## 3 Results
### Exhaustive Descriptions of Included Studies
#### Group 5: Others  
_Regularization and optimization [67]:_ [PERSON] et al. proposed parameter-reducing formulations and consecutive-hour-related regularizations for forecasting concentration of air pollutants for the next day. The dataset consisted of meteorological and air pollution data from 2006 to 2015 (Chicago area). The main steps of this study are to explicitly control the number of model parameters and then, to enforce a certain regularization on the model parameter explicitly. For the first step, three models were selected, including Baseline, Heavy and Light. For regularization task, Frobenius norm regularization, \(\ell\)2,1-norm regularization, nuclear norm regularization and Consecutive Close (CC) regularization were proposed. The following models were compared, including the baseline model with standard Frobenius norm regularization (Baseline), the heavy model with standard Frobenius norm regularization (Heavy-F), the light model with standard Frobenius norm regularization (Light-F), the heavy model with \(\ell\)2,1-norm regularization (Heavy-\(\ell\)2,1), the heavy model with nuclear-norm regularization (Heavy-nuclear), the heavy model with CC regularization using the \(\ell\)2-norm (Heavy-CCL2), the heavy model with CC regularization using the the light model with \(\ell\)2,1-norm regularization (Light-\(\ell\)2,1), the light model with nuclear-norm regularization (Light-nuclear), the light model with CC regularization using the \(\ell\)2-norm (Light-CCL2), the light model with CC regularization using the \(\ell\)1-norm (Light-CCL1). As evaluation metric was chosen RMSE, and comparatively better results performed Light-CCL1 for Lansing Municipal Airport-Alsip Village (LMA-AV): O\({}_{3}\) and Lewis University-Lemont Village (LU-LV): SO\({}_{2}\) with the score 0.11535 and 0.03248 respectively, Light-nuclear for LMA-AV: PM\({}_{2.5}\) with score 0.0368, and Light-CCL2 for LU-LV: O\({}_{3}\) with score 0.0845. As a limitation was mentioned that similarities between nearby meteorology stations were not considered which could improve the prediction.  
_Predictive mapping of urban air pollution using Apache Spark on a Hadoop cluster [68]:_ represents the platform based on Apache Spark and Hadoop cluster, which predicts air pollution in city Tehran for the next 24 hours. To provide efficient prediction, the authors used Multinomial Naive Bayes and Multinomial Logistic Regression algorithms. Then, applying the IDW method, the predictive map was generated for the whole city. The dataset used in this study consisted of air pollution data (CO, SO\({}_{2}\), PM\({}_{10}\), PM\({}_{2.5}\), NO\({}_{2}\), O\({}_{3}\)) captured from 21 monitoring stations and meteorological data (temperature, pressure, cloud cover, relative humidity, wind speed, wind direction) obtained from 4 weather stations between 2009 and 2013. The results showed that the Naive Bayes model creates more classes than the Logistic Regression model. To compare models, the following metrics were used: precision, recall and F1 score. The logistic regression has comparable higher accuracy (0.68), but it failed to predict classes 4, 5, 6, 7. Meanwhile, the Naive Bayes model could perform better results for those classes. Overall, the two methods provided good outcomes; however, there were problems related to handling imbalanced data. Based on the latter limitation for future work, more advanced machine learning techniques should be used.