1. ESEL Chung-buk National University Environment System Engineering Laboratory Identifying of a Pollution Delivery Coefficient for a stream water quality Analysis model introducing a Watershed Form ratio 2007. 6. 7. Ha Sung-Ryong, Park Jung-Ha Department of Urban Engineering Chungbuk National University, Korea

2. ESEL Chung-buk National University Environment System Engineering Laboratory Contents  Introduction  Objective of the research  Conventional simple rate method (SRM)  Nonlinear Regression Method (NRM)  Limitation of Nonlinear Regression Method (NRM)  Innovation Process for estimating K –The bypassed load –The leaking load –The weighting factor of flow rate –The characteristic of the nitrogen wash-off  Study watershed  Result and discussion  Conclusions  References

3. ESEL Chung-buk National University Environment System Engineering Laboratory Introduction  TMDL(Total Maximum Daily Load) in Korea - TMDL has been established for watershed-based water quality management since 1998. - Observation of water quality and flow, researching present condition of the pollution sources, calculation of the discharge pollution load, water quality modeling, checking for approaching a water quality standard, allocation of pollution load.  Delivery Coefficient, K –relation between discharge and delivery pollution load - Using for environmental volume characteristics of the watershed

4. ESEL Chung-buk National University Environment System Engineering Laboratory Introduction  Conventional simple rate method( Son et al., 1995) –A general method for calculating delivery coefficient which dividing delivery pollution load by discharge pollution load. –This method can not only reflect variation of environmental volume in the watershed but calculate delivery pollution load for un-observed watershed.  Method for calculating pollution load reduction coefficient(Ha et al., 1998) –Monte-carlo simulation  Nonlinear Regression Method(Ha et al., 2001, 2003, 2004, 2005) - Deducing nonlinear regression method through relation between observed delivery pollution coefficient of water quality observation points and calculated pollution run-off characteristic coefficient of watershed. - Definition delivery coefficient using watershed form ratio - In case of calculated discharge pollution load of watershed less than observed delivery pollution load, impossible to define nonlinear regression method  Suggest a technical Innovated method for estimating the pollution delivery coefficient (Park et al., 2007) –Analyzed the cause of that discharge pollution load less than observed pollution load. And suggested innovation method.

5. ESEL Chung-buk National University Environment System Engineering Laboratory Objective of the research  The objective of this study is to identify what the reason causes the limitation of NRM and suggest how we can purify the process to evaluate a pollution delivery coefficient using many field observed cases

6. ESEL Chung-buk National University Environment System Engineering Laboratory Delivery Function of Pollution Load  Conventional simple rate method (SRM) to determine a delivery coefficient, K. P M = P O × K where P M : the pollution load monitored at a river mouth with enough observed data. P O : the total pollution load discharged from a specific with enough observed data. P O : the total pollution load discharged from a specific catchment area. catchment area. (to be available by unit loading factor application) (to be available by unit loading factor application) ---- Eq. (1)

7. ESEL Chung-buk National University Environment System Engineering Laboratory Limitation of Conventional SRM K of watershed which has water quality monitoring station (WQMS) can be calculated using SRM Conventional method adapt the similar K to nearby watersheds even though the different circumstances. How can we get K of watershed which has no WQMS? Can it be similar K?

8. ESEL Chung-buk National University Environment System Engineering Laboratory Nonlinear Regression Method (NRM) P M = P O × K K = e -Φ ·Sf ---- Eq. (1) ---- Eq. (2) where S f : the watershed form ratio of the watershed Φ : the retention coefficient of the watershed ---- Eq. (3) where, L : a sum of stream length and A : a area of specific watershed.  Using GIS spatial analysis of GRID DEM (Digital Elevation Model), the L, A and S f can be derived as a deterministic variable of NRM.

9. ESEL Chung-buk National University Environment System Engineering Laboratory Nonlinear Regression Method (NRM) Φ = a ( Sf ) b  Deducing nonlinear regression method through relation between watershed form ratio and retention coefficient. where, a and b : where, a and b : pollution load characteristic function of watershed pollution load characteristic function of watershed

10. ESEL Chung-buk National University Environment System Engineering Laboratory Limitation of Nonlinear Regression Method (NRM)  Calculated discharge pollution load < observed delivery pollution load, Delivery coefficient > 1, Delivery coefficient > 1, Retention coefficient < 0 Retention coefficient < 0  In this case, can not define linear regression method - retention coefficient of watershed showed negative - retention coefficient of period which large-scale flow showed negative - retention coefficient of period which large-scale flow showed negative - retention coefficient of T-N compared with the other water quality items - retention coefficient of T-N compared with the other water quality items showed negative showed negative

11. ESEL Chung-buk National University Environment System Engineering Laboratory Innovation Process for estimating K Nonlinear Regression Method (NRM) retention coefficient, Φ Application of bypassed load Application of leaking load Application of flow weight Application of nitrogen wash-off Re-checking of observed water quality Delivery coefficient, K-NRM Delivery coefficient, K-Innovation RMSE analysis Φ < 0 Φ ≥ 0Φ ≥ 0Φ ≥ 0Φ ≥ 0

12. ESEL Chung-buk National University Environment System Engineering Laboratory The bypassed load  The bypassed load at a waste water treatment plant P h = P i × bypassed load rate Φ = -ln ( P m /(P o + P h ))×S f Where, P h : the bypassed load Pi : pollution load transported through pipeline Pi : pollution load transported through pipeline bypassed load ratio : data suggested from Total Water Pollution Load bypassed load ratio : data suggested from Total Water Pollution Load Management Plan for Geum River Management Plan for Geum River (Chung-buk province, 2005) (Chung-buk province, 2005)

13. ESEL Chung-buk National University Environment System Engineering Laboratory The leaking load  The leaking load while pollution load transported through pipeline P k = total pollution load of sewage catchment area – total inflow pollution load of waste water treatment plant pollution load of waste water treatment plant Φ = -ln( P m /( P o ) / P k )) × S f Where, P k : The leaking load

14. ESEL Chung-buk National University Environment System Engineering Laboratory The weighting factor of flow rate  The cause of that observed pollution load of raining season showed high is flowed accumulated pollution load on surface out of dry season at a time.  thus, apply flow weight for each watershed Flow weight = monthly average flow / yearly average flow Monthly average discharge pollution load = monthly average discharge pollution load before improvement × flow weight flow weight

15. ESEL Chung-buk National University Environment System Engineering Laboratory The characteristic of the nitrogen wash-off  Retention coefficient of T-N water quality showed negative through compared with the other water quality items, it's cause of nitrogen's discharge characteristic.  Plenty of nitrogen element might be separated from soil by increased flow at raining season.  Nitrogen is closely connected with infiltration water of farmland.  we applied base-flow for definition weight of nitrogen which moved by infiltration water. Baseflow rate = monthly baseflow / yearly baseflow P f = P i × Baseflow rate Φ = -ln ( P m /(P o +P f ))×S f

16. ESEL Chung-buk National University Environment System Engineering Laboratory Study watershed: Miho stream and Geum River upstream Area: about 4,709 Km 2 Area: about 4,709 Km 2 Division of watersheds: 191 (data source: Korea Water Resources Corporation) Division of watersheds: 191 (data source: Korea Water Resources Corporation) 15 WQMS was used to calculate the relationship between S f and , base on mean of watershed area. 15 WQMS was used to calculate the relationship between S f and , base on mean of watershed area.

17. ESEL Chung-buk National University Environment System Engineering Laboratory Result and discussion Before improvement After improvement  Comparison of nonlinear regression curve

18. ESEL Chung-buk National University Environment System Engineering Laboratory Result and discussion  Comparison of nonlinear regression curve As a result, the critical point of nonlinear regression method(NRM) which retention coefficient showed negative has improved. Before improvement After improvement

19. ESEL Chung-buk National University Environment System Engineering Laboratory Result and discussion  Non-exceedance probability of Retention coefficient (Φ) Before improvement After improvement

20. ESEL Chung-buk National University Environment System Engineering Laboratory Result and discussion  Non-exceedance probability of Retention coefficient (Φ) Retention coefficient calculated by Improved nonlinear regression method(NRM) has shown stabilized value. Before improvement After improvement

21. ESEL Chung-buk National University Environment System Engineering Laboratory Result and discussion  Non-exceedance probability of Delivery coefficient (K) Before improvement After improvement

22. ESEL Chung-buk National University Environment System Engineering Laboratory Result and discussion  Non-exceedance probability of Derivery coefficient(K) Retention coefficient calculated by Improved nonlinear regression method(NRM) has shown stabilized value. Before improvement After improvement

23. ESEL Chung-buk National University Environment System Engineering Laboratory Result and discussion  RMSE comparison analysis(Delivery coefficient, K) –As a result of analysis, delivery coefficient after improvement has more accurate value than before. –RMSE analysis is compared delivery coefficient calculated by nonlinear regression method with improved nonlinear regression method based on observed delivery coefficient. Month BODT-NT-P beforeafterbeforeafterbeforeafter Jan0.0510.0490.9640.9521.2221.170 Feb0.0520.0440.2620.4300.2370.258 Mar0.0480.0471.3401.3230.2650.268 Apr0.1030.0400.3890.3280.1270.096 May0.1620.1020.1600.1830.0530.038 Jun0.2620.2190.1390.2660.0840.058 Jul1.1640.9161.1720.7950.2400.080 Aug4.6534.7674.7344.5400.2200.081 Sep0.4820.5190.5380.6660.2100.047 Oct0.3240.3420.8890.7770.3530.350 Nov0.2040.0850.3960.4140.1580.161 Dec0.1900.1290.4750.3820.1750.177 RMSE 합 7.6957.26011.45911.0553.3442.785

24. ESEL Chung-buk National University Environment System Engineering Laboratory Conclusions  This study analyzed limitation of nonlinear regression method(NRM) through application of NRM to real river, and then improved for nonlinear regression method. By improved nonlinear regression method, we could calculate more accurate delivery coefficient to verify that validity.  The limitation of pollution load delivery function calculation method –Discharge pollution load < observed pollution load, –Delivery coefficient > 1, retention coefficient < 0  Application of innovated pollution load delivery coefficient calculation method for solving the limitation. –application of the bypassed load, –application of leaking load, application of flow weight, –application of nitrogen’s discharge characteristic weight  As a result of apply innovated pollution load delivery calculation method to Keum river and Miho stream, we could verify that improved method more accurate than non-improved method through comparing of nonlinear regression curve, non-exceedance probability distribution analysis, RMSE analysis.

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