The Use of Artificial Neural Networks to Analyze Greenhouse Gas and Air Pollutant Emissions from the Mining and Quarrying Sector in the European Union

Abstract

The European Union (EU) is considered one of the most economically developed regions worldwide. It was driven by the mining industry for several decades. Despite certain changes in this area, a number of mineral and energy resources are still being mined in the EU. Nevertheless, mining activities are accompanied by many unfavorable phenomena, especially for the environment, such as greenhouse gas and air pollutant emissions. The great diversity of the EU countries in terms of the size of the "mining and quarrying" sector means that both the volume and structure of these emissions in individual countries varies. In order to assess the current state of affairs, research was conducted to look at the structure and volume of these emissions in individual EU countries. The aim of the study was to divide these countries into homogenous groups by structure and volume of studied emissions. In order to reflect both the specificity and diversity of the EU countries, this division was based on the seven most important gases (CO 2 , CH 4 , N 2 O, NH 3 , NMVOC, CO, NO x ) and two types of particulate matter (PM 2.5, PM 10) emitted into the atmosphere from the sector in question. The volume of studied emissions was also compared to the number of inhabitants of each EU country and the gross value added (GVA) by the mining and quarrying sector. This approach enabled a new and broader view on the issue of gas and air pollutant emissions associated with mining activities. The artificial Kohonen's neural networks were used for the analysis. The developed method, the analyses and the results constitute a new approach to studying such emissions in the EU. Research that looks only at the emission of harmful substances into the environment in relation to their absolute values fail to fully reflect the complexity of this problem in individual EU countries. The presented approach and the results should broaden the knowledge in the field of harmful substance emissions from the mining and quarrying sector, which should be utilized in the process of implementing the new European climate strategy referred to as "The European Green Deal".

Studying the Level of Sustainable Energy Development of the European Union Countries and Their Similarity Based on the Economic and Demographic Potential

“…The output neuron can only update its weight according to the input vector that feeds it [70][71][72][73][74][75][76][77]. The general procedure of using neural networks to group objects according to their similarity, defined by features, consists of the following stages [77,79]:…”

“…Initiate vector weights of all neurons in the network. The general procedure of using neural networks to group objects according to their similarity, defined by features, consists of the following stages [77,79]:…”

Analysis of Similarities Between the European Union Countries in Terms of Sustainable Energy and Climate Development

“…This training process is repeated until the design vector and the nodes of the neurons in the competitive layer are fully matched (Zhou et al, 2017). The algorithm used in the Kohonen's method is as follows (Brodny and Tutak, 2020b): − To determine the size of the topological map (1):…”

Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review

“…To eliminate the human factor and the subjectivity and bias in the planning of mining operations, as well as limit the soil, water, and air pollution by quarries and find the most effective solutions for reclamation, Kohonen's neural network has proven itself well. Based on the results of its application, promising directions for the adjustment of "The European Green Deal" (modern climate strategy) were identified [81].…”