| Title |
Statistical Assessment of Flow-Dependent, Time Series, and Seasonal Patterns in Long-Term Water Quality of the Kyeongan Stream |
| DOI |
https://doi.org/10.15681/KSWE.2025.41.4.285 |
| Keywords |
Kyeongan stream; Log-linear model; Statistical analysis; Total maximum daily load; Water quality |
| Abstract |
This study analyzed long-term water quality trends at the Kyeongan B site, a TMDL (Total Maximum Daily Load) target point along the Kyeongan stream, which flows into the Paldang reservoir, a key water source for the Seoul metropolitan area. A log-linear model that incorporated flow, time-series data, and seasonal functions was utilized. Upgrading the time-series component from a second-order to a third-order polynomial resulted in minimal statistical changes but improved the model’s capacity to capture complex trends, especially for TOC and total nitrogen. Each water quality parameter responded differently to the model components: TOC was mainly influenced by flow, BOD? and total nitrogen were affected by seasonality, while total phosphorus was driven by the time-series component. These variations reflect the characteristics of each parameter in terms of environmental pool, chemical speciation, and regulatory controls. BOD? and total nitrogen showed strong seasonal fluctuations due to monsoonal wash-off, whereas TOC and total phosphorus, which were more influenced by short-term rainfall events, displayed weaker seasonal patterns. A significant decrease in total phosphorus since 2012 was linked to the implementation of stricter effluent standards, highlighting the impact of policy-driven time-series effects. After controlling for the confounding influence of flow variability, a comparison between the early TMDL period (2005?2007) and recent years (2022?2024) indicated substantial improvements in water quality: total phosphorus decreased by 74%, BOD? by 52%, total nitrogen by 38%, and TOC by 17%. These findings underscore the effectiveness of water quality management efforts, even in the face of increasing pollutant sources. |