# Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review
## 3 Results and Discussion
### Synthesis  
In order to synthetize the results of this systematic review, an MCA is performed. This factorial analysis looks for the principal dimension explaining the variability between the papers and to closely examine relationships between them. The MCA dimensions are quantitative variables which summarize the qualitative variables that characterize each study. This statistical method suggests links between these qualitative variables by the observation of their proximity in the point cloud-based representation of the categories (Figure 5). Here, the total percentage of inertia is 28%, which is an acceptable value considering the fact the papers are located in a high K-J dimensional space. The \(x\)-axis (F1) shows an opposition between forecasting models and estimation models. On the one hand, the forecasting models seem more used to predict Air Quality Index (prediction-AQI) and are mainly based on NN (algorithm-NN) and SVM (algorithm-SVM) algorithms. They do not rely on land use predictors (land-F), but a lot on the targeted predicted parameter (target-T). Since a forecasting usually involves a time series analysis, it makes sense to use the targeted parameter as predictive feature for predicting its evolution over time. Considering the large distance between'model-forecasting' and 'prediction-NOX', it is also possible to conclude that this kind of modelling is not used to predict the nitrogen oxides in the atmosphere. On the other hand, the estimation models constitute the main approach to predict ozone (prediction-O3) and nanoparticles (prediction-PM01) and are mostly based on Ensemble Learning algorithms (algorithm-Ensemble). Contrary to forecasting, they do not use the predicted factor as feature (target-F), because this type of model focuses on estimating the concentration of a determined pollutant from other sources of contamination or dispersion. This aspect explains the reason why atmospheric parameters (atmosphere-T), time (time-T), satellite images (image-T), and land use (land-T) tend to be located on the same side as'model-estimation'. The \(y\)-axis (F2) explains less variables than the \(x\)-axis (F1). Nevertheless, it highlights a clear opposition between 2 peculiars algorithms that appear only in 2 different papers (algorithm-Rules and algorithm-Lazy). If we look at the data, it is possible to confirm that these two studies address distinct types of modelling (estimation vs. forecasting) and predicted parameters (PM\({}_{10}\) vs. AQI). Overall, more studies are dedicated to estimation (28) than forecasting (18). There are only two out of six categories in which the number of forecast motivations is higher than estimation purposes (Figure 6). This is the case of Category 4, which presents hybrid solutions to predict air contamination and includes deep and extreme learning. These techniques are usually characterized by a high performance of prediction, which is particularly adapted to tackle the uncertainty to forecast concentrations of pollutant in the near or far future. The other group is Category 5, which focuses on implementing practical applications. Intuitively, it makes more sense to develop an App or a Web portal that provides the users with a forecasting information that supports the planning of their next activities than to propose a theoretical assessment of air quality. On the contrary, the other four categories are  
Figure 5: MCA representation of the point cloud of the categories that characterize the selected papers. The studies are described from nine variables (J) and 27 categories (K). Variable 1 is the type of modelling and has two categories: Estimation (model-estimation) and forecast (model-forecasting). Variable 2 is the type of algorithm and has six categories: Ensemble Learning (algorithm-Ensemble), Neural Network (algorithm-NN), Support Vector Machine (algorithm-SVM), Multiple Regression (algorithm-Regression), Decision Rules (algorithm-Rules), and Lazy methods (algorithm-Lazy).