# Hybrid LSTM Model to Predict the Level of Air Pollution in Montenegro
## 4 Results and Discussion  
### Data Summary  
After the necessary eliminations and reductions of the initial measurement set, the CAQI values were calculated by applying Equation (14) for the remaining 16, 913 readings of each pollutant. The descriptive statistics (Count, Mean, Standard Deviation (Std), Minimum (Min), First Quartile (Q1), Median (Q2), Third Quartile (Q3), and Maximum (Max)) of the input dataset are shown in the first column of Table 2. Columns two to five correspond to the input features of our model and the last column presents the calculated values of CAQI. CAQI represents the output variable that our model aims to predict based on the known input feature concentrations, \(O_{3}\), \(PM_{2.5}\), \(PM_{10}\), and \(NO_{2}\), displayed in \(\upmu\)g/m\({}^{3}\). A concise summary of the dataset is presented with the boxplot displayed in Figure 4. The boxplot illustrates the distribution of concentrations for four pollutants: \(PM_{2.5}\), \(PM_{10}\), \(NO_{2}\), and \(O_{3}\), and CAQI values. Figure 4 reveals that \(PM_{2.5}\) and \(PM_{10}\) exhibit similar distributions, with both pollutants showing positively skewed tendencies. The \(PM_{2.5}\) interquartile range (IQR) is narrower than the IQR for \(PM_{10}\), suggesting that \(PM_{2.5}\) concentrations are relatively less variable. Both pollutants display many outliers, indicating extreme pollution events. The percentage of outliers in \(PM_{2.5}\) is 11.87% and that in \(PM_{10}\) is 7.15%. The \(NO_{2}\) dataset shows a moderately positively skewed distribution with a narrow IQR and the percentage of outliers in \(NO_{2}\) is 5.87%. The median concentration of \(O_{3}\) indicates a lower central tendency, and there are no outliers in \(O_{3}\). The range of CAQI values is considerably extensive and these values have the greatest dispersion, resulting in a challenge for modeling. Additionally, there are no outliers within the CAQI dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & \(NO_{2}\) (\(\upmu\)g/m\({}^{3}\)) & \(PM_{2.5}\) (\(\upmu\)g/m\({}^{3}\)) & \(PM_{10}\) (\(\upmu\)g/m\({}^{3}\)) & \(O_{3}\) (\(\upmu\)g/m\({}^{3}\)) & **CAQI** \\ \hline Count & 16,913 & 16,913 & 16,913 & 16,913 & 16,91 Figure 5 shows the Pearson correlation coefficients between selected air pollutants, \(PM_{2.5}\), \(PM_{10}\), \(NO_{2}\), O\({}_{3}\), and the CAQI values. The strength and direction of the linear correlation between the CAQI values and the measured pollutants are shown as values falling between \(-1\) and \(+1\). The correlation coefficient of 0.8 between \(PM_{2.5}\) and \(PM_{10}\) indicates a strong positive correlation between the two pollutants. The correlation coefficient between \(PM_{2.5}\) and \(NO_{2}\) is 0.61, indicating a moderate positive correlation. On the other hand, the correlation coefficient between \(O_{3}\) and \(PM_{2.5}\) is \(-0.46\), and that between \(O_{3}\) and \(NO_{2}\) is \(-0.47\), indicating a moderate negative correlation. There is a moderate positive correlation between the CAQI values and \(PM_{2.5}\) (0.55), as well as between the CAQI values and \(PM_{10}\) (0.6). The correlation coefficient between the CAQI values and \(O_{3}\) (\(-0.35\)) indicates a moderate negative correlation. Figure 6 presents the values of four air pollutants (\(PM_{2.5}\), \(PM_{10}\), \(NO_{2}\), and \(O_{3}\)) collected over the 3-year period, together with five CAQI levels. The most frequent CAQI levels in relation to \(PM_{2.5}\) and \(PM_{10}\) are high and very high. In relation to \(NO_{2}\) and \(O_{3}\), low, medium, and high CAQI levels are equally distributed. In the legends of the plots shown in Figure 6, we provide percentages that describe how many measurements of the parameter shown on the y-axis influenced some of the CAQI levels.