Contributing towards representative pm data coverage by utilizing artificial neural networks

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 pre-dicted 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 neigh-bouring stations. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Addressing missing environmental data via a machine learning scheme

An important aspect in environmental sciences is the study of air quality, using statistical methods (environmental statistics) which utilize large datasets of climatic parameters. The air-quality-monitoring networks that operate in urban areas provide data on the most important pollutants, which, via environmental statistics, can be used for the development of continuous surfaces of pollutants’ concentrations. Generating ambient air-quality maps can help guide policy makers and researchers to formulate measures to minimize the adverse effects. The information needed for a mapping application can be obtained by employing spatial interpolation methods to the available data, for generating estimations of air-quality distributions. This study used point-monitoring data from the network of stations that operates in Athens, Greece. A machine-learning scheme was applied as a method to spatially estimate pollutants’ concentrations, and the results can be effectively used to implement missing values and provide representative data for statistical analyses purposes. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Spatial estimation of urban air pollution with the use of artificial neural network models

The deterioration of urban air quality is considered worldwide one of the primary environmental issues and scientific evidence associates the exposure to ambient air pollution with serious health effects. This fact highlights the importance of generating accurate fields of air pollution for quantifying present and future health related risks. Interpolation methods for point estimations in the field of air pollution modelling enable the estimation of pollutant concentrations in unmonitored locations. The main objective of this study is to evaluate two interpolation methodologies, Artificial Neural Networks and Multiple Linear Regression, using data from a real urban air quality monitoring network located at the greater area of metropolitan Athens in Greece. The results for five regulated air pollutants (Nitrogen dioxide, Nitrogen monoxide, Ozone, Carbon monoxide and Sulphur dioxide) are compared through the use of a set of correlation and difference statistical measures and residuals distribution. Artificial neural networks are found in most cases to be significantly superior, especially where the air quality network density is limited, leading to a decreased degree of spatial correlations among the monitoring sites. © 2018 Elsevier Lt