1. 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)
2ndAQUAREHAB Consortium meeting, Delft, NL
January 14-15, 2010

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

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. 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. 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. 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. 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. POLLUTANT LIST
Piet Seuntjes, VITO-RMA

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

10. Selection of substances
POLLUTANT LIST
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

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

12. Selected substances WP6
POLLUTANT LIST
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.

13. WP6 13

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

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 15

16. Implementation FATE MODELING: Scheldt river
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 16

17. Nutrient fluxes FATE MODELING: Scheldt river
Preliminary results for nitrogen in the soil and groundwater:
N storage in soil
[kg/km2]
N storage in shallow groundwater
[kg/km2]
N storage in deep groundwater
[kg/km2] 17

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

19. WP6 19

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

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) 21

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

23. CASE STUDY: SENNE RIVER BASIN, BELGIUM
FATE MODELING: Senne river
CASE STUDY: SENNE RIVER BASIN, BELGIUM
Vilvoorde/
Machelen
Area: 1011 km2
Average flow: 9 m3/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
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. FATE MODELING: Senne river
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.
Upland Processes
Channel/Flood Plain
Processes

25. HEC-RAS MODEL FUTURE DEVELOPMENT
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. Groundwater flow and Transport modeling (Girma Yimer)
FATE MODELING: Senne river
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

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

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 28

29. FATE MODELING: Odense river
Area: approx. 1,050 km2, including 1,015 km of watercourse
The River Odense, which is about 60 km long and drains a catchment of 625 km2, 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%)

30. FATE MODELING: Odense river
Pressure on water quality
Atmosphere
Industry
25 WWTPs > 30PE
489 stormwater outfalls, 204 from combined and 285 from separate sewerage system
WWTPs and
stormwater outfalls
Households
1870 registered farms in 2000
960 is livestock holdings
Livestock density: 0.9 unit/ha
Agriculture

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

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

33. WP6 33

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

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

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

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

39. Ecotoxicological assessment
Species Sensitivity Distribution – AQUAREHAB application (mixtures)
PAF (risks)
0.60
0.35
0.25
(Modelled) conc. of 3 compounds: conc. 1 / conc. 2. / conc. 3
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

40. WP6 40

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

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

43. WP6 43

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

45. Results of 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.

46. What is not needed Stakeholder consultation 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)”

47. What is needed Stakeholder consultation 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.

48. Conclusions for set up Stakeholder consultation
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)

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

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

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. 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)
2002
2003
2004
2005

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

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. 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. 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)