The use of plants and natural processes for wastewater treatment is an issue that arouses interest among technologists and scientists around the world. The aim of the article was to analyze the influence of the air temperature and insolation on the removal of nitrate nitrogen from the wastewater treated in the hydroponic system, under greenhouse conditions. Samples of sewage for its quality tests were taken from a wastewater treatment plant (WWTP) located in the southwestern part of Poland. Data regarding daily sunshine duration and average daily air temperature values in selected periods of 2013–2016 come from a meteorological station located 30 km from WWTP. The conducted research and analyses of the results clearly indicate that under moderate climate conditions, the amount of solar radiation reaching the Earth’s surface is insufficient to ensure the year-round, effective wastewater treatment process in the hydroponic system. In the case of air temperature, no correlation was found between the tested parameters, which indicates the lack of temperature influence on the efficiency of NO3 removal from the wastewater by macrophytes growing in the lagoon.

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The climate change that has been observed in recent years has affected the water balance, including the groundwater resources recharge. The paper is an attempt to evaluate the groundwater recharge in dry years. The initial stage of the research consisted of selecting the years when meteorological and hydrological droughts occurred, with use of the standardized indices Standardized Precipitation Index (SPI) and Standardized Water Level Index (SWI). With the use of the WetSpass model for selected periods and for comparative long-term periods the volume of groundwater recharge was estimated. It was determined that the meteorological drought of 1982 led to a considerable decrease in the mean groundwater recharge to a negative level in the summer half-year in the Western Pomeranian region in Poland. On the other hand, the winter season was characterised by positive values, but they were still lower than those characteristic for the comparative long-term periods. The hydrological drought in 1992 did not have such noticeable consequences.

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An accurate air-temperature prediction can provide the energy consumption and system load in advance, both of which are crucial in HVAC (heating, ventilation, air conditioning) system operation optimisation as a way of reducing energy losses, operating costs, as well as pollution and dust emissions while maintaining residents’ thermal comfort. This article presents the results of an outdoor air-temperature time-series prediction for a multifamily building with the use of artificial neural networks during the heating period (October–May). The aim of the research was to analyse in detail the created neural models with a view to select the best combination of predictors and the optimal number of neurons in a hidden layer. To meet that task, the Akaike information criterion was used. The most accurate results were obtained by MLP 3-3-1 (r = 0.986, AIC = 1300.098, SSE = 4467.109), with the ambient-air-temperature time series observed 1, 2, and 24 h before the prognostic temperature as predictors. The AIC proved to be a useful method for the optimum model selection in a machine-learning modelling. What is more, neural network models provide the most accurate prediction, when compared with LR and SVR. Additionally, the obtained temperature predictions were used in HVAC applications: entering-water temperature and indoor temperature modelling.

The rivers of agricultural catchment areas are particularly vulnerable to eutrophication, which causes nitrate nitrogen (N-NO3) that can be easily leached from the cropland. In 1991, the EU implemented the Nitrates Directive (ND) to identify and reduce the negative effects of nitrates in water. According to this regulation, in 2018, the whole territory of Poland was classified as Nitrate Vulnerable Zone (NVZ). The aim of the study was to assess the validity of the introduction of NVZs in large areas of the river catchment level. Statistical data on agricultural changes for individual provinces of Poland and for the whole country were analyzed. A one-way analysis of variance (ANOVA) was used to assess the N-NO3 content in the water at different locations along the river within four rivers in the Odra basin. The results indicated that higher concentrations are observed in the upper part of the studied catchments, which reached a maximum of 25.0 mg N-NO3·dm−3. However, average values rarely exceeded 11.3 mg N-NO3·dm−3, the limit according to the Nitrates Directive. The large variability in N-NO3 content suggests the need to redefine the actual NVZs since it is essential for the appropriate implementation of programs aimed at restoring water quality according to ND.

Modern solutions in water distribution systems are based on monitoring the quality and quantity of drinking water. Identifying the volume of water consumption is the main element of the tools embedded in water demand forecasting (WDF) systems. The crucial element in forecasting is the influence of random factors on the identification of water consumption, which includes, among others, weather conditions and anthropogenic aspects. The paper proposes an approach to forecasting water demand based on a linear regression model combined with evolutionary strategies to extract weekly seasonality and presents its results. A comparison is made between the author's model and solutions such as Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Random Forest (RF). The implemented daily forecasting procedure allowed to minimize the MAPE error to even less than 2% for water consumption at the water supply zone level, that is the District Metered Area (DMA). The conducted research may be implemented as a component of WDF systems in water companies, especially at the stage of data preprocessing with the main goal of improving short-term water demand forecasting.