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MANAGING WATER QUALITY OF S.W. EUROPEAN MARINE SITES (SEPT 03) Monitoring and modelling nutrients in catchments PERC M Y H T U O L P F O U N I V Y E T.

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Presentation on theme: "MANAGING WATER QUALITY OF S.W. EUROPEAN MARINE SITES (SEPT 03) Monitoring and modelling nutrients in catchments PERC M Y H T U O L P F O U N I V Y E T."— Presentation transcript:

1 MANAGING WATER QUALITY OF S.W. EUROPEAN MARINE SITES (SEPT 03) Monitoring and modelling nutrients in catchments PERC M Y H T U O L P F O U N I V Y E T I S R Prof Paul Worsfold Biogeochemistry & Environmental Analytical Chemistry Group Plymouth Environmental Research Centre University of Plymouth, UK email pworsfold@plymouth.ac.uk

2 Environmental monitoring Objective: Provide high quality analytical data to: Elucidate environmental processes and biogeochemical cycles Elucidate environmental processes and biogeochemical cycles Monitor compliance with legislation, e.g. WFD Monitor compliance with legislation, e.g. WFD Archive data and provide baseline surveys e.g. EIA Archive data and provide baseline surveys e.g. EIA Study chemical fluxes, pathways and fates Study chemical fluxes, pathways and fates BUT sampling is expensive and time consuming BUT sampling is expensive and time consuming AND sample integrity may be lost AND sample integrity may be lost THEREFORE we need in situ monitoring

3 In situ environmental monitoring Provides high quality data with excellent temporal and spatial resolution for process studies, catchment management and mapping but requires: Provides high quality data with excellent temporal and spatial resolution for process studies, catchment management and mapping but requires: Rugged, portable, automated instrumentation Rugged, portable, automated instrumentation Contamination free environment Contamination free environment – Reagents, containers, sampling apparatus, ship Sensitive and selective detection Sensitive and selective detection Removal of matrix interferences e.g. sea salts Removal of matrix interferences e.g. sea salts Stability (reagents, standards, pumps, detector,) Stability (reagents, standards, pumps, detector,) Filtration and prevention of biofouling Filtration and prevention of biofouling Regular on-site calibration, maintenance & communication Regular on-site calibration, maintenance & communication THEREFORE WE NEED FLOW INJECTION ANALYSIS

4 Temporal changes in river TP load 0 100 200 300 400 500 600 TP kgP/day Dec-94 Feb-95 Apr-95 Jun-95 Aug-95 Oct-95 Dec-95 Feb-96 Apr-96 Jun-96 Aug-96 Oct-96 Dec-96 Feb-97 Apr-97 Jun-97 Aug-97 Oct-97 Dec-97 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

5 Storage effects for R. Frome P 0 1 2 3 4 020406080 Day PO 4 -P (uM) 0.0 0.5 1.0 1.5 2.0 020406080 Day PO 4 -P (uM) 0 1 2 3 4 020406080 Day PO 4 -P (uM) 0 1 2 3 4 020406080 Day PO 4 -P (uM) 0 1 2 3 4 020406080 Day PO 4 -P (uM) 0 1 2 3 4 020406080 Day PO 4 -P (uM) Fridge Control Fridge Chloroform Freezer Chloroform FridgeFreezer Deep Freezer Water Research 35 (2001) 3670

6 Submersible Nitrate Manifold Ammonium chloride (10 g l -1 ) Mixed colour reagent 0.32 0.16 Flow cell 1 m reaction coil ml min Packed 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) RESPONSE

7 Submersible Monitor Specifications Tidal cycle (13 h), diurnal cycle (24 h) and transect deployment with high frequency Tidal cycle (13 h), diurnal cycle (24 h) and transect 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  M N (oligotrophic waters) Detection limit 0.1  M N (oligotrophic waters) Rugged (protected cage), compact and light Rugged (protected cage), compact and light Variable operational modes e.g. event triggered Variable operational modes e.g. event triggered Communication with base station Communication with base station On-board filtration & calibration On-board filtration & calibration

8 Submersible deployment Paulo Gardolinski Feb 2001 Protective cage and reagents Pressure housing FI manifold

9 0.501.001.502.002.50 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 4 5 6 7 8 9 10 11 12 13 14 15 17 18 19 20 21 22 23 24 25 26 27 31 29 32 28 33 34 ug L -1 Nitrate Dogger Bank The Wash Humber Estuary England North Sea North Sea surface nitrate mapping Talanta 58 (2002) 1015

10 Integrated Chemical/Biological Monitor Physical probes logger Ammonia CAPMON Computer Landfill leachate from Chelson Meadow waste treatment facility Pump Overflow Holding tank Test organisms - crayfish ( Pacifastacus leniusculus ) Sample

11 0.1 1.0 5.01530 Ammonia (mg l -1 ) Maximum Heart rate (bpm) of P. Leniusculus (n=12) Control 40 80 120 160 Relation between ammonia and heart rate Ecotoxicology 8 (1999) 225

12 High temporal resolution P monitoring Talanta 58 (2002) 1043

13 Historical Tamar data week W51W46W41W36W31W26W21W16W11W06W01 PHO (mg/L) 1.0.8.6.4.2 0.0 -.2 1989 1987 1996 1981 1978 1979 1990 1976 1981 1988 1989 1978 1988 1991 1986 1990 1980 1979 19951976 1998 1983 1986 1991 1992 1996 1984 1981 1990 1983 1984 1983 week W51W46W41W36W31W26W21W16W11W06W01 TEM (C) 30 25 20 15 10 5 0 -5 1991 1985 1993 1975 1985 1989 1976 1989 1984 1979 1987 1986 1990 1993 1986 1991 c week W51W46W41W36W31W26W21W16W11W06W01 CHL (microg/L) 175 150 125 100 75 50 25 0 1980 1982 1981 1978 1997 1995 1982 1978 1994 1983 1984 1995 1992 1984 week W51W46W41W36W31W26W21W16W11W06W01 SS (mg/L) 600 500 400 300 200 100 0 1974 1977 1978 1988 1992 1994 1976 1994 1977 1996 1994 1985 1990 1984 1974 1976 1981 1976 1974 1993 1994 1993 1986 1974 1980 1977 1986 1992 1986 1980 1986 1974 1986 1985 1993 1985 19801992 1993 1975 1988 1984 1976 1978 1988 1977 1985 19971985 1981 1986 1979 1974 1979 1981 1993 1997 1975 1986 1974 1977 1986 1979 1986 1975 1986 1992 1979 19891985 1977 1987 1995 1998 1976 1977 1981 1975 1987 1991 1989 1977 1978 1990 1996 1981 1980 1988 1977 1990 1984 1975 1985 1984 1988 1984 i 371520191422171618201916111417 1320142116151218211718 211420182112111619 1419 171317161813 191516N = 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 1994 1993 1986 1980 1977 1986 1992 1986 1980 1986 1974 1986 1985 1993 1985 19801992 1993 1988 1984 1976 1978 1988 1977 1985 1997 1985 1981 1986 1979 1974 1979 1981 1993 1997 1975 1986 1974 1986 1979 1975 1986 1992 1979 1989 1985 1977 1987 1995 1998 1977 1981 1975 1987 1991 1989 1977 1978 1990 1981 1980 1988 1977 1985 1988 g week W51W46W41W36W31W26W21W16W11W06W01 FLW (m3/S) 200 175 150 125 100 75 50 25 0 -25 1979 1999 1992 1986 2000 1998 1981 1976 2000 19871988 1993 2000 1981 1974 1994 1981 1980 1993 1974 1997 1986 1985 1992 1988 1974 1985 1992 1988 1986 1985 1986 1992 19791974 1985 1998 1993 1985 1981 1993 1988 1998 1993 1988 1987 1980 1991 1998 1993 1998 1980 1993 1981 1979 1981 1979 1993 1986 1993 1983 1996 1981 1986 1981 1983 1977 1983 1986 1999 1998 2000 1986 20001994 1985 1994 1978 1981 1978 1990 1974 1999 1974 1996 1998 week W51W46W41W36W31W26W21W16W11W06W01 RAI (mm/day) 25 20 15 10 5 0 -5 1998 1979 2000 1992 1997 2000 1987 1981 1993 1976 1980 1981 1992 1986 1997 1988 1979 2000 1985 1986 1984 1988 1987 1993 1998 2000 1988 1985 1982 1980 1987 1999 1998 1986 19931983 1981 1983 1988 1979 1985 19901997 2002 1990 1999 1993 1996 week W51W46W41W36W31W26W21W16W11W06W01 NIT (mg/L) 7 6 5 4 3 2 1 0 1994 1989 1996 1976 1985 1976 1985 1986 1995 1998 1997 1986 1991 1983 1996 1974 1996 1997 1986 1996 1997 1996 ab d 1312151710191412151716146913121015101811108151813 1516915 17961214 101617 13111415 111017911N = 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 1978 1979 1981 1988 1989 1978 1988 1986 1990 1980 1979 19951976 1998 1983 1986 1991 1992 1996 1984 1981 1990 1984 e f h Rainfall Flow Temperature Phosphate Nitrate Suspended solids Chlorophyll

14 Modelled Tamar data 1974 - 1998 Nitrate + nitritePhosphate

15 Export Coefficients for ITE Land Cover Types ITE grid code Landcover type% catchment area Export coeff. (kg ha -1 y -1 ) kg P y 1Sea / Estuary0.00 0 2Inland water0.020.000 3Beach and Coastal bare0.00 0 4Saltmarsh0.00 0 5Grass heath1.270.0210.5 6Mown / Grazed turf19.40.201611 7Meadow/Verge/Semi-natural31.00.202563 8Rough / Marsh grass0.980.028.10 12Bracken0.00050.020.004 13Dense shrub heath0.500.024.22 14Scrub / Orchard0.920.027.64 15Deciduous woodland7.370.0261.0 16Coniferous woodland2.640.0221.9 18Tilled land28.00.667666 20Suburban / Rural dev.4.320.831486 21Continuous urban0.190.8365.8 22Inland bare ground0.520.70151 24Lowland bog0.100.000 25Open shrub heath0.630.025.22 Unclassified2.130.48424 Total10014,085

16 Export Coefficients for Animal Waste and Population Equivalents Nutrient sourceExport coefficientskg P y Animals: Horses2.85 % 27 Cattle2.85 %330 Pigs2.55 %713 Sheep3.00 %250 Humans: Sewage systems0.38 kg P capita8,869 Septic systems0.24 kg P capita y y 1,331 Total 10,200 Total1,320 Total export modeled 25,605

17 GIS of Frome catchment land use Modelled export (1998) 25,605 kg y -1 P Observed export (1998) 23,400 kg y -1 P GIS plot prepared by Grady Hanrahan & Gordon Irons J. Env. Qual. 30 (2001) 1738

18 Phosphorus reduction scenarios for STWs within the Frome catchment Implementation of phosphorus removal technology (Urban Wastewater Directive) All data in kg P y -1 DorchesterAll STWsTotal Original4942886925605 Treatment at2768669523431 Dorchester STW Treatment at 2768496721703 all STWs

19 Organic P release from sediment Ian McKelvie & Paulo Gardolinski

20 Nutrient monitoring & modelling 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


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