Water and sewage management in Poland has systematically been transformed in terms of quality and quantity since the 1990s. Currently, the most important problem in this matter is posed by areas where buildings are spread out across rural areas. The present work aims to analyse the process of changes and the current state of water and sewage management in rural areas of Poland. The author intended to present the issues in their broader context, paying attention to local specificity as well as natural and economic conditions. The analysis led to the conclusion that there have been significant positive changes in water and sewage infrastructure in rural Poland. A several-fold increase in the length of sewage and water supply networks and number of sewage treatment plants was identified. There has been an increase in the use of water and treated sewage, while raw sewage has been minimised. Tap-water quality and wastewater treatment standards have improved. At the same time, areas requiring further improvement—primarily wastewater management—were indicated. It was identified that having only 42% of the rural population connected to a collective sewerage system is unsatisfactory. All the more so, in light of the fact that more than twice as many consumers are connected to the water supply network (85%). The major ecological threat that closed-system septic sewage tanks pose is highlighted. It is pointed out that they are mainly being replaced by household wastewater treatment systems with ineffective filtering drainage. Furthermore, recommendations were also made for the future development of selected aspects of water and sewage management, including the legal and the political.
The study focuses on short-term changes in surface water temperature in Polish lakes, and is based upon the experimental measurements of water temperature conducted every 60 minutes during the years 1971–2015. 19 lakes were selected on the grounds of their morphometric properties. The examinations were carried out in the system of expeditionary measurements (up to 8 days) and stationary measurements (over 2 months), and included temperature of surface water and its vertical distribution. The analysis of the results showed that temperature differences of water (daily amplitudes) were observed in both time and spatial distribution. The biggest differences in water temperature occurred during spring warming, and often reached 4–5°C, while rarely exceed 2°C in the remaining periods of the yearly cycle. The mean day value occurs twice; in the morning between 8:00 (7:00 GMT) and 11:00 (10:00 GMT), and in the evening at 20:00 (19:00 GMT) and 22:00 (21:00 GMT). Daily changes in the vertical distribution of water temperature are clearly visible down to the depth of 2.5–3.5 m, whereas are just perceptible to the depth of 5.5–7.0 m.
This paper presents Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) methods for predicting future daily water consumption values based on three antecedent records of water consumption and humidity forecast for a given day, which are considered as independent variables. Mean Absolute Percentage Error (MAPE) is obtained for different configurations of the input sets and of the ANN model structure. Additionally, sets of explanatory variables are enhanced with dummy variables indicating typical days: working day, Saturday, Sunday/public holidays. The results indicated the superiority of the ANN approach over MLR, although the observed difference in performance was very limited.
Abstract. Renewable energy sources (RES) exhibit various characteristics when it comes to their availability in time and space domain. Some are characterised by significant variability and limited predictability. This makes their integration to the power grid a complicated task. Temporal and spatial complementarity of RES is perceived as one of the possible ways to facilitate the process of integration. This paper investigates the concept of temporal complementarity of solar wind and hydrokinetic energy in case of two sites in Poland. Obtained results indicate existence of some beneficial complementarity on inter-annual and annual time scale. Combination of those three RES in one hybrid system makes power source more reliable.
The aim of this study is to assess the possibility of forecasting water level fluctuations in a relatively small (<100 km2), post-glacial lake located in a temperate climate zone by means of artificial neural networks and multiple linear regression. The area of study was Lake Serwy, located in northeastern Poland. Two artificial neural network (ANN) multilayer perceptron (MLP) and multiple linear regression (MLR) models were built. The following explanatory variables were considered: maximal and minimal temperature (Tmax, Tmin) wind speed (WS), vertical circulation (VC) and water level from previous periods (WL). Additionally, a binary variable describing the period of the year (winter, summer) has been considered in one of the two MLP and MLR models. The forecasting models have been assessed based on selected criteria: mean absolute percentage error (MAPE), root mean squared error (RMSE), coefficient of determination (R2) and mean biased error. Considering their values and absolute deviations from observed values it was concluded that the ANN model using an additional binary variable (MLP_B+) has the best forecasting performance. Absolute deviations from observed values were the determining factor which made this model the most efficient. In the case of the MLP_B+ model, those values were about 10% lower than in other models. The conducted analyses indicated good performance of ANN networks as a forecasting tool for relatively small lakes located in temperate climate zones. It is acknowledged that they enable water level forecasting with greater precision and lower absolute deviations than the use of multiple linear regression models.
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