# Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review
## 3 Results and Discussion
### Detailed Description
2.2 Category 2: Image-Based Monitoring and Tackling Low Spatial Resolution from Non-Specific/Low Resolution Sensors  
This group is composed of a set of seven papers. All of them address estimation issues only. It is quite logical that no forecasting studies are included in this category, because the principal objective of this kind of work is to increase the spatial resolution of the current pollution spreading. To do so, a first paper presents a method to improve the spatial resolution and accuracy of satellite-derived ground-level PM\({}_{2.5}\) by adding a geostatistical model (Random Forest-based Regression Kriging--RFRK) to the traditional geophysical model (i.e., Chemical Transport Models--CTM) [49]. This is a two-step procedure in which a Random Forest regression models the nonlinear relationship between PM\({}_{2.5}\) and the geographic variables (step 1) then the kriging is applied to estimate the residuals (or error) of step 1 (step 2). This work is carried out in the USA and is based on a 14 years dataset. The predictive features include CTM, satellite-derived dataset (meteorology and emissions), geographic variables (brightness of Night Time Lights--NTL, Normalized Difference Vegetation Index--NDVI, and elevation), and in situ PM\({}_{2.5}\). The accuracy is evaluated by comparing the RFRK to CTM-derived PM\({}_{2.5}\) models and using ground-based PM\({}_{2.5}\) monitor measurements as reference. The results show that the RFRK significantly outperforms the other models. In addition, this method has a relatively low computational cost and high flexibility to incorporate auxiliary variables. Among the three geographic variables, elevation contributes the most and NDVI contributes the less to the prediction of PM\({}_{2.5}\) concentrations. The main limitation of the technique is to rely on satellite images that are subject to uncertainties (e.g., data quality, completeness, and calibration). A similar approach is proposed by [PERSON] et al. [50]. The motivation of this study is to enhance satellite-derived Aerosol Optical Depth (AOD) prediction, by applying a correction based on three different Ensemble Learning algorithms: Random Forest, Gradient Boosting, and Extreme Gradient Boosting (XGBoost) The correction is implemented by considering additional inputs as predictive factors (e.g., land use and meteorology) than AOD only. This technique is applied to predict the concentration of PM\({}_{2.5}\) in Northeastern USA during a period of 14 years. The accuracy of the method is assessed by comparing the coefficient of determination (predicted value/actual value) between the three models. The result shows that XGBoost outperforms slightly the other two algorithms. In addition, the study demonstrates that including land use and meteorological parameters in the algorithms improves significantly the accuracy when compared to the raw AOD. Nevertheless, a limitation of the technique is the fact that it requires lots of features (total number = 52). Furthermore, [PERSON] et al. [51] propose an improved version of the Gradient Boosting algorithm by considering the spatial nonlinearity between PM\({}_{2.5}\) and AOD and meteorology. They develop a Geographically-Weighted Gradient Boosting Machine (GW-GBM) based on spatial smoothing kernels (i.e., gaussian) to weight the optimized loss function. This model is applied to all China and uses data of 2014. The GW-GBM provides a better coefficient of determination than the traditional GBM. The study shows that the best features in descending order are: day of year, AOD, pressure, temperature, wind direction, relative humidity, solar radiation, and precipitation. [PERSON] et al. [52] estimate ozone profile shapes from satellite-derived data. They develop a NN-based algorithm, called the Physics Inverse Learning Machine. The proposed method is composed of five steps. Step 1 is a clustering that gets groups of ozone profiles according to their similarity (k-means clustering procedure).