MANAGING WATER QUALITY OF S.W. MANAGING WATER QUALITY OF S.W. EUROPEAN MARINE SITES (SEPT 03)EUROPEAN MARINE SITES (SEPT 03)

Monitoring and modelling nutrients in Monitoring and modelling nutrients in catchmentscatchments

PERCMY

HT

UOL

P

FO

UN

I V

Y

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T

ISR

Prof Paul WorsfoldProf Paul WorsfoldBiogeochemistry & Environmental Biogeochemistry & Environmental

Analytical Chemistry GroupAnalytical Chemistry GroupPlymouth Environmental Research Plymouth Environmental Research

CentreCentreUniversity of Plymouth, UKUniversity of Plymouth, UK

email pworsfold@plymouth.ac.ukemail pworsfold@plymouth.ac.uk

Environmental monitoringEnvironmental monitoringObjective: Provide high quality analytical data to:Objective: Provide high quality analytical data to:• Elucidate environmental processes and Elucidate environmental processes and

biogeochemical cyclesbiogeochemical cycles• Monitor compliance with legislation, e.g. WFDMonitor compliance with legislation, e.g. WFD• Archive data and provide baseline surveys e.g. Archive data and provide baseline surveys e.g.

EIAEIA• Study chemical fluxes, pathways and fates Study chemical fluxes, pathways and fates • BUT sampling is expensive and time consumingBUT sampling is expensive and time consuming• AND sample integrity may be lost AND sample integrity may be lost

THEREFORE we need THEREFORE we need in situin situ monitoring monitoring

In situ environmental In situ environmental monitoringmonitoring

• Provides high quality data with excellent temporal Provides high quality data with excellent temporal and spatial resolution for process studies, and spatial resolution for process studies, catchment management and mapping but requires:catchment management and mapping but requires:

• Rugged, portable, automated instrumentationRugged, portable, automated instrumentation• Contamination free environmentContamination free environment

– Reagents, containers, sampling apparatus, shipReagents, containers, sampling apparatus, ship• Sensitive and selective detectionSensitive and selective detection• Removal of matrix interferences e.g. sea saltsRemoval of matrix interferences e.g. sea salts• Stability (reagents, standards, pumps, detector,)Stability (reagents, standards, pumps, detector,)• Filtration and prevention of biofoulingFiltration and prevention of biofouling• Regular on-site calibration, maintenance & Regular on-site calibration, maintenance &

communicationcommunication

THEREFORE WE NEED FLOW INJECTION ANALYSISTHEREFORE WE NEED FLOW INJECTION ANALYSIS

Temporal changes in river Temporal changes in river TP loadTP load

0

100

200

300

400

500

600

TP

kg

P/d

ay

Dec-9

4

Feb

-95

Ap

r-9

5

Jun

-95

Au

g-9

5

Oct-

95

Dec-9

5

Feb

-96

Ap

r-9

6

Jun

-96

Au

g-9

6

Oct-

96

Dec-9

6

Feb

-97

Ap

r-9

7

Jun

-97

Au

g-9

7

Oct-

97

Dec-9

7

Lough Conn, Ireland (1995-1997)

•Periodic sampling ok for estimating annual loads.•However 90% of flow occurs in 10% of time.•Short term event driven pulses.•Diurnal cycles•Therefore need frequent analysis during these events to predict daily/monthly loads and study in-stream processes

Storage effects for R. Storage effects for R. Frome PFrome P

0

1

2

3

4

0 20 40 60 80Day

PO

4 -P

(u

M)

0.0

0.5

1.0

1.5

2.0

0 20 40 60 80Day

PO

4 -P

(u

M)

0

1

2

3

4

0 20 40 60 80Day

PO

4 -P

(u

M)

0

1

2

3

4

0 20 40 60 80Day

PO

4 -P

(u

M)

0

1

2

3

4

0 20 40 60 80Day

PO

4 -P

(u

M)

0

1

2

3

4

0 20 40 60 80Day

PO

4 -P

(u

M)

Fridge ControlFridge Control Fridge ChloroformFridge ChloroformFreezer ChloroformFreezer Chloroform

FridgeFridge FreezerFreezer Deep FreezerDeep Freezer

Water Research

35 (2001) 3670

Submersible Nitrate Submersible Nitrate ManifoldManifold

Ammonium chloride

(10 g l-1)

Mixed colour reagent

0.32

0.16

Flow cell

1 m reaction coil

ml min -1Packed reduction column

260 ul sample injected via 5 um filter

20 mm path: LOD 2.8 ug L-1 N

Linear range 5 - 100

10 mm path: LOD 85 ug L-1 N Linear range 100 - 2500

ACA 361 (1998) 63

TIME (s)

RE

SP

ON

SE

Submersible Monitor Submersible Monitor SpecificationsSpecifications• Tidal cycle (13 h), diurnal cycle (24 h) and Tidal cycle (13 h), diurnal cycle (24 h) and

transect deployment with high frequencytransect deployment with high frequency• Submersible to 50 m (mixed layer)Submersible to 50 m (mixed layer)• Multiparameter e.g. nitrate & phosphate Multiparameter e.g. nitrate & phosphate • Detection limit 0.1 Detection limit 0.1 M N (oligotrophic waters)M N (oligotrophic waters)• Rugged (protected cage), compact and lightRugged (protected cage), compact and light• Variable operational modes e.g. event Variable operational modes e.g. event

triggeredtriggered• Communication with base stationCommunication with base station• On-board filtration & calibrationOn-board filtration & calibration

Submersible deploymentSubmersible deploymentPaulo Gardolinski

Feb 2001

Protective cage and reagents

Pressure housing

FI manifold

0.50 1.00 1.50 2.00 2.50

53.00

53.50

54.00

54.50

0.0

8.0

16.0

24.0

32.0

40.0

48.0

56.0

64.0

72.0

1

2

3

45

6

78

910

1112

13

1415

17

1819

20 2122

23

24

25

26

27

31

29

3228

33

34

ug L-1 Nitrate

Dogger Bank

The Wash

Humber Estuary

England

North Sea

North Sea surface nitrate North Sea surface nitrate mappingmapping

Talanta 58 (2002) 1015

Integrated Chemical/Biological Integrated Chemical/Biological MonitorMonitor

Physical probeslogger

Ammonia

CAPMON Computer

Computer

ComputerLandfill leachate from Chelson Meadow waste treatment facility

PumpOverflow

Holding tank

Test organisms - crayfish (Pacifastacus leniusculus)

Sample

0.1 1.0 5.0 15 30

Ammonia (mg l-1)

Maximum Heart rate (bpm) of P. Leniusculus (n=12)

Control

40

80

120

160

Relation between ammonia and heart rateRelation between ammonia and heart rate

Ecotoxicology 8 (1999) 225

High temporal resolution P High temporal resolution P monitoringmonitoring

Talanta 58 (2002)

1043

Historical Tamar dataHistorical Tamar data

week

W51W46W41W36W31W26W21W16W11W06W01

PH

O (

mg/

L)

1.0

.8

.6

.4

.2

0.0

-.2

1989

19871996

1981197819781979

1990

1976

1981

1988

198919781988

19881991

1986

1990

1980

1979

199519761998

198319861991

1992

1996

1984

19811990

1983

1984

1983

week

W51W46W41W36W31W26W21W16W11W06W01

TE

M (

C)

30

25

20

15

10

5

0

-5

1991

1985

19931975

1985

1989

19761976

19891984

1979

1987

1986

1990

1993

19861991

c

week

W51W46W41W36W31W26W21W16W11W06W01

CH

L (m

icro

g/L)

175

150

125

100

75

50

25

01980

1982

1981

1978

1997

199519821978

1994

1983

1984

1995

1992

1984

week

W51W46W41W36W31W26W21W16W11W06W01

SS

(m

g/L)

600

500

400

300

200

100

0

1974

1977

19771978

1988

1992

1994

1976

19941977

1996

19941985

1990

1984

1974

1976

19811976

1974

1993

199419931986

1974

19801977

198619921986

1980198619741974198619851993

1985198519801992

1993

1975

1988

198419761978

1988

19771985

1997198519811986

1979

19741979

1979

1981

1993

1997

197519861974

1977

1977

1986

1979

1986

19751986

1992

1979

19891985

1977

1987

1995

1998

1976

19771981

19751987

1991

1989

1977

1978

1990

1996

1981

198019881977

1990

19841975

1985

1984

1988

1984

i

37152019142217161820191611141717132014211615121821171818211420182112111619191419191917131716181313191516N =

W52

W49

W46

W43

W40

W37

W34

W31

W28

W25

W22

W19

W16

W13

W10

W07

W04

W01

150

125

100

75

50

25

0

-25

1974

1977

1988

1992

1994

1977

1994

1985

1984

1974

1976

1974

19941993

1986

19801977

1986

1992

1986

19801986

1974

1974198619851993

1985

1985

19801992

1993

1988

19841976

1978

1988

1977

1985

199719851981

1986

1979

1974

1979

1979

1981

1993

1997

19751986

1974

1986

1979

1975

1986

1992

1979

19891985

1977

1987

1995

1998

1977

1981

19751987

1991

1989

1977

1978

1990

1981

1980

1988

1977

1985

1988

g

week

W51W46W41W36W31W26W21W16W11W06W01

FLW

(m

3/S

)

200

175

150

125

100

75

50

25

0

-25

1979

1999

19921992

1986

2000

1998

19811976

200019871988

19932000

19811974

19941981

1980

19931974

19971986198519921988

1974

1985

19921988

1986

1985

1986

199219791974

1985

19981993

198519811993

19881998

1993

19881987198019911998

1993

199819801993

1993

19811979198119791993

19861993

19831996198119861981

1983

197719831986199919982000

1986

20001994

19851985

1994

1978

1981

1978

19901990

1974

199919741996

1998

week

W51W46W41W36W31W26W21W16W11W06W01

RA

I (m

m/d

ay)

25

20

15

10

5

0

-5

19981979

2000

19921997

20001987

1981

19931976

19801981

1992198619971988

1979

2000

1985

1986198419881987199319982000

1988

1985

1982

1980

19871999

19981986

199319831981

19831988

19791985

19901997

2002

19901999

1993

1996

week

W51W46W41W36W31W26W21W16W11W06W01

NIT

(m

g/L)

7

6

5

4

3

2

1

0

-1

1994

1989

1996

1976

1976

1976

1976

1985

1976

1985

1986

1995

19981997

1986

1991

1983

1996

1974

1996

1997

1986

1996

19971996

a b

d

1312151710191412151716146913121015101811108151813131516915151796121414101617171311141515111017911N =

W52

W49

W46

W43

W40

W37

W34

W31

W28

W25

W22

W19

W16

W13

W10

W07

W04

W01

.2

0.0

1989

1987

1996

1981

19781978

1979

1981

1988

19891978

1988

1986

1990

1980

1979

19951976

1998

1983

1986

1991

1992

1996

1984

19811990

1984

e f

h

•Rainfall

•Flow

•Temperature

•Phosphate

•Nitrate

•Suspended solids

•Chlorophyll

Modelled Tamar data 1974 Modelled Tamar data 1974 - 1998- 1998

(a)

0

1

2

3

4

5

6

1

10

19

28

37

46

55

64

73

82

91

10

0

10

9

11

8

12

7

13

6

14

5

15

4

16

3

17

2

week

co

nc

. (m

g L

-1 N

-NO

3)

measured

predicted

(b)

0.00

0.05

0.10

0.15

0.20

0.25

1

10

19

28

37

46

55

64

73

82

91

10

0

10

9

11

8

12

7

13

6

14

5

15

4

16

3

17

2

week

co

nc

. (m

g L

-1 P

-PO

4)

measured

predicted

Nitrate + nitrite Phosphate

Export Coefficients for ITE Land Cover Export Coefficients for ITE Land Cover TypesTypes

ITE gridcode

Landcover type % catchmentarea

Export coeff.(kg ha-1 y-1)

kg P y -1

1 Sea / Estuary 0.00 0.00 02 Inland water 0.02 0.00 03 Beach and Coastal bare 0.00 0.00 04 Saltmarsh 0.00 0.00 05 Grass heath 1.27 0.02 10.56 Mown / Grazed turf 19.4 0.20 16117 Meadow/Verge/Semi-natural 31.0 0.20 25638 Rough / Marsh grass 0.98 0.02 8.1012 Bracken 0.0005 0.02 0.00413 Dense shrub heath 0.50 0.02 4.2214 Scrub / Orchard 0.92 0.02 7.6415 Deciduous woodland 7.37 0.02 61.016 Coniferous woodland 2.64 0.02 21.918 Tilled land 28.0 0.66 766620 Suburban / Rural dev. 4.32 0.83 148621 Continuous urban 0.19 0.83 65.822 Inland bare ground 0.52 0.70 15124 Lowland bog 0.10 0.00 025 Open shrub heath 0.63 0.02 5.22

Unclassified 2.13 0.48 424Total 100 14,085

Export Coefficients for Animal Export Coefficients for Animal Waste and Population Waste and Population

EquivalentsEquivalentsNutrient source Export coefficients kg P y-1

Animals: Horses 2.85 % 27 Cattle 2.85 % 330 Pigs 2.55 % 713 Sheep 3.00 % 250

Humans: Sewage systems0.38 kg P capita 8,869 Septic systems0.24 kg P capita

-1 y-1-1 y-1 1,331

Total 10,200

Total 1,320

Total export modeled 25,605

N KEY

UrbanArablePastureRough GrazingScrub / TreesWet / WaterOther / unclassified

Copyright ITE5

km0 10

GIS of Frome catchment GIS of Frome catchment land useland use Modelled

export (1998)25,605 kg y-1 P

Observed export (1998)23,400 kg y-1 P

GIS plot GIS plot prepared by prepared by Grady Hanrahan Grady Hanrahan & Gordon Irons& Gordon Irons

J. Env. Qual. 30 (2001) 1738

Phosphorus reduction scenarios Phosphorus reduction scenarios for STWs within the Frome for STWs within the Frome

catchmentcatchmentImplementation of phosphorus removal technology (Urban Wastewater Directive) All data in kg P y-1

Dorchester All STWsTotal

Original 4942 886925605

Treatment at 2768 669523431

Dorchester STW

Treatment at 2768 496721703

all STWs

Organic P release from Organic P release from sedimentsediment

0

10

20

30

40

50

60

70

70 90 110 130 150 170 190

Time elapsed (hr)

P c

on

cen

trati

on

(µ

g L

P

)

Inorganic P Organic P

5 ‰10 ‰

15 ‰

20 ‰

Ian McKelvie & Paulo Gardolinski

Nutrient monitoring & Nutrient monitoring & modellingmodelling•Reliable field instrumentation for in situ

monitoring and ground truthing models

•High temporal resolution for studying in stream processes (diurnal, storm event)

•High spatial monitoring for global mapping

•Integration with ecotoxicological monitoring

•PLS models of large historical data sets

•Empirical models based on export coefficients

•Respond to policy drivers e.g. Water Framework Directive