Table 5 Summary of air pollution source apportionment studies using data mining techniques
From: A systematic review of data mining and machine learning for air pollution epidemiology
Author | Year | Sub-field | Environmental agent of interest | Data mining techniques | Objective |
|---|---|---|---|---|---|
Chen et al. [22] | 2010 | Outdoor air pollution | Inorganic acids & basic air pollutants | Hierarchical Clustering | Explore relationship between climate and air pollutants |
Singh et al. [24] | 2013 | Outdoor air pollution | AQI | PCA, SVM, DT | Predicting air quality and identifying air pollution sources. |
Fernández- Camacho et al. [51] | 2015 | Urban air and noise pollution by traffic | NOx, O3, SO2, Black Carbon | Fuzzy Clustering | Find the relationship of noise to the traffic emission |
Chen et al. [23] | 2015 | Outdoor air pollution | Multiple air pollutants | Clustering | Source apportionment for air pollutants |
Li et al. [45] | 2017 | Outdoor air pollution | PM | Trajectory clustering | Use clustering to understand how seasonality and meteorology effects pollution sources for Beijing |