Novartis Foundation Symposium 220 - Environmental Statistics: Analysing Data for Environmental Policy

291 Pages·9.302 MB·English

ENVIRONMENTAL
STATISTICS:
ANALYSING DATA FOR
ENVIRONMENTAL
POLICY
The Novartis Foundation is an international scientific and educational
charity (UK Registered Charity No. 313574). Known until September 1997
as the Ciba Foundation, it was established in 1947 by the CIBA company
of Basle, which merged with Sandoz in 1996, to form Novartis. The
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law. It was formally opened on 22 June 1949.
The Foundation promotes the study and general knowledge of
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.
participants
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Novartis Foundation Symposium 220
ENVIRONMENTAL
STATISTICS:
ANALYSING DATA FOR
ENVIRONMENTAL
POLICY
1999
JOHN WILEY & SONS
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Novartis Foundation Symposium 220
viii+283 pages, 57 figures, 19 tables
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Contents
@mposium on Environmentalstatistics:a nahsing dataf or environmentaLpoliy, heldatthe Novartis
Foundation, London, 11-13 May 1998
Editors: Grtgoy R. Bock (Organixed and Jamie A. Goode
This ynposium is based on aproposalmade by Vic Barneft
V. Barnett Chairman’s introduction 1
A. Stein, K. ETurkman, P. Bermudez, R. van Heerd and P. de Bruijn
In search of spatial extremes 8
Discussion 2 6
P. D. Sampson and P. Guttorp Operational evaluation of air quality models 33
Discussion 46
General discussion I 52
R. L. Smith, J. M. Davis and P. Speckman Assessing the human health risk of
atmospheric particles 59
Discussion 72
C.W. Anderson and R. I. Smith Ozone dose mapping and the utility of models 80
DiscusJion 96
General discussion I1 101
J.V.Z idek and N. D. Lee Measuring and modelling pollution for risk analysis 105
Discussiola 116
L. H. Cox, P. Guttorp, P. D. Sampson, D. C. Caccia and M.-L.Thomspon
A preliminary statistical examination of the effects of uncertainty and variability on
environmental regulatory criteria for ozone 122
Discussion 138
R. Goudey and G. Laslett Statistics and environmental policy: case studies from
long-term environmental monitoring data 144
Discussion 154
V
vi CONTENTS
A. E.Warn Better late than never? Injecting statistical know-how into legislation on
water quality 158
Discussion 16 6
General discussion I11 Setting quality standards 170
Principles of sampling 172
J. Riley Indicator quality for multidisciplinary systems 178
Discussion 186
J. Heycox Integrating data for sustainable development: introducing the
distribution of resources framework 191
Discussion 207
M. P. J. Pulles Does environmental data collection need statistics? 213
Discwsion 222
R. I. Smith Air pollution statistics in policy applications 227
Discussion 239
R. M. House, H. McDowell, M. Peters and R. Heimlich Agriculture sector
resource and environmental policy analysis: an economic and biophysical
approach 243
Discussion 261
Final discussion 265
Index of contributors 273
Subject index 275
Participants
Clive W. Anderson School of Mathematics and Statistics, University of Sheffield,
PO Box 597, Sheffield S10 ZUN, UI<
Andrzej Balicki Department of Statistics, Univeristy of Gdansk, ul. Armii
Krajowej 101,81-824 Sopot, Poland
Vic Barnett (Chairman) Department of Mathematics, University of Nottingham,
University Park, Nottingham NG7 2RD, UK
Jennifer Brown (Novurtis Foundation Bursar) Biomathematics Research Centre,
Department of Mathematics and Statistics, University of Canterbury, Private Bag
4800, Christchurch, New Zealand
Lawrence H. Cox US Environmental Protection Agency, National Exposure
Research Laboratory (MD-75), ResearchTriangle Park, NC 27711, USA
Abdel Hamid H. El-Shaarawi National Water Research Institute, PO Box 5050,
867 Lakeshore Road, Burlington, Ontario, Canada L7R 4A6
Jeannette Heycox Australian Bureau of Statistics, PO Box 10, Belconnen, ACT
2616, Australia
Robert M. House Resource and Environmental Policy Branch, Economic Research
Service, US Department of Agriculture, 1800 M Street NW, Room S4007,
Washington, DC 20036-5831, USA
Geoff Laslett CSIRO Mathematical and Information Sciences, Private Bag 10,
Clayton South MDC,Victoria 3169, Australia
Georg Lindgren Department of Mathematical Statistics, Solvegatan 18,
PO Box 118,221 00 Lund, Sweden
Tinus Pulles TNO Institute of Environmental Sciences, Energy Research and
Process Innovation, Laan van Westenenk 501, PO Box 342,7300 AH Apeldoorn,
The Netherlands
vii
...
Vlll PARTICIPANTS
Janet Riley Department of Statistics, IACR-Rothamsted, Harpenden,
Hertfordshire AL5 2 JQ, UK
Paul D. Sampson National Research Center for Statistics and the Environment,
University of Washington, Box 351720, Seattle,W A 98195-1720, USA
Richard L. Smith Department of Statistics, University of North Carolina, Chapel
Hill, NC 27599-3260, USA
Rognvald I. Smith Institute of Terrestrial Ecology, Edinburgh Research Station,
Bush Estate, Penicuik, Midlothian EH26 OQB, UK
Alfred Stein Department of Environmental Sciences,W ageningen Agricultural
University, PO Box 37,6700 AA Wageningen, The Netherlands
Kamil ETurkman DEIO, Fac. de Ciencas, Bloco C2, Campo Grande, 1700 Lisboa,
Portugal
Wolfgang Urfer Department of Statistics, University of Dortmund,
D-44221 Dortmund, Germany
Tony Warn Environment Agency, Kingfisher House, Goldhay Way, Orton Goldhay,
Peterborough, Cambs PE2 6N2, UK
JamesV. Zidek Department of Statistics, University of British Columbia,
6356 Agricultural Road,Vancouver, BC, CanadaV6T 122
Novartis Foundation Symposium
Edited by GregoIy R. Bock, Jamie A. Goode
Copyright 0 1999 by Novartis Foundation
Chairman’s introduction
Vic Batnett
Department of Mathematics, Universio of Nottingham, Uniuersit_y Park, Nottingham
NG72RD, UK
This meeting brings together an influential group of workers from around the
world who share a common interest in environmental statistics. We are all
interested in seeking to understand and interpret environmental effects and
influences, in efforts to reliably measure the state of our natural and man-made
surroundings, and to formulate policies for protection, control and conservation.
We also all share our approach to this, which is by use of appropriate statistically
expressed measures, models, methods and standards.
Where we differ, and this is the crucial element of our deliberations, is in the
emphasis with which we approach environmental statistics. This is readily
illustrated. Consider the question of the quality of our water resources-
whether contained in rivers, lakes, reservoirs or seas, whether for drinking,
leisure use, or culture of plant, animal or fish populations. A specific case of
interest might be water in rivers, influenced as it is by leachage of agricultural
treatment processes, by industrial or urban effluent and by natural replenishment.
All can contribute to deterioration effects, e.g. pollution or drought. As
statisticians, how do we approach this?
We might:
(a) Measure specific pollution levels, e.g. by ongoing monitoring networks or
spatial/temporal selection of sampling sites.
(b) Determine indicators of quality, or standards for pollution levels.
(c) Construct models to explain effects -f rom discharge to site condition -a nd
methods to interpret relationships and conduct estimation, testing or policy-
making activities.
All involve mathematical principles and inevitably, due to the intrinsic
variability and uncertainty of the world we live in, probabilistic/statistical
concepts and principles. In simple terms, we might need increasing emphasis on
numerical outcomes as we go from (c) to (a) and increasing mathematical/
statistical sophistication as we go from (a) to (c). We can represent this
diagrammatically (Fig. 1).
1
2 BARNETT HHH
Increasing
data
emphasis
Increasing modellmethod emphasis
FIG. 1. Different approaches to environmental statistics.
Such a schematic view is over-simplified -v arying data and model/method
emphases arise in many statistical problems but it does reflect an operational
-
distinction which exists and cannot be ignored. This is the tendency for those
working in environmental statistics to be seen to be involved at different points
in the spectrum shown in Fig. 2.
This is the vital mix of emphases in our meeting this week. Each participant has
been deliberately chosen to represent in broad terms the different points of this
spectrum. The aim is for us to discuss our interests and problems from these
different (perceived) standpoints and to bring to bear fruitful emphasis-transfer
effects.
We should not pigeon-hole our activities even if working roles and conventional
attitudes seem to imply this.
0 Measuring pollution levels should imply using the most powerful sampling
methods for optimal efficiency.
0 Regulations and standards should be set by use of relevant detailed models of
effects.
0 Developing models and methods should be done using extensive data for
validation.
4 b
Data collection/ Regulations1 Model and method
tabulation policy formation development
FIG. 2. The spectrum of different roles for environmental statisticians.