This paper describes the development of a model based on artificial neural networks (ANN) which aims to predict the concentration of nitrates in river water. Another 26 water quality parameters were also monitored and used as input parameters. The models were trained and tested with data from ten monitoring stations on the Danube River, located in its course through Serbia, for the period from 2011 to 2016. Multilayer perceptron (MLP), standard three-layer network is used to develop models and two input variable selection techniques are used to reduce the number of input variables. The obtained results have shown the ability of ANN to predict the nitrate concentration in both developed models with a value of mean absolute error of 0.53 and 0.42 mg/L for the test data. Also, the application of IVS has contributed to reduce the number of input variables and to increase the performance of the model, especially in the case of variance inflation factor (VIF) analysis where the estimation of multicollinearity among variables and the elimination of excessive variables significantly influenced the prediction abilities of the ANN model, r – 0.91.

Soil samples were collected near the thermal power plant and coal mine (Gacko, Republic of Srpska, Bosnia and Herzegovina) and analyzed to measure the concentration of 16 toxic metals (Pb, Cd, Cr, Ni, Zn, Cu, Hg, As, Mo, Co, Ba, V, Tl, B, S and F). The pollutant with the highest mean concentration was B (3,210.02 mg/kg), followed by the Ba (242 mg/kg), Zn (109.50 mg/kg), V (90 mg/kg), Ni (82.10 mg/kg), Cr (60.22 mg/kg), Cu (46.28 mg/kg), Pb (38.90 mg/kg) and Co (19.25 mg/kg). From the evaluation of the soil pollution indices (contamination factor (CF), pollution load index (PLI), ecological risk assessment (ERI), geoaccumulation index (Igeo) and degree of soil load), it can be seen that the studied soil samples in most cases are contaminated. CF values indicate that all values for Cu, Co, Ba and Va in all samplers, Ni in three samplers and Zn in one sampler indicate a moderate degree of contamination. In all soil samples, the values PLI indicates the presence of soil pollution. This study reveals that Ni for RI index is the toxic metal that poses the highest ecological threat. In according average values for Igeo for all toxic metals are >5, it can be concluded that the soil belongs to the class “extremely polluted”. The values of degree of soil load for Ba and V load show that V class is a land of very high load (limit values in according of national legislation for Ba is 160 mg/kg and for V is 42 mg/kg). Values for Ni load show that IV class is a land of high loads (limit value 35 mg/kg). In accordance with the obtained results, it is necessary to prioritize the work on sustainable solutions to mitigate the current risks.

The construction industry is a major contributor to dust, greenhouse gases, and other air pollutants. Implementing effective and sustainable practices in managing construction site operations can greatly mitigate the environmental effects of a project. To achieve this, a collaboration between a scientific research institution and a construction company enabled the real-time monitoring of air quality parameters at a construction site using Internet of Things (IoT) technologies. They implemented an IoT-based system framework that integrated a distributed sensor network to collect real-time data from the construction site. Various sensors were utilized to gather data on the concentration of NO₂ and particulate matter (PM_2.5 and PM₁₀), as well as meteorological parameters such as wind speed, wind direction, humidity, pressure, and temperature. The real-time measurements yielded insights into the level of air pollution at the construction site and its association with earth excavation, the primary construction activity. This information can be utilized to manage excavation work and reduce the levels of polluting gases (NO₂) and suspended particles. By conducting an on-site monitoring of these three pollutants, the study discovered that the dust levels resulting from excavation activities were relatively high. When comparing the wind direction with NO₂ and PM concentrations, it was concluded that earth excavation significantly influenced the air quality in the construction area. However, in terms of the primary factors affecting NO₂ and construction dust concentrations, the analysis revealed that meteorological factors did not exhibit a significant correlation with NO₂ and dust levels at the construction site. The multiple linear regression (MLR) and the artificial neural network (ANN) models for predicting PM_2.5, PM₁₀ and NO₂ concentration in air using meteorological parameters as predictors were applied. The ANN model showed greater accordance with the measured concentrations in air than the MLR model.