# Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review
## 3 Results and Discussion
### Detailed Description
#### 3.2.5 Category 5: Towards an Application System  
With only four papers out of 46, the application category is one of the smallest. This result means that the majority of the published studies are still very theoretical. A first interesting application system is proposed by [PERSON] et al. [71]. The purpose of their model is to regulate the traffic, in order to optimize pollution level while maximize the vehicle flow. The method is based on a hybrid model that involves problem-solving, multi-agent system, and NN to estimate the concentration of O\({}_{3}\) in Marrakech-City (Morocco). The predictive features are traffic, pollutant covariates, and meteorological parameters (relative humidity, wind speed, temperature, and solar radiation). The accuracy of the model is assessed by comparing the predicted values to the measurements at the monitoring stations. The system is optimized by using the Hadoop framework that manages big data and a multi-agent software architecture. The objective is to provide the drivers with recommendations on the best path (sum of the shortest + least polluted road) after applying a search graph algorithm (Dijkstra). An app of the system is currently available.  
Other studies intend to provide a public application to forecast air quality. This is the case of the work carried out by [PERSON] et al. [72], who develop a tool that is integratable with existing Environmental Management Systems implemented in Greece. The machine learning model implements Decision Rules to forecast pollution peaks of O\({}_{3}\) and PM\({}_{10}\). The obtained model is a trade-off between performance and understandability. It is less accurate than a linear regression to forecast PM\({}_{10}\) but it is better than the other tested models to predict O\({}_{3}\). A more advanced system is based on the operational forecasting platform Prev'Air [73]. This platform aims at forecasting maps, on a daily basis, for O\({}_{3}\), NO\({}_{2}\), and PM\({}_{10}\). This study implements a Ridge Regression method that reduces the errors of the forecasts (RMSE) for all the contaminants (by 35% for O\({}_{3}\), 26% for NO\({}_{2}\), and 19% for PM\({}_{10}\)). However, the technique presents some limitations to predict pollution peaks. In that sense, it is not adapted for cities with frequent violations of air quality standards.  
The last work consists of a network installation of low-cost pollution sensors [74]. The whole system is able to store data, to process these raw data for a posterior forecasting, and to present the prediction through different channels, such as mobile application and Web portal. Different machine learning methods (SVM, Regression, Model Trees, and NN) are tested, in order to identify the best algorithm to forecast the concentration levels of O\({}_{3}\), NO\({}_{2}\), and SO\({}_{2}\). It seems that the Model Trees provide the lowest RMSE. This solution is particularly relevant to tackle low density monitoring stations and could be advantageously applied in developing countries that suffer high pollution rates. Overall, the majority of the application systems implement traditional machine learning techniques.