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 Foundation operates independently in London under English trust law. It was formally opened on 22 June 1949. The Foundation promotes the study and general knowledge of science and in particular encourages international co-operation in scientific research. To this end, it organizes internationally acclaimed meetings (typically eight symposia and allied open meetings, 1520 discussion meetings, a public lecture and a public debate each year) and publishes eight books per year featuring the presented papers and discussions from the symposia. 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Information on all Foundation activities can be found at http://www.novartisfound.demon.co.uk Novartis Foundation Symposium 220 ENVIRONMENTAL STATISTICS: ANALYSING DATA FOR ENVIRONMENTAL POLICY 1999 JOHN WILEY & SONS Chichester New York . Weinheim . Brisbane . Singapore . Toronto Copyright ONovartis Foundation 1999 Published in 1999 by John Wiley & Sons Ltd, Baffins Lane, Chichester, West Sussex PO19 IUD, England National 01243 779777 International (+44) 1243 779777 e-mail (for orders and customer service enquiries): [email protected] Visit our Home Page on http://www.wiley.co.uk or http://www.wiley.com All Rights Reserved. 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Printed and bound in Great Britain by Biddles Ltd, Guildford and King’s Lynn. This book is printed on acid-free paper responsibly manufactured from sustainable forestry, in which at least two trees are planted for each one used for paper production. 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.