# Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review
## 3 Results and Discussion
### Detailed Description
The model performance is validated with observed data and the QRM model shows a better performance (r\({}^{2}\) = 0.66) in predicting hourly PM\({}_{10}\) concentrations due to the contribution of covariates at different quantiles of the dependent variable (PM\({}_{10}\)), instead of considering the central tendency of PM\({}_{10}\). [PERSON] et al. [39] aim to identity sources of pollution and predict the air quality by using Hybrid Model of Principal Components Analysis, Tree-based Ensemble Learning (Single Decision Tree (SDT),Decision Tree Forest (DTF) and Decision Tree Boost (DTB)) vs. Support Vector Machine Model. Five years of air quality and meteorological parameter data are used for Lucknow (India). SDT, DTF, DTB and SVM are used to predict the Air Quality Index (AQI), and Combined AQI (CAQI)), and to determine the importance of predictor features. The model performance is validated by a comparison between the models and with the observed data. Decision Tree models--SDT (r2 = 0.9), DTF (r2 = 0.95), and DTB at r2 = 0.96) outperform SVM (r2 = 0.89). [PERSON] et al. [40] aim to predict a greenhouse gas N2O emission using Random Forest vs. Two Regression models (linear and nonlinear) by employing global data of environmental and crop variables (e.g., fertilization, type of crop, experiment duration, country, etc.). The extreme values from data are removed, excluding the boreal ecosystem; input variable features are ranked by importance; controlling number of input variables to result in better prediction. The model is validated by a comparison to the regression model and the simple non-linear model (10-fold cross validation). RF outperforms the regression models by 20-24% of misclassifications. Meanwhile, [PERSON] et al. [41] aim to predict air pollution (NO2, SO2 and PM10) in Oviedo, Spain based on a number of factors using Multivariate Adaptive Regression Splines and ANN Multilayer Perceptron (MLP). Three years of NOx, CO, SO2, O3, and PM10 data modeling result in a good estimation of NO2: r2 = 0.85; SO2: r2 = 0.82; PM10 r2 = 0.75 when compared with observed data. [PERSON] et al. [42] aim to identify the meteorology effects on PM2.5, by predicting it using six years of meteorological data (wind speed and direction, and precipitation) for Quito, Ecuador in regression modeling. This is a good simplified technique and an economic option for the cities with no air quality equipment. The model gets validated by regression between observed and predicted PM2.5, and by a 10-fold cross validation of predicted vs. observed PM2.5, which shows that this method is a fair approach to estimate PM2.5 from meteorological data. In addition, the model performance is improved in the more extreme weather conditions (most influential parameters), as previously seen in [PERSON] et al. [35]. [PERSON] et al. [43] use the lazy learning technique to establish the relationship between precursor emissions (PM10) and pollutants (AQI of PM10) for the Lombard region (Italy), using hourly Sulfur Dioxide (SO2), Nitrogen oxides (NOx), Carbon Monoxide (CO), PM10, NH3 data for one year. The Dijkstra algorithm is deployed in the large-scale data processing system. Eighty percent of the data are used as an example set and 20% as validation set (every 5 th cell). The validation phase of surrogate models is performed comparing the output to deterministic model simulations, not the observations. Constant, linear, quadratic and combination approximation based on elevation of the area (0, 1, 2 and all polynomial approximations) are applied. The performance of the model is very comparable (r2 = 0.99-1) to the Transport Chemical Aerosol Model (TCAM), a model that is computationally much costlier, currently used in decision making. [PERSON] et al. [44] on the other hand, aim to estimate the dependence between primary and secondary pollutants; and most contributing factors in air quality using SVM radial (Gaussian), linear, quadratic, Pearson VII Universal Kernels (PUK) and multilayer perceptron (MLP) to predict NOx, CO, SO2, O3, and PM10.