Although close relationships between the water quality of streams and the types of land use within their watersheds have been well-documented in previous studies, many aspects of these relationships remain unclear. We examined the relationships between urban land use and water quality using data collected from 527 sample points in five major rivers in Korea—the Han, Geum, Nakdong, Younsan, and Seomjin Rivers. Water quality data were derived from samples collected and analyzed under the guidelines of the Korean National Aquatic Ecological Monitoring Program, and land use was quantified using products provided by the Korean Ministry of the Environment, which were used to create a Geographic Information System. Linear models (LMs) and generalized additive models were developed to describe the relationships between urban land use and stream water quality, including biological oxygen demand (BOD), total nitrogen (TN), and total phosphorous (TP). A comparison between LMs and non-linear models (in terms of R² and Akaike’s information criterion values) indicated that the general additive models had a better fit and suggested a non-linear relationship between urban land use and water quality. Non-linear models for BOD, TN, and TP showed that each parameter had a similar relationship with urban land use, which had two breakpoints. The non-linear models suggested that the relationships between urban land use and water quality could be categorized into three regions, based on the proportion of urban land use. In moderate urban land use conditions, negative impacts of urban land use on water quality were observed, which confirmed the findings of previous studies. However, the relationships were different in very low urbanization or very high urbanization conditions. Our results could be used to develop strategies for more efficient stream restoration and management, which would enhance water quality based on the degree of urbanization in watersheds. In particular, land use management for enhancing stream water quality might be more effective when urban land use is in the range of 1.1%–31.5% of a watershed. If urban land use exceeds 31.5% in a watershed, a more comprehensive approach would be required because water quality would not respond as rapidly as expected.

Understanding the complex relationships between land use and stream water quality is critical for water pollution control and watershed management. This study aimed to investigate the relationship between land use types and water quality indicators at multiple spatial scales, namely, the watershed and riparian scales, using the ordinary least squares (OLS) and geographically weighted regression (GWR) models. GWR extended traditional regression models, such as OLS to address the spatial variations among variables. Our results indicated that the water quality indicators were significantly affected by agricultural and forested areas at both scales. We found that extensive agricultural land use had negative effects on water quality indicators, whereas, forested areas had positive effects on these indicators. The results also indicated that the watershed scale is effective for management and regulation of watershed land use, as the predictive power of the models is much greater at the watershed scale. The maps of estimated local parameters and local R² in GWR models showcased the spatially varying relationships and indicated that the effects of land use on water quality varied over space. The results of this study reinforced the importance of watershed management in the planning, restoration, and management of stream water quality. It is also suggested that planners and managers may need to adopt different strategies, considering watershed characteristics—such as topographic features and meteorological conditions—and the source of pollutants, in managing stream water quality.

The relationships between land cover characteristics in riparian areas and the biological integrity of rivers and streams are critical in riparian area management decision-making. This study aims to evaluate such relationships using the Trophic Diatom Index (TDI), Benthic Macroinvertebrate Index (BMI), Fish Assessment Index (FAI), and random forest regression, which can capture nonlinear and complex relationships with limited training datasets. Our results indicate that the proportions of land cover types in riparian areas, including urban, agricultural, and forested areas, have greater impacts on the biological communities in streams than those offered by land cover spatial patterns. The proportion of forests in riparian areas has the greatest influence on the biological integrity of streams. Partial dependence plots indicate that the biological integrity of streams gradually improves until the proportion of riparian forest areas reach about 60%; it rapidly decreases until riparian urban areas reach 25%, and declines significantly when the riparian agricultural area ranges from 20% to 40%. Overall, this study highlights the importance of riparian forests in the planning, restoration, and management of streams, and suggests that partial dependence plots may serve to provide insightful quantitative criteria for defining specific objectives that managers and decision-makers can use to improve stream conditions.

Urban green spaces provide various social, economic, health, aesthetic, environmental, and ecological benefits. This study aimed to investigate the influence of green spaces on apartment prices, with a particular emphasis on visual greenery and the proportion of green spaces. Hedonic pricing models have often been used to assess the impact of green spaces on housing prices. Herein, 16 variables were considered as factors affecting housing prices and divided into housing, neighborhood, and green space characteristics. The findings indicate that the presence of green spaces enhanced the value of apartment complexes. Moreover, both visual greenery and the proportion of green spaces within apartment complexes influenced housing prices. Additional analysis demonstrated the impact of green space characteristics within Seoul apartment complexes on housing price changes from 2016 to 2022, finding that higher green space proportions and visual greenery led to approximately 20% higher price increases, and structural equation modeling revealed that the proportion of green spaces within apartment complexes, directly and indirectly, influenced housing prices through visual greenery. Overall, this study emphasized the importance of ensuring well-managed green spaces within and around apartment complexes.

Understanding the relation between watershed land use and stream conditions is critical for watershed planning and management. This study investigated the effects of land use on stream water quality and biological conditions in sub-watersheds and micro-watersheds across the Han River watershed in South Korea. We developed random forest models for each water quality and biological indicator using the proportions of urban, agricultural, and forested areas. Our results indicate that water quality and biological indicators were significantly affected by forest area at both scales, and the sub-watershed models performed better than the micro-watershed models. Accumulated local effects were used to interpret the effect of each explanatory variable on the response variable. The plots for water quality and biological indicators with proportions of watershed land use demonstrated similar patterns at both scales, although the relation between land use and stream conditions was slightly more sensitive in micro-watersheds than in sub-watersheds. Urban and agricultural areas showed a lower proportion of water quality and biological condition variability in the micro-watersheds than in the sub-watersheds, while forests showed the opposite results. The findings of this study suggest that different spatial scales should be considered when developing effective watershed management strategies to maintain stream ecosystems.

It is imperative to develop a methodology to identify river impairment sources, particularly the relative impact of socioeconomic sources, to enhance the efficiency of various river restoration schemes and policies and to have an internal diagnosis system in place. This study, therefore, aims to identify and analyze the relative importance of the socioeconomic factors affecting river ecosystem impairment in South Korea. To achieve this goal, we applied the Analytical Hierarchy Process (AHP) to evaluate expert judgement of the relative importance of different socioeconomic factors influencing river ecosystem impairment. Based on a list of socioeconomic factors influencing stream health, an AHP questionnaire was prepared and administered to experts in aquatic ecology. Our analysis reveals that secondary industries form the most significant source of stream ecosystem impairment. Moreover, the most critical socioeconomic factors affecting stream impairment are direct inflow pollution, policy implementation, and industrial wastewater. The results also suggest that the AHP is a rapid and robust approach to assessing the relative importance of different socioeconomic factors that affect river ecosystem health. The results can be used to assist decision makers in focusing on actions to improve river ecosystem health.