# Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review
## 3 Results and Discussion
### Detailed Description
#### 3.2.3 Category 3: Considering Land Use and Spatial Heterogeneity/Dependence  
Four out of five papers classified in this category are estimation models. The most general manuscript is written by [PERSON] et al. [56]. The objective is to build a land use model to predict Black Carbon (PM\({}_{10}\)) concentrations. The approach is divided into two steps. First, a land use regression model is improved by using a nu-Support Vector Regression (nu-SVR). Second, a generalized additive model is used to refit residuals from nu-SVR. This study is applied in New England States (USA) and exploits a 12 years dataset, which stores data from 368 monitoring stations. A broad spectrum of variables is used as features, such as: Filter analysis, spatial predictors (elevation, population density, traffic density, etc. ), and temporal predictors (meteorology, planet boundary layer, etc.). The model is tested in cold and warm seasons and is assessed by comparison to actual data. Overall, the results provide a high coefficient of determination (r\({}^{2}\) = 0.8). In addition, it appears that this coefficient is significantly higher in the cold than in the warm season. [PERSON] et al. [57] also build a spatiotemporal model based on land use to capture interactions and non-linear relationships between pollutants and land. The study consists of comparing two algorithms--Land Use Random Forest (LURF) vs. Land Use Regression (LUR)--to estimate the concentration levels of NO\({}_{2}\). This work takes place in the region of Amagasaki, in Japan, over a period of four years. Besides land use, the authors use population, emission intensities, meteorology, satellite-derived NO\({}_{2}\), and time as features. The accuracy of LURF outperforms slightly LUR, and the coefficient of determination gets a same range of values as in the above study. LURF is a bit better than LUR, because the former allows for a non-linear modelling whereas the latter is limited to a linear relationship. Another advantage of using random forest is to produce an automatic selection of the most relevant features. The results show that the best predictors in descending order are--green area ratio, satellite-based NO\({}_{2}\), emission sources, month, highways, and meteorology. The variable population is discarded from the model. Nevertheless, the interpretation of the regression model is easier than the random forest-based model, because the former provides coefficients representing the direction and magnitude of the effects of predictor variables. The variable selection issue related to LUR is tackled in the study carried out by [PERSON] et al. [58]. They develop a hybrid model that mix LUR with deletion, substitution, and addition machine learning. Thanks to this method, no more than 12 variables are used to predict the concentration of PM\({}_{2.5}\) and NO\({}_{2}\). The case study is applied to all California and uses data on the period 1999-2002 for PM\({}_{2.5}\), and 1988-2002 for NO\({}_{2}\). The overall performance of the model is fair, but the accuracy for estimating PM\({}_{2.5}\) is significantly lower than NO\({}_{2}\). [PERSON] et al. [31] perform a deeper analysis based on a prediction of the concentration of the chemical composition of PM\({}_{2.5}\). As [PERSON] et al. [57], they compare the performance of a LURF versus LUR approach. The dataset is built from the measurements of 24 monitoring stations in the city of Cincinnati (USA) during the period 2001-2005. Over 50 spatial parameters are registered, which include transportation, physical features, socioeconomic characteristics, greenspace, land cover, and emission sources. Again, LURF outperforms slightly LUR. The originality of this work is to create models that predict not only the air quality, but also the individual concentration of metal components in the atmosphere from land use parameters. [PERSON] et al. [59] is the only study of this category that provides a forecasting model. The approach is applied in three steps.