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.

Bathymetric measurements play an important role in assessing the sedimentation rate, deposition of pollutants, erosion rate, or monitoring of morphological changes in a river, lake, or accumulation basin. In order to create a coherent and continuous digital elevation model (DEM) of a river bed, various data interpolation methods are used, especially when single-beam bathymetric measurements do not cover the entire area and when there are areas which are not measured. Interpolation methods are based on numerical models applied to natural landscapes (e.g., meandering river) by taking into account various morphometric and morphologies and a wide range of scales. Obviously, each interpolation method, used in standard or customised form, yields different results. This study aims at testing four interpolation methods in order to determine the most appropriate method which will give an accurate description of the riverbed, based on single-beam bathymetric measurements. The four interpolation methods selected in the present research are: inverse distance weighting (IDW), radial basis function (RBF) with completely regularized spline (CRS) which uses deterministic interpolation, simple kriging (KRG) which is a geo-statistical method, and Topo to Raster (TopoR), a particular method specifically designed for creating continuous surfaces from various elevation points, contour, or polygon data, suitable for creating surfaces for hydrologic analysis. Digital elevation models (DEM’s) were statistically analyzed and precision and errors were evaluated. The single-beam bathymetric measurements were made on the Siret River, between 0 and 35 km. To check and validate the methods, the experiment was repeated for five randomly selected cross-sections in a 1500 m section of the river. The results were then compared with the data extracted from each elevation model generated with each of the four interpolation methods. Our results show that: 1) TopoR is the most accurate technique, and 2) the two deterministic methods give large errors in bank areas, for the entire river channel and for the particular cross-sections.

Water quality indices are suitable tools used for assessing water quality because of their capacity to reduce a large number of water quality indicators into one value which defines the water quality class. In this study, Water Quality Index (WQI), Water Pollution Index (WPI) and Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) were applied in order to evaluate the seasonal and spatial variation of the water quality in the Romanian Lower Danube sector. Fourteen physico-chemical parameters, i.e., pH, DO, BOD5, COD, N-NH4+, N-NO3−, N-NO2−, N-total, P-total, SO42−, Cl−, Fe-total, Zn2+ and Cr-total, were monitored along the Danube course (on a distance of about 120 km), during the four seasons between the autumn of 2018 and the summer of 2019 in order to calculate the three indices mentioned above. Indices results showed that the water analysed was ranked into different water quality classes, although the same dataset was used. These differences were due to the contribution of each parameter taken into account in the calculation formula. Thus, the WQI scores were mostly influenced by those parameters whose maximum allowable concentration was low (e.g., heavy metals, N-NO2−), while the WPI and CCME-WQI scores were influenced by those parameters which exceeded the maximum allowable concentration (BOD5, DO, COD, N-NO3−, N-NO2−). Based on the WQI results, the water was ranked into quality classes II and III. WPI and CCME-WQI assessed water only in quality class II, with one exception in the case of CCME-WQI when water was ranked into quality class III. The temporal assessment identified the seasons in which the water quality was lower, namely summer and autumn. The variation of the indices values between the sampling stations demonstrates the existence of pollution sources in the study area. Moreover, the indices results illustrated the contribution of the main tributaries (Rivers Siret and Prut) to the Danube River water quality. The appropriate applicability of the three indices was also discussed in this study.

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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.