1 2 nd aquarehab consortium meeting, delft, nl january 14-15, 2010 aquarehab – wp6 ludek blaha...

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2ndAQUAREHAB Consortium meeting, Delft, NL

January 14-15, 2010

AQUAREHAB – WP6Ludek 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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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

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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

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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.

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TASKS

T6.1 Fate model integrating the fluxes of chemicals at river basin scale: T6.1.1 OdenseT6.1.2 Scheldt riverT6.1.3 Senne riverT6.2. Ecological effect assessment of chemicals in river basinsT6.2.1 Ecotoxicological database (MU)T6.2.2.Ecotoxicological database (MU)T6.3 Economical analyses of water quality remediation measuresT6.3.1 Information from existing remediation measuresT6.3.2 Information from “AQUAREHAB” measuresT6.4 Integration of fate, effect assessment and economic analyses in a management tool REACH-ERT6.4.1 identification of the pollutant listT6.4.2 Conceptual designT6.4.3 Optimisation issuesT6.4.4 ImplementationT6.4.5 Application on Scheldt and Odense riverT6.5 Rehabilitation Guidelines

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DELIVERABLESD.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

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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


WP 6 Conclusions

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POLLUTANT LIST

Piet Seuntjes, VITO-RMA

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Aquarehab substances

• DoW– Nitrate– Pesticides– Chlorinated aliphatic hydrocarbons (CAH)– Other substances: BTEX, chlorobenzenes,

metals, …

• Surface water + groundwater

• WP6: model substances

POLLUTANT LIST

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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

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Selection

• Green: suitable• Orange: moderately

suitable• Red: not suitable

Aquarehab ID

Aquarehab substance group

WFD priority substances ID Substance Scheldt river*Registration Odense river Modelkey** Socopse*** Score-PP**** Other EU catchments*****logKoc******

1 Nitrate x x2 Pesticides 1 Alachlor excluded Annex 1 x 1.63-2.28 moderately mobile

3 Atrazine xexcluded Annex 1 (10/09/2005, some x x x 1.95-2.71 moderately mobile

8 Chlorfenvinphosexcluded Annex 1 (31/12/2003) x 2.83 slightly mobile

9 Chlorpyrifos x x 3.44-4.49 non-mobile13 Diuron x excluded Annex 1 x x 2.67-3.22 slightly mobile14 Endosulfan x excluded Annex 1 x 4.06 non-mobile16 Hexachlorobenzene excluded Annex 1 x x 4.70 non-mobile17 Hexachlorobutadiene x18 Hexachlorocyclohexane x19 Isoproturon x 31/12/2012 x x x 1.56-2.38 moderately mobile

POLLUTANT LIST

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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

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WP6

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FATE MODELING: Scheldt river

Pieter Jan Haest (UA – VITO)Piet Seuntjens, Steven Broekx and Paul Campling (VITO)

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FATE MODELING: Scheldt river Modelling tool: PCRaster

Area: 20000 km²

Pollutants: Nitrate (+Pesticide)

Resolution: 1 km2

Time step: monthly

Purpose of the modelling

Fate for nitrate and pesticides

Link to AQUAREHAB WP’s

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• 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

ImplementationFATE MODELING: Scheldt river

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Nutrient fluxes

N storage in soil[kg/km2]

N storage in shallow groundwater[kg/km2]

N storage in deep groundwater[kg/km2]

» Preliminary results for nitrogen in the soil and groundwater:


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Nutrient fluxes

N-load [kg/year] at the outflow point of the Scheldt basin

» Preliminary results for nitrogen in the river network:


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WP6

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FATE MODELING: Senne river

Claudio Avella (UNESCO-IHE/University of Milano)Girma Yimer (UNESCO-IHE)Ann van Griensven (UNESCO-IHE)

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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)

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CASE STUDY: SENNE RIVER BASIN, BELGIUM


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20 21

1918

1514

1312

24 25

2322

1716

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1110

8

976

Aa

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1

Siphons of Vil voorde

2728

South

North

Woluwe

EPPEGEM

V ILVOORDE

AND ERLECHT

LOT

TUBIZE

HALLE

1

2

3

Overflow Lembeek

Flow ca libration point

addition by overf low

abstraction by overflow

constant point source

dynamic point source

subbasin: land and r iver

river reach

#410000

#345000

#344000

#348000

#350000

#342000

quenast

Water quality measurement site

Water flow me asurement site

CASE STUDY: SENNE RIVER BASIN, BELGIUM

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 useAgricultural 48% Urbanised 38%Pasture 8%Forest 6%

SoilLoam


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

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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


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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

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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


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FATE MODELING: Odense river

Linh Hoang (UNESCO-IHE)Lars Troldborg (GEUS)Ann van Griensven (UNESCO-IHE)

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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

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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%)


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Pressure on water quality

Agriculture Households

Industry25 WWTPs > 30PE

489 stormwater outfalls, 204 from combined and 285 from separate sewerage system

1870 registered farms in 2000

960 is livestock holdings

Livestock density: 0.9 unit/ha

Atmosphere

WWTPs and stormwater outfalls


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Data collection

No. Data Purpose1 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)

Inputs for water quality models


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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

BuildSWAT model


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WP6

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Inventory of toxicological data: conceptual design

Ludek Blaha, Karel Brabec, Martina Nešporová [email protected] University, Faculty of Science, RECETOX (Research Centre for Environmental Chemistry and Ecotoxicology), www.recetox.cz

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CA model for community Species Sensitivity Distribution (SSD)

One compound - organisms have variable sensitivities (example: diethyl phthalate, DEP)

Ecotoxicological assessment

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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 %


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Species Sensitivity Distribution - APPLICATIONS

1) PROSPECTIVE – EQC definition

5 % canbe lost(95% protected) Safe

concentration


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Species Sensitivity Distribution - APPLICATIONS1) PROSPECTIVE – EQC definition

5 % canbe lost(95% protected) Safe

concentration

2) RETROSPECTIVE – Relative risk evaluation

Measured (modelled)

concentrations

Potentially affected fraction (PAF) of

community


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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)

0.60

0.35

0.25

(Modelled) conc. of 3 compounds: conc. 1 / conc. 2. / conc. 3


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WP6

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Economical assessment

Geraldine Ducos (INERIS)Steven Broekx (VITO)

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• Concept design & planning of activities• Inventory of data on existing remediation

measures

Question:• Costs of AQUAREHAB measures????

Economical assessment

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WP6

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Stakeholde consultation of DSS systems

Steven Broekx (VITO)

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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

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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)”


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What is neededWe 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.


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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)


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Piet Seuntjens (VITO)Paul Campling (VITO)Steven Broekx (VITO)

Ann van Griensven (UNESCO-IHE)

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REACH-ER

Pressures

Driving forces

StateImpact

Responses

(1)

(2)

(3)

(4)

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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

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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

1

2

3

4

5

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

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ConclusionsStarting 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

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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

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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)

1 2 nd aquarehab consortium meeting, delft, nl january 14-15, 2010 aquarehab – wp6 ludek blaha...

Documents

odense t6

tasks t6

aquarehab measures t6

implementation t6

odense river t6

scheldt river t6

pollutant list t6

river basins t6