# Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review
## 3 Results and Discussion
### General Description  
Figure 2 shows the evolution of the number of journal papers using a machine learning approach to model air quality, since 2010. The number of studies in this field shows an increasing trend over the last eight years. An important boom can be observed from 2015. The average number of studies has been multiplied by a factor of 5, since the period 2011-2015. This high value tends to remain stable during the last two years. It is of note that the literature review was performed in September 2018, which supposes a possibly superior number of documents by the end of the present year. While the number of machine learning approaches for predicting atmospheric pollutants has increased dramatically, this growth has not been uniformed globally. Figure 3 shows that the number of studies is much higher in the northern hemisphere, specifically in Eurasia and North America. In addition, the more complex list of pollutants has been studied in Asia and Europe, including all criteria pollutants and even a specific pollutant or an overall Air Quality Index (AQI). While the biggest number of studies has been concentrated in China (13 studies) and United States (seven studies), other parts of the world have more limited published research. Especially unstudied regions of the world are South America and Oceania, both with one study, focusing on a single specific pollutant. Figure 3: Number of papers per country and types of atmospheric contaminants studied by continent. The world map is colored by number of studies per country (yellow-brown), while the grey color indicates regions with no studies. The pollutant studies were considered per continent: O\({}_{3}\)—ozone, PM\({}_{10}\)—particulate matter with aerodynamic diameters \(\leq\)10 \(\mu\)m, BC—black carbon, PM\({}_{2.5}\)—particulate matter with aerodynamic diameters \(\leq\)2.5 \(\mu\)m, AQI—Air Quality Index, NO\({}_{2}\)—nitrogen dioxide, NO\({}_{x}\)—nitrogen oxides (NO + NO\({}_{2}\)), SO\({}_{2}\)—sulfur dioxide, CO—carbon dioxide, NanoPM—nano particles (particles with diameter in nm). Figure 2: The evolution of published studies in scientific journals regarding machine learning techniques used in atmospheric sciences. The systematic review shows that the most used algorithms in descending order are: Ensemble Learning Methods, Artificial Neural Networks, Support Vector Machines, and Linear Regressions (Figure 4). Two papers use more peculiar approaches related to Decision rules and Lazy methods. Ensemble Learning is a class of machine learning techniques that creates multiple predictors to address the same problem and make a single prediction by combining the results from the predictors. Thus, the result of the predictive model is obtained by taking an average or the majority voting. The multiple predictors are created by introducing stochasticity into the data or the prediction algorithm. In the first case, different datasets and respective predictors are produced from the original database. In the second case, the randomness is introduced by using different types of algorithms (e.g., Decision Trees and Neural Network) to solve a single problem. The advantage of using an Ensemble Learning method is to provide a better accuracy, if we compare to the performance of each predictor taken individually (also called weak predictors). Bagging (or Bootstrap Aggregation) is the simplest way to introduce stochasticity in the data. In this technique, several datasets are created by taking random samples with replacement. Then, one specific predictor is produced for each dataset, and theses predictors are aggregated to give the final model. Boosting is another way to create stochasticity by sequentially training each weak classifier. These predictors are weighted in some way that is usually related to the accuracy of each predictor. It means that observations which are incorrectly predicted get a higher weight. So, the best predictor is the one that has to perform better on these critical observations.