# Contributing towards Representative PM Data Coverage by Utilizing Artificial Neural Networks
## 1 Introduction  
Advances in the field of air quality estimations have been rapid, particularly during the last few decades, demonstrating an increasing interest and attention in both the research community and authorities responsible for the impact assessment of air quality in modern communities. Many cities worldwide are struggling with poor air quality conditions and subsequently with increased mortality and hospital admission rates, mainly due to cardiovascular and respiratory illnesses [1, 2]. This is mostly evident for cities with limited access to clean energy, resulting in an increased need for electric power generation and oil/gas extraction, both procedures responsible for emissions amplification, thus citing air pollution levels as an indicator of sustainable development goals [3]. However, all modern socioeconomic centers, where the majority of human activities transpire, need to carefully monitor and evaluate outdoor and indoor pollutants [4] which, in combination with global climate change (global warming), can lead to higher mortality rates [5]. The connectionbetween high air quality parameters concentrations and health-related effects has been further established in various studies [6; 7; 8; 9; 10; 11]. Apart from the health aspect, high pollutant concentration values are associated with non-health-related effects in crucial fields, such as agriculture, building materials, objects of cultural heritage, forest ecosystems, etc., and are contributing to a more severe deterioration of overall air quality and consequently of the environment [12; 13; 14; 15; 16; 17]. A portion of air pollution of significant importance, which, in contrast with other pollutants, is responsible not only for long-term but also short-term effects on human health, consists of particles of small diameters and various compositions [18; 19]. Particulate Matter (PM), due to its long-time suspension and the ability to travel far distances in the atmosphere, is characterized as one of the leading causes of worldwide mortality and is associated with direct and indirect effects on the climate system [20; 21], as well as a wide range of health problems, mostly depending on the size of the particles [22; 23; 24; 25; 26; 27]. The most studied and adequately monitored categories of PM are the PM\({}_{10}\) and PM\({}_{2.5}\)[28; 29]. [PERSON] and [PERSON] performed a study at a national level in the US, analyzing the critical effects of PM\({}_{10}\) and PM\({}_{2.5}\), and concluded that both are associated with increased rates of mortality [30]. [PERSON] et al. found a close relationship between these two PM size fractions and all-cause and cause-specific mortality, by using data from Statistics Netherlands during the 2008-2009 time period and for the entire Dutch population [31]. The need for spatially continuous data of air quality parameters can be satisfied by using interpolation methods at locations where there are no available observations. [PERSON] et al. (2010) provided a thorough review of commonly used interpolation techniques in which they presented their basic principle as well as their most important advantages and disadvantages and, additionally, they included scenarios in which these methods would be at their best potential [32]. The process of spatial interpolation eventually was supported by utilizing more advanced machine learning methodologies, such as Artificial Neural Networks (ANNs), in cases where radioactive gases concentrations data were needed [33]. Neural Network approaches have been continuously modified regarding their structure, leading to models with enhanced generalization ability and extrapolation capability and, most importantly, improved accuracy [34; 35]. [PERSON] et al. (2014) introduced two more regression techniques for modelling, specifically, a Random Forest Regression and a Support Vector Regression (SVR), and evaluated their usefulness and limitations in comparison with conventional methods and ANN-based approaches [36].