Research hotspots and current challenges of lakes and reservoirs: a bibliometric analysis

Diatoms as indicators in running waters: trends of studies on biological assessment and monitoring

Despite the biodiversity and ecosystem services provided by lotic ecosystems, they are strongly affected by anthropogenic activities. Therefore, biological monitoring and assessment strategies are crucial in helping maintain these ecosystems and developing mitigation policies. We provide a global overview of the use of benthic diatoms as bioindicators in lotic environments, by analyzing 764 articles published in the past 20 years. We analyzed the influence of substrate type on samplings, which species have been highlighted as indicators and for which type of impacts, which anthropogenic impacts have been most commonly evaluated, and which metrics have been commonly used in studies using diatoms to assess and monitor the quality of lotic environments. We found that the most studied anthropogenic impact is artificial eutrophication and that some species, especially Nitzschia palea, have been thoroughly mentioned as indicators of this impact. Indicator species related to other types of impact are less common, demonstrating the need for studies on this issue. Moreover, we verified that traditional taxonomic metrics, such as diversity and diatom indices, have been widely used. Some alternative metrics have been used recently, such as those based on teratological valves, lipid bodies, valve size, and DNA metabarcoding. The number of biomonitoring and assessment studies based on diatoms has increased considerably in the past 20 years. Nonetheless, the demand for natural resources and consequently the degradation of lotic ecosystems have accelerated significantly. Thus, the development of low-cost and time-efficient biological assessment and monitoring strategies is essential for evaluating the health of lotic environments.

Greenhouse gas dynamics in an urbanized river system: influence of water quality and land use

Rivers act as a natural source of greenhouse gases (GHGs). However, anthropogenic activities can largely alter the chemical composition and microbial communities of rivers, consequently affecting their GHG production. To investigate these impacts, we assessed the accumulation of CO₂, CH₄, and N₂O in an urban river system (Cuenca, Ecuador). High variation of dissolved GHG concentrations was found among river tributaries that mainly depended on water quality and land use. By using Prati and Oregon water quality indices, we observed a clear pattern between water quality and the dissolved GHG concentration: the more polluted the sites were, the higher were their dissolved GHG concentrations. When river water quality deteriorated from acceptable to very heavily polluted, the mean value of pCO₂ and dissolved CH₄ increased by up to ten times while N₂O concentrations boosted by 15 times. Furthermore, surrounding land-use types, i.e., urban, roads, and agriculture, could considerably affect the GHG production in the rivers. Particularly, the average pCO₂ and dissolved N₂O of the sites close to urban areas were almost four times higher than those of the natural sites while this ratio was 25 times in case of CH₄, reflecting the finding that urban areas had the worst water quality with almost 70% of their sites being polluted while this proportion of nature areas was only 12.5%. Lastly, we identified dissolved oxygen, ammonium, and flow characteristics as the main important factors to the GHG production by applying statistical analysis and random forests. These results highlighted the impacts of land-use types on the production of GHGs in rivers contaminated by sewage discharges and surface runoff.

A Classification-Based Machine Learning Approach to the Prediction of Cyanobacterial Blooms in Chilgok Weir, South Korea

Cyanobacterial blooms appear by complex causes such as water quality, climate, and hydrological factors. This study aims to present the machine learning models to predict occurrences of these complicated cyanobacterial blooms efficiently and effectively. The dataset was classified into groups consisting of two, three, or four classes based on cyanobacterial cell density after a week, which was used as the target variable. We developed 96 machine learning models for Chilgok weir using four classification algorithms: k-Nearest Neighbor, Decision Tree, Logistic Regression, and Support Vector Machine. In the modeling methodology, we first selected input features by applying ANOVA (Analysis of Variance) and solving a multi-collinearity problem as a process of feature selection, which is a method of removing irrelevant features to a target variable. Next, we adopted an oversampling method to resolve the problem of having an imbalanced dataset. Consequently, the best performance was achieved for models using datasets divided into two classes, with an accuracy of 80% or more. Comparatively, we confirmed low accuracy of approximately 60% for models using datasets divided into three classes. Moreover, while we produced models with overall high accuracy when using logCyano (logarithm of cyanobacterial cell density) as a feature, several models in combination with air temperature and NO3-N (nitrate nitrogen) using two classes also demonstrated more than 80% accuracy. It can be concluded that it is possible to develop very accurate classification-based machine learning models with two features related to cyanobacterial blooms. This proved that we could make efficient and effective models with a low number of inputs.

Progress on Pharmaceutical Sciences/Pharmacy Postgraduate Education: a Bibliometric Perspective

Objective

The study quantitatively investigated the related research progress in pharmaceutical sciences/pharmacy education from a bibliometric angle and provided feasible suggestions to facilitate the development of pharmaceutical sciences/pharmacy postgraduate education.

Methods

Bibliometric analysis was conducted using the database of Web of Science Core Collection. The literature published in 1985–2021 was screened and selected. The overall profile description, citation analysis, and research hotspot mining were performed using the citation report of Clarivate Analytics, bibliometrics online platform, and VOSviewer software. The bibliometric results and profiles were plotted and illustrated.

Results

The bibliometric analysis of 485 papers of interest showed that the research frontier was continuously expanding; especially the institutions from the USA were the main contributors. The numbers of citing papers have been ascending, and a considerable part of citations were from the areas other than the education research. Mining results showed that the in-school and residency education of pharmacy postgraduates was a research hotspot, as well as interprofessional training and new education styles for Coronavirus Disease 2019 (COVID-19) therapy were the emerging trends in the field.

Conclusion

Through the analysis of the studies, it was found that encouraging relevant research programs, establishing financial supports, and launching specified publication sources could be helpful to boost the development of pharmaceutical sciences/pharmacy postgraduate education. Besides, the results suggested that this was a less discussed topic and was worthy for the investigators to pay more attention to such an issue.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12247-021-09611-z.

Alterations to sediment nutrient deposition and transport along a six reservoir sequence

Reservoir presence and construction has become commonplace along rivers due to the multitude of ecosystem services they provide. Many services are well recognized, including the effectiveness of sequestering both sediments and sediment-bound nutrients such as silts and phosphorus (P). Reservoirs are also capable of transforming or sequestering significant quantities of nutrients with more complex biogeochemical pathways, like nitrogen (N). Reservoir assessments, independent of inflow-outflow models, have primarily focused on a small number of systems creating a growing need to understand how reservoirs function both individually and as reservoir sequences within large rivers and their watersheds. Models have simulated the overall efficiency and drivers of reservoir nutrient deposition, but few have considered how a sequence of reservoirs alters deposition as an interdependent watershed-sediment-transport-system. In this study, we collected sediment cores from a six-reservoir sequence along a 5th - 6th order stream receiving treated waters from a large metropolitan area in the subtropical southeastern United States. Paleolimnological studies of subtropical reservoirs are underrepresented and are needed to understand the history of reservoir development. Using paleolimnological techniques and a known 30 year flux of riverine nutrient loading from waste water treatment facilities, we compared nutrient deposition to reservoir morphological qualities and primary producer community structure during the past ~50 years. Our findings suggest phosphorus deposition is associated with reservoir order downstream of the primary nutrient source, nitrogen deposition is linked to reservoir water retention time, and N:P is most strongly linked to reservoir surface area and watershed population density. Our results were strongly influenced by a large upstream and metropolitan nutrient source, common in large rivers, but under different conditions of nutrient loading (i.e. nonpoint source), reservoirs may express other nutrient depositional patterns.