# Contributing towards Representative PM Data Coverage by Utilizing Artificial Neural Networks
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**PM\({}_{10}\)**} & \multicolumn{3}{c}{**PM\({}_{2.5}\)**} \\ \hline  & MAE & SD of MAE & \(R^{2}\) & MAE & SD of MAE & \(R^{2}\) \\  & (\(\upmu\)g/m\({}^{3}\)) & (\(\upmu\)g/m\({}^{3}\)) & & (\(\upmu\)g/m\({}^{3}\)) & \\ PM & 4.96 & 5.4 & 0.83 & 2.99 & 3.18 & 0.55 \\ PM + T & 4.97 & 5.39 & 0.83 & 2.97 & 3.15 & 0.56 \\ PM + WS & 4.93 & 5.35 & 0.83 & 2.99 & 3.18 & 0.55 \\ PM + T + WS & 4.93 & 5.34 & 0.84 & 2.97 & 3.15 & 0.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: MAE, SD of MAE and \(R^{2}\) values that correspond to the MLR methodology, for PM\({}_{10}\) and PM\({}_{2.5}\) and the four different input cases. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**PM\({}_{10}\)**} & \multicolumn{3}{c}{**PM\({}_{2.5}\)**} \\ \hline  & MAE & SD of MAE & \(R^{2}\) & MAE & SD of MAE & \(R^{2}\) \\  & (\(\upmu\)g/m\({}^{3}\)) & (\(\upmu\)g/m\({}^{3}\)) & & (\(\upmu\)g/m\({}^{3}\)) & \\ PM & 3.99 & 4.85 & 0.83 & 2.45 & 2.39 & 0.70 \\ PM + T & 3.89 & 4.15 & 0.91 & 2.44 & 2.38 & 0.70 \\ PM + WS & 3.85 & 4.40 & 0.90 & 2.47 & 2.12 & 0.65 \\ PM + T + WS & 3.67 & 3.86 & 0.94 & 2.39 & 2.16 & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MAE, SD of MAE and \(R^{2}\) values that correspond to the FFNN methodology, for PM\({}_{10}\) and PM\({}_{2.5}\) and the four different input cases. The scatter diagrams for PM\({}_{10}\) and PM\({}_{2.5}\) (Figure 4) are consistent with the MAE and \(R^{2}\) performance statistics (Table 5). The degree of dispersion for the PM\({}_{10}\) (Figure 4a-d) is lower compared to PM\({}_{2.5}\) (Figure 4e-h). This can be explained by the lower number of inputs provided in order to train the models. Specifically, during the training process, the number of input stations is eight for PM\({}_{10}\) and five for PM\({}_{2.5}\), meaning that the air quality network density for the latter was lower. On the contrary, there are no notable differences when the diagrams are compared based on the different inputs. According to the MAE values, the performance of all the models, when studied separately for each figure, reveals that there is not a scheme that identifies as substantially superior. However, a closer examination reveals that the models which include both meteorological parameters (T and WS) produce scatter diagrams with lower dispersion across the line of optimum agreement. This is especially evident regarding the higher concentration values (upper right) for the PM\({}_{10}\) models, where the markers are closer to the diagonal. Finally, the results of the [PERSON] methodology are presented in Table 7. The percentage of contribution for the meteorological parameters is nearly half when compared with the monitoring stations concentrations in the case of the PM\({}_{10}\) models. For PM\({}_{2.5}\), the corresponding percentages are at a similar level (\(\sim\)15%). Additionally, the monitoring stations which are of the same type (Suburban/Background) as AGP (KOR, LYK and THR), and those which are at proximity (MAR, LYK and ARI), are expected to contribute more to the AGP concentrations estimations. However, Table 7 reveals that all stations have a significant importance for the models. \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline  & **ARI** & **ELE** & **THR** & **KOR** & **LYK** & **MAR** & **PIR** & **PER** & **T** & **WS** \\ \hline PM\({}_{10}\) & 8.38 & 12.06 & 13.85 & 10.02 & 8.93 & 13.61 & 11.07 & 10.45 & 5.07 & 6.56 \\ PM\({}_{2.5}\) & 14.14 & 11.92 & 14.70 & & 13.54 & & 15.42 & & 16.69 & 13.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Relative importance (%) of the input data for the two cases of PM\({}_{10}\) and PM\({}_{2.5}\) FFNN models, where both PM concentrations and meteorological parameters (T, WS) are used.