# Contributing towards Representative PM Data Coverage by Utilizing Artificial Neural Networks
## 1 Introduction
In the field of PM modeling, spatial and temporal estimations of PM\({}_{10}\) and PM\({}_{2.5}\) concentrations can be performed by utilizing ANN statistical models, which have been proven to effectively simulate PM pollution fields in previous studies [37; 38; 39; 40]. In general, the importance of using advanced techniques (e.g., auto-regressive models, tensor-based approaches, deep neural networks, etc.) for spatiotemporal predictive modeling, by combining data from different locations, has been demonstrated in relevant works [41; 42; 43; 44; 45], as these techniques consistently outperform more conventional methods. Regarding ANNs applications, the input parameters used for developing the models can be different in many cases, utilizing air quality concentrations from ground stations, satellite data and/or values from numerical climate models [39; 46; 47; 48; 49]. Adding parameters as inputs may be beneficial for estimation purposes as more information is inserted to the networks, however, the latter can become more complex and time-consuming. An essential task in this context is to carefully compare different scenarios of input parameters which will help choose the optimum input set which can be used effectively to provide accurate estimations. This work proposes a framework that can be applied in urban environments, characterized by topographically complex terrain and high variability regarding climatic conditions, at points of interest where PM pollution measurements are needed. At these points of interest, by carrying out an experimental campaign for a short period, the results can be utilized to train ANN models. However, for the latter, meteorological predictors are also of extreme importance due to the heavy influence of climatic conditions on PM pollution distribution fields. The overall methodology which is presented examines and evaluateshow both PM concentrations and meteorological values can support PM concentrations' point estimations. Specifically, a Feed-Forward Neural Networks (FFNNs) approach was used, in order to make spatial point estimations of PM\({}_{10}\) and PM\({}_{2.5}\) concentrations, aiming to develop a simple yet effective scheme which has the ability to provide representative PM datasets for stations with data gaps or to expand the available data. The spatial estimation of PM concentrations by using FFNNs and data from neighboring stations has been performed before successfully, when compared with other schemes [38]. However, this study evaluates additionally the incorporation of crucial meteorological parameters, such as the surface temperature and wind speed, and how these additions affect the performance of the networks. The methodology utilizes data from ground-based observations, obtained from monitoring stations located in the city of Athens, Greece, which is a densely populated metropolitan area, characterized by regional variability considering the type of each subsidiary area that is part of the city. Furthermore, an important part of the presented methodology is to provide an approach for understanding the contribution of each model input to the output by utilizing an approach proposed by [PERSON] [50]. This approach can contribute to addressing the lack of explanatory power, which is a common problem associated with ANNs. and provide insight on the structure of the function being approximated, which associates input and output parameters.