore, the use of coarse grids in the assessment of the impact on the air quality carried out using the CALPUFF model not only results in lowering the highest concentration values in the analyzed average periods, but also indicates different locations of these values in the adopted calculation area. A more detailed analysis of this problem is possible by comparing the air concentrations calculated in a given spot to the reference concentration, which was the result of the calculation obtained for Variant V1 (a grid with the resolution of 100 m). Table 4 compares the values of the particular statistical indicators obtained in the adopted calculation area for three cases reflecting a relative comparison of the results of concentration calculations obtained for the following pairs of variants: V1 and V2 (V1-V2), V1 and V3 (V1-V3), and V1 and V4 (V1-V4). Only the concentrations calculated in 676 discrete receptors were subject to this analysis, accurately coinciding with the points of the grid with the spacing of 1000 m.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Indicator**} & \multicolumn{3}{c}{**C\({}_{1\_{\text{max}}}\)**} & \multicolumn{3}{c}{**C\({}_{2\_{\text{max}}}\)**} & \multicolumn{3}{c}{**C\({}_{\text{a}}\)**} \\ \cline{2-10}  & **V1–V2** & **V1–V3** & **V1–V3** & **V1–V2** & **V1–V3** & **V1–V3** & **V1–V2** & **V1–V3** & **V1–V3** \\ \hline FB & \(-\)0.05 & \(-\)0.14 & \(-\)0.06 & \(-\)0.06 & \(-\)0.22 & \(-\)0.13 & \(-\)0.04 & \(-\)0.17 & \(-\)0.05 \\ FB\({}_{\text{FN}}\) & 0.08 & 0.08 & 0.11 & 0.04 & 0.020 & 0.05 & 0.02 & 0.01 & 0.04 \\ FB\({}_{\text{FP}}  
The results of the calculations of the average fractional bias FB, presented in Table 4, clearly indicate that Variants V2, V3 and V4 are characterized by, on average, an overestimation of calculation results during all analyzed concentration averaging periods, as compared to Variant V1. The aforementioned overestimation does not exceed 30%, which means that the analyzed computational variants are in the range of the so-called good models, which in turn means that the obtained results did not differ significantly from the reference values (Variant V1). The fact that the analyzed cases show mostly average overestimation of the results in Variants V2, V3 and V4, as compared to Variant V1, is additionally proven by the fact that the values of the positive components of a systematic deviation FBFP are higher than the values of the negative components FBFN. It is worth emphasizing that the highest values of FBFP and the lowest values of FBFN, as well as the highest average deviation of the results in all examined concentration averaging periods were recorded for Case V1-V3, comparing Variants V1 and V3. In the case of a geometric mean bias (MG), the good models include those which fit in the range of 0.7 \(\leq\) MG \(\leq\) 1.3 [54]. MG value = 1 means that the compared results are perfectly convergent. The MG values, presented in Table 4, show that each analyzed case deals with overestimation of the results, as compared to the V1 variant. The underestimation does not exceed 25% of the reference value. The analysis of the negative and positive MG components provides similar observations, as in the case of the values obtained for FBFP and FBFN.