The health and quality of the Danube River ecosystems is strongly affected by the nutrients loads (N and P), degree of contamination with hazardous substances or with oxygen depleting substances, microbiological contamination and changes in river flow patterns and sediment transport regimes. Water quality index (WQI) is an important dynamic attribute in the characterization of the Danube River ecosystems health and quality. The WQ index scores do not reflect the actual condition of water quality. We proposed a new forecast scheme for water quality based on the following qualitative classes very good (0-25), good (26-50), poor (51-75), very poor (76-100) and extremely polluted/non-potable (>100). Water quality forecasting by using Artificial Intelligence (AI) is a meaningful method of protecting public health because of its possibility to provide early warning regarding harmful water pollutants. The main objective of the present study is to forecast the WQI time series data based on water physical, chemical and flow status parameters and associated WQ index scores. The Cascade-forward network (CFN) models, along with the Radial Basis Function Network (RBF) as a benchmark model, were developed using data from 2011 to 2017 and WQI forecasts were produced for the period 2018-2019 at all sites. The nineteen input water quality features represent the initial dataset. Moreover, the Random Forest (RF) algorithm refines the initial dataset by selecting eight features considered the most relevant. Both datasets are employed for constructing the predictive models. According to the results of appraisal, the CFN models produced better outcomes (MSE = 0.083/0,319 and R-value 0.940/0.911 in quarter I/quarter IV) than the RBF models. In addition, results show that both the CFN and RBF models could be effective for predicting time series data for water quality when the eight most relevant features are used as input variables. Also, the CFNs provide the most accurate short-term forecasting curves which reproduce the WQI for the first and fourth quarters (the cold season). The second and third quarters presented a slightly lower accuracy. The reported results clearly demonstrate that CFNs successfully forecast the short-term WQI as they may learn historic patterns and determine the nonlinear relationships between the input and output variables.

Currently, there are many different interpretations in the literature of what a circular economy is and how it functions. As cities are still facing challenges to become fully sustainable, the need for a comprehensive analysis of how the circular economy can be implemented in urban areas is increasing. This article aims at outlining circular cities by their key characteristics and to further explore and provide a framework for fostering circularity at the city level. In order to achieve this goal, we performed a systematic review and analyzed key papers published in the field of circular economy to determine how circular economy practices form circular cities. We discovered that cities play a focal role in facilitating the transition towards circularity through the closing of the loops, recirculation, technical innovation, policy elaboration and citizens’ support. However, city policymakers are still uncertain about how a circular city looks like and what its purpose is, as views are ranging from a strategic ambition to a niche concept of a smart city. Such uncertainty brings challenges, especially in the transition phase that many cities are in at the moment. This further implies that circular economy applied at the urban level still needs effort and innovation to successfully pass the transition phase from the linear economy. Therefore, lastly, we developed a framework model that can be adapted in other cities to facilitate their transition to circular cities.

It is a well–known fact that heavy metal pollution in sediments causes serious problems not only in the Danube basin, but also in the large and small adjacent river streams. A suitable method for assessing the level of heavy metals and their toxicity in sediments is the calculation of pollution indices. The present research aims to assess heavy metal pollution in the Lower Danube surface sediments collected along the Danube course (between 180 and 60 km) up to the point where the Danube River flows into the Danube Delta Biosphere Reserve (a United Nations Educational, Scientific and Cultural Organization—UNESCO, protected area). In addition, this monitored area is one of the largest European hydrographic basins. Five heavy metals (Cd, Ni, Zn, Pb, Cu) were analyzed in two different seasons, i.e., the autumn of 2018 and the spring of 2019, using the Inductively Coupled Plasma Mass Spectrometry (ICP– MS) technique. Our assessment of heavy metal pollution revealed two correlated aspects: 1. a determination of the potential risks of heavy metals in sediments by calculating the Potential Ecological Risk Index (RI), and 2. an evaluation of the influence of anthropogenic activities on the level of heavy metal contamination in the surface sediments, using three specific pollution indices, namely, the Geo–Accumulation Index (Igeo), the Contamination Factor (CF), and the Pollution Load Index (PLI). The results of this pioneering research activity in the region highlighted the presence of moderate metal (Ni and Cd) pollution and a low potential ecological risk for the aquatic environment.

The aim of the present paper is to quantify water quality in the Lower Danube Region by using a series of multivariate techniques and the Water Quality Index (WQI). In this paper were measured 18 parameters upstream and downstream the city of Galati along the Danube River, namely: pH, Dissolved Oxygen (DO), Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), N-NH₄⁺, N-NO₂⁻, N-NO₃⁻, N total, P-PO₄³⁻, SO₄²⁻, Cl⁻, Fe-total, Cr-total, Pb²⁺, Ni²⁺, Mn²⁺, Zn²⁺, As²⁺, in the interval winter 2013–winter 2016. The samples were either analyzed on the field, or sent for testing to the laboratory. The physicochemical parameters mentioned above were analyzed in accordance with the Romanian and International standards in force. The WQI was calculated according to Weighted Arithmetic Water Quality Index Method. The interdependencies between the selected physicochemical parameters were used for determining potential sources of pollution. Monitoring water quality dynamics in the period mentioned above favoured a series of relevant conclusions about the anthropic influence on water quality. Water quality was assessed by processing the measurements results, by calculating the water quality index (WQI), and by using the principal component analyses (PCA) and the response surface method (RSM) with the aim of correlating the indices for the physico-chemical parameters.

This paper presents optical solitons with the concatenation model having spatio-temporal and chromatic dispersions. This model can advantageously curtail the Internet bottleneck effect. Two integration schemes yield these solitons. By utilizing the multipliers approach, the conservation laws are also derived.

This review highlights recent advances in the development of electrochemical sensors and biosensors for detecting pharmaceutical contaminants in water samples, including surface water, wastewater, and bottled water. The electrode modification materials—such as nanomaterials, conductive polymers, and eco-friendly nanocomposites—have shown notable improvements in sensor sensitivity and selectivity, enabling the detection of various pharmaceutical compounds, including diclofenac, ibuprofen, antibiotics, and hormones. Future research directions suggest the use of eco-friendly nanocomposites, the development of multiplexed platforms for simultaneous contaminant detection, and the integration of sensors into portable devices for in situ monitoring. Additionally, integrating biosensors with specific biomolecules and IoT technology can expand their applicability, enabling continuous and efficient water quality monitoring, thus supporting public health and environmental protection.