# Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review
## 3 Results and Discussion
### Detailed Description
#### 3.2.6 Category 6: Nanoparticles as a New Challenge  
In the last category, the studies focus on estimating the concentrations and peaks of nanoparticles. A number of years of research indicates that the levels of PM\({}_{2.5}\) concentrations are dropping in most of the high- and mid-income countries [75; 76; 77; 78]. However, in the case of nanoparticles, the harm comes not from their mass (particle concentration, usually high for the larger size aerosols), which compared to micro-sized particles is minute, but the quantity (particle number, usually very high for the smaller size aerosols) entering the human blood stream and damaging the cardiovascular system.  
Recent study indicates that nanoparticles attack the injured blood vessels and may cause heart attacks and strokes, while long-term exposure causes vascular damage [79]. Therefore, studying these ultrafine particles (UFP) is quite important due to the increased production of very small particles by modern engines. However, not many cities have the infrastructure to monitor these complex pollutants. Therefore, [PERSON] et al. [80] aim to build a model that estimates the concentration of nanoparticles from accessible and routinely-monitored pollution and meteorological factors using a feed-forward Neural Network with back-propagation. First, different architectures of NN (number of layers and neurons) are tested through an empiric approach (i.e., trial and error), in order to select the models that provide the minimum error of prediction according to the features considered, then the seven best models (each one with different types and number of features) are compared, and finally, in order to assess the prediction of the models for high-pollution concentrations 75 th percentile values are considered. One month of five pollutant covariates (PM\({}_{10}\), SO\({}_{2}\), O\({}_{3}\), NOx\({}_{x}\), and CO), and/or meteorological data (wind speed, and temperature) are used in Fahaheel City (Kuwait). The model performance is assessed by comparison between the seven models (i.e., type and number of features considered) based on the determination coefficient. The models that include meteorology have the best results r\({}^{2}\) = 0.8 (with meteorology), and r\({}^{2}\) = 0.74 (without meteorology). The accuracy of the models is slightly lower for extreme concentrations (>75 th percentile values). The study shows that the concentration in nanoparticles is very sensitive to meteorological factors.  
Similarly, [PERSON] et al. [81] aim to understand the relationships between the concentration of submicron particles and meteorological and traffic factors in order to estimate nanoparticles (particulate matter less than 1 \(\mu\)m--PM\({}_{1}\), and UFP) in Hangzhou (China). As in the previous study [80], a short term, three days in winter data of meteorological (temperature, relative humidity, wind speed and direction, precipitation and pressure), traffic (flow, speed) and time parameters are used. Tree-based classification models, SVM, Naive Bayes, Bayesian Network, NN, k-NN, Ripper and RF are implemented. Correlation with PM and each predictor is performed, to know which single feature has better predicting power, and based on that the multiple parameter model is used. Data are split into two (low vs. high) and three (low vs. medium vs. high) ranges of pollution classes. Model performance is validated against the observed data (precision, area under curve--AUC) resulting in tree algorithm AUC and f-measure 1 for PM\({}_{1}\) (ADTree, RF), and for UFP 0.84 (RF). This study is a promising example in predicting nanoparticles without actual measurements. It shows that it is easier to predict PM\({}_{1}\), than UFP. Tree algorithms are able to produce almost completely accurate prediction of PM\({}_{1}\), and very good (r\({}^{2}\) = 0.844) for UFP. A binary split is better than ternary for all classifiers. Finally, weather also shows a strong relationship with PM\({}_{1}\), while traffic parameter is important for UFP.