Conserving and enhancing freshwater biodiversity are global issues to ensure ecosystem integrity and sustainability. To meet this, it is critical to understand how the biological assemblages are determined by environmental gradients in different spatial scales. Nevertheless, information on their large-scale environmental relationships remains scarce in Korea. We aimed to understand nationwide spatial distribution patterns of benthic macroinvertebrates and important environmental factors affecting their distribution in 388 streams and rivers across Korea. A total of 340 taxa, belonging to 113 families in 23 orders of five phyla, were identified. Assemblage composition in most Korean streams included a few predominant colonizers and a majority of rare taxa. Cluster analysis based on benthic macroinvertebrates classified a total of 720 sampling sites into five clusters according to the pollution levels from fast-flowing less polluted streams with low electrical conductivity to moderately or severely polluted streams with high electrical conductivity and slow water velocity. Canonical correspondence analysis revealed that altitude, water velocity and streambed composition were the most important determinants, rather than watershed and water chemistry variables, for explaining the variation in macroinvertebrate assemblage patterns. The results provide basic information for establishing the conservation and restoration strategies of macroinvertebrate biodiversity against anthropogenic disturbances and developing more confident bio-assessment tools for diagnosing stream ecosystem integrity.

Reliable water quality monitoring data, identifying potential pollution sources, and quantifying the corresponding potential pollution source apportionment are essential for future water resource management and pollution control. Here, we collected water quality data from seven monitoring sites to identify spatiotemporal changes in surface water in the Imjin River Watershed (IRW), South Korea, distinguish potential pollution sources, and quantify the source apportionment from 2018–2020. An analysis was performed based on multivariate statistical techniques (MST) and the absolute principal component score-multiple linear regression (APCS-MLR) model. Statistically significant groups were created based on spatiotemporally similar physicochemical water quality characteristics and anthropogenic activities: low-pollution (LP) and high-pollution (HP) regions, and dry season (DS) and wet season (WS). There were statistically significant mean differences in water quality parameters between spatial clusters, rather than between temporal clusters. We identified four and three potential factors that could explain 80.75% and 71.99% in the LP and HP regions, respectively. Identification and quantitative evaluation of potential pollution sources using MST and the APCS-MLR model for the IRW may be useful for policymakers to improve the water quality of target watersheds and establish future management policies.

Increasing public awareness about the aesthetics and safety of water sources has shifted researchers’ attention to the adverse effects of volatile organic compounds (VOCs) on humans and aquatic organisms. A total of 17 VOCs, including 10 volatile halogenated hydrocarbons and seven volatile non-halogenated hydrocarbons, were investigated at 36 sites of the Han River Basin, which is the largest and most important drinking water source for residents of the Seoul metropolitan area and Gyeonggi province in South Korea. The VOC concentrations ranged from below detection limits to 1.813 µg L⁻¹. The most frequently detected VOC was 1,2-dichloropropane, with a detection frequency of 80.56%, as it is used as a soil fumigant, chemical intermediate, and industrial solvent. In terms of geographical trends, the sampling sites that were under the influence of sewage and industrial wastewater treatment plants were more polluted with VOCs than other areas. This observation was also supported by the results of the principal component analysis. In the present study, the detected concentrations of VOCs were much lower than that of the predicted no-effect concentrations, suggesting low ecological risk in the Han River. However, a lack of available ecotoxicity data and limited comparable studies warrants further studies on these compounds.

The Paldang Reservoir (PDR) in South Korea is vital for supplying drinking water and maintaining ecosystems; thus, a comprehensive understanding of its water quality is necessary. Spatiotemporal changes in reservoir water quality were evaluated by applying Korean water quality indices and multivariate statistical techniques (MSTs). A dataset of 15 water quality parameters at five sites in the PDR were evaluated from 2017 to 2021. The organic matter, suspended matter, total phosphorus (TP), chlorophyll a (Chl-a), and total coliforms in the PDR exhibited a fair grade or higher. Chemical oxygen demand was found to correlate with biochemical oxygen demand, Chl-a, and TP. The average real-time water quality index (RTWQI) and average trophic state index (TSI_KO) of the PDR were excellent and mesotrophic, respectively, and 46% of eutrophic conditions occurred during the monsoon season. For a hierarchical cluster analysis (HCA), the five sites were grouped into three polluted areas and 12 months were grouped into dry and wet seasons. Principal component analysis and factor analysis identified four potential pollution sources (domestic sewage, industrial wastewater, intensive agricultural activities, and livestock wastewater) in the PDR and explained 79.7% of the total changes. Thus, the RTWQI, TSI_KO, and MSTs are useful tools for assessing freshwater quality in Korea, predicting potentially harmful conditions, and potentially assisting policymakers in PDR management.

This study evaluated the spatiotemporal variability of water quality in the Han River Basin (HRB) as well as the contributions of potential pollution sources using multivariate statistical and absolute principal component score-multiple linear regression (APCS-MLR) modeling techniques. From 2011 to 2020, data on water quality parameters were collected from 14 sites in the Ministry of Environment’s water quality monitoring network. Using spatiotemporal cluster analysis, these sites were classified into two periods over the year (dry and wet seasons) and into three regions: low pollution region (LPR), moderate pollution region (MPR), and high pollution region (HPR). Through principal component analysis, we identified four potential factors accounting for 80.1% and 74.1% of the total variance in the LPR and MPR, respectively, and three that accounted for 72.7% of the total variance in the HPR. APCS-MLR results indicated domestic sewage and phytoplankton growth (25%), domestic sewage and seasonal influence (29%), and point pollution sources caused by domestic sewage and industrial wastewater discharge (31%) as potential factors for the LPR, MPR, and HPR. These results demonstrate that the multivariate statistical techniques and the APCS-MLR model can be effectively used to monitor network design, quantitatively evaluate potential pollution sources, and establish efficient water quality management policies.

To investigate the effects of rapid urbanization on water pollution, the water quality, daily unit area pollutant load, water quality score, and real-time water quality index for the Jinwi River watershed were assessed. The contribution of known pollution sources was identified using multivariate statistical analysis and absolute principal component score-multiple linear regression. The water quality data were collected during the dry and wet seasons to compare the pollution characteristics with varying precipitation levels and flow rates. The highest level of urbanization is present in the upstream areas of the Hwangguji and Osan Streams. Most of the water quality parameter values were the highest in the downstream areas after the polluted rivers merged. The results showed a dilution effect with a lower pollution level in the wet season. Conversely, the daily unit area pollutant load was higher in the rainy season, indicating that the pollutants increased as the flow rate increased. A cluster analysis identified that the downstream water quality parameters are quite different from the upstream values. Upstream is an urban area with relatively high organic matter and nutrient loads. The upstream sewage treatment facilities were the main pollution sources. This study provides basic data for policymakers in urban water quality management.