Daechung Reservoir has been suffering from severe cyanobacterial blooming periodically due to the water pollutants from the watershed, especially nutrients from nonpoint sources. As a countermeasure, an artificial wetland was constructed to mitigate the pollutant load from the watershed by utilizing the vegetation. We investigated the water quality of the influent and outflow of the wetland during years 2014~2020 to evaluate the performance of pollutant removal through the wetland. Major pollutants (e.g. BOD, COD, SS, T-N, and T-P) were largely reduced during the retention in the wetland while nutrients removal was more efficient than that of organic matters. Pollutant removal efficiency for different inflow concentrations was also investigated to estimate the wetland’s capability as a way of managing nonpoint sources. The efficiency of water treatment was significantly higher when inflow concentrations were above 75th percentile for all pollutant, implying the wetland can be applied to the pre-treatment of high pollution load including initial rainfall runoff. Furthermore, the yearly variation of removal efficiency for seven years was analyzed to better understand long-term trends in water treatment of the wetland. The annual treatment efficiency of T-P was very high in the early stages of vegetation growth with high concentration of inflow water. However, it was confirmed that the concentration of inflow water decreased, vegetation stabilized, and the treatment efficiency gradually decreased as the soil was saturated. The findings of the study suggest that artificial wetlands can be an effective method for controlling harmful algal blooms by alleviating pollutant load from the tributaries of Daechung Reservoir.

This study was conducted to investigate runoff characteristics of non-point pollutants source at the urban and rural zones in sangju area. The monitoring was conducted with seven events for ten months and Event mean Concentration(EMC) and First Flush Effect(FFE) of SS and BOD were calculated on the result of the water quality parameters. During rainfall event, the peak concentrations of SS and BOD were observed after 3∼4 hours of rainfall in rural areas. Whereas, the peak concentrations occurred within 1∼2 hours after rainfall and then the highest concentration of NPS pollutants sharply decreased, showing strong first flush effect in urban areas. The cumulative load curves for NPS pollutants showed above the 45° straight line, indicating that fist flush effect occurred in urban areas. The mean SS EMC values of rural areas ranged from 0.9∼3.3mg/L, it was higher value when compare to urban areas. While the mean BOD values of urban areas were shown the highest values.

This study analyzed the characteristics of stormwater runoff by rainfall type in orchard areas for two years. Effluents were monitored to calculate the EMCs and runoff loads of each pollutant. The runoff characteristics for nonpoint sources from vineyards were also inspected based on independent variables that affect runoff such as rainfall and rainfall intensity. The average runoff loads of each pollutant from vineyard_A and vineyard_B were found as follows: BOD 39.13 ㎎/㎡, COD 112.13 ㎎/㎡, TOC 54.98 ㎎/㎡, SS 1,681.8 ㎎/㎡, TN 18.29 ㎎/㎡, and TP 4.06 ㎎/㎡, which indicates that the COD's runoff load was especially high. The average EMCs from vineyard_A and vineyard_B, which represents the quality of rainfall effluent, were also analyzed: BOD 3.5 ㎎/L, COD 11.5 ㎎/L, TOC 5.2 ㎎/L, SS 211.7 ㎎/L, TN 1.774 ㎎/L, and TP 0.324 ㎎ /L. This suggested that the COD, as an indicator of organic pollutants, is high in terms of EMCs as well. As rainfall increased, the EMCs of BOD, COD, TOC and SS kept turning upward. At a point, however, the high rainfall brought about dilution effects and began to push down the EMCs. Higher rainfall intensities led to the increase in the EMCs that displays the convergence of rainfall. Low rainfall intensities also raised pollutant concentrations, although the concentrations themselves were slightly different among pollutants.

Growth in population and urbanization has progressively increased the loading of pollutants from nonpoint sources as well as point sources. Especially in case of road regions such as city trunk road, national road and highway are rainfall and pollutants runoff intensive landuses since they are impervious and emit a lot of pollutants from vehicle activity. This research was conducted to investigate the nonpoint sources concentration and quantifying stormwater pollutants which are contained in rainfall runoff water. Three different monitoring sites in Jinju and Changwon city were equipped with an automatic rainfall gauge and flow meter for measuring rainfall and the volume of rainfall runoff. In the case of average EMC value, city trunk road was shown the highest value in target water quality items like as BOD, COD, SS, TN and TP. Or the amount of runoff loads by water quality items showed the highest value in city trunk road. And runoff load in city trunk road was 43.8 times high value compared to highway by value of city trunk road 356.7 ㎎/㎡, highway 8.150 ㎎/㎡, national road 19.99 ㎎/ ㎡ in the case of BOD.

Agriculture nonpoint pollution source is a significant contributor to water quality degradation. To establish effective water quality control policy, environpolitics establishment person must be able to estimate nonpoint source loads to lakes and streams. To meet this need for orchard area, we investigated a real rainfall runoff phenomena about it. We developed nonpoint source runoff estimation models for vineyard area that has lots of fertilizer, compost specially between agricultural areas. Data used in nonpoint source estimation model gained from real measuring runoff loads and it surveyed for two years(2008-2009 year) about vineyard. Nonpoint source runoff loads estimation models were composed of using independent variables(rainfall, storm duration time(SDT), antecedent dry weather period(ADWP), total runoff depth(TRD), average storm intensity(ASI), average runoff intensity(ARI)). Rainfall, total runoff depth and average runoff intensity among six independent variables were specially high related to nonpoint source runoff loads such as BOD, COD, TN, TP, TOC and SS. The best regression model to predict nonpoint source runoff load was Model 6 and regression factor of all water quality items except for was R² =0.85.