Where is environmental statistics going?

Peter GuttorpUniversity of Washington

peter@stat.washington.edu

NRCSE

Thanks

Noel Cressie

David Fox

Mark Kaiser

Doug Nychka

Eric Smith

Michael Stein

Jim Zidek

Colleagues at NRCSE

Outline

Space-time (global) processes

Deterministic/stochastic models

Health effects

Emissions modelling

Statisticians in environmental decision-making

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

Outline

Space-time (global) processes

Deterministic/stochastic models

Health effects

Emissions modelling

Statisticians in environmental decision-making

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

French precipitation data

Altitude-adjusted 10-day aggregated rainfall data Nov-Dec 1975-1992 for 39 sites from Languedoc-Rousillon region of France.

California ozone

Spatial correlation structure depends on hour of the day (non-separable):

Global temperature

Global Historical Climatology Network 7280 stations with at least 10 years of data. Subset with 839 stations with data 1950-1991 selected.

Global correlations

isotropic nonstationary

Outline

Space-time (global) processes

Deterministic/stochastic models

Health effects

Emissions modelling

Statisticians in environmental decision-making

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

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

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

Outline

Space-time (global) processes

Deterministic/stochastic models

Health effects

Emissions modelling

Statisticians in environmental decision-making

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

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

pDR

PUFHPEM

Ogawa sampler

HI

Ogawasampler

T/RH logger

Nephelometer

Quiet Pump Box

CO2 monitor

CAT

PM2.5 measurements

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

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

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!

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

Outline

Space-time (global) processes

Deterministic/stochastic models

Health effects

Emissions modelling

Statisticians in environmental decision-making

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

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

Identifiability problems

Need to choose chemicals so that –source profiles are distinctive–little or no chemical change in atmosphere

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

A different approach

Time series of proportions (ignoring total mass)

Allows estimation of both P and a

Outline

Space-time (global) processes

Deterministic/stochastic models

Health effects

Emissions modelling

Statisticians in environmental decision-making

Multi-disciplinary research projects

Are statisticians good at herding cats?

Modern statistics focuses on collaboration more than consulting

Collaborative research centers

How to tell the prime minister the facts

Need tools to describe uncertainty concisely

Need to teach decision-makers to want two numbers

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