Classification and Prediction of Fecal Coliform in Stream Waters Using Decision Trees (DTs) for Upper Green River Watershed, Kentucky, USA

The classification of stream waters using parameters such as fecal coliforms into the classes of body contact and recreation, fishing and boating, domestic utilization, and danger itself is a significant practical problem of water quality prediction worldwide. Various statistical and causal approaches are used routinely to solve the problem from a causal modeling perspective. However, a transparent process in the form of Decision Trees is used to shed more light on the structure of input variables such as climate and land use in predicting the stream water quality in the current paper. The Decision Tree algorithms such as classification and regression tree (CART), iterative dichotomiser (ID3), random forest (RF), and ensemble methods such as bagging and boosting are applied to predict and classify the unknown stream water quality behavior from the input variables. The variants of bagging and boosting have also been looked at for more effective modeling results. Although the Random Forest, Gradient Boosting, and Extremely Randomized Tree models have been found to yield consistent classification results, DTs with Adaptive Boosting and Bagging gave the best testing accuracies out of all the attempted modeling approaches for the classification of Fecal Coliforms in the Upper Green River watershed, Kentucky, USA. Separately, a discussion of the Decision Support System (DSS) that uses Decision Tree Classifier (DTC) is provided.

Determining the Regional Geochemical Background for Dissolved Trace Metals and Metalloids in Stream Waters: Protocol, Results and Limitations–The Upper Loire River Basin (France)

To avoid the improper disqualification of a watershed for which the water–rock interaction (WRI) may produce trace element concentrations exceeding established guidelines, it is of the utmost importance to properly establish natural geochemical backgrounds. Using the example of the crystalline Upper Loire River Basin, we are proposing a methodology based on the selection and chemical characterization of water and sediment samples from 10 monolithologic watersheds supposedly lowly impacted by anthropogenic inputs. We collected water samples from each watershed’s spring down to its outlet and measured dissolved major, minor and selected trace elements (Al, As, Ba, Cd, Co, Cr, Cs, Cu, La, Ni, Pb, U, V and Zn) at low- and high-water stages. Results show that the chemical signature of the stream waters is controlled by mineral weatherability rather than by the available rock stock. As a result, the variability in dissolved metal concentrations between the principal lithologies is similar to that observed within each of them. While some elements mostly result from WRI, others clearly identify high inputs from topsoil leaching. Comparison with published data evidences the need to subdivide studied watersheds into distinct sectors, according to the distance from the spring, in order to define reliable natural backgrounds.

Anthropogenic impact on urban rivers: insights from the Mugnone Creek (Florence, Italy) waters and sediments characterization in the framework of the multidisciplinary SENECA project

<p>Urbanization is a striking phenomenon, responsible for the development of cities as complex and highly dynamic systems. One of the most pressing issues in urban areas is water cycle management, which directly influences the availability and the quality of this resource. Urban streams are highly vulnerable to the impacts resulting from the increasing urbanization and they have often lost most of their pristine, natural character.</p><p>We hereby present the preliminary results of the multidisciplinary StrEams urbaN Ecological City plAnning (SENECA) project, which is not only but mainly devoted to characterizing the chemical features of stream waters and sediments of the Mugnone Creek (MC). MC is a typical example of an urban stream, crossing the city of Florence (Italy) and eventually discharging to the Arno River, that has suffered an intense denaturation due to the urban sprawl, such as several diversions, canalizations and rectifications, alterations of the stream bed and riparian habitat, widespread concrete revetment of the banks and burial of short stream tracts.</p><p>Different sites along the 17-km long MC were investigated for water and sediment geochemistry from upstream of the Florence urban area (“blank” sites) to the MC outlet (Cascine Park), passing through variably urban-impacted areas, including sites located along traffic-congested roads, close to the new tramway construction sites and under the railway line. Stream waters were sampled twice a year (June and December 2019 and 2020) to account for seasonal variability. Stream discharge at three representative points was simultaneously determined during water sampling to allow mass load calculations of contaminants. In details, major dissolved ions (Ca, Mg, Na, K, HCO<sub>3</sub>, SO<sub>4</sub>, Cl, NO<sub>3</sub>) and reduced nitrogen (NH<sub>4</sub>), trace elements (TEs: Sb, As, Cu, Zn, Cd, Co, Ba, Li, Pb, Ni) and microplastics (MPs) in stream sediments and waters of MC were determined. Major dissolved ions and NH<sub>4</sub> were analysed via acidimetric titration and IC. TEs were determined in both sediments and waters (0.45 μm filtered and unfiltered) by ICP-OES and ICP-MS, respectively. MPs (1 μm-5 mm) were identified in sediments and waters by HD binocular stereomicroscope.</p><p>Chemical composition (major element constituents) of water reflected both the seasonality and the progress of the water-rock interaction acting in MC from its spring toward the outlet. In addition, the pattern of abundances of Cu, Zn, Sb and Pb and especially their mass loads reflect the increasing anthropogenic impact on the water and sediments, even if is important to stress that the contents are within the limits defined by law, except for stream sediments at a few sites. For MPs, up to 10<sup>9</sup> particles, coming mainly from the NW side of Florence, are estimated to be discharged daily to the Arno River during the winter season, much higher than creeks with similar urbanization contexts worldwide. Increasing data collection will make it possible to identify and better understand what type of sources and processes are responsible for the anthropogenic impacts in the MC and could help in better safeguarding the creek within the framework of the urban social-ecological systems of Florence.</p>