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2000 Progress Report: A National Research Center on Statistics and the Environment
EPA Grant Number: R825173Title: A National Research Center on Statistics and the Environment
Investigators: Gutorp, Peter
Institution: University of Washington - Seattle
EPA Project Officer: Saint, Chris
Project Period: October 1, 1996 through September 30, 2001
Project Period Covered by this Report: October 1, 1999 through September 30, 2000
Project Amount: $4,997,429
RFA: Environmental Statistics (1996)
Research Category: Environmental Statistics
Description:
Objective:The National Research Center for Statistics and the Environment (NRCSE) is a research organization focusing on problems of statistical methodology for environmental applications. The Center has a core group of faculty at the University of Washington, and anticipates a large number of visitors from other institutions. In addition, collaborative research technologies will be employed to enable long distance collaborative work. Although many projects will originate from particular U.S. Environmental Protection Agency (EPA) problems, the Center's work will focus on five areas: (1) environmental sampling; (2) space-time modeling; (3) model assessment; (4) ecological assessment; and (5) risk assessment. Work at the Center will enable environmental scientists and EPA personnel to use the most modern statistical methodology in complex situations. It also will spearhead the development of environmental statistics as a mature subarea of statistics.
Progress Summary:The fourth year of NRCSE's operation has been extremely productive. The Center has had a steady stream of visitors, both in person and on the Web. Center faculty and experts frequently present work at scientific meetings, and technical reports and papers are produced in a variety of areas. As outlined below, the Center has made a significant amount of progress in both research and outreach activities.
Research Activities
NRCSE has made progress on several specific research activities during the year. Research sampling, modeling, assessments, review papers, and workshops have covered some of the following areas:
- Rank-set sampling cost analyses and sampling schemes: Ranked set sampling (RSS; McIntyre, 1952) is a two-phase sampling procedure that reduces the number of samples required from a more exacting and expensive measurement by using expert knowledge or other frugal measurement to select sample values. Current work focuses on: (1) generalizing the cost analyses to unbalanced designs (i.e., unequal set sizes where each order statistic appears an unequal number of times); and (2) other sampling schemes where the initial data are not just ranked, but also placed in strata based upon expected ranges (double sampling with cutpoints).
- Stochastic analysis of precipitation: The Center uses stochastic models of precipitation in assessing climate variability and climate change; and in downscaling (doing sub-grid scale simulation) general circulation models of global climate.
Current work focuses on developing a model for precipitation amounts in Washington State using a small network of 10 stations. Present research concerns include sensitivity to measurement error, particularly for small precipitation amounts, and determining the number of weather states.
A hierarchic Bayesian approach to estimating precipitation rate using data from different sources, such as rain gauges, weather radar, and distrometers, is being developed by Tamre Cardoso. Traditionally, rain gauge data have been regarded as "ground truth" for calibration purposes, although gauges have known biases, particularly in windy conditions. This modeling project will enable researchers to improve radar-gauge calibration exercises, and will eventually be used to improve precipitation observation networks and satellite calibration.
- Statistical aspects of setting and implementing environmental standards: The typical environmental standard might be referred to as an ideal standard. Based on various health effects studies, a target value not to be exceeded is determined, and the standard may be that this value is not to be exceeded, or only to be exceeded with a certain probability, or a certain number of times per year.
With Center visitors Nirel and Sanso, we have started to develop theoretical tools needed for defining areal standards, and to estimate the associated quantities. This work also will enable the use of process model output. Additional funding for this project has been received from the EPA.
- Nonhomogeneous global covariance estimation: We have developed tools for analyzing meteorological time-series on a global scale, taking into account spatial heterogeneity and the fact that data are collected on a globe (an oriented sphere). A flexible class of parametric nonstationary global covariance functions has been developed and applied to global temperature data, with tools that enable use of incomplete monitoring data without requiring imputation. The methodology enables realistic estimates of prediction variance for regional and global averages, and allows comparison of gridded model output data to suitably processed observational data.
- Trend estimation using wavelets: A common problem in environmental time-series analysis is how to deal with a trend component, usually thought of as large-scale (or low frequency) variations or patterns in the series that may be better modeled separately from the rest of the series. Trend often is confounded with low frequency stochastic fluctuations, particularly in the case of models that can account for long memory dependence (slowly decaying auto-correlation) and nonstationary processes exhibiting significantly low frequency components.
We have developed both an approach for estimating trend at a given temporal scale and procedures for testing the presence of a trend, valid for a large range of assumptions. The trend estimation procedure also has been used in health effects studies for particulate matter air pollution.
- Bayesian methods for assessment of environmental fate and transport models: The aim of this project is to develop Bayesian methods for assessing uncertainty and variability in risk assessment models, building on the Bayesian melding approach of Poole and Raftery (2000). The work this year has focused on the development and implementation of this Bayesian approach in the context of risk assessment. There have been four main foci of our work: (1) development and application of the sampling-importance-resampling (SIR) algorithm for making inference about the parameters of the deterministic simulation models; (2) extension of these methods to multiple-compartment models, such as extending plant to Alison Cullen's air-to-soil model; (3) development from the observation that the SIR algorithm is inefficient in high-dimensional models with the ridge-like posteriors characteristic of these models; and (4) development of model validation methods based on the Bayesian melding approach.
- Agricultural modeling for watershed management: The objectives of this project are to build a Web site for EPA programs to address agriculture issues and to identify the agricultural-related interrelationships among the EPA programs and state agencies.
A Web site was needed to organize and understand the relationships between departments within the EPA that deal with similar issues. It was believed that many of the departments shared the same agricultural issues and were not collaborating with each other to find further information. The project was to create and design a site for staff within the EPA to determine which EPA departments and state agencies had common agricultural interests.
The Web site has been designed and is near completion. It is still a work in progress because of the lack of EPA staff available to maintain the site using Lotus Notes software. Access to this Web site is restricted because the EPA has not yet decided whether it should be made available to the public or be limited to internal use.
Outreach Activities
The Center sponsored three major off site workshops, one at EPA Las Vegas, one in Slovakia, and one at the National Center for Atmospheric Research (NCAR) in Colorado. Smaller workshops complemented the Center seminar series during the year. In addition, the Center Web Site now has free, downloadable software on topics of spatial statistics, model assessment, visualization, and regression with censored data.
- Las Vegas EPA Workshop: The Center has pursued a series of workshops at various EPA locations intended to give EPA researchers a better understanding of the kind of research being conducted and to initiate new research contacts. The second of these workshops took place at the EPA laboratory in Las Vegas, NV, December 13-14, 1999. Center representatives gave eight talks. Presentations alternated with group discussions of statistical issues arising from the EPA's laboratory work. For example, there was a discussion on the best ways to sample and assess pollution levels at industrial sites. Follow-up contacts between NRCSE researchers and the EPA laboratory have resulted from this convening in Las Vegas, and we hope this relationship continues.
- Slovakia Workshop: The purpose of this workshop was to enhance capabilities to identify, assess, and manage high-priority environmental and/or occupational health issues. The Slovakia workshop was attended by approximately 40 Slovak and Czech participants, among them were decisionmakers who deal with contemporary environmental and occupational health problems, and scientists who support the decisionmaking. The workshop was designed to follow a case where an environmental/occupational issue has been identified through planning, implementation, analysis, and communication of a data collection program to support risk management decisionmaking.
- Colorado Large Data Sets Workshop: This workshop, sponsored by NCAR, the Geophysical Statistics Project, and NRCSE, acquainted statisticians with substantive scientific problems that hinge on the analysis of large data sets, and presented recent statistical advances for large data set problems. Topics included: visualization strategies, computational algorithms and new methods, including techniques from data mining. Although the statistical methodology is relevant to a wide range of problems, the focus was on continuous variables and multivariate or spatial-temporal contexts. About 50 researchers participated in the workshop, including 10 from government laboratories or industry.
- Mini-Workshops: Mini-workshops were held over the course of the year, covering several topics such as Statistical Downscaling of Precipitation (May 24, 2000), Environmental Statistics Teaching at the University of Washington (May 26, 2000), and Spatial Deformation Methods (August 21, 2000).
- NRCSE Web Site: The NRCSE Web Site is the main source of information about the Center. During the time period covered by this report, there were 207,893 successful requests for Web pages from over 14,000 different hosts. As in the previous year, the highest demand was for Technical Report 15: "Meteorological Adjustment of Western Washington and Northwest Oregon Surface Ozone Observations with Investigation of Trends."
The link for software from the Center Web Site was activated. It currently contains four links: the Orca visualization software, Doug Nychka's Funfit package implemented for SPlus, the Pareto optimal Model Assessment Cycle (POMAC), and SPlus code for maximum likelihood estimation in linear regression with interval or left censored data.
Future Activities:Since the EPA site visit, the Center has devoted considerable effort to rethinking the current form of NRCSE. In the course of developing proposals for continued funding of the Center, we have considered its educational aspects, as well as its role as a national focal point for environmetric work, and the consequent need to involve researchers at other institutions in the work performed here. Several different directions are being considered, and funding will be sought from different sources.
There are several upcoming conferences at which NRCSE will organize sessions. Sampson, Faustman, and Wakefield will highlight NRCSE work at the EPA Statistician's meeting in Philadelphia in May. The Joint Statistical Meetings in Atlanta in August will, as has become a tradition, have two NRCSE-organized sessions-one on receptor modeling, and one on monitoring network design. At the International Environmetrics Society meeting in Portland in August, there also will be two NRCSE-organized sessions: one on nonstationary covariance models, and one on meteorological adjustment of air quality data.
In May 2001, there will be a workshop on Spatial Moving Average Models at the University of Washington. This workshop is being organized by Dave Higdon from Duke University, and Jay Ver Hoef from the Alaska Department of Fish and Game. In June, a Regional Conference on Environmental Statistics also is planned at the University of Washington. The key speaker is Richard Smith from the University of North Carolina, who will give 10 lectures on the subject. Additional lectures will be given by Peter Guttorp, (NRCSE), Doug Nychka, (NCAR), Paul D. Sampson, Paul Switzer, and Jim Zidek.
Journal Articles on this Report: 36 Displayed | Download in RIS Format
| Other project views: | All 165 publications | 103 publications in selected types | All 64 journal articles |
| Type | Citation | ||
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Aylin P, Maheswaran R, Wakefield J, Cockings S, Jarup L, Arnold R, Wheeler G, Elliott P. A national facility for small area disease mapping and rapid initial assessment of apparent disease clusters around a point source: the UK Small Area Health Statistics Unit. Journal of Public Health Medicine 1999;21(3):289-298. |
R825173 (1999) R825173 (2000) |
not available |
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Banga SJ, Patil GP, Taillie C. Sensitivity of normal theory methods to model misspecification in the calculation of upper confidence limits on the risk function for continuous responses. Environmental and Ecological Statistics 2000;7(2):177-189 |
R825173 (2000) R825385 (Final) |
not available |
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Banga S, Patil GP, Taillie C. Likelihood contour method for the calculation of asymptotic upper confidence limits on the risk function for quantitative responses. Risk Analysis 2001;21(4):613-623 |
R825173 (2000) R825385 (Final) |
not available |
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Best N, Wakefield J. Accounting for inaccuracies in population counts and case registration in cancer mapping studies. Journal of the Royal Statistical Society, Series A-Statistics in Society 1999;162(Part 3):363-382. |
R825173 (1999) R825173 (2000) |
not available |
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Billheimer D. Compositional receptor modeling. Environmetrics 2001;12(5):451-467. |
R825173 (1999) R825173 (2000) |
not available |
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Brauer M, Hruba F, Mihalikova E, Fabianova E, Miskovic P, Plzikova A, Lendacka M, Vandenberg J, Cullen A. Personal exposure to particles in Banska Bystrica, Slovakia. Journal of Exposure Analysis and Environmental Epidemiology 2000;10(5):478-487. |
R825173 (1999) R825173 (2000) |
not available |
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Clyde M, Guttorp P, Sullivan E. Effects of ambient fine and coarse particles on mortality in Phoenix, Arizona. Journal of Exposure Analysis and Environmental Epidemiology 2000. |
R825173 (1999) R825173 (2000) |
not available |
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Craigmile PF, Guttorp P, Percival DB. Wavelet-based parameter estimation for polynomial contaminated fractionally differenced processes. IEEE Transactions on Signal Processing 2005;53(8):3151-3161 Part 2. |
R825173 (1999) R825173 (2000) |
not available |
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Cullen AC, Guttorp P, Smith RL. Special issue: Statistical analysis of particulate matter air pollution data. Environmetrics 2000;11(6):609-610. |
R825173 (2000) |
not available |
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Damian D, Sampson PD, Guttorp P. Bayesian estimation of semi-parametric non-stationary spatial covariance structures. Environmetrics 2001;12(2):161-178. |
R825173 (1999) R825173 (2000) |
not available |
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Diggle PJ, Morris SE, Wakefield JC. The analysis of matched case-control studies in spatial epidemiology. Biostatistics 2000;1:89-105. |
R825173 (1999) R825173 (2000) |
not available |
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Doberstein CP, Karr JR, Conquest LL. The effect of fixed-count subsampling on macroinvertebrate biomonitoring in small streams. Freshwater Biology 2000;44(2):355-371. |
R825173 (1999) R825173 (2000) R825284 (Final) |
not available |
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Elliott P, Arnold R, Cockings S, Eaton N, Jarup L, Jones J, Quinn M, Rosato M, Thornton I, Toledano M, Tristan E, Wakefield J. Risk of mortality, cancer incidence, and stroke in a population potentially exposed to cadmium. Occupational and Environmental Medicine 2000;57(2):94-97. |
R825173 (1999) R825173 (2000) |
not available |
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Faustman EM, Silbernagel SM, Fenske RA, Burbacher TM, Ponce RA. Mechanisms underlying children’s susceptibility to environmental toxicants. Environmental Health Perspectives 2000;108(Suppl 1):13-21. |
R825173 (1999) R825173 (2000) R826886 (2000) R831709 (2005) |
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Gertler N, Cullen A. Effects of a transient cancer scare on property values: Implications for risk valuation and the value of life. Human and Ecological Risk Assessment Volume 6, Issue 5, 2000, Pages 731-745. |
R825173 (1999) R825173 (2000) |
not available |
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Gneiting T. Addendum to 'isotropic correlation functions on d-dimensional balls'. Advances in Applied Probability 2000;32(4):960-961 |
R825173 (1999) R825173 (2000) |
not available |
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Gneiting T. Power-law correlations, related models for long-range dependence, and their simulation. Journal of Applied Probability 2000;37(4):1104-1109. |
R825173 (1999) R825173 (2000) |
not available |
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Gneiting T. Nonseparable, stationary covariance functions for space-time data. Journal of the American Statistical Association 2002;97(458):590-600. |
R825173 (1999) R825173 (2000) |
not available |
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Kang SH, Park ES. The actual size of the chi-squared and the likelihood ratio test of independence in a contingency table. Journal of Statistical Computation and Simulation 2000. |
R825173 (1999) R825173 (2000) |
not available |
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Levy D, Lumley T, Sheppard L, Kaufman J, Checkoway H. Referent selection in case-crossover analyses of acute health effects of air pollution. Epidemiology 2001;12(2):186-192. |
R825173 (1999) R825173 (2000) R827355 (2004) R827355 (Final) R827355C001 (2000) R827355C001 (2001) |
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Maheswaran R, Morris S, Falconer S, Grossinho A, Perry I, Wakefield J, Elliott P. Magnesium in drinking water supplies and mortality from acute myocardial infarction in north west England. Heart 1999;82:455-460. |
R825173 (1999) R825173 (2000) |
not available |
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Mode NA, Conquest LL, Marker DA. Ranked set sampling for ecological research: Accounting for the total costs of sampling. Environmetrics 1999;10(2):179-194. |
R825173 (1999) R825173 (2000) |
not available |
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Mode NA, Conquest LL, Marker DA. Incorporating prior knowledge in environmental sampling: ranked set sampling and other double sampling procedures. Environmetrics 2002;13(5-6):513-521 |
R825173 (1999) R825173 (2000) |
not available |
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Morris SE, Sale RC, Wakefield JC, Falconer S, Elliott P, Boucher BJ. Hospital admissions for asthma and chronic obstructive airways disease in east London hospitals and proximity to main roads. Journal of Epidemiology and Community Health 2000;54(1):75-76. |
R825173 (1999) R825173 (2000) |
not available |
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Park ES, Spiegelman CH, Henry RC. Bilinear estimation of pollution source profiles and amounts by using multivariate receptor models. Environmetrics 2002;13(7):775-798. |
R825173 (1999) R825173 (2000) R826238 (Final) |
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Park ES, Henry RC, Spiegelman CH. Estimating the number of factors to include in a high-dimensional multivariate bilinear model. Communications in Statistics – Simulation and Computation 2000;29(3):723-746. |
R825173 (1999) R825173 (2000) R826238 (Final) |
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Park ES, Guttorp P, Henry RC. Multivariate receptor modeling for temporally correlated data by using MCMC. Journal of the American Statistical Association 2001;96(456):1171-1183. |
R825173 (1999) R825173 (2000) R826238 (2001) R826238 (Final) |
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Park ES, Oh MS, Guttorp P. Multivariate receptor models and model uncertainty. Chemometrics and Intelligent Laboratory Systems, Volume 60, Issues 1-2, 28 January 2002, Pages 49-67. |
R825173 (1999) R825173 (2000) |
not available |
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Pascutto C, Wakefield JC, Best NG, Richardson S, Bernardinelli L, Staines A, Elliott P. Statistical issues in the analysis of disease mapping data. Statistics in Medicine 2000;19(17-18):2493-2519. |
R825173 (1999) R825173 (2000) |
not available |
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Poole D, Raftery AE. Inference for deterministic simulation models: The Bayesian melding approach. Journal of the American Statistical Association 2000;95(452):1244-1255. |
R825173 (1999) R825173 (2000) |
not available |
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Sheppard L, Levy D, Checkoway H. Correcting for the effects of location and atmospheric conditions on air pollution exposures in a case-crossover study. Journal of Exposure Science and Environmental Epidemiology 2001;11(2):86-96. |
R825173 (1999) R825173 (2000) R827355 (2004) R827355 (Final) R827355C001 (2001) R827355C009 (Final) |
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Steel EA, Guttorp P, Anderson JJ, Caccia DC. Modeling juvenile salmon migration using a simple Markov chain. Journal of Agricultural, Biological, and Environmental Statistics 2001;6(1):80-88 |
R825173 (1999) R825173 (2000) |
not available |
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Thompson M.L., Reynolds J., Cox L.H., Guttorp P. and Sampson P.D. A review of statistical methods for the meteorological adjustment of tropospheric ozone. Atmospheric Environment, Volume 35, Issue 3, 2001, Pages 617-630. |
R825173 (1999) R825173 (2000) |
not available |
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Wakefield J, Elliott P. Issues in the statistical analysis of small area health data. Statistics in Medicine 1999;18(17-18):2377-2399. |
R825173 (1999) R825173 (2000) |
not available |
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Whitcher B, Guttorp P, Percival DB. Multiscale detection and location of multiple variance changes in the presence of long memory. Journal of Statistical Computation and Simulation 2000;68(1):65-87. |
R825173 (1999) R825173 (2000) |
not available |
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Van Belle G, Griffith WC, Edland SD. Compositions to composite sampling. Environmental and Ecological Statistics 2001;8(2):171-180 |
R825173 (1999) R825173 (2000) |
not available |
particulate matter, PM, air pollution, modeling, National Ambient Air Quality Standards, NAAQS. , Economic, Social, & Behavioral Science Research Program, Scientific Discipline, RFA, Social Science, Environmental Statistics, Environmental Monitoring, risk assessment, University of Washington, statistical models, model assessment, statistical methods, ecosystem assessment, environmental risks, National Research Center on Statistics & the Environment, environmental sampling
Relevant Websites:
http://www.nrcse.washington.edu
http://www.nrcse.washington.edu/NRCSE/pm-workshop/pm-workshop.html
Progress and Final Reports:
1999 Progress Report
Original Abstract