Scale and Complexity in Environmental Systems

Office of Research and DevelopmentNational Exposure Research Laboratory

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Scale and Complexity in Environmental Systems

Daniel A. Vallero, Ph.D.

National Exposure Research LaboratoryU.S. Environmental Protection Agency

Office of Research and DevelopmentNational Exposure Research Laboratory 2

Environmental Protection: A Child of the ’60s

Systematic mandates from NEPA:•EIS•CEQ


Scale is crucial


Regulatory Focus Varies

• Policy– National consistency– Command and control

• Technology (Clean Air Act in the 1990s)• Risk

– Assessment (science)– Management (policy)– Communication (everything)– Residual risk (Clean Air Act now)– Safe products (TSCA/FIFRA)– Health based standards (Clean Water and Safe Drinking Water Acts)– Manifests (RCRA, Right to Know)– Response (Superfund, Contingency Plan, Spill Response…)


Mission of Engineers(Adapted from: Department of Materials Science and Engineering, State University of New York at Stony Brook)

• The engineer must envision and allow for the creation of something, following certain specifications, which performs a given function.

• What we design must perform its function without fail.


But eventually, everything fails…

• So, designers must strive to avoid failure, in all of its forms.• In particular, we must avoid catastrophic failures:

– loss of designed property or properties potentially affected by the application of the design;

–damage to the environment where the design is applied, and;–Most importantly injury and loss of life.

• Modern designers can learn what to do and NOT to do to create designs with less of a chance of failure.


Example of Range of Acceptability

–Design of a barrier under a waste facility may reduce the flow of water carrying hazardous materials to 10-9 m s –1


Design Success

Clay liner

Water Table

ContaminantsContaminants




–But, it does not eliminate the flow entirely–The designer must keep the flow rate low


Design Success

Clay liner

Water Table

Contaminants

Contaminants




–But, it does not eliminate the flow entirely–The designer must keep the flow rate low –Catastrophic failure at Q = 10-2 m s –1!


Perception is crucial

•Which line is longer?

The Müller-Lyer Illusion.


But sometimes, perception is pretty accurate….

Source: Pardon, ca. 1970.


Risk Perception

• Failures become "disasters" as a function of public perception of risk. –For example, in 1992, same number of U.S. fatalities in

transportation accidents involving airplanes (775), trains (755), and bicycles (722).

–Public perception of the risk from air travel is often much higher than that for trains and bicycles.

• Two apparent reasons: – large loss of life and associated media attention from an air crash,

and –air passenger's lack of control over their environment in the case

of air or, to a lesser degree, rail accidents. • But there are many reasons behind these perceptions


Risk Communication

Report

Data True Meaning(Signal)

Data Reduction

Interpretation (Information)

S/N = ∞

ReportReportReportReportReportReportReportReport

?

Noise

S/N = Low


Different Processes at Work*

Analytical Phase Risk Assessment Processes Risk Perception Processes

Identifying risk Physical, chemical, and biological monitoring and measuring of the event

Personal awareness

Deductive reasoning Intuition

Statistical inference

Estimating risk Magnitude, frequency and duration calculations

Personal experience

Cost estimation and damage assessment

Intangible losses and non-monetized valuation

Economic costs

Evaluating risk Cost/benefit analysis Personality factors

Community policy analysis Individual action

*Adapted from K. Smith, 1992


Risk is quantifiable ...

Risk = f(Hazard x Exposure)• A probability, a fraction• Part of our everyday lives

–Different for each of us–Basis for decision-making


Risk Assessment Defined:

Risk assessment is a process where information is analyzed to determine if an environmental hazard might cause harm to exposed persons and ecosystems.

Paraphrased from the “Risk Assessment in the Federal Government” (National Research Council, 1983)


A Paradigm for Risk


A few words about toxicity and uncertainty in scale

• Cancer versus non-cancer• Cancer uses slope factor• Non-cancer uses reference dose (RfD) or reference concentration (RfC)

• RfC is for air, RfD for other exposure pathways• No safe level of exposure to a carcinogen (no threshold, no NOAEL)


Dose-Response: A Way to Define a Hazard

A

B B

C

Adverse Effect

DoseNOAEL


Dose-Response: No threshold for cancer

Cancer

Non-cancer

Adverse Effect

DoseNOAEL


Dose-Response: Safety in Reference Dose

Adverse Effect

DoseNOAEL

RfD = NOAELMFUF


Dose-Response: Safety in Reference Dose

Adverse Effect

DoseNOAEL

RfD

RfD = NOAELMFUF


Improved Certainty Includes Better Scale and Complexity Factors

Adverse Effect

DoseNOAEL

RfD RfD

RfD = NOAELMFUF


Calculating Exposures: Amount of Hazard Reaching Us

Where,

E = personal exposure during time period from t1 to t2

C(t) = concentration at interface, at t.

2

1

)(tt

tt

dttCE


Exposure bridges the physical and social sciences

2

1

)(tt

tt

dttCE

Where,

E = personal exposure during time period from t1 to t2

C(t) = concentration at interface, at t.

Chemistry & Physics

Psychology & Sociology


TRANSPORT /

TRANSFORMATION

Dispersion

Kinetics

Themodynamics

Distributions

Meteorology

ALTERED STRUCTURE /

FUNCTION

Edema

Arrhythmia

Enzymuria

Necrosis

etc.

ENVIRONMENTALCHARACTERIZATION

Air

Water

Diet

Soil & dust

SOURCE / STRESSORFORMATION

Chemical

Physical

Microbial

Magnitude

Duration

Timing

DOSE

Absorbed

Target

Internal

Biologically EffectivePathway

Route

Duration

Frequency

Magnitude

EXPOSURE

Activity

Patterns

EARLY BIOLOGICALEFFECT

Molecular

Biochemical

Cellular

Organ

Organism

PBPKModels

Transport,Transformation

& Fate Models

ExposureModels

DISEASE

Cancer

Asthma

Infertility

etc.

• Individual• Community

• Population

Statistical Profile

Reference Population

Susceptible Individual

Susceptible Subpopulations

Population Distributions

Components of Exposure Science

Measurements (Orange Boxes) Models (Green Lines)

Deposition to aquatic ecosystem

M0, M2+ M-CxHy

Linking Human and Exposure Analysis for a Single Contaminant (Mangis et al.)

SpeciationEn

viro

nm

enta

l M

easu

rem

ents

&

Mod

elin

g


M0, M2+ M-CxHy

Food Chain Uptake

Linking Human and Exposure Analysis for a Single Contaminant

SpeciationEn

viro

nm

enta

l M

easu

rem

ents

&

Mod

elin

g

Ecosystem function & structure

Act

ivit

y an

d

Fu

nct

ion

M

easu

rem

ents

&

Mod

elin

g


M0, M2+ M-CxHy

Food Chain Uptake


Atmospheric emissionsNatural: Forest fires, volcanoes

Industrial: Power plants

Population DietUncertainties:•Amounts consumed

•Fish species consumed•Fish preparation etc.

Ground water transportNatural & industrial sources

Temporal VariabilityUncertainties:•Intra-annual•Inter-annual•Fish species

•Fish maturation•Fish size etc.

Regional EconomyUncertainties:•Local vs. imported fish

•Pricing and availability•Processing, storage etc.

SpeciationEn

viro

nm

enta

l M

easu

rem

ents

&

Mod

elin

g


Act

ivit

y an

d

Fu

nct

ion

M

easu

rem

ents

&

Mod

elin

g


M0, M2+ M-CxHy

Food Chain Uptake











SpeciationEn

viro

nm

enta

l M

easu

rem

ents

&

Mod

elin

g


Act

ivit

y an

d

Fu

nct

ion

M

easu

rem

ents

&

Mod

elin

g

Dietary Ingestion





Absorption, Distribution Metabolism, Elimination and Toxicity (ADMET) ModelingUncertainties:•Age, gender, lifestyle differences•Physiological variability•Physicochemical and biochemical variabilities

•Health status, activities•Pregnancy/nursing•Genetic susceptibilities





M0, M2+ M-CxHy

Target Tissue DoseBrain

KidneyBreast milk

Fetus / fetal brain

Food Chain Uptake


Toxicity/Adverse EffectNeurological

RenalCardiovascular

[Genomic / Cytomic]



Dietary Ingestion

SpeciationEn

viro

nm

enta

l M

easu

rem

ents

&

Mod

elin

g


Act

ivit

y an

d

Fu

nct

ion

M

easu

rem

ents

&

Mod

elin

g

PB

TK

an

d

BB

DR

M

odel

ing

Bio

mar

ker

s &

E

co-

Ind

icat

ors





Absorption, Distribution Metabolism, Elimination and Toxicity (ADMET) ModelingUncertainties:•Age, gender, lifestyle differences•Physiological variability•Physicochemical and biochemical variabilities

•Health status, activities•Pregnancy/nursing•Genetic susceptibilities





M0, M2+ M-CxHy

Target Tissue DoseBrain

KidneyBreast milk

Fetus / fetal brain

Food Chain Uptake

LOOKING BACK: RECONSTRUCTION

Toxicity/Adverse EffectNeurological

RenalCardiovascular

[Genomic / Cytomic]



Dietary Ingestion

SpeciationEn

viro

nm

enta

l M

easu

rem

ents

&

Mod

elin

g


Act

ivit

y an

d

Fu

nct

ion

M

easu

rem

ents

&

Mod

elin

g

PB

TK

an

d

BB

DR

M

odel

ing

Bio

mar

ker

s &

E

co-

Ind

icat

ors


Advances in the Bayesian Network Applications

• Usually, limited available data• And, limited resources• Need reliable information for human and eco decision making

• Need a predictive link between actions & results (eco & health)


Typical Approach

• Models try to combine understanding from many projects into one predictive framework Simulating all physical, chemical and biological

processes at some state Highly variable interrelationships among these

processes So, probably better to tailor each relationship’s detail

than to choose a scale identical for all processes.


Bayesian Networks

• When data and resources are limited…• Graphical structure represents cause-and-effect assumptions between system variables

• Such assumptions let causal chain from actions to eco and human consequences to be factored into sequence of conditional probabilities


Rate of contaminant release

Contaminant characteristics

Value (e.g., rate of destruction, uptake)

Time

Inst

anta

neou

s P

roba

bili

ty

Pro

babi

lity

Fluid/matrix characteristics

Value (e.g., flow rate, partitioning)

Pro

babi

lity

Transport, Transformation, and Fate Characterization of Contaminant

Air

Residence time

Pre

dict

ed m

ass

or

conc

entr

atio

n

Residence time

Pre

dict

ed m

ass

or

conc

entr

atio

n

Soil

Residence time

Pre

dict

ed m

ass

or

conc

entr

atio

n

Sediment

Confidence intervalResidence time

Pre

dict

ed m

ass

or

conc

entr

atio

n

Biota

Characteristics of target organisms, habitats

Characteristics of human populations (e.g., activity patterns, sensitivities, diet, residential structures)

Population characteristics

Value (e.g., activities)

Pro

babi

lity Organism

characteristics

Value (e.g., uptake)

Pro

babi

lity

Eco-Exposure Assessment

Dos

e

Time

Human Exposure Factors and Algorithms

Ecological Exposure Factors and Algorithms

Human ExposureAssessment

Residence time

Pre

dict

ed m

ass

or

conc

entr

atio

n

Water (Ground & Surface)

Rate of contaminant release

Contaminant characteristics

Value (e.g., rate of destruction, uptake)

Time

Inst

anta

neou

s P

roba

bili

ty

Pro

babi

lity

Fluid/matrix characteristics

Value (e.g., flow rate, partitioning)

Pro

babi

lity

Transport, Transformation, and Fate Characterization of Contaminant

Air

Residence time

Pre

dict

ed m

ass

or

conc

entr

atio

n

Residence time

Pre

dict

ed m

ass

or

conc

entr

atio

n

Soil

Residence time

Pre

dict

ed m

ass

or

conc

entr

atio

n

Sediment

Confidence intervalResidence time

Pre

dict

ed m

ass

or

conc

entr

atio

n

Biota

Characteristics of target organisms, habitats

Characteristics of human populations (e.g., activity patterns, sensitivities, diet, residential structures)

Population characteristics

Value (e.g., activities)

Pro

babi

lity Organism

characteristics

Value (e.g., uptake)

Pro

babi

lity

Eco-Exposure Assessment

Dos

e

Time

Human Exposure Factors and Algorithms

Ecological Exposure Factors and Algorithms

Human ExposureAssessment

Residence time

Pre

dict

ed m

ass

or

conc

entr

atio

n

Water (Ground & Surface)

Bayes Theorem allows myriad forms of information like this to be combined:


Sample(monitoringdata)

Posterior (integrating modelingand monitoring)

Bayesian Analysis: Combining Information

Prior (model forecast)

Criterion Concentration


Log

chl

a

Log P

Log(chla)=-.95+1.5Log(P)

Std. Err. = .120

Prior

Sample

Posterior Probability

Lake

Consequences of actions on ecosystem and human exposure can be predicted.


These are conditional probability models that: • can be mechanistic, statistical, judgmental• use probability to express uncertainty• use Bayes theorem for adaptive implementation updating.

Bayes (Probability) Networks

NitrogenInputs

RiverFlow

Cause and EffectRelationships

Cause and EffectRelationships

AlgalDensity

Carbon Production

Frequency of Hypoxia

Number ofFishkills

FishHealth

ShellfishAbundance

Duration of Stratification

HarmfulAlgal Blooms

SedimentOxygenDemand

ChlorophyllViolations



RiverFlow

AlgalDensity

Carbon Production


Number ofFishkills

NitrogenInputs

FishHealth


HarmfulAlgal Blooms

ShellfishAbundance

Dependencies are described by Dependencies are described by conditional probability distributions.conditional probability distributions.

p(Hypoxiap(Hypoxia |SOD, Strat.)|SOD, Strat.)

All model relationships can be disaggregated into a series of conditional distributions.



RiverFlow

AlgalDensity

Carbon Production


Number ofFishkills

NitrogenInputs

FishHealth


HarmfulAlgal Blooms

ShellfishAbundance

p(C|N)p(C|N) = p(C|A)= p(C|A) p(A|N,R)p(A|N,R) p(R)p(R)

Each conditional distribution can be represented by a separateseparate sub-model.




RiverFlow

AlgalDensity

Carbon Production


Cross-System Cross-System ComparisonComparison

Simple Simple MechanisticMechanistic

Expert Expert ElicitationElicitation

Number ofFishkills

NitrogenInputs

FishHealth


HarmfulAlgal Blooms

ShellfishAbundance

Empirical ModelEmpirical Model

Seasonal RegressionSeasonal Regression

Site-Specific ApplicationSite-Specific Application

Survival ModelSurvival Model


p(Health = “Poor”p(Health = “Poor” | N inputs = “X”)| N inputs = “X”)



RiverFlow

AlgalDensity

Carbon Production


Number ofFishkills

NitrogenInputs

FishHealth


HarmfulAlgal Blooms

ShellfishAbundance

Once the model is complete, conditional

probabilities can easily be computed.


0

0.04

0.08

0.12

0.16

0.2

0 5 10 15 20 25 30

Exceedance Frequency (%)

Pro

bab

ility

Den

sity

Example of how outcomes can be predictedExample of how outcomes can be predicted

90%90% Risk Riskof Exceedanceof Exceedance




Example of how outcomes can be predictedExample of how outcomes can be predicted

0

0.04

0.08

0.12

0.16

0.2

0 5 10 15 20 25 30

Exceedance Frequency (%)

Pro

ba

bili

ty D

en

sity

No Action

45% Nutrient reduction


Application: Fecal-origin pathogen exposure


New emphases

•Multimedia, compartmental• Interfaces and integrations

–Human and Ecosystem–Time and Space


Emerging Technologies

• Always a part of engineering• Balance between innovation and carelessness…• Ignorance is not an option, nor is ignoring the breakthroughs….

• So, we need to manage the risks and take advantage of the opportunities.


Nanotechnology – Good or Bad?

• Answer: Yes…• Things are different down there.• Carbon is not carbon….


Membranes

Adsorbents

Oxidants

Catalysts

Sensing

Analytical

The Good: Nanomaterial-enabled tools for environmental engineers*

*Thanks to Mark Wiesner.


Conventional ‘permeable reactive barrier’ made with millimeter-sized construction-grade granular Fe

Tratnyek and Johnson (2005)


‘Reactive treatment zone’

• Formed by sequential injection of nano-sized Fe• Makes overlapping zones of particles adsorbed to the grains of native aquifer material


Treating much more mobile contaminants

• Same approach can be used to treat nonaqueous phase liquid (DNAPL) contamination by injection of mobile nanoparticles


The Bad: Nanomaterials themselves can change physical and chemical behavior and may be hazardous.

• Much variability in mobility of nanoparticles even in the same size range (Wiesner again)

nanoparticle mobility

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

0 2 4 6 8 10

V/Vp

C/C

o

Silica 57nm

Silica 135nm

Anatase 198nm

Alumox 74nm

Ferrox 303nm

Tracer

Cha

nge

in c

once

ntra

tion

[1-C

/C0


And, toxicity is even more uncertain…

Human cell line toxicity (Sayes, Colvin, et al., 2004)

toxic but not mobile

COOHHOOC

HOOC

HOOC

HOOC

COOH

OHOH

OH

OH

HO

HO

OH

OHHO

HO

HOOH

OHHO

OHOH

HO

HO

OHHO

OH

O

OH

O

O

Na

Nanot toxic, but mobile


Group Project: What is a Pollutant?

• Hypothetical* Case: ZGA and the Forklift• Answer 3 Questions:

1. Is air a hazard?

2. Is air a pollutant?

3. Is the company entitled to coverage per the pollution exclusion clause?

*Sort of….


Near Road Exposures

•Concentration gradient•Micrometeorology•Fluid dynamics•Traffic dynamics


Near Road Exposures: Importance of Variability

•5 yrs of hourly data

•Saw the same thing in at Ground Zero (WTC)


Near Road Exposures: What Should Be Measured?

Continuous sampling:Continuous sampling:•PMPM2.52.5

•COCO•NONOxx

•Elemental CarbonElemental Carbon

1-hr integrated sampling:1-hr integrated sampling:•BenzeneBenzene•1,3-butadiene1,3-butadiene•FormaldehydeFormaldehyde•AcetaldehydeAcetaldehyde•AcroleinAcrolein•PMPM2.5 2.5 (24-hr)(24-hr)


Environmental Justice

• Toxic Waste and Race (United Church of Christ study)• Found direct correlation between minority population and

likelihood of waste site• EJ neighborhood defined:

–Disproportionate exposure to contaminants–SES and racial makeup


Unique challenges of EJ

• Historically, communities have had little or no “voice”• So, the prototypical environmental response models don’t work well–Based upon complaints

• Must deal with trust issues• … and disenfranchisement.• So, we need a different paradigm

–Intervention–Outreach and how we report what we find.


EJ: Culture Is Crucial

Left: Brick making kiln in Ciudad Juarez. Right: El Paso-Ciudad Juarez airshed during a thermal inversion.

Photo credit: Environmental Defense


But sometimes, EJ is less obvious (but more ubiquitous)

• Vinclozolin, a fungicide, is an endocrine disruptor• But two of its degradation products are even more anti-

androgenic than the parent vinclozolin• How are people exposed?


Time integrated dicarboximide flux from sterilized soil with pore water pH7.5, after incorporation of 5mL of 2g L-1 fungicide suspension and a 2.8mm rain event (95% CI).


0

50

100

150

200

250

300

350

1 55 450 1020

T im e s ince sp ray event (m in)

Flu

x (n

g m

-2 h

r-1)

Vincloz olin

M 1-but enoic acid

M 2-enanilide

3,5-dichloroaniline

Time integrated dicarboximide flux from non-sterile soil with pore water pH7.5, after incorporation of 5mL of 2g L-1 fungicide suspension and a 2.8mm rain event (95% CI).


Worker and family exposures shortly after field re-entry:

• Greater inhalation exposures to more toxic endocrine disruptors in first few hours.

• Farm worker and family activities are determinants of risk

0

50

100

150

200

250

300

350

155 450 1020

Time since spray event (min)

Flu

x (n

g m

-2 hr

-1)

Vinclozolin

M1-butenoic acid

M2-enanilide

3,5-dichloroaniline


Greenhouse effect is a physical concept

So, what is this truth that is so inconvenient?


But, you can have too much of a good thing.


What is that we value?Sentinel species?


Let’s talk about models….


Our way of life…?


And who’s to blame…?


Inconvenient

• You• Me• Or that guy… behind the tree?


Let’s talk about science

• Objectivity• Soundness• Precision• Accuracy• Relevance


But, when is risk acceptable?


Good science also means we need to

be open to the possibility that we

are wrong.


In fact, environmentalism is not science…

"...it is now time for us to make a major shift in our thinking about the environment, similar to the shift that occurred around the first Earth Day in 1970, when this

awareness was first heightened. But this time around, we need to get environmentalism out of the sphere of religion. We need to stop the mythic fantasies, and we need to stop the doomsday predictions. We need to start

doing hard science instead.”

--Michael Crichton


The Truth

• Whether it is inconvenient or not, we need to be scientific.• Scientist search for truth.• We must be open and honest about what we do not know (ala Socrates).• We must be open-minded to new paradigms.• The one requirement of science is that the truth be told at all times (C.F.

Snow).


The need for scientists is growing

• We need new technologies to address these problems….

• We need better data to see what is really happening…

• We need a better informed public, especially about things scientific

• And most importantly, we need YOUYOU!


The Very Bottom Line

• Need a balance:• … between risk assessment and risk management• … between risk assessment and risk perception• … and between opportunities and risks.

Office of Research and DevelopmentNational Exposure Research Laboratory

Photo image area measures 2” H x 6.93” W and can be masked by a collage strip of one, two or three images.

The photo image area is located 3.19” from left and 3.81” from top of page.

Each image used in collage should be reduced or cropped to a maximum of 2” high, stroked with a 1.5 pt white frame and positioned edge-to-edge with accompanying images.

Contact me:

[email protected]

Scale and Complexity in Environmental Systems

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Transcript of Scale and Complexity in Environmental Systems