# Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review
## 3 Results and Discussion
### Detailed Description  
This section consists of a detailed explanation of the selected papers. It is organized into six categories based on the main motivation of the study. In each group, we first present manuscripts on estimation models and, second, studies that involve forecasting models. An estimation model is defined as an approach that seeks to estimate the value of a pollutant (or index of contamination) from the measurement of other predictive parameters at the same time step. It is mostly used to give an approximation of the concentrations on an extended geographic area. On the contrary, the objective of a forecasting model is to predict the concentration levels of the contaminants in the near future. We call both estimation and forecasting prediction or predictive models. 2.1 Category 1: Identifying Relevant Predictors and Understanding the Non-Linear Relationship with Air Pollution  
Category 1 is the biggest group and accounts for total of 16 articles--twelve in estimation modeling and four in forecasting. In the estimation modeling there are five cases of Random Forest (RF) or tree-based Ensemble learning algorithms (e.g., M5P), four regression models, one lazy learning, and two SVM. The latter was once used in forecasting studies, NN was used twice and M5P once. This category highly concentrates on identifying the most contributing parameters to a successful prediction of atmospheric pollution. A variety of tests are used to differentiate between the influential predictors, like Principal Component Analysis (PCA), Quantile Regression Model (QRM), Pearson correlation, etc. Among the most recent studies we find [PERSON] et al. [22]. This study aims to (i) build a predictive model of PM\({}_{10}\) based on meteorological, atmospheric, and temporal factors and (ii) analyze PM\({}_{10}\) trend during the last ten years. The Random Forest model, a simple, efficient, and easy interpretable method, is used. First of all, many out-of-bag samples (randomly sampling observations with replacement and sampling of feature variables) of the training set are used to grow different Decision Trees (DT), and then all the trees are aggregated to form a single prediction. The algorithms are trained and run on 20 years of data in Switzerland from 31 monitoring stations. Daily average data of meteorology (wind speed, wind direction, and temperature), synoptic scale, boundary layer height, and time are used. The model is validated by comparison between the observed and predicted value, resulting in average value in 31 sites: r\({}^{2}\) = 0.62. The best predictors are--wind speed, day of the year (seasonal effect), and boundary layer height, whereas the worst predictors are day of the week, and synoptic scale. The model performance is low and the predictive accuracy is inconsistent (0.53 \(<\) r\({}^{2}\) \(<\) 0.71) for different locations, especially varying for the rural mountain sites. Another study also relies on RF to build a spatiotemporal model to predict O\({}_{3}\) concentration across China [35]. First the RF model is built by averaging the predictions from 500 decision/regression trees, and then Random Forest Regressor from the python package scikit-learn is used to run the algorithm. For that meteorology, planetary boundary height, elevation, anthropogenic emission inventory, land use, vegetation index, road density, population density, and time are used from 1601 stations over one year in all of China. The model is validated by comparison of the r\({}^{2}\) and RMSE (predicted value/actual value) to Chemical Transport Models, and shows a good performance of r\({}^{2}\) = 0.69 (specifically, r\({}^{2}\) = 0.56 in winter, and r\({}^{2}\) = 0.72 in fall). Interestingly, the model results are quite comparable or even higher than the predictive performance of the CTMs for lower computational costs.