where is environmental statistics going? peter guttorp university of washington...
Post on 20-Dec-2015
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![Page 1: Where is environmental statistics going? Peter Guttorp University of Washington peter@stat.washington.edu NRCSE](https://reader031.vdocuments.mx/reader031/viewer/2022032309/56649d4c5503460f94a29d71/html5/thumbnails/1.jpg)
Where is environmental statistics going?
Peter GuttorpUniversity of Washington
NRCSE
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Thanks
Noel Cressie
David Fox
Mark Kaiser
Doug Nychka
Eric Smith
Michael Stein
Jim Zidek
Colleagues at NRCSE
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Outline
Space-time (global) processes
Deterministic/stochastic models
Health effects
Emissions modelling
Statisticians in environmental decision-making
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Topics I will not talk about
Focus on air quality–very similar issues in water and soil qualityMore work needed particularly in water quality issuesEcologySocial aspects of the environment
standardssocial ecologyenvironmental justiceenvironmental accounting
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Outline
Space-time (global) processes
Deterministic/stochastic models
Health effects
Emissions modelling
Statisticians in environmental decision-making
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Space-time modelsX(z,t) = (z,t) + Y(z,t) + E(z,t)
mean + smooth + error
Simplifying assumptions:isotropystationarity (in time and/or space)separabilityz R2
Richard Smith’s talk tomorrow Nonstationary spatial models
Need non-separable, spatially and temporally heterogeneous processes on the globe, taking into account heightOften processes operate on different scales
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Vertical distribution of ozone
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French precipitation data
Altitude-adjusted 10-day aggregated rainfall data Nov-Dec 1975-1992 for 39 sites from Languedoc-Rousillon region of France.
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California ozone
Spatial correlation structure depends on hour of the day (non-separable):
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Global temperature
Global Historical Climatology Network 7280 stations with at least 10 years of data. Subset with 839 stations with data 1950-1991 selected.
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Global correlations
isotropic nonstationary
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Outline
Space-time (global) processes
Deterministic/stochastic models
Health effects
Emissions modelling
Statisticians in environmental decision-making
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The issue(s)
Air quality model components:EmissionsAtmospheric transportAtmospheric chemistryDepositionVisualization
Data from deposition monitoringImportant model use: scenarios•Compare model output and data•Combine model with stochastic components
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Model assessment
Geostatistical approachTemporally and spatially data rich
Bayesian melding approachTemporally rich, spatially poor
Requires many model runs
Approximation approachStatistical modelling of model output
TIES session Thursday
Needed: assessment of uncertainty about model structure
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Combining models and data
Mark Berliner’s talk tomorrow: Bayesian hierarchical models
Data assimilation
Stochastic downscaling
Stochastic partial differential equations
dc(t) = (q(t) – d(t)c(t))dt + c(t)dB(t)
source deposition concentration
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Outline
Space-time (global) processes
Deterministic/stochastic models
Health effects
Emissions modelling
Statisticians in environmental decision-making
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Exposure issues for particulate matter (PM)
Personal exposures vs. outdoor and central measurements
Composition of PM (size and sources)
PM vs. co-pollutants (gases/vapors)
Susceptible vs. general population
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2 years, 26 10-day sessions
A total of 167 subjects: 56 COPD subjects 40 CHD subjects 38 healthy subjects(over 65 years old, non-smokers)
33 asthmatic kids
A total of 108 residences: 55 private homes 23 private apartments 30 group homes
Seattle health effects study
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pDR
PUFHPEM
Ogawa sampler
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HI
Ogawasampler
T/RH logger
Nephelometer
Quiet Pump Box
CO2 monitor
CAT
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PM2.5 measurements
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Where do the subjects spend their time?
Asthmatic kids: – 66% at home– 21% indoors away from home– 4% in transit– 6% outdoors
Healthy (CHD, COPD) adults:– 83% (86,88) at home– 8% (7,6) indoors away from home– 4% (4,3) in transit– 3% (2,2) outdoors
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Panel results
Asthmatic children not on anti-inflammatory medication:
decrease in lung function related to indoor and to outdoor PM2.5, not to personal exposure
Adults with CV or COPD:increase in blood pressure and heart rate related to indoor and personal PM2.5
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Modeling approach
Estimate space-time field from monitoring data
Estimate individual paths from population data
Estimate ambient exposure from path integral over space-time field and house type infiltration estimate
Estimate non-ambient exposure from predictor variables
UNCERTAINTY!
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Difficulties with health effects studies
Most studies deal with acute effects
Chronic effects potentially more serious
Opportunistic studies limit power
Very small health effects
Model uncertainty/model selection
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Outline
Space-time (global) processes
Deterministic/stochastic models
Health effects
Emissions modelling
Statisticians in environmental decision-making
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Emissions data in US
Point sourcesmost data from industry self-reporting
daily or hourly data created from annual reports
only worst offenders are required to monitor
allowable emissions can depend on weather
Diffuse sourcestraffic data
heating–no data collected
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Source-receptor models
Mass balance equation
mt = P at
m vector of mass of different componentsP matrix of emissions signatures
each row corresponds to a source
a vector of relative source contributions
Observe with additive errors
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Identifiability problems
Need to choose chemicals so that –source profiles are distinctive–little or no chemical change in atmosphere
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Generalizations
Space-time dependencemultiple receptorserrors spatially dependentsource profiles time dependentsource contributions spatiotemporally dependentuse air quality models to evaluate
chemistry in the airadvection and deposition
Back-trajectories to estimate actual emissions
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A different approach
Time series of proportions (ignoring total mass)
Allows estimation of both P and a
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Outline
Space-time (global) processes
Deterministic/stochastic models
Health effects
Emissions modelling
Statisticians in environmental decision-making
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Multi-disciplinary research projects
Are statisticians good at herding cats?
Modern statistics focuses on collaboration more than consulting
Collaborative research centers
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How to tell the prime minister the facts
Need tools to describe uncertainty concisely
Need to teach decision-makers to want two numbers
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International projects
United Nations Environment ProgramIntergovernmental Panel on Climate Change
30 co-chairs and vice-chairs in three working groups. No statisticians.
Global Environment FacilityScientific and Technological Advisory
Panel15 members. No statisticians
STAP Roster of Experts430 scientists. No statisticians