A comparison study of runoff characteristics of non-point source pollution from threewatersheds in South Korea

Abstract

Three watersheds in Korea (Dochoncheon, Gongjicheon, Seolseongcheon) with different land cover characteristics were selected for non-point source pollution monitoring. Event mean concentration (EMC) was calculated, and runoff characteristics were compared through first-flushing and statistical analyses. The mean of the water quality parameters was the highest in Seolseongcheon during dry days among the three watersheds. EMCs of biochemical oxygen demand (BOD) and total nitrogen (TN) were higher in Dochoncheon and Gongjicheon during rainy days, respectively. The upper Seolseongchun watershed showed overall greater values of chemical oxygen demand (COD), suspended solids (SS), total organic carbon (TOC), and total phosphorus (TP). First-flush analyses indicated that SS had the strongest and TN had the weakest effects on the first flush. BOD was the highest in Dochoncheon (urban watershed) and increased with increased number of antecedent dry days. Rainfall intensity appeared to affect SS runoff strongly in Gongjicheon and Seolseongcheon. COD showed strong correlation with SS and TOC in all watersheds, and organic matter (COD and TOC) demonstrated high factor loads during dry and rainy days. Thus, organic matter-related factors were classified as the major factors in pollutant loads. TP and TN were separately classified during dry days in Gongjicheon and Seolseongcheon, whereas these were the secondary factors during rainfall when the influence of non-point pollution was substantial. Cluster analyses showed that the monitoring sites in Dochoncheon and Gongjicheon watersheds were closer than Seolseongcheon. As a result of the comparison of non-point source pollution runoff in the three watersheds, it was difficult to explain the non-point source pollution runoff by specific characteristics such as land cover. For science-based management of non-point pollution, it is necessary to obtain additional survey data considering the climatic, geographical and major industries.