# Contributing towards Representative PM Data Coverage by Utilizing Artificial Neural Networks
## 2 Materials and Methods  
### Data  
The area of study is metropolitan Athens, which is part of the Attica region in Greece. Important characteristics of the functional urban area of Athens are the considerably high population density, the complexity of its meteorological and geophysical features and the agglomeration of the majority of economic activities in Greece, which are being associated with various PM pollution sources (vehicular traffic, domestic fuel burning, natural dust and salt, industrial activities, etc.). The Athens basin is defined by four major mountain ranges. These are Mounts Parnitha, Pentelikon, Hymmetus and Aigaleo, which are natural borders at the north, northeast, east central and west respectively, and they affect the air pollutants dispersion and transportation mechanisms. Additionally, the city lies on the north coast of the Saronic Gulf and the west coast of the Euboean Gulf and thus is affected by the sea breeze and other flows. Subsequently, the complex topography and fluctuation of climatic conditions are associated with complex PM\({}_{10}\) and PM\({}_{2.5}\) concentration profiles, characterized by spatial variability even for stations at close proximity [51; 52]. The area of study and the locations of each monitoring station are presented in Figure 1.  
The importance of the area, considering the PM pollution fields, over the last few years is additionally connected to the post-2010 time period and the economic crisis that affected Greece, during which particle concentrations in all major cities, and especially Athens, increased significantly due to the residential extensive burn of low-cost biomass as an alternative source of fuel for heating [53; 54]. For this study, PM\({}_{10}\) and PM\({}_{2.5}\) hourly data were obtained from the air quality monitoring network operated by the Hellenic Ministry of the Environment, Energy and Climate Change (MEE), which has operated in the Attica region since 1984. More specifically, data from nine (AGP, ARI, ELE, THR, KOR, LYK, MAR, PIR and PER) and six stations (AGP, ARI, ELE, THR, LYK and PIR) for PM\({}_{10}\) (\(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\)) and PM\({}_{2.5}\) (\(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\)), respectively, were used in order to create the PM database, and they are presented in Table 1.  
The selection of the stations was mostly based on data availability in each case. Additionally, daily data for two meteorological parameters (wind speed in km/h and temperature in \({}^{\circ}\)C) were obtained for the target station (AGP) from the automatic weather stations NOAA (National Observatory of Athens Automated Network) network of the National Observatory of Athens (NOA). The methodology used in this study could be applied for a different target station in the area. However, the AGP station was selected among the six common stations of PM\({}_{10}\) and PM\({}_{2.5}\) due to the temperature (T) and wind speed (WS) values' availability, which can help in better supporting the methodology. Ultimately, the analysis covers a three-year time period (2016-2018) for both the PM and the meteorological parameters. Figure 2 depicts the average monthly evolution for both PM\({}_{10}\) and PM\({}_{2.5}\) and for the 2016 to 2018 time period at the AGP station. All three years'