## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Categories**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Pollutant**} & \multicolumn{4}{c}{**SD of the Five Model Runs**} \\ \cline{3-6}  & & & **RMSE** & **MAE** & **PCC** & **KTC** \\ \hline \multirow{3}{*}{Deep Learning Model} & \multirow{3}{*}{ANN} & PM\({}_{10}\) & 4.06 & 3.78 & 0.06 & 0.06 \\  & & **PM\({}_{\mathbf{2.5}}\)** & 1.02 & 0.76 & **0.03** & **0.03** \\  & & NO\({}_{2}\) & 2.03 & 1.71 & 0.05 & 0.05 \\  & & CO & 0.21 & 0.19 & 0.09 & 0.07 \\ \hline \multirow{3}{*}{Deep Learning Model} & \multirow{3}{*}{GRU} & PM\({}_{10}\) & 6.21 & 5.41 & 0.08 & 0.07 \\  & & **PM\({}_{\mathbf{2.5}}\)** & 1.28 & 1.03 & **0.03** & **0.02** \\  & & NO\({}_{2}\) & 2.31 & 2.00 & 0.04 & 0.05 \\  & & CO & 0.06 & 0.06 & 0.04 & 0.03 \\ \hline \multirow{3}{*}{Deep Learning Model} & \multirow{3}{*}{LSTM} & PM\({}_{\mathbf{10}}\) & 1.68 & 1.47 & **0.02** & **0.02** \\  & & PM\({}_{2.5}\) & 2.54 & 2.29 & 0.03 & 0.04 \\  & & NO\({}_{2}\) & 2.48 & 2.36 & 0.02 & 0.03 \\  & & CO & 0.04 & 0.04 & 0.07 & 0.06 \\ \hline \multirow{3}{*}{Machine Learning Model} & \multirow{3}{*}{RF} & PM\({}_{10}\) & 0.04 & 0.05 & 0.00 & 0.00 \\  & & **PM\({}_{\mathbf{2.5}}\)** & 0.03 & 0.01 & **0.00** & **0.00** \\  & & NO\({}_{2}\) & 0.02 & 0.02 & 0.00 & 0.00 \\  & & CO & 0.00 & 0.00 & 0.00 & 0.01 \\ \hline \multirow{3}{*}{Deep Learning Model} & \multirow{3}{*}{RNN} & PM\({}_{10}\) & 2.93 & 2.63 & 0.06 & 0.06 \\  & & PM\({}_{2.5}\) & 4.35 & 3.62 & 0.09 & 0.09 \\  & & NO\({}_{2}\) & 2.08 & 1.96 & 0.04 & 0.04 \\  & & **CO** & 0.10 & 0.08 & **0.04** & **0.02** \\ \hline \multirow{3}{*}{Machine Learning Model} & \multirow{3}{*}{SVR} & PM\({}_{10}\) & 0.00 & 0.00 & 0.00 & 0.00 \\  & & **PM\({}_{\mathbf{2.5}}\)** & 0.00 & 0.00 & **0.00** & **0.00** \\ \cline{1-1}  & & NO\({}_{2}\) & 0.00 & 0.00 & 0.00 & 0.00 \\ \cline{1-1}  & & CO & 0.00 & 0.00 & 0.00 & 0.00 \\ \hline \hline \end{tabular} The best performance in each category is shown in bold.  
\end{table} 
Table 4: SD of the average five model runs for the different DL and ML models. The novelty of this work is that an air pollution prediction model has been successfully developed and may become operational at the discretion of the local authorities. This is a significant advancement for a small region such as Macau, where an air pollution prediction was not in place for many years. In addition, by identifying and reducing the air pollution problems, the effect of climate change may also be mitigated for a double win. The results obtained in this study may be due to DL models requiring a more extensive dataset to reach optimal performance. In contrast, the ML models do fine with a relatively small dataset, with over 190,000 entries of data being applied in this study. A comparison study between ML and DL models in the prediction of air pollution in major Chinese cities with a sample size of 7 years also confirms that ML models outperform DL models [56], with the reason being the consideration of smaller sample size, which is similar to the study period of this work, a sample size of 9 years. The RF and SVR models may be considered the priority choice for air pollution predictions in future datasets. Nevertheless, the application of hybrid DL and ML methods to more datasets globally may be considered for future studies to validate that ML models outperform DL models in air pollution prediction. Conceptualization, T.M.T.L. and J.C.; methodology, T.M.T.L. and J.C.; software, J.C.; validation, T.M.T.L. and S.S.-K.K. ; formal analysis, J.C.; investigation, T.M.T.L. and T.A.K. ; resources, T.M.T.L. ; data curation, J.C.; writing--original draft preparation, T.M.T.L., A.H.M., T.A.K. and S.S.-K.K. ; writing--review and editing, T.M.T.L., A.H.M., T.A.K. and S.S.-K.K. ; visualization, J.C.; supervision, T.M.T.L. and T.A.K. ; project administration, T.M.T.L. ; funding acquisition, T.M.T.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.