ESEL Chung-buk National University Environment System Engineering Laboratory Identifying of a...

ESEL Chung-buk National UniversityEnvironment System Engineering Laboratory

Identifying of a Pollution Delivery Coefficient for Identifying of a Pollution Delivery Coefficient for a stream water quality Analysis model introducing a stream water quality Analysis model introducing

a Watershed Form ratioa Watershed Form ratio

2007. 6. 7.

Ha Sung-Ryong, Park Jung-HaHa Sung-Ryong, Park Jung-Ha

Department of Urban EngineeringDepartment of Urban Engineering

Chungbuk National University, KoreaChungbuk National University, Korea


ContentsContents

IntroductionIntroduction Objective of the researchObjective of the research Conventional simple rate method (SRM)Conventional simple rate method (SRM) Nonlinear Regression Method (NRM)Nonlinear Regression Method (NRM) Limitation of Nonlinear Regression Method (NRM)Limitation of Nonlinear Regression Method (NRM) Innovation Process for estimating KInnovation Process for estimating K

– The bypassed loadThe bypassed load– The leaking loadThe leaking load– The weighting factor of flow rateThe weighting factor of flow rate– The characteristic of the nitrogen wash-offThe characteristic of the nitrogen wash-off

Study watershedStudy watershed Result and discussionResult and discussion ConclusionsConclusions ReferencesReferences


IntroductionIntroduction 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


IntroductionIntroduction 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 d

elivery 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 observ

ed delivery pollution load, impossible to define nonlinear regression method Suggest a technical Innovated method for estimating the pollution delivery c

oefficient (Park et al., 2007)– Analyzed the cause of that discharge pollution load less than observed p

ollution load. And suggested innovation method.


Objective of the researchObjective 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


Delivery Function of Pollution LoadDelivery Function of Pollution Load

Conventional simple rate method (SRM) to determine a delivery Conventional simple rate method (SRM) to determine a delivery coefficient, coefficient, KK. .

PM = PO × K

where where PPMM : the pollution load monitored at a river mouth : the pollution load monitored at a river mouth

with enough observed data.with enough observed data. PPOO : the total pollution load discharged from a specific : 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)


Limitation of Conventional SRMLimitation of Conventional SRM

0.0

0.2

0.4

0.6

0.8

1.0

Forest Cultivate Water Barren Residential Road

(%)

0.0

0.2

0.4

0.6

0.8

1.0

Forest Cultivate Water Barren Residential Road

(%)

K of watershed whichhas water qualitymonitoring 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 watershedwhich has no WQMS?

Can it be similar K?


Nonlinear Regression Method (NRM)Nonlinear Regression Method (NRM)

PM = PO × K

K = e -Φ ·Sf

---- Eq. (1)

---- Eq. (2)

where Sf : the watershed form ratio of the watershed Φ : the retention coefficient of the watershed

ALS f /)( 2 ---- 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 Sf can be derived as a deterministic variable of NRM.



Φ = a ( Sf ) b

Deducing nonlinear regression method through relation between watershed Deducing nonlinear regression method through relation between watershed form ratio and retention coefficient.form ratio and retention coefficient.

where, where, a and b : a and b :

pollution load characteristic pollution load characteristic function of watershed function of watershed

ESEL Chung-buk National UniversityEnvironment System Engineering LaboratoryLimitation of Limitation of Nonlinear Regression Method (NRM)Nonlinear Regression Method (NRM)

BOD

- 0.40

- 0.20

0.00

0.20

0.40

0.60

0.80

1.00

0 10 20 30 40 50 60 70 80

Watershed form ratio, Sf

Rete

ntion c

oeffi

cie

nt,

ψ

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

TN

- 0.40

- 0.20

0.00

0.20

0.40

0.60

0.80

1.00

0 10 20 30 40 50 60 70 80


Rete

ntion c

oeffi

cie

nt,

ψ


TP

- 0.20

0.00

0.20

0.40

0.60

0.80

1.00

0 10 20 30 40 50 60 70 80


Rete

ntion c

oeffi

cie

nt,

ψ


Calculated discharge pollution load < Calculated discharge pollution load < observed delivery pollution load, observed delivery pollution load,

Delivery coefficient > 1, Delivery coefficient > 1,

Retention coefficient < 0Retention coefficient < 0

In this case, can not define linear regression methodIn this case, can not define linear regression method

- retention coefficient of watershed showed negative- 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 negativeshowed negative


Innovation Process for estimating KInnovation Process for estimating K


retention coefficient, retention coefficient, ΦΦ

retention coefficient, retention coefficient, ΦΦ

Application of Application of bypassed loadbypassed load

Application of Application of leaking load leaking load

Application of flow weightApplication of flow weight

Application of nitrogen wash-offApplication of nitrogen wash-off

Re-checking of observed water qualityRe-checking of observed water quality

Delivery coefficient, K-NRMDelivery coefficient, K-NRM Delivery coefficient, K-InnovationDelivery coefficient, K-Innovation

RMSE analysisRMSE analysis

ΦΦ < 0 < 0

ΦΦ ≥≥ 0 0


The bypassed loadThe bypassed load

The bypassed load at a waste water treatment plant

PPh h = P= Pii ×× bypassed load rate bypassed load rate

ΦΦ = -ln ( P= -ln ( Pmm /( /(PPoo + + PPh h ))×S))×Sff

Where, PWhere, Ph h : the bypassed load: the bypassed load

PiPi : pollution load transported through pipeline : 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 RiverManagement Plan for Geum River (Chung-buk province, 2005)(Chung-buk province, 2005)


The leaking loadThe leaking load

The leaking load while pollution load transported through pipeline

PPk k = total pollution load of sewage catchment area – total inflo= total pollution load of sewage catchment area – total inflo

w w pollution load of pollution load of waste water treatment plantwaste water treatment plant ΦΦ = -ln( P= -ln( Pmm /( /( PPoo ) ) / P/ Pk k )) × S)) × Sff

Where, PWhere, Pk k : The leaking load: The leaking load


The weighting factor of flow rateThe 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 weightFlow weight = monthly average flow / yearly average flow= monthly average flow / yearly average flow

Monthly average discharge pollution loadMonthly average discharge pollution load= monthly average discharge pollution load before improvement= monthly average discharge pollution load before improvement× × flow weightflow weight


The characteristic of the nitrogen wash-offThe 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 rateBaseflow rate= monthly baseflow / yearly baseflow= monthly baseflow / yearly baseflow

PPf f = P= Pii ×× Baseflow rate Baseflow rate

ΦΦ = -ln ( P= -ln ( Pmm /( /(PPoo + +PPf f ))×S))×Sff


Study watershed: Miho stream and Geum River upstreamStudy watershed: Miho stream and Geum River upstream

• Area: about 4,709 KmArea: about 4,709 Km22

• 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 15 WQMS was used to calculate the relationship between between SSff and and , base on mean of watershed area., base on mean of watershed area.


Result and discussionResult and discussion

BOD

- 0.40

- 0.20

0.00

0.20

0.40

0.60

0.80

1.00

0 10 20 30 40 50 60 70 80

Watershed form ratio(Sf)

Ret

entio

n co

effici

ent

(∮)


BOD

y = 3.9818x- 0.9676

R2 = 0.6862

0.000.100.200.300.400.500.600.700.800.901.00

0 10 20 30 40 50 60 70 80


Ret

entio

n co

effici

ent

(∮)

Jan Feb Mar AprMay Jun Jul AugSep Oct Nov Dec

(Mar)거듭제곱

TN

- 0.40

- 0.20

0.00

0.20

0.40

0.60

0.80

1.00

0 10 20 30 40 50 60 70 80


Ret

entio

n co

effici

ent

(∮)


TN

y = 2.0441x- 0.9074

R2 = 0.8721

0.000.100.200.300.400.500.600.700.800.901.00

0 10 20 30 40 50 60 70 80


Ret

entio

n co

effici

ent

(∮)


(Aug)거듭제곱

Before improvementBefore improvement After improvementAfter improvement

Comparison of nonlinear regression curve



TP

- 0.20

0.00

0.20

0.40

0.60

0.80

1.00

0 10 20 30 40 50 60 70 80


Ret

entio

n co

effici

ent

(∮)


Comparison of nonlinear regression curve

TP

y = 1.7227x- 0.7925

R2 = 0.4838

0.000.100.200.300.400.500.600.700.800.901.00

0 10 20 30 40 50 60 70 80


Ret

entio

n co

effici

ent

(∮)


(Nov)거듭제곱

As a result, the critical point of nonlinear regression method(NRM) which retention coefficient showed negative has improved.



Result and discussionResult and discussion Non-exceedance probability of Retention coefficient (Φ)

BOD5

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.00 1.00 2.00 3.00 4.00

Retention coefficient, ψ

Non-exc

eedance p

robability

(-)

JanFebMarAprMayJunJul

AugSepOctNovDec

BOD5

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.00 1.00 2.00 3.00 4.00Retention coefficient, ψ

Non-exc

eedance p

robabili

ty (

-)

J an

Feb

Mar

Apr

May

J un

J ul

Aug

Sep

Oct

Nov

Dec

TN

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.00 0.50 1.00 1.50 2.00 2.50Retention coefficient, ψ

Non-exc

eedance p

robabili

ty (

-)

J an

Feb

Mar

Apr

May

J un

J ul

Aug

Sep

Oct

Nov

Dec

TN

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0


Non-exc

eedance p

robabili

ty (

-)

J an

Feb

Mar

Apr

May

J un

J ul

Aug

Sep

Oct

Nov

Dec




Non-exceedance probability of Retention coefficient (Φ)TP

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0


Non-exc

eedance p

robabili

ty (

-)

J an

Feb

Mar

Apr

May

J un

J ul

Aug

Sep

Oct

Nov

Dec

TP

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0


Non-exc

eedance p

robabili

ty (

-)

J an

Feb

Mar

Apr

May

J un

J ul

Aug

Sep

Oct

Nov

Dec

Retention coefficient calculated by Improved nonlinear regression method(NRM) has shown stabilized value.




BOD5

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.00 0.20 0.40 0.60 0.80 1.00Delivery coefficient,K

Non-exc

eedance p

robabili

ty (

-)

J an

Feb

Mar

Apr

May

J un

J ul

Aug

Sep

Oct

Nov

Dec

BOD5

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0


Non-exc

eedance p

robabili

ty (

-)

J an

Feb

Mar

Apr

May

J un

J ul

Aug

Sep

Oct

Nov

Dec

TN

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0


Non-exc

eedance p

robabili

ty (

-)

J an

Feb

Mar

Apr

May

J un

J ul

Aug

Sep

Oct

Nov

Dec

TN

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0


Non-exc

eedance p

robabili

ty (

-)

J an

Feb

Mar

Apr

May

J un

J ul

Aug

Sep

Oct

Nov

Dec

Non-exceedance probability of Delivery coefficient (K)




Non-exceedance probability of Derivery coefficient(K)TP

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0


Non-exc

eedance p

robabili

ty (

-)

J an

Feb

Mar

Apr

May

J un

J ul

Aug

Sep

Oct

Nov

Dec

T- P

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0


Non

-exc

eeda

nce

prob

abili

ty (

-)

J an

Feb

Mar

Apr

May

J un

J ul

Aug

Sep

Oct

Nov

Dec

Retention coefficient calculated by Improved nonlinear regression method(NRM) has shown stabilized value.



Result and discussionResult 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.

MonthBOD T-N T-P

before after before after before after

Jan 0.051 0.049 0.964 0.952 1.222 1.170

Feb 0.052 0.044 0.262 0.430 0.237 0.258

Mar 0.048 0.047 1.340 1.323 0.265 0.268

Apr 0.103 0.040 0.389 0.328 0.127 0.096

May 0.162 0.102 0.160 0.183 0.053 0.038

Jun 0.262 0.219 0.139 0.266 0.084 0.058

Jul 1.164 0.916 1.172 0.795 0.240 0.080

Aug 4.653 4.767 4.734 4.540 0.220 0.081

Sep 0.482 0.519 0.538 0.666 0.210 0.047

Oct 0.324 0.342 0.889 0.777 0.353 0.350

Nov 0.204 0.085 0.396 0.414 0.158 0.161

Dec 0.190 0.129 0.475 0.382 0.175 0.177

RMSE 합 7.695 7.260 11.459 11.055 3.344 2.785


ConclusionsConclusions 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 accur

ate 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.


ReferencesReferences 1. 강선홍 , 서동일 , 1997, 초강천의 퇴적물 분석을 이용한 총인 및 질소의 유달특성평가 ,

대한상하수도학회지 , 11(4), 99-109. 2. 박대희 , 2005, GIS-HSPF 기반 유역 오염유출 해석을 통한 하천 수질오염 영향도 평가 , 충북대학교

박사학위 논문 . 3. 배명순 , 2003, 지형정보학을 이용한 오염부하유출계수 산정 , 충북대학교 박사학위 논문 . 4. 손명기 , 서수용 , 서동일 , 1995, 금강 하류부의 오염물질 발생현황과 유달율을 고려한 유입오염부하량

산정 , 대한환경공학회 , 401-404. 5. 이광호 , 하성룡 , 1993, 농경유역으로부터의 오염부하 장기유출해석 , 한국수자원학회 , 501-508. 6. 이은실 , 1998, 하천 수질 보전을 고려한 토지이용 적지분석 , 충북대학교 석사학위 논문 . 7. 충청북도 , 2005, 충청북도 금강 오염총량관리 기본계획 . 8. EPA, 1987, The Enhanced Stream Water Quality Models QUAL2E. 9. ESRI, 1994, Using ARC/INFO Network/TIN/COGO/ArcStorm/ArcScan. 10. ESRI, 1995, Using GRID with ARC/INFO. 11. Horton, R. E., 1945, Erosional development of streams and their drainage basins : Hydrophysical approa

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