Linking Pollution to Water Body Integrity- First Year of Research

Vladimir Novotny

CDM Chair Professor

Northeastern University

STAR WATERSHED PROJECT FUNDED BY USEPA 2003-2007

Development of Risk Propagation Model for Estimating Ecological Response of Streams to Anthropogenic Stresses and Stream Modification Project Team

PI - Vladimir Novotny, NEU Center for Urban Environmental Studies

Co-PI’s NEU CUER Elias Manolakos Ferdinand Hellweger Ramanitharan Kandiah

Co-PI Univ. of Wisconsin Milwaukee Timothy Ehlinger

Co-PI Marquette University Neal O’Reilly

Co-PI Illinois State Water Survey Alena Bartosova

5 graduate students

Project objectives

A model that will include stresses such as Pollutant inputs Watershed and water body modification

Land use changes Chanelization and impoundments Riparian corridor modifications

Development of a quantitative layered risk propagation from basic landscape and watershed stressors to the biotic IBI endpoints

Study the possibility of mitigating the stresses that would have the most beneficial impact on the biotic endpoints

Apply the model to another geographic region

NUMERIC INDICES OF BIOTIC INTEGRITY

Fish

Benthic macroinvertebrates

Physical - Habitat

% IMPERVIOUSNESS

0

10

20

30

40

0 10 20 30 40 50

GOOD

FAIR

POOR

SIMPLISTIC RELATIONSHIPS

A more realistic relationship of IBI to a single stressor Yoder

Oh

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oa

tab

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Up

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Bra

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Fo

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un

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Fo

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low

ing

Gre

en

Ro

ck

10

20

30

40

50

60

Impact of pollutants and channel modification Northeast Illinois Rivers

Reference impounded streams

Navigable impounded

Des Plaines R. CSSC

Model DevelopmentModel Development

AnalyzeAnalyze individual risks of stressorsindividual risks of stressorsAssemble a large data baseAssemble a large data base– Midwest (Illinois, Wisconsin, Ohio)Midwest (Illinois, Wisconsin, Ohio)

Define structural and functional components of Define structural and functional components of the modelthe modelDevelop a layered hierarchical modelDevelop a layered hierarchical modelAssemble a data base for testing and Assemble a data base for testing and transferability of the model (e.g., Charles River)transferability of the model (e.g., Charles River)Test the model and its Test the model and its a prioria priori predictability predictability

Stream morphology(slope, width, depth,order)

Fish IBI and itsmetrics

Macroinvertebrate IBI and its metrics

LAYER 1

HABITAT WATERCHEMICALS

SEDIMENTCONTAMINATION

FRAGMENTATION

STRESSOR 1 STRESSOR 2 STRESSOR 3 STRESSOR 4 STRESSOR 5 STRESSOR 6

LAYER 2

LAYER 3

Landscape morpho-logical/ riparianfactors and stresses

ECOREGION

Land use changefactors and stresses,imperviousness

Pollutant loads fromland, point sourcesand atmosphere

Hydrologic/hydraulicstresses

LAYER 4

Periphyton and its metrics

BIOTICENDPOINTS

RISKS

IN-STREAM STRESSES

LANDSCAPE/ATMOSPHERIC STRESSES

STRUCTURAL AND FUNCTIONAL MAZE

RISKSRISKSPollutant (chemical) risks, acute and chronic, in the water column– Key metrics: Priority (toxic) pollutants, dissolved oxygen, turbidity

(suspended sediment), temperature, pH.– Variability: flow , DO, temperature

Pollutant risk (primarily chronic) in sediment– Key metrics: Priority pollutants, ammonium, dissolved oxygen in the

interstitial layer (anoxic/anaerobic or aerobic), organic and clay content

Habitat degradation risk– Key metrics: Texture of the sediment, clay and organic contents,

embeddedness, pools and riffle structure, bank stability, riparian zone quality, channelization and other stream modifications

Fragmentation risk Longitudinal - presence of dams, drop steps, impassable culvertsLateral - Lining, embankments, loss of riparian habitat (included in the habitat evaluation), reduction or elimination of refugiaVertical - lack of stream - groundwater interchange, bottom scouring by barge traffic, thermal stratification/heated discharges, bottom lined channel

Fragmentation RiskFragmentation Risk

Fragmentation can result from any factor (biotic or abiotic) that causes decrease in the ability of species to move/migrate among sub-populations or between portions of their habitat necessary for different stages of their life (e.g spawning migrations) and it can be both physical (e.g., biologically impassable culverts, dams, waterfalls, road crossings and bridges) and caused by pollutants (e.g., localized fish kills or a polluted mixing zone without a zone of passage or a thermal plume or stratification).

DATA BASEDATA BASE

NEUNortheast

UW-MMidwest

ISWSData basecreation

Local Access baseddata bases

Main SQL based data base

FOX RIVER DATA BASE

Data base keeper

Functional componentsFunctional componentsMaximum Species RichnessMaximum Species Richness

Magnitude of the risk or stressor Magnitude of the risk or stressor

Reference or maximum

Magnitude of the risk or stressor Magnitude of the risk or stressor

Reference or maximum Reference or maximum

Reference or maximum

A B

C D

RISK

Stressor – Endpoint RelationshipsStressor – Endpoint RelationshipsFish vs. Dissolved OxygenFish vs. Dissolved Oxygen

NO IMPACT

OutlierLargemouthbass

ACUTE

CHRONIC

0 1 2 3 4 5 6 7 8 9

DISSOLVED OXYGEN [ mg/L ]

0

2

4

6

8

10

OPTIMAL

GROWTHRETARDATION

LETHAL

RISK

Example of simple risk model Example of simple risk model

p pEtaxa

Staxa hab i

i

N( ) ( | _ )

1

1

where pE (taxa) is the joint probability of taxa extinction,

pS(taxa|hab_i) is the probability of taxa survival due to habitat condition I and N is the total number of habitat characteristics influencing the taxa.

Probability of taxa survival

IC I a p b p cW Q d S ed eEta xE

Eg a vg

c IC I a p b p cW Q d S ed eEta xE

Eg a vg

c

The Model (additive risks)The Model (additive risks)

IC I a p b p cW Q d S ed eEta xE

Eg a vg

c

ICI = index of biotic integrity (macroinvertebrate)

pEtaxE and pE

gavg = respective risks due to habitat impairment to mayfly taxa and a geometric mean of all habitat risk components respectively

WQc = the summation of chronic risks due to water column contamination

Sed =is the summation of the chronic risks due to contamination of sediments

Single vs. multiple stressor/IBI Single vs. multiple stressor/IBI relationship relationship

-4 -2-3 -1 0

40

30

20

10

0

Log10 (sediment risk)

Line of best fit

MSR line

predicted

observed

0 10 20 30 400

10

20

30

40

Single stressor effect

Multivariate model – predicted and calculated from observed metrics – Regional data – Southeastern Wisconsin

Artificial (Feed forward/backward) Artificial (Feed forward/backward) Neural Nets or More Advance Neural Nets or More Advance

Learning ModelsLearning ModelsStressorsandhazards

MulticomponentEcosystem

Bioticendpoints

Structure component

Transfer function

IBI

Metrics

Randomness (white noise)

Network based schemes which can flag the formation of parameter patterns that start affecting severely metrics that contribute to IBI

CATEGORIZATION OF STRESSCATEGORIZATION OF STRESS

Stream ClassificationStream Classification– Rosgen morphologicalRosgen morphological– Stream orderStream order

EcoregionalEcoregional– Reference water bodiesReference water bodies

Hydraulic modificationHydraulic modification– NaturalNatural– ImpoundedImpounded– ChannelizedChannelized– NavigationNavigation

Chemical risksChemical risks– WaterWater– SedimentSediment

Etc.Etc.

1 2 3 4 5 6

0

10

20

30

40

STREAM ORDER

7

maximum species

richness line

1

3

5

STREAMS RIVERS LARGE RIVERS

TROUT ZONE

GRAYLINGZONE

BARBEL ZONEBREAM ZONE

Linked to the Habitat risk Linked to the Habitat risk and IBI metricsand IBI metrics

USE OF THE MODELUSE OF THE MODEL

Watershed and water/body vulnerability Watershed and water/body vulnerability classification (another project)classification (another project)

Assist watershed manager with selection of Assist watershed manager with selection of priority watershedspriority watersheds

Development of watershed wide best Development of watershed wide best management practicesmanagement practices

Watershed mapping based on vulnerabilityWatershed mapping based on vulnerability

TMDLTMDL

Northeastern University Northeastern University established Center for Urban established Center for Urban

Environmental StudiesEnvironmental Studies

www.coe.neu/environmentwww.coe.neu/environment

novotny@coe.neu.edunovotny@coe.neu.edu

First year accomplishmentsFirst year accomplishments

Interdisciplinary team was formed Interdisciplinary team was formed Northeastern UniversityNortheastern University

University of Wisconsin/Marquette UniversityUniversity of Wisconsin/Marquette University

Illinois Water SurveyIllinois Water Survey

Two Technical Review ReportsTwo Technical Review Reports

Methodology was developedMethodology was developed

Data Base developmentData Base development

Four review publications submittedFour review publications submitted