Prediction of groundwater quality index to assess suitability for drinking purpose using averaged neural network and geospatial analysis.

Groundwater quality management is pivotal for ensuring public health and ecological resilience. However, the conventional water quality indices often face challenges related to parameter selection, geographic coverage, and scalability. The integration of machine learning and spatial analysis represents a promising methodological shift, allowing for high accuracy and adaptive management strategies. The Safe Groundwater Project in Unsupplied Areas (2017-2020) employed a comprehensive Groundwater Quality Index (GQI) to evaluate potable groundwater quality across South Korea, utilizing a large dataset comprising 28 water quality parameters and 3552 wells. This study revealed that over 50 % of the evaluated wells (Total 8326 wells) were inappropriate as sources of drinking water, indicating a pressing need for policy revision. The averaged neural network model achieved a high predictive accuracy of approximately 95 % for GQI grades, outperforming other classification models. The introduction of 2D spatial analysis in conjunction with machine learning algorithms notably increased the predictive accuracy for unevenly distributed groundwater samples. Moreover, this combined approach enabled the intuitive visualization of groundwater vulnerability across various regions, which can inform targeted interventions for effective resource allocation and management. This research represents a methodologically robust, interdisciplinary approach that holds significant implications for a framework for future groundwater quality management and vulnerability assessment.

Flow cytometric monitoring of the bacterial phenotypic diversity in aquatic ecosystems.

Flow cytometry is a promising tool used to identify the phenotypic features of bacterial communities in aquatic ecosystems by measuring the physical and chemical properties of cells based on their light scattering behavior and fluorescence. Compared to molecular or culture-based approaches, flow cytometry is suitable for the online monitoring of microbial water quality because of its relatively simple sample preparation process, rapid analysis time, and high-resolution phenotypic data. Advanced statistical techniques (e.g., denoising and binning) can be utilized to successfully calculate phenotypic diversity by processing the scatter data obtained from flow cytometry. These phenotypic diversities were well correlated with taxonomic-based diversity computed using next-generation 16S RNA gene sequencing. The protocol provided in this paper should be a useful guide for a fast and reliable flow cytometric monitoring of bacterial phenotypic diversity in aquatic ecosystems.

Water quality drives the regional patterns of an algal metacommunity in interconnected lakes.

The metacommunity approach provides insights into how the biological communities are assembled along the environmental variations. The current study presents the importance of water quality on the metacommunity structure of algal communities in six river-connected lakes using long-term (8 years) monitoring datasets. Elements of metacommunity structure were analyzed to evaluate whether water quality structured the metacommunity across biogeographic regions in the riverine ecosystem. The algal community in all lakes was found to exhibit Clementsian or quasi-Clementsian structure properties such as significant turnover, grouped and species sorting indicating that the communities responded to the environmental gradient. Reciprocal averaging clearly classified the lakes into three clusters according to the geographical region in river flow (upstream, midstream, and downstream). The dispersal patterns of algal genera, including Aulacoseira, Cyclotella, Stephanodiscus, and Chlamydomonas across the regions also supported the spatial-based classification results. Although conductivity, chemical oxygen demand, and biological oxygen demand were found to be important variables (loading > |0.5|) of the entire algal community assembly, water temperature was a critical factor in water quality associated with community assembly in each geographical area. These results support the notion that the structure of algal communities is strongly associated with water quality, but the relative importance of variables in structuring algal communities differed by geological regions.