Protection of the environment has been of public concern for many years in Italy and many institutions here are involved in this field. However, the advantages offered by the improvement of the quality of the environment are not always considered in economic terms, even though a rigorous evaluation of the relationships between economy and environment should help us to better understand how protection of the environment is related to economic development and social benefits.

Water quality observation is an important task which must performed at every water reservoir, especially at those whose water is used for water supply. In the purpose of easier and faster entering to storied data, for all potential users, and in the purpose of performing faster verification and handling data, two models are designed: 1) data base and 2) information system for dam observation and water quality management. These two models are parts of the Information System of Serbia for Water Resources Management, who is in development. Methodological bases of those two systems are presented in the paper.

The adverse health effects associated with the inhalation and ingestion of naturally occurring radon gas produced during the uranium decay chain mean that there is a need to identify high-risk areas. This study detected radon-prone areas using a geographic information system (GIS)-based probabilistic and machine learning methods, including the frequency ratio (FR) model and a convolutional neural network (CNN). Ten influencing factors, namely elevation, slope, the topographic wetness index (TWI), valley depth, fault density, lithology, and the average soil copper (Cu), calcium oxide (Cao), ferric oxide (Fe₂O₃), and lead (Pb) concentrations, were analyzed. In total, 27 rock samples with high activity concentration index values were divided randomly into training and validation datasets (70:30 ratio) to train the models. Areas were categorized as very high, high, moderate, low, and very low radon areas. According to the models, approximately 40% of the study area was classified as very high or high risk. Finally, the radon potential maps were validated using the area under the receiver operating characteristic curve (AUC) analysis. This showed that the CNN algorithm was superior to the FR method; for the former, AUC values of 0.844 and 0.840 were obtained using the training and validation datasets, respectively. However, both algorithms had high predictive power. Slope, lithology, and TWI were the best predictors of radon-affected areas. These results provide new information regarding the spatial distribution of radon, and could inform the development of new residential areas. Radon screening is important to reduce public exposure to high levels of naturally occurring radiation.

Understanding the key drivers of eutrophication in floodplain lakes has long been a challenge. In this study, the Chlorophyll a (Chla) variations and associated relationships with environmental stressors along the temporal hydrological connectivity gradient were investigated using a 11-year dataset in a large floodplain lake (Poyang Lake). A geostatistical method was firstly used to calculate the hydrological connectivity curves for each sampling campaign that was further classified by K-means technique. Linear mixed effect (LME) models were developed through the inclusion of the site as a random effect to identify the limiting factors of Chla variations. The results identified three clear hydrological connectivity variation patterns with remarkable connecting water area changes in Poyang Lake. Furthermore, hydrological connectivity changes exerted a great influence on environmental variables in Poyang Lake, with a decrease in nutrient concentrations as the hydrological connectivity enhanced. The Chla exhibited contrast variations with nutrient variables along the temporal hydrological connectivity gradient and generally depended on WT, DO, EC and TP, for the entire study period. Nevertheless, the relative roles of nutrient and non-nutrient variables in phytoplankton growth varied with different degrees of hydrological connectivity as confirmed by the LME models. In the low hydrological connectivity phase, the Chla dynamics were controlled only by water temperature with sufficient nutrients available. In the high hydrological connectivity phase, the synergistic influences of both nutrient and physical variables jointly limited the Chla dynamics. In addition, a significant increasing trend was observed for Chla variations from 2008 to 2018 in the HHC phase, which could largely be attributed to the elevated nutrient concentrations. This study confirmed the strong influences of hydrological connectivity on the nutrient and non-nutrient limitation of phytoplankton growth in floodplain lakes. The present study could provide new insights on the driving mechanisms underlying phytoplankton growth in floodplain lakes.