# Breathable Cities: Dynamic Machine Learning Modelling Approaches for Advanced Air Pollution Control
## 3 Materials and Methods  
### Overview  
First of all, the data part of the experiment consisted of data collection, data preprocessing and data preparation; Table 1 shows the main data sources. Hourly data in this research refers to the frequency at which air quality data are collected, and predictions are made. In this study, 'hourly' refers to the collection and analysis of air quality data at one-hour intervals. Further, predictions are generated for the subsequent hour using machine learning models trained on historical data. It should be mentioned that the study for this work started in 2019, and historical data were requested from the Ministry of Environment. The data provided pertained to hourly data requests from 2016 to 2018. Data completeness was a very important factor in deciding on data selection. To conduct a comparable study with England's data and to observe the behavior of the modeling with larger datasets, data from 2014 to 2018 were selected. Additionally, data from Italy were used in this study as a new source of data (data not seen previously by the model) to ensure model generalization. Data preparation was conducted using multiple methods, as described below. Input Data: day, month, year, hour, humidity, temperature, wind speed, wind direction. Day, month, year, and hour columns were created by splitting the date time from the original raw data (from monitors) into separate columns. The year column was transformed to years 1, 2, 3, etc. (instead of 2014, 2015, 2016, etc.). Output Data:  
* England: CO (\(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\)), NO (\(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\)), NO\(\mathrm{\SIUnitSymbolMicro g}\) (\(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\)), O\(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\)), PM10 (\(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\)), SO\(\mathrm{\SIUnitSymbolMicro g}\) (\(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\)). * Jordan: PM10 (\(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\)), NO\(\mathrm{\SIUnitSymbolMicro g}\) (ppb), CO (ppb), SO\(\mathrm{\SIUnitSymbolMicro g}\) (ppb). * Figure 1 shows pollutants' concentration correlations. Variables were selected based on several factors. Firstly, a literature review was conducted to understand previous research in the air quality index domain. Additionally, this research focuses on traffic areas and related pollution at the selected locations, which were provided by the Ministry of Environment for Jordan data and pulled from open-source data for England. Data were selected based on completeness and hourly frequency as needed for the experiment. The flowchart of the experimental design stages shown in Figure 2 explains the major stages of the experimental work conducted in this paper. It can be seen that datasets were collected from three sources during the data collection process, and subsequent data preparation and pre-processing ensured data quality. Models were developed in phases based on the aim and objectives of this study. Following the model setup, data splitting and model training, testing, and validation were performed. Details that summarize the stages of the experimental design are presented in Figure 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **London** & **Jordan** \\ \hline Air quality station & & \\ (data capturing) & & \\ Collection years & 2014–2018 & 2016–2018 \\ Data points & 43,824 & 26,268 \\ Frequency & Hourly & Hourly \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main data sources. Figure 1: Data correlation. Figure 2: Experimental design stages. The setup of model parameters is ultimately one of the most important parts of this research experiment, as attaining the desired reliability and accuracy of the model depends on fine-tuning parameters to fulfill the objectives of the study.