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1 2 nd AQUAREHAB Consortium meeting, Delft, NL January 14-15, 2010 AQUAREHAB – WP6 Ludek Blaha (RECETOX) Jean-Marc brignon (INERIS) Geraldine Ducos (INERIS)

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Presentation on theme: "1 2 nd AQUAREHAB Consortium meeting, Delft, NL January 14-15, 2010 AQUAREHAB – WP6 Ludek Blaha (RECETOX) Jean-Marc brignon (INERIS) Geraldine Ducos (INERIS)"— Presentation transcript:

1 1 2 nd AQUAREHAB Consortium meeting, Delft, NL January 14-15, 2010 AQUAREHAB – WP6 Ludek Blaha (RECETOX) Jean-Marc brignon (INERIS) Geraldine Ducos (INERIS) Broekx Steven (VITO-RMA) Campling Paul (VITO-RMA) Seuntjens Piet (VITO-RMA) Haest Pieter Jan (VITO-RMA) Jaroslav Slobodnik (EI) Corina Carpentier (EI) Nilsson, Bertel (GEUS) Troldborg, Lars (GEUS) Giuliano Di Baldassarre (IHE) Linh Hoang (IHE) Girma Yimer (IHE) Zhu Xuan (IHE) Ann van Griensven (IHE)

2 2 AQUAREHAB Linh Hoang (IHE) Zhu Xuan (IHE) Seleshi Yalew (IHE) Geraldine Ducos (INERIS) Pieter Jan Haest (VITO-RMA) Lars Troldberg (GEUS) Corina Carpentier (EI) WP6

3 3 CONTENT  WP 6 Objectives  WP 6 Tasks  WP 6 Deliverables  WP 6 Results  Pollution list  Fate models  Inventory of data on remediation measures  Inventory of toxicological data  Inventory of DSS systems  Draft design of REACH-ER  WP 6 Conclusions

4 4 OBJECTIVES The objective of WP6 is to develop a generic collaborative management tool ‘REACH-ER’ that can be used by stakeholders, citizens or water managers to evaluate the ecological and economical effects of different remedial actions on waterbodies. How important are innovative technologies compared to more conventional measures? Ecological: potentially large impact on local and/or basin scale, on substances which are problematic. Economic: cost-effective compared to conventional measures.

5 5 TASKS T6.1 Fate model integrating the fluxes of chemicals at river basin scale: T6.1.1 Odense T6.1.2 Scheldt river T6.1.3 Senne river T6.2. Ecological effect assessment of chemicals in river basins T6.2.1 Ecotoxicological database (MU) T6.2.2.Ecotoxicological database (MU) T6.3 Economical analyses of water quality remediation measures T6.3.1 Information from existing remediation measures T6.3.2 Information from “AQUAREHAB” measures T6.4 Integration of fate, effect assessment and economic analyses in a management tool REACH-ER T6.4.1 identification of the pollutant list T6.4.2 Conceptual design T6.4.3 Optimisation issues T6.4.4 Implementation T6.4.5 Application on Scheldt and Odense river T6.5 Rehabilitation Guidelines

6 6 DELIVERABLES D.6.1 (Month 12): Conceptual fate model framework D.6.1 (Month 18): Economic assessments methodology applied to the cases D.6.3 (Month 24): Coupled fate-ecological modelling framework for pollutants D.6.4 (Month 30): Coupled fate-ecological-economical modelling framework for rehabilitation technologies D.6.5 (Month 30): Management tool for rehabilitation technologies D.6.6 (Month 36): Guidance document on rehabilitation and restoration technologies D.6.7 (Month 42): Report on evaluation of management scenarios for rehabilition technologies for critical areas within the Scheldt and the Odense river

7 7 CONTENT  WP 6 Objectives  WP 6 Tasks  WP 6 Deliverables  WP 6 Results  Pollution list  Fate models  Inventory of toxicological data  Inventory of data on remediation measures  Stakeholder analysis for DSS system  Draft design of REACH-ER  WP 6 Conclusions

8 8 POLLUTANT LIST Piet Seuntjes, VITO-RMA

9 9 Aquarehab substances DoW –Nitrate –Pesticides –Chlorinated aliphatic hydrocarbons (CAH) –Other substances: BTEX, chlorobenzenes, metals, … Surface water + groundwater WP6: model substances POLLUTANT LIST

10 10 Selection of substances Criteria –Aquarehab substance group –WFD priority chemicals –Registration period –Presence in pilot river basins (Scheldt, Odense) –Ecological relevance –Data from other projects (Modelkey, Socopse, Score-PP, Footprint) –Moderately sorbing compounds POLLUTANT LIST

11 11 Selection Green: suitable Orange: moderately suitable Red: not suitable POLLUTANT LIST

12 12 Selected substances WP6 Nitrate Pesticides: Isoproturon, Simazine, Terbutylazine, MCPA, Bentazon, Glyphosate, AMPA, Mecoprop. The selection of the pesticides was done based on their occurrence in EU rivers, their presence on the market, their inclusion in the WFD priority substance list, their physical chemical properties (moderately sorbing), and the existence of physical and chemical data from other EU projects. Chlorinated aliphatics: trichloro-ethylene. This substance is considered the model substance for the CAHs. It was chosen because of the presence on the WFD priority substance list and information collected in the SCORE- PP project BTEX: toluene and benzene. These substances are representative for the BTEXs. They were chosen because of their revelance for groundwater pollution and their inclusion in the WFD list (benzene). Nonylphenol, DEHP: these substances were chosen for a dedicated study related to the Zenne river case where they occur and have a strong ecological relevance as evidenced in the Modelkey project. They are also included in the WFD priority substances list. POLLUTANT LIST

13 13 WP6

14 14 FATE MODELING: Scheldt river Pieter Jan Haest (UA – VITO) Piet Seuntjens, Steven Broekx and Paul Campling (VITO)

15 15 FATE MODELING: Scheldt river  Modelling tool: PCRaster  Area: km²  Pollutants: Nitrate (+Pesticide)  Resolution: 1 km2  Time step: monthly  Purpose of the modelling  Fate for nitrate and pesticides  Link to AQUAREHAB WP’s

16 16 PCRaster environmental modelling language –Raster based GIS, suitable for distributed dynamic modelling –Easy to modify code –Easy to replace/update Series of maps Tables with temporal data Time series Implementation FATE MODELING: Scheldt river

17 17 Nutrient fluxes N storage in soil [kg/km 2 ] N storage in shallow groundwater [kg/km 2 ] N storage in deep groundwater [kg/km 2 ] »Preliminary results for nitrogen in the soil and groundwater: FATE MODELING: Scheldt river

18 18 Nutrient fluxes N-load [kg/year] at the outflow point of the Scheldt basin »Preliminary results for nitrogen in the river network: FATE MODELING: Scheldt river

19 19 WP6

20 20 FATE MODELING: Senne river Claudio Avella (UNESCO-IHE/University of Milano) Girma Yimer (UNESCO-IHE) Ann van Griensven (UNESCO-IHE)

21 21 FATE MODELING: Senne river  Modelling tool: SWAT + HEC-RAS  Area: 1100 km²  Pollutants: CAH, Nitrate  Resolution: 30 m data  Time step: daily  Purpose of the modelling:  Model the transport of pollutants from ‘Vilvoode-Machelen’ site -> REACH-ER  View pollution from Vilvoorde-Machelen in relation to the urban pollution and operations of the WWTP’s of Brussels -> Scheldt model.  Compute nitrate loads and nitrification/denitrification processes -> Scheldt model.  Link to AQUAREHAB WP’s  WP3-WP7: Vilvoorde/Machelen (remedial technology + MODFLOW model)

22 22 CASE STUDY: SENNE RIVER BASIN, BELGIUM FATE MODELING: Senne river

23 23 CASE STUDY: SENNE RIVER BASIN, BELGIUM Area: 1011 km 2 Average flow: 9 m 3 /sec Average velocity: 0.2 – 0.3 m/sec Receives waste-water from 1.4 mln of inhabitants: Land use Agricultural 48% Urbanised 38% Pasture 8% Forest 6% Soil Loam FATE MODELING: Senne river Vilvoorde/ Machelen 5 models: SWAT: Rainfall-runoff, nitrate KOSIM: Sewer/WWTP in Brussels MODFLOW: Groundwater/contaminant flux HEC-RAS: Hydrodynamic river model downstream AQUASIM model for denitrification in river bed

24 24 SWAT MODEL SWAT (Soil and Water Assessment Tool) is a conceptual hydrological model that works on daily time step. It can simulate hydrological processes as well as water quality and sediment transportation and processes at soil phase, taking into account for weather conditions and land management. Channel/Flood Plain Processes FATE MODELING: Senne river

25 25 HEC-RAS MODEL A hydraulic model of the last streams of the river is being built. HEC-RAS model also include a water-quality module. The two models will be linked to have a global model of the river. FUTURE DEVELOPMENT Calibration/validation for the flows Run SWAT with sub-daily time step Calibration/validation for nitrate Link to Scheldt estuary model

26 26 Groundwater flow and Transport modeling (Girma Yimer) DONE: The groundwater flow and Transport modeling of Vilvoorde- Machelen region has been carried out by VITO (Touchant, Bronders et al. 2007) and VUB (Boel 2008) TO DO: - Implementing transport models that incorporates multiple chemical and biological reactions (e.g. RT3D) at finer scale - Uncertainty and probabilistic risk assessment FATE MODELING: Senne river

27 27 FATE MODELING: Odense river Linh Hoang (UNESCO-IHE) Lars Troldborg (GEUS) Ann van Griensven (UNESCO-IHE)

28 28 FATE MODELING: Odense river  Modelling tool: SWAT / MIKE-SHE-DAISY  Area: ~1000 km²  Pollutants: Nitrate, pesticides  Resolution: 30 m data  Time step: daily  Purpose of the modelling:  Compute nitrate and pesticide pollutions to the river Odense  Evalute effectiveness of restored wetlands to reduce pollution to the river  Link to AQUAREHAB WP’s  WP1(+WP7): Removal of pesticides and nitrate

29 29 Area: approx. 1,050 km 2, including 1,015 km of watercourse The River Odense, which is about 60 km long and drains a catchment of 625 km 2, is the largest river Population: 246,000, 10% not serviced by sewerage system Monthly precipitation: 40 mm (April)- 90 mm (December/January) Soil type: clay soil (51%), sandy soil (49%) Land use: Farmland (68%), urban area (16%, woodland (10%) and natural/ semi-natural areas (6%) FATE MODELING: Odense river

30 30 Pressure on water quality Agriculture Households Industry 25 WWTPs > 30PE 489 stormwater outfalls, 204 from combined and 285 from separate sewerage system 1870 registered farms in is livestock holdings Livestock density: 0.9 unit/ha Atmosphere WWTPs and stormwater outfalls FATE MODELING: Odense river

31 31 Data collection No.DataPurpose 1Catchment data (topology, geology, land use, soil map) Build catchment models 2Meteorological dataInput for catchment models 3Hydrological data (discharge, groundwater head) Calibrate hydrological models 4Water quality dataCalibrate water quality models 5Pollutant loadings from point sources (households, industries, WWTPs, etc) Inputs for water quality models 6Diffuse source data (Agriculture and farming data) Inputs for water quality models FATE MODELING: Odense river

32 32 Integrate wetland model in catchment models Integrate in SWAT Integrate in DAISY-MIKE SHE Update SWAT model Update the existing DAISY-MIKE SHE model Compare the performance of 2 models Build SWAT model FATE MODELING: Odense river

33 33 WP6

34 34 Inventory of toxicological data: conceptual design Ludek Blaha, Karel Brabec, Martina Nešporová Masaryk University, Faculty of Science, RECETOX (Research Centre for Environmental Chemistry and Ecotoxicology),

35 35 CA model for community Species Sensitivity Distribution (SSD) One compound - organisms have variable sensitivities (example: diethyl phthalate, DEP) Ecotoxicological assessment

36 36 CA model for community Species Sensitivity Distribution (SSD) Increasing concentration Diethyl phthalate - distribution of sensitivities (based on ECx, NOECs…) Increasing concentration Frequency% species 100 % 50 % 0 % Ecotoxicological assessment

37 37 Species Sensitivity Distribution - APPLICATIONS 1) PROSPECTIVE – EQC definition 5 % can be lost (95% protected) Safe concentration Ecotoxicological assessment

38 38 Species Sensitivity Distribution - APPLICATIONS 1) PROSPECTIVE – EQC definition 5 % can be lost (95% protected) Safe concentration 2) RETROSPECTIVE – Relative risk evaluation Measured (modelled) concentrations Potentially affected fraction (PAF) of community Ecotoxicological assessment

39 39 Species Sensitivity Distribution – AQUAREHAB application (mixtures) MIXTURE PAF – msPAF (multisubstance PAF) – e.g. dissimilar mode of action (response addition) msPAF = 1 – (1-0.6)*(1-0.35)*(1-0.25) = 0.8 => 80% of species will be affected PAF (risks) (Modelled) conc. of 3 compounds: conc. 1 / conc. 2. / conc. 3 Ecotoxicological assessment

40 40 WP6

41 41 Economical assessment Geraldine Ducos (INERIS) Steven Broekx (VITO)

42 42 Concept design & planning of activities Inventory of data on existing remediation measures Question: Costs of AQUAREHAB measures???? Economical assessment

43 43 WP6

44 44 Stakeholde consultation of DSS systems Steven Broekx (VITO)

45 45 Results of stakeholder consultation Stakeholder consultation –Potential end users at all levels: national, subbasin, administrations –Check potential added value –What do we need and what do we not need? General conclusions on consultation: –It is time consuming and requires resilience. (up untill now: 6 presentations, 8 interviews,…). –Not every administration is equally happy about an integrative approach esp. those who execute the projects (interference with own policy, cost- effective sollution could implicate a shift in budgets). –If you want people to use it, they need to be involved from the start and have impact on outline. Stakeholder consultation

46 46 What is not needed We do not need: –Additional work: new reporting demands, run complicated models –New models doing the same things but different: “we have models for basic water quality parameters, water quantity (floods)” Stakeholder consultation

47 47 What is needed We do need: –Estimate how far we reach in achieving different targets with certain measures. (compose and compare scenario’s) –Support in prioritisation. (now: a lot of expert judgement) –Impact of measures on different water aspects (biological quality and links to physico-chemical quality and hydromorphology), cfr. bufferstrips: a single aspect approach is disadvantageous –Local vs. central reporting levels: handle different scales –Upstream-downstream impacts –Transparancy in data, indicate uncertainty is important. Possibility for users to insert data. Stakeholder consultation

48 48 Conclusions for set up A strict integration with models reduces added value. (no user interface embedded in models) Waterbody vs. river basin: both are needed. Take into account multi-objective impacts: quality (ecological + physico-chemical), quantity Modular: easy to extend DSS with other modules Transparency: possibility to go back to basic figures and possibility to insert own figures. Time frame: some measures take long time to be at full impact (decades) Stakeholder consultation

49 49 Draft design of REACH-ER Piet Seuntjens (VITO) Paul Campling (VITO) Steven Broekx (VITO) Ann van Griensven (UNESCO-IHE)

50 50 REACH-ER Pressures Driving forces State Impact Responses (1) (2) (3) (4)

51 51 REACH-ER Identification of components  Drivers: List of pollutant, pollution maps (e.g. maps of pesticide use)  Pressures (“Hazard” “fluxes): e.g.MODFLOW model  State: Fate models (“Exposure”): e.g. SWAT/PCRASTER -> Covert time series to statistical descriptors -> Probabilities of concentrations at various locations  Impacts: ecotoxicoligy “Risk” -> Species Sensitivity Distribution -> Potentially affected fraction (PAF)  Responses: - WP7 models -> remedial measures database/rules - Optimisation/Multi-criteria analysis

52 52 REACH-ER REQUIREMENTS  Spatial visualisation (GIS) Shape files for river reaches, fluxes  Time dynamic variability within a year changes over the years/decades  Web-based: accessible over the internet  (Uncertainties)  Model independent (OpenMI compliant)

53 53 + Link to groundwater + Link to models + Remedial measure database + Optimisation

54 54 Conclusions  Starting of modelling for all cases  Inventories for:  Remedial measures  Toxicological data  DSS Systems  Stakeholder consultation for DSS  First design of DSS  Planning of further activities

55 55 Questions/issues  Link with other modelling activities (Odense wetlands, Vilvoorde/Machelen site)!  Data (&tools) for pesticide fate modelling  Several ecologically effective pollutants NOT modeled/studies in AQUAREHAB  Upscaling issues, general conclusions  Temperature dependence on ecotoxicology  Estimation of benefits?  Costs for AQUAREHAB measures?  Use of existing DSS such as MODELKEY?  Case specific versus generic data/rules

56 56 THANK YOU!!! Ludek Blaha (RECETOX) Jean-Marc brignon (INERIS) Geraldine Ducos (INERIS) Broekx Steven (VITO-RMA) Campling Paul (VITO-RMA) Seuntjens Piet (VITO-RMA) Haest Pieter Jan (VITO-RMA) Jaroslav Slobodnik (EI) Corina Carpentier (EI) Nilsson, Bertel (GEUS) Troldborg, Lars (GEUS) Giuliano Di Baldassarre (IHE) Linh Hoang (IHE) Girma Yimer (IHE) Zhu Xuan (IHE) Ann van Griensven (IHE)


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