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The study evaluated the effect of environmental conditions and morphometric parameters on lake water temperature changes. The analysis was carried out on the basis of 14 lakes located in northern Poland. The assessment was based on the daily water and air temperatures from 1972 to 2016. It took into account the location of lakes (latitude, longitude, altitude) morphometric parameters (surface area, maximum and mean depth, volume), hydrological processes (rate of water exchange, course of ice phenomena), and trophic status (water transparency) as factors that can modify lake water temperature changes. Direction and rate of air and water temperature changes were analysed by means of Mann–Kendall’s and Sen’s tests. Cluster analysis (CA) was applied to group lakes characterised by similar water temperature changes. The effect of climatic and non-climatic parameters on a lake’s water temperature was assessed on the basis of principal component analysis (PCA). Water temperatures in the lakes in the years 1972–2016 were characterised by a higher rate of increase of 0.43 °C·dec−1 than the air temperature decrease of 0.34 °C·dec−1. The analysis showed a faster rate of heating of waters in western Poland. This can be explained by shorter duration of ice cover. Moreover, the changes of water temperature were affected by other factors, including the location of the lakes, their morphometric parameters, wind speed, water transparency and water exchange time.

The study evaluated the effect of environmental conditions and morphometric parameters on lake water temperature changes. The analysis was carried out on the basis of 14 lakes located in northern Poland. The assessment was based on the daily water and air temperatures from 1972 to 2016. It took into account the location of lakes (latitude, longitude, altitude) morphometric parameters (surface area, maximum and mean depth, volume), hydrological processes (rate of water exchange, course of ice phenomena), and trophic status (water transparency) as factors that can modify lake water temperature changes. Direction and rate of air and water temperature changes were analysed by means of Mann–Kendall’s and Sen’s tests. Cluster analysis (CA) was applied to group lakes characterised by similar water temperature changes. The effect of climatic and non-climatic parameters on a lake’s water temperature was assessed on the basis of principal component analysis (PCA). Water temperatures in the lakes in the years 1972–2016 were characterised by a higher rate of increase of 0.43 °C·dec−1 than the air temperature decrease of 0.34 °C·dec−1. The analysis showed a faster rate of heating of waters in western Poland. This can be explained by shorter duration of ice cover. Moreover, the changes of water temperature were affected by other factors, including the location of the lakes, their morphometric parameters, wind speed, water transparency and water exchange time.

The conversion of chemical energy contained in organic matter into electricity has become an object of interest for many scientists worldwide. This technology is used in microbial fuel cells (MFC). Apart from generating electrical energy, these cells can be used simultaneously for wastewater treatment. Although the technology is constantly being improved, currently functioning microbial fuel cells cannot provide appropriate output parameters to use on an industrial scale. One of the barriers is so-called extracellular electron transfer, which in turn depends on the electrode type used, its material, shape, and size. According to current literature, carbon, graphite, stainless steel, and ceramics are the most frequently used electrode materials. However, more and more often, scientists are turning to other, unusual materials, the production of which uses the newest technologies, and one of them is graphene. This material is modified in different ways and connected with other materials, and the results of this seem to be very promising. Scientists manage to get a higher level of extracellular electron transfer and, hence, higher output parameters of the whole system. This article describes chosen technologies and attempts made by scientists worldwide to use graphene in MFC and their results.

The conversion of chemical energy contained in organic matter into electricity has become an object of interest for many scientists worldwide. This technology is used in microbial fuel cells (MFC). Apart from generating electrical energy, these cells can be used simultaneously for wastewater treatment. Although the technology is constantly being improved, currently functioning microbial fuel cells cannot provide appropriate output parameters to use on an industrial scale. One of the barriers is so-called extracellular electron transfer, which in turn depends on the electrode type used, its material, shape, and size. According to current literature, carbon, graphite, stainless steel, and ceramics are the most frequently used electrode materials. However, more and more often, scientists are turning to other, unusual materials, the production of which uses the newest technologies, and one of them is graphene. This material is modified in different ways and connected with other materials, and the results of this seem to be very promising. Scientists manage to get a higher level of extracellular electron transfer and, hence, higher output parameters of the whole system. This article describes chosen technologies and attempts made by scientists worldwide to use graphene in MFC and their results.

Aim of the study: The current paper aims to give a detailed evaluation and analysis of some extreme rainfall events that happened in the last decade in terms of spatial and temporal rainfall distribution, intensity rate and exceedance probability. Moreover, it examines the effects of each analysed aspect on the resulting flash floods in the study area. Material and methods: American Meteorology Society (AMS) in their glossary of meteorology divided rainfall intensity types into four groups (light, moderate, heavy, and violent). Also, for estimating the exceedance probability, Lognormal distribution was applied as a statistical model of the precipitation probability distribution function. Results and conclusions: Out of six episodes, five of the analysed events were classified as heavy rainfalls. However, the duration of heavy rainfalls was not more than two hours. Four events of maximum daily rainfall (for a 39-year dataset) were rated from 1 – 10% of exceedance probability. To conclude, the current study can be an initial step in modelling hydrological events in the study area and in the process of transforming precipitation into the outflows of urban basins in the future.

Aim of the study: The current paper aims to give a detailed evaluation and analysis of some extreme rainfall events that happened in the last decade in terms of spatial and temporal rainfall distribution, intensity rate and exceedance probability. Moreover, it examines the effects of each analysed aspect on the resulting flash floods in the study area. Material and methods: American Meteorology Society (AMS) in their glossary of meteorology divided rainfall intensity types into four groups (light, moderate, heavy, and violent). Also, for estimating the exceedance probability, Lognormal distribution was applied as a statistical model of the precipitation probability distribution function. Results and conclusions: Out of six episodes, five of the analysed events were classified as heavy rainfalls. However, the duration of heavy rainfalls was not more than two hours. Four events of maximum daily rainfall (for a 39-year dataset) were rated from 1 – 10% of exceedance probability. To conclude, the current study can be an initial step in modelling hydrological events in the study area and in the process of transforming precipitation into the outflows of urban basins in the future.

With regard to underground mining, methane is a gas that, on the one hand, poses a threat to the exploitation process and, on the other hand, creates an opportunity for economic development. As a result of coal exploitation, large amounts of coal enter the natural environment mainly through ventilation systems. Since methane is a greenhouse gas, its emission has a significant impact on global warming. Nevertheless, methane is also a high-energy gas that can be utilized as a very valuable energy resource. These different properties of methane prompted an analysis of both the current and the future states of methane emissions from coal seams, taking into account the possibilities of its use. For this reason, the following article presents the results of the study of methane emissions from Polish hard coal mines between 1993–2018 and their forecast until 2025. In order to predict methane emissions, research methodology was developed based on artificial neural networks and selected statistical methods. The multi-layer perceptron (MLP) network was used to make a prognostic model. The aim of the study was to develop a method to predict methane emissions and determine trends in terms of the amount of methane that may enter the natural environment in the coming years and the amount that can be used as a result of the methane drainage process. The methodology developed with the use of neural networks, the conducted research, and the findings constitute a new approach in the scope of both analysis and prediction of methane emissions from hard coal mines. The results obtained confirm that this methodology works well in mining practice and can also be successfully used in other industries to forecast greenhouse gas and other substance emissions.

With regard to underground mining, methane is a gas that, on the one hand, poses a threat to the exploitation process and, on the other hand, creates an opportunity for economic development. As a result of coal exploitation, large amounts of coal enter the natural environment mainly through ventilation systems. Since methane is a greenhouse gas, its emission has a significant impact on global warming. Nevertheless, methane is also a high-energy gas that can be utilized as a very valuable energy resource. These different properties of methane prompted an analysis of both the current and the future states of methane emissions from coal seams, taking into account the possibilities of its use. For this reason, the following article presents the results of the study of methane emissions from Polish hard coal mines between 1993–2018 and their forecast until 2025. In order to predict methane emissions, research methodology was developed based on artificial neural networks and selected statistical methods. The multi-layer perceptron (MLP) network was used to make a prognostic model. The aim of the study was to develop a method to predict methane emissions and determine trends in terms of the amount of methane that may enter the natural environment in the coming years and the amount that can be used as a result of the methane drainage process. The methodology developed with the use of neural networks, the conducted research, and the findings constitute a new approach in the scope of both analysis and prediction of methane emissions from hard coal mines. The results obtained confirm that this methodology works well in mining practice and can also be successfully used in other industries to forecast greenhouse gas and other substance emissions.