Scale and Complexity in Environmental Systems
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Transcript of 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
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Environmental Protection: A Child of the ’60s
Systematic mandates from NEPA:•EIS•CEQ
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Scale is crucial
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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…)
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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.
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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.
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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
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Design Success
Clay liner
Water Table
ContaminantsContaminants
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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
–But, it does not eliminate the flow entirely–The designer must keep the flow rate low
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Design Success
Clay liner
Water Table
Contaminants
Contaminants
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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
–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!
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Perception is crucial
•Which line is longer?
The Müller-Lyer Illusion.
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Perception is crucial
•Which line is longer?
The Müller-Lyer Illusion.
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Perception is crucial
•Which line is longer?
The Müller-Lyer Illusion.
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Perception is crucial
•Which line is longer?
The Müller-Lyer Illusion.
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Perception is crucial
•Which line is longer?
The Müller-Lyer Illusion.
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Perception is crucial
•Which line is longer?
The Müller-Lyer Illusion.
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But sometimes, perception is pretty accurate….
Source: Pardon, ca. 1970.
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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
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Risk Communication
Report
Data True Meaning(Signal)
Data Reduction
Interpretation (Information)
S/N = ∞
ReportReportReportReportReportReportReportReport
?
Noise
S/N = Low
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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
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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
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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)
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A Paradigm for Risk
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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)
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Dose-Response: A Way to Define a Hazard
A
B B
C
Adverse Effect
DoseNOAEL
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Dose-Response: No threshold for cancer
Cancer
Non-cancer
Adverse Effect
DoseNOAEL
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Dose-Response: Safety in Reference Dose
Adverse Effect
DoseNOAEL
RfD = NOAELMFUF
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Dose-Response: Safety in Reference Dose
Adverse Effect
DoseNOAEL
RfD
RfD = NOAELMFUF
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Improved Certainty Includes Better Scale and Complexity Factors
Adverse Effect
DoseNOAEL
RfD RfD
RfD = NOAELMFUF
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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
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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
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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
Deposition to aquatic ecosystem
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
Deposition to aquatic ecosystem
M0, M2+ M-CxHy
Food Chain Uptake
Linking Human and Exposure Analysis for a Single Contaminant
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
Ecosystem function & structure
Act
ivit
y an
d
Fu
nct
ion
M
easu
rem
ents
&
Mod
elin
g
Deposition to aquatic ecosystem
M0, M2+ M-CxHy
Food Chain Uptake
Linking Human and Exposure Analysis for a Single Contaminant
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
Ecosystem function & structure
Act
ivit
y an
d
Fu
nct
ion
M
easu
rem
ents
&
Mod
elin
g
Dietary Ingestion
Atmospheric emissionsNatural: Forest fires, volcanoes
Industrial: Power plants
Population DietUncertainties:•Amounts consumed
•Fish species consumed•Fish preparation etc.
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
Ground water transportNatural & industrial sources
Temporal VariabilityUncertainties:•Intra-annual•Inter-annual•Fish species
•Fish maturation•Fish size etc.
Deposition to aquatic ecosystem
M0, M2+ M-CxHy
Target Tissue DoseBrain
KidneyBreast milk
Fetus / fetal brain
Food Chain Uptake
Linking Human and Exposure Analysis for a Single Contaminant
Toxicity/Adverse EffectNeurological
RenalCardiovascular
[Genomic / Cytomic]
Regional EconomyUncertainties:•Local vs. imported fish
•Pricing and availability•Processing, storage etc.
Dietary Ingestion
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
PB
TK
an
d
BB
DR
M
odel
ing
Bio
mar
ker
s &
E
co-
Ind
icat
ors
Atmospheric emissionsNatural: Forest fires, volcanoes
Industrial: Power plants
Population DietUncertainties:•Amounts consumed
•Fish species consumed•Fish preparation etc.
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
Ground water transportNatural & industrial sources
Temporal VariabilityUncertainties:•Intra-annual•Inter-annual•Fish species
•Fish maturation•Fish size etc.
Deposition to aquatic ecosystem
M0, M2+ M-CxHy
Target Tissue DoseBrain
KidneyBreast milk
Fetus / fetal brain
Food Chain Uptake
LOOKING BACK: RECONSTRUCTION
Toxicity/Adverse EffectNeurological
RenalCardiovascular
[Genomic / Cytomic]
Regional EconomyUncertainties:•Local vs. imported fish
•Pricing and availability•Processing, storage etc.
Dietary Ingestion
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
PB
TK
an
d
BB
DR
M
odel
ing
Bio
mar
ker
s &
E
co-
Ind
icat
ors
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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)
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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.
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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
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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:
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Sample(monitoringdata)
Posterior (integrating modelingand monitoring)
Bayesian Analysis: Combining Information
Prior (model forecast)
Criterion Concentration
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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.
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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
SedimentOxygenDemand
Duration of Stratification
RiverFlow
AlgalDensity
Carbon Production
Frequency of Hypoxia
Number ofFishkills
NitrogenInputs
FishHealth
ChlorophyllViolations
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.
SedimentOxygenDemand
Duration of Stratification
RiverFlow
AlgalDensity
Carbon Production
Frequency of Hypoxia
Number ofFishkills
NitrogenInputs
FishHealth
ChlorophyllViolations
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.
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SedimentOxygenDemand
Duration of Stratification
RiverFlow
AlgalDensity
Carbon Production
Frequency of Hypoxia
Cross-System Cross-System ComparisonComparison
Simple Simple MechanisticMechanistic
Expert Expert ElicitationElicitation
Number ofFishkills
NitrogenInputs
FishHealth
ChlorophyllViolations
HarmfulAlgal Blooms
ShellfishAbundance
Empirical ModelEmpirical Model
Seasonal RegressionSeasonal Regression
Site-Specific ApplicationSite-Specific Application
Survival ModelSurvival Model
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p(Health = “Poor”p(Health = “Poor” | N inputs = “X”)| N inputs = “X”)
SedimentOxygenDemand
Duration of Stratification
RiverFlow
AlgalDensity
Carbon Production
Frequency of Hypoxia
Number ofFishkills
NitrogenInputs
FishHealth
ChlorophyllViolations
HarmfulAlgal Blooms
ShellfishAbundance
Once the model is complete, conditional
probabilities can easily be computed.
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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
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50%50% Risk Riskof Exceedanceof Exceedance
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
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Application: Fecal-origin pathogen exposure
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New emphases
•Multimedia, compartmental• Interfaces and integrations
–Human and Ecosystem–Time and Space
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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.
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Nanotechnology – Good or Bad?
• Answer: Yes…• Things are different down there.• Carbon is not carbon….
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Membranes
Adsorbents
Oxidants
Catalysts
Sensing
Analytical
The Good: Nanomaterial-enabled tools for environmental engineers*
*Thanks to Mark Wiesner.
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Conventional ‘permeable reactive barrier’ made with millimeter-sized construction-grade granular Fe
Tratnyek and Johnson (2005)
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‘Reactive treatment zone’
• Formed by sequential injection of nano-sized Fe• Makes overlapping zones of particles adsorbed to the grains of native aquifer material
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Treating much more mobile contaminants
• Same approach can be used to treat nonaqueous phase liquid (DNAPL) contamination by injection of mobile nanoparticles
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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
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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
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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….
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Near Road Exposures
•Concentration gradient•Micrometeorology•Fluid dynamics•Traffic dynamics
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Near Road Exposures: Importance of Variability
•5 yrs of hourly data
•Saw the same thing in at Ground Zero (WTC)
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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)
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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
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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.
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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
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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?
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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).
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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).
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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
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Greenhouse effect is a physical concept
So, what is this truth that is so inconvenient?
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But, you can have too much of a good thing.
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What is that we value?Sentinel species?
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Let’s talk about models….
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Our way of life…?
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And who’s to blame…?
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Inconvenient
• You• Me• Or that guy… behind the tree?
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Let’s talk about science
• Objectivity• Soundness• Precision• Accuracy• Relevance
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But, when is risk acceptable?
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Good science also means we need to
be open to the possibility that we
are wrong.
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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
Office of Research and DevelopmentNational Exposure Research Laboratory 86
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).
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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!
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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.
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