# Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review
## 2 Method
### Analyzed Parameters  
All the papers are analyzed according to 14 aspects. The first parameter concerns the motivation of the study. The second is the type of modelling, which is divided into two categories--estimation and forecasting models. An estimation model uses predictive features (e.g., contaminant covariates, meteorology, etc.) to estimate the concentration of a determined pollutant at the same time. A forecasting model takes into account historical data to predict the concentration of a pollutant in the future. The third analysis is based on the type of machine learning algorithm used. The main categories are artificial neural networks, support vector machines, ensemble learning, regressions, and hybrid versions of these algorithms. The fourth analysis describes the method applied by the authors. The fifth point focuses on the nature of the predicted parameter. Again, two groups are identified. On the one hand, there are authors interested in predicting specific air contaminants, which are: Micro and nano-size particulate matter (PM\({}_{10}\), PM\({}_{2.5}\), PM\({}_{1}\)); nitrogen oxides (NO\({}_{\rm x}={\rm NO}+{\rm NO}_{2}\)); Sulphur oxides (SO\({}_{\rm x}\)); carbon monoxide (CO); and ozone (O\({}_{3}\)). On the other hand, some authors work on a prediction of air quality in general by searching for the Air Quality Index (AQI), which may include the concentrations of several pollutants. The sixth parameter identifies the geographic location of the study. The seventh gives details of the characteristics of the dataset, such as--time span; quantity of monitoring stations; and number of instances. Furthermore, the eighth point provides information on the specificity of the dataset in terms of the used predictive attributes. The main features are related to--pollutant covariates; meteorology; land use; time; human activity; and atmospheric phenomena. The ninth and tenth factors address the evaluation method and the performance of the tested algorithms, respectively. The assessment is mainly based on a comparison of the accuracy between the models and/or a comparison of the prediction of the actual value. The most popular evaluation criteria are the ratio of correctly classified instances (Accuracy), the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the coefficient of determination (R\({}^{2}\)). The Accuracy represents the overall performance of a classifier by providing the proportion of the whole test set that is correctly classified, as described in Equation (1). \[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{1}\]  
where TP, TN, FP and FN stand for True Positives, True Negatives, False Positives and False Negatives, respectively. The higher the Accuracy value is, the better is the model performance. The MAE shows the degree of difference between the predicted values and the actual values. The RMSE  
Figure 1: PRISMA-based flowchart of the systematic selection of the relevant studies. is another relative error estimator that focuses on the impact of extreme values based on the MAE. The R\({}^{2}\) represents the fitting degree of a regression. The MAE, RMSE and R\({}^{2}\) are calculated according to Equations (2)-(4), respectively. \[\mathrm{MAE}=\ \frac{1}{n}\ \sum_{i=1}^{n}\left|\mathrm{E_{i}-A_{i}}\right| \tag{2}\]  
\[\mathrm{RMSE}=\ \left(\frac{1}{n}\sum_{i=1}^{n}\left(\mathrm{E_{i}-A_{i}} \right)^{2}\right)^{\frac{1}{2}} \tag{3}\]  
\[\mathrm{R}^{2}=\left(\frac{\sum_{i=1}^{n}\left(\mathrm{A_{i}-\overline{A}} \right)\ \left(\mathrm{E_{i}-\overline{E}}\right)}{\sqrt{\sum_{i=1}^{n}\left( \mathrm{A_{i}-\overline{A}}\right)^{2}\ \sum_{i=1}^{n}\left(\mathrm{E_{i}-\overline{E}}\right)^{2}}} \right)^{2} \tag{4}\]  
where n is the number of instances, A\({}_{i}\) and E\({}_{i}\) are the actual and estimated values, respectively. \(\overline{A}\) and \(\overline{E}\) stand for the mean measured and mean estimated value of the contaminant, respectively.