| Title |
Development of Regression Models for Estimation of Unmeasured Dissolved Organic Carbon C oncentrations in Mixed L and-use Watersh eds |
| Authors |
박민경 ( Min Kyeong Park ) ; 범진아 ( Jin A Beom ) ; 정민혁 ( Minhyuk Jeung ) ; 정지연 ( Ji Yeon Jeong ) ; 윤광식 ( Kwang Sik Yoon ) |
| DOI |
https://doi.org/10.15681/KSWE.2023.39.2.162 |
| Keywords |
Correlation Analysis; DOC; Organic matter; Regression model; TOC |
| Abstract |
In order to prevent water pollution caused by organic matter, Total Organic Carbon(TOC) has been adopted indicator and monitored. TOC can be divided into Dissolved Organic Carbon(DOC) and Particulate Organic Carbon(POC). POC is largely precipitated and removed during stream flow, which making DOC environmentally significant. However, there are lack of studies to define spatio-temporal distributions of DOC in stream affected by various land use. Therefore, it is necessary to estimate the past DOC concentration using other water quality indicators to evaluate status of watershed management. In this study, DOC was estimated by correlation and regression analysis using three different organic matter indicators monitored in mixed land-use watersheds. The results of correlation analysis showed that DOC has the highest correlation with TOC. Based on the results of the correlation analysis, the single- and multiple-regression models were developed using Biochemical Oxygen Demand(BOD), Chemical Oxygen Demand(COD), and TOC. The results of the prediction accuracy for three different regression models showed that the single-regression model with TOC was better than those of the other multiple-regression models. The trend analysis using extended average concentration DOC data shows that DOC tends to decrease reflecting watershed management. This study could contribute to assessment and management of organic water pollution in mixed land-use watershed by suggesting methods for assessment of unmeasured DOC concentration. |