# Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review
## 3 Results and Discussion
### Synthesis
Variable 3 is the predicted parameter and has seven categories: Air Quality Index (prediction-AQI), PM\({}_{10}\) (prediction-PM10), PM\({}_{2.5}\) (prediction-PM25), nanoparticles (prediction-PM01), carbon monoxide (prediction-CO), nitrogen oxides (prediction-NOx), and ozone (prediction-O3). Variable 4 is the feature target and has two categories: Used (target-T) and not used (target-F). Variable 5 is the feature pollutant covariates and has two categories: Used (covariates-T) and not used (covariates-F). Variable 6 is the feature atmospheric parameters and has two categories: Used (atmosphere-T) and not used (atmosphere-F). Variable 7 is the feature land use and has two categories: Used (land-T) and not used (land-F). Variable 8 is the feature time and has two categories: Used (time-T) and not used (time-F). Variable 9 is the feature image processing and has two categories: Used (image-T) and not used (image-F). significantly more represented by estimation than forecasting models. Obviously, the group with the most estimations is Category 1, because it is interested in understanding the relationship between the sources of contamination and air quality. According to the MCA, it also tends to prefer models that involve a high interpretability (e.g., Regression or Decision Tree-based models) than accuracy. A similar interpretation can be made for Category 4, which includes land use parameters and other spatial heterogeneities of the environment to increase the comprehensiveness of the models. Interestingly, there are two groups (Categories 2 and 6) in which forecasting models are not represented at all. Only estimation models are built in Category 2, certainly because the priority of this kind of study is improving the spatial resolution of the current pollution spreading and not predicting it hours ahead. Finally, considering that Category 6 is a new challenge, it is natural that the only two studies on the topic focus on an estimation before carrying out a forecast. The identification of these two unexplored axes allows us to suggest where the new research should be evolving next. Finally, the performances of all the studied models were evaluated in general per algorithm and then each algorithm performance per pollutant for estimation and forecasting studies. For the first analysis, we selected the most used algorithms: Ensemble, NN, SVM and Regression modeling to evaluate coefficients of determination resulting from all 46 studies. The best overall performance is registered for the Ensemble modeling (r\({}^{2}\) = 0.79) with a low variability in the results between different studies. Second best is the Regression modeling (r\({}^{2}\) = 0.74) with also low standard deviation. These two types of algorithms are mostly used in the estimation modeling (see Figure 5), thus resulting in a better predictive performance. SVM has also showed a good performance (r\({}^{2}\) = 0.67 \(\pm\) 0.15). This algorithm is used for the forecasting and a few studies on estimation modeling. The lowest performance was registered from NN modeling (r\({}^{2}\) = 0.64 \(\pm\) 0.27), often used in forecasting studies (see Figure 5). In order to further identify a success of forecasting and estimation studies we separated the resulting available coefficients of determination for each pollutant. Figure 7 shows that in almost all the cases, with the exception of PM\({}_{2.5}\) the estimation modeling results in better accuracy than the forecasting modeling. However, the variation of the results is significantly higher in the forecasting. The estimation modeling performs quite well for CO (r\({}^{2}\) = 0.76), NOx (r\({}^{2}\) = 0.78), O\({}_{3}\) (r\({}^{2}\) = 0.74) and PM\({}_{2.5}\) (r\({}^{2}\) = 0.72). Meanwhile, the forecasting models reach a very high level of performance for  
Figure 6: Number of types of studies (estimation vs. forecasting) per group. Category 1: Identifying relevant predictors and understanding the non-linear relationship with air pollution.