Contributing towards Representative PM Data Coverage by Utilizing Artificial Neural Networks

• Published in: Applied sciences Vol. 11; no. 18; p. 8431
• Main Authors: Tzanis, Chris G., Alimissis, Anastasios
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2021
• Subjects: Air pollution; artificial neural networks; Cities; Climate change; climatic parameters; Cultural heritage; feed-forward networks; Ground stations; machine learning; Methods; Mortality; Neural networks; Observatories; particulate matter; Pollutants; spatiotemporal predictions; Greece
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11188431

Atmospheric aerosol particles have a significant impact on both the climatic conditions and human health, especially in densely populated urban areas, where the particle concentrations in several cases can be extremely threatening (increased anthropogenic emissions). Most large cities located in high-income countries have stations responsible for measuring particulate matter and various other parameters, collectively forming an operating monitoring network, which is essential for the purposes of environmental control. In the city of Athens, which is characterized by high population density and accumulates a large number of economic activities, the currently operating monitoring network is responsible, among others, for PM10 and PM2.5 measurements. The need for satisfactory data availability though can be supported by using machine learning methods, such as artificial neural networks. The methodology presented in this study uses a neural network model to provide spatiotemporal estimations of PM10 and PM2.5 concentrations by utilizing the existing PM data in combination with other climatic parameters that affect them. The overall performance of the predictive neural network models’ scheme is enhanced when meteorological parameters (wind speed and temperature) are included in the training process, lowering the error values of the predicted versus the observed time series’ concentrations. Furthermore, this work includes the calculation of the contribution of each predictor, in order to provide a clearer understanding of the relationship between the model’s output and input. The results of this procedure showcase that all PM input stations’ concentrations have an important impact on the estimations. Considering the meteorological variables, the results for PM2.5 seem to be affected more than those for PM10, although when examining PM10 and PM2.5 individually, the wind speed and temperature contribution is on a similar level with the corresponding contribution of the available PM concentrations of the neighbouring stations.