DOCSLIB.ORG

Model-Based Demography Essays on Integrating Data, Technique a N D E O R Y Demographic Research Monographs

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Model-Based Demography Essays on Integrating Data, Technique a N D E O R Y Demographic Research Monographs Demographic Research Monographs omas K. Burch Model-Based Demography Essays on Integrating Data, Technique a n d e o r y Demographic Research Monographs A Series of the Max Planck Institute for Demographic Research Editor-in-chief James W. Vaupel Max Planck Institute for Demographic Research Rostock, Germany More information about this series at http://www.springer.com/series/5521 Thomas K. Burch Model-Based Demography Essays on Integrating Data, Technique and Theory Thomas K. Burch Department of Sociology and Population Research Group University of Victoria Victoria, BC, Canada ISSN 1613-5520 ISSN 2197-9286 (electronic) Demographic Research Monographs ISBN 978-3-319-65432-4 ISBN 978-3-319-65433-1 (eBook) DOI 10.1007/978-3-319-65433-1 Library of Congress Control Number: 2017951857 © The Editor(s) (if applicable) and The Author(s) 2018. This book is published open access. Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland For my wife, Karen Halling Burch, and in memory of my son, Thaddeus J. Burch Preface The papers in this collection – most of them previously published – are the fruits of an intellectual odyssey over the last decades of my career as a sociologist/demog- rapher. Beginning in the late 1980s, longstanding questions about the status of demography as a science came to the surface, and I began to pursue them actively. Looking back, I realize that at some point I became less a demographer and more a demography critic – cf. art critic or music critic – and an amateur philosopher of science. My central concern has been with the role and status of theory in demography. For some, it was enough that demography did rigorous analysis of data using standard demographic and statistical techniques, notably emerging methods of multivariate analysis as applied to micro-data files. Theoretical explanations and models of behavioral processes often were left to other disciplines. Becker and the microeconomists had become the leading theorists of demographic behavior, while social demographers made relatively little systematic use of the large fund of relevant theory from sociology, social psychology, and cultural anthropology. Microeconomic theory enjoyed widespread acceptance, if not consensus, among economists. And it was stated in clear, unambiguous form, often mathematically. Social-behavioral theory, by contrast, was formulated with less rigor, in loose verbal form, and commanded nothing approaching consensus. As a graduate student in sociology and demography in the late 1950s, I had taken several excellent courses on social theory and cultural anthropology (Wilbert Moore; Marion J. Levy, Jr.; and Melvin Tumin) and trained in demography and statistics with leaders in the field – Frank Notestein, Ansley Coale, and Frederick Stephan. But there was little integration. My dissertation was a largely technical work on measurement of internal migration, with virtually no behavioral content and no theory. Some of my sociology professors were dismayed. My demography and statistics professors were satisfied if not ecstatic. As I pursued my career, I lived this schizoid life as an empirical demographer with an interest in theory – a small example of the split between theory and empirical research famously described by Robert Merton (1957). With a primary commitment to demography, my vii viii Preface relationship to theory, like that of the discipline, was characterized by ambivalence and malaise. In reviewing the development of my thinking on these matters, I can single out three works as crucial. Robert Hanneman’s Computer-Assisted Theory Building: Modeling Dynamic Social Systems (1988) provided a detailed introduction to dynamic systems modeling as a potential theoretical tool for demographers and other empirical social scientists. It promised rigor in the statement and manipula- tion of theoretical models – including complex dynamic models with feedbacks and delays – and reoriented thinking away from comparative statics and equilibrium toward process and change. To this day, I remain puzzled why social scientists, including demographers, have made so little use of this powerful analytic tool. An earlier work – discovered much later and by accident – was Explanation in Social Science: A System Paradigm by Eugene Meehan, a political scientist (1968). Meehan provided a convincing critique of logical positivism as a dead-end approach to social science and set forth a practical alternative involving ‘systems’ – roughly equivalent to theoretical models. He also insisted on the importance of purpose or aim, as well as logical consistency with data, in evaluating models. A model well-suited to one purpose may not be adequate for another. Ronald Giere’s Science Without Laws (1999) appeared to me to support Meehan’s general approach, while placing it in the context of late twentieth- century philosophy of science. Accessible to the nonprofessional philosopher, this work argues that the model, not the law, is the central element in science. Models are not ‘true’ in any strong sense of that word. They simply fit some portion of the real world closely enough in certain respects to make them useful for certain purposes. At best, they embody ‘realism without truth.’ Taken together, these works convinced me that demography had more and better theory than generally recognized and pointed the way toward fruitful systematiza- tion and codification. Demography could be a full-fledged discipline, with its ample foundation of empirical data and technique balanced by a rich body of theory. From time to time, I have wondered whether I had touched bottom with respect to the philosophical and methodological issues involved in demography as a science. Eventually, I realized there probably is no bottom. Professional students of science – philosophers, sociologists of science, and cognitive psychologists – disagree on many points. It is not likely that I would be able beat them at their own game and come up with a definitive view on science. I agree with Paul Teller (2001), who has warned against ‘the perfect model model’ of science, and with Samir Okasha who writes: ‘Like most philosophical questions, these questions do not admit of final answers, but in grappling with them we learn much about the nature and limits of scientific knowledge’ (2002, p. 39). In any case, I am convinced that the model-based view of science as developed by Giere and others has much to offer demography as a liberating view of demo- graphic theory. Its acceptance and routine application to our work could lead to a rich collection – a toolkit – of useful theoretical models, general, middle range, and Preface ix “low range.” As noted above, we can achieve a better balance among data, technique, and theory and become a complete science of human population.1 Even after a career of nearly 60 years in demography, however, I may be presumptuous to sit in judgment on the discipline and to suggest directions for its future development. But I have been encouraged by many other demographers who, over the years, have expressed their concern for the character and status of the field, their lingering feeling that something was missing. It seems to me that the model- based approach to science will encourage and enable us to provide what has been missing, notably a carefully crafted body of theory. But just as there is no perfect model in science, there is no perfect model of science. And I am not a philosopher of science nor familiar with the practice and accomplishments of all the sciences, social, behavioral, biological, and physical. I can do no better than to close with a quote from E.O. Wilson. In the Preface to On Human Nature, in which he argues for the usefulness of evolutionary biology for understanding human behavior, he comments: “I might easily be wrong” (p. x). But it will be enough if this work promotes a lively discussion of what demography is and might become. As lightly edited versions of papers written at different times and in different contexts, many of the following chapters repeat central ideas, for example, the contrasts between logical empiricism and the model-based approach to science, or the idea that much of ‘technical’ demography can be viewed as theory. Sometimes, this repetition may seem unnecessary.
Load more

Recommended publications
  • Modeling Population Dynamics
    Modeling Population Dynamics Gary C. White INTRODUCTION Population modeling is a tool used by wildlife managers. At its simplest, population modeling is a book keeping system to keep track of the four components of population change: births, deaths, immigration, and emigration. In mathematical symbolism, a population model can be expressed as Nt+1 = Nt + Bt - Dt + It - Et . Population size (N) at time t+1 is equal to the population size at time t plus births (B) minus deaths (D) plus immigrants (I) minus emigrants (E). The simplicity of the relationship belies its usefulness. If you were managing a business, you would be interested in knowing how much money was being paid out of the business, relative to how much money was coming in. The net difference represents your “profit”. The same is true for managing a wildlife population. By knowing the net change in the size of the wildlife population from one year to the next, you as a wildlife manager know whether the population is “profitable” (i.e., gaining in size), or headed towards bankruptcy (i.e., becoming smaller). Two examples will demonstrate typical uses of population models in big game management. In November 1995, voters in Colorado passed an amendment to the state’s constitution that outlawed hunting black bears during spring. Wildlife managers were asked if the bear population would increase because of no spring harvest. A population model can be used to answer this question. The second example of the use of a model is to determine which of two management schemes (e.g., no antlerless harvest versus significant antlerless harvest) will result in Modeling Population Dynamics – Draft May 6, 1998 2 the largest buck harvest in a mule deer population.
    [Show full text]
  • Evaluating the Interconnectedness of the Sustainable Development Goals Based on the Causality Analysis of Sustainability Indicators
    sustainability Article Evaluating the Interconnectedness of the Sustainable Development Goals Based on the Causality Analysis of Sustainability Indicators Gyula Dörg˝o 1,† , Viktor Sebestyén 2,† and János Abonyi 1,* 1 MTA-PE “Lendület” Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, Egyetem u. 10, P.O. Box 158, H-8201 Veszprém, Hungary; [email protected] 2 Institute of Environmental Engineering, University of Pannonia, Egyetem u. 10, P.O. Box 158, H-8201 Veszprém, Hungary; [email protected] * Correspondence: [email protected] † These authors contributed equally to this work. Received: 20 June 2018; Accepted: 15 October 2018; Published: 18 October 2018 Abstract: Policymaking requires an in-depth understanding of the cause-and-effect relationships between the sustainable development goals. However, due to the complex nature of socio-economic and environmental systems, this is still a challenging task. In the present article, the interconnectedness of the United Nations (UN) sustainability goals is measured using the Granger causality analysis of their indicators. The applicability of the causality analysis is validated through the predictions of the World3 model. The causal relationships are represented as a network of sustainability indicators providing the opportunity for the application of network analysis techniques. Based on the analysis of 801 UN indicator types in 283 geographical regions, approximately 4000 causal relationships were identified and the most important global connections were represented in a causal loop network. The results highlight the drastic deficiency of the analysed datasets, the strong interconnectedness of the sustainability targets and the applicability of the extracted causal loop network.
    [Show full text]
  • Immigration and the Stable Population Model
    IMMIGRATION AND THE STABLE POPULATION MODEL Thomas J. Espenshade The Urban Institute, 2100 M Street, N. W., Washington, D.C. 20037, USA Leon F. Bouvier Population Reference Bureau, 1337 Connecticut Avenue, N. W., Washington, D.C. 20036, USA W. Brian Arthur International Institute for Applied Systems Analysis, A-2361 Laxenburg, Austria RR-82-29 August 1982 Reprinted from Demography, volume 19(1) (1982) INTERNATIONAL INSTITUTE FOR APPLIED SYSTEMS ANALYSIS Laxenburg, Austria Research Reports, which record research conducted at IIASA, are independently reviewed before publication. However, the views and opinions they express are not necessarily those of the Institute or the National Member Organizations that support it. Reprinted with permission from Demography 19(1): 125-133, 1982. Copyright© 1982 Population Association of America. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage or retrieval system, without permission in writing from the copyright holder. iii FOREWORD For some years, IIASA has had a keen interest in problems of population dynamics and migration policy. In this paper, reprinted from Demography, Thomas Espenshade, Leon Bouvier, and Brian Arthur extend the traditional methods of stable population theory to populations with below-replacement fertility and a constant annual quota of in-migrants. They show that such a situation results in a stationary population and examine how its size and ethnic structure depend on both the fertility level and the migration quota. DEMOGRAPHY© Volume 19, Number 1 Februory 1982 IMMIGRATION AND THE STABLE POPULATION MODEL Thomos J.
    [Show full text]
  • The Science of Demography
    Annotations 421 an introduction by the editors to each Part. It is impossible to discuss these chapters individually, but in order that this re­ view may be informative as to the coverage and contributors, the Table of Contents is appended. A serious problem faced by every editor of symposia is that of achieving some unity to the undertaking. The editors of the present volume unified the work through the extent of their own writing, through the “ Overview and Conclusions” which pulls the whole volume together, and through the instructions to the individual contributors to guide them in the organization of the material desired. Although there is considerable variation in the extent to which the suggested outline was followed, this guide must have been an enormous aid as the contributors ap­ proached assignments any one of which could easily have de­ veloped into a book of its own. Included in the guide was a request for a selected bibliography. Since the chapters range widely over the whole field of demography, the selected bibliog­ raphy prepared by each author for his subject may well prove to be one of the most useful features of the book. In the “ Overview and Conclusions” the editors consider at some length the question as to what constitutes the science of demography, debating the merits of the restricted definition of demography as synonymous with demographic analysis, versus the more comprehensive definition embracing all population studies. They argue persuasively for the more limited defini­ tion as designating a single theoretical discipline with a co­ herent frame of reference.
    [Show full text]
  • A Synopsis: Limits to Growth: the 30-Year Update
    do nellameado ws.o rg http://www.donellameadows.org/archives/a-synopsis-limits-to-growth-the-30-year-update/ A Synopsis: Limits to Growth: The 30-Year Update By Donella Meadows, Jorgen Randers, and Dennis Meadows Chelsea Green (United States & Canada) Earthscan (United Kingdom and Commonwealth) Diamond, Inc (Japan) Kossoth Publishing Company (Hungary) A Synopsis: Limits to Growth: The 30-Year Update The signs are everywhere around us: Sea level has risen 10–20 cm since 1900. Most non-polar glaciers are retreating, and the extent and thickness of Arctic sea ice is decreasing in summer. In 1998 more than 45 percent of the globe’s people had to live on incomes averaging $2 a day or less. Meanwhile, the richest one- f if th of the world’s population has 85 percent of the global GNP. And the gap between rich and poor is widening. In 2002, the Food and Agriculture Organization of the UN estimated that 75 percent of the world’s oceanic f isheries were f ished at or beyond capacity. The North Atlantic cod f ishery, f ished sustainably f or hundreds of years, has collapsed, and the species may have been pushed to biological extinction. The f irst global assessment of soil loss, based on studies of hundreds of experts, f ound that 38 percent, or nearly 1.4 billion acres, of currently used agricultural land has been degraded. Fif ty-f our nations experienced declines in per capita GDP f or more than a decade during the period 1990–2001. These are symptoms of a world in overshoot, where we are drawing on the world’s resources f aster than they can be restored, and we are releasing wastes and pollutants f aster than the Earth can absorb them or render them harmless.
    [Show full text]
  • Investigations Into the Club of Rome's World3 Model : Lessons for Understanding Complicated Models
    Investigations into the Club of Rome's World3 model : lessons for understanding complicated models Citation for published version (APA): Thissen, W. A. H. (1978). Investigations into the Club of Rome's World3 model : lessons for understanding complicated models. Technische Hogeschool Eindhoven. https://doi.org/10.6100/IR79372 DOI: 10.6100/IR79372 Document status and date: Published: 01/01/1978 Document Version: Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.
    [Show full text]
  • Mathematical Analysis of Population Growth Subject to Environmental Change
    SCIENTIA MANU E T MENTE MATHEMATICAL ANALYSIS OF POPULATION GROWTH SUBJECT TO ENVIRONMENTAL CHANGE A thesis submitted for the degree of Doctor of Philosophy By Hamizah Mohd Safuan M.Sc.(Mathematics) Applied and Industrial Mathematics Research Group, School of Physical, Environmental and Mathematical Sciences, The University of New South Wales, Canberra, Australian Defence Force Academy. November 2014 PLEASE TYPE THE UNIVERSITY OF NEW SOUTH WALES Thesis/Dissertation Sheet Surname or Family name: Mohd Safuan First name: Hamizah Other name/s: - Abbreviation for degree as given in the University calendar: Ph.D (Mathematics) School: Faculty: UNSW Canberra School of Physical, Environmental and Mathematical Sciences Tille: Mathematical analysis of population growth subject lo environmental change Abstract 350 words maximum: (PLEASE TYPE) Many ecosystems are pressured when the environment is perturbed, such as when resources are scarce, or even when they are over-abundant. Changes in the environment impact on its ability lo support a population of a given species. However, most current models do not lake the changing environment into consideration. The standard approach in modelling a population in its environment is lo assume that the carrying capacity, which is a proxy for the state of the environment, is unchanging. In effect, the assumption also posits that the population is negligible compared lo the environment and cannot alter the carrying capacity in any way. Thus, modelling the interplay of the population with its environments is important lo describe varying factors that exist in the system. This objective can be achieved by treating the carrying capacity as lime- and space-dependent variables in the governing equations of the model.
    [Show full text]
  • Social Demography
    2nd term 2019-2020 Social demography Given by Juho Härkönen Register online Contact: [email protected] This course deals with some current debates and research topics in social demography. Social demography deals with questions of population composition and change and how they interact with sociological variables at the individual and contextual levels. Social demography also uses demographic approaches and methods to make sense of social, economic, and political phenomena. The course is structured into two parts. Part I provides an introduction to some current debates, with the purpose of laying a common background to Part II, in which these topics are deepened by individual presentations of more specific questions. In Part I, read all the texts assigned to the core readings, plus one from the additional readings. Brief response papers (about 1 page) should identify the core question/debate addressed in the readings and the summarize evidence for/against core arguments. The response papers are due at 17:00 the day before class (on Brightspace). Similarly, the classroom discussions should focus on these topics. The purpose of the additional reading is to offer further insights into the core debate, often through an empirical study. You should bring this insight to the classroom. Part II consists of individual papers (7-10 pages) and their presentations. You will be asked to design a study related to a current debate in social demography. This can expand and deepen upon the topics discussed in Part I, or you can alternatively choose another debate that was not addressed. Your paper and presentation can—but does not have to—be something that you will yourself study in the future (but it cannot be something that you are already doing).
    [Show full text]
  • Demography and Democracy
    TOURO LAW JOURNAL OF RACE, GENDER, & ETHNICITY & BERKELEY JOURNAL OF AFRICAN-AMERICAN LAW & POLICY DEMOGRAPHY AND DEMOCRACY PHYLLIS GOLDFARB* Introduction During oral arguments in Shelby County v. Holder, 1 Justice Antonin Scalia provoked audible gasps from the audience when he observed that in 2006 Congress had no choice but to reauthorize the Voting Rights Act, because it had become “a racial entitlement.”2 Later in the argument, Justice Sonia Sotomayor obliquely challenged Scalia’s surprise comment, eliciting a negative answer from the attorney for Shelby County to a question about whether “the right to vote” was “a racial entitlement.” 3 As revealed by these dueling remarks from the highest bench in the land, demography and democracy are linked in the public consciousness of Americans, but in dramatically different ways. Minority voters have learned from decades of experience that in a number of jurisdictions, the Voting Rights Act is nearly synonymous with their unfettered ability to vote. 4 Without the Act, their right to vote, American democracy’s fundamental precept, would be compromised to a far greater degree.5 So although Justice Scalia stated that, in his view, the Voting Rights Act had become a racial entitlement, that could only be because minority voters knew—as Congressional findings confirmed—that it was necessary to protect their access to the ballot.6 The powerful evidence of the Act’s indispensability in *Jacob Burns Foundation Professor of Clinical Law and Associate Dean for Clinical Affairs, George Washington University Law School. Special thanks to Anthony Farley and Touro Law Center for organizing this symposium, to George Washington University Law School for research support, and to Andrew Holt for research assistance.
    [Show full text]
  • Relationship Between Demography and Economic Growth from the Islamic Perspective: a Case Study of Malaysia
    Munich Personal RePEc Archive Relationship between demography and economic growth from the islamic perspective: a case study of Malaysia Abdul, Salman and Masih, Mansur INCEIF, Malaysia, Business School, Universiti Kuala Lumpur, Kuala Lumpur, Malaysia 31 July 2018 Online at https://mpra.ub.uni-muenchen.de/108463/ MPRA Paper No. 108463, posted 30 Jun 2021 06:46 UTC Relationship between demography and economic growth from the islamic perspective: a case study of Malaysia Salman Abdul1 and Mansur Masih2 Abstract: There have been various theoretical and empirical studies which analyze the relationship between demography and economic growth using different methodologies, which led to different results, interpretations and continuous debates. Demography as a statistical study of human population, has a significant impact on economic growth given certain area and period of time. This paper aims to include some of Islamic theory of demography and socio economics especially regarding family planning issue, along with other commonly used theories and bring them into the investigation of the long- and short- run relationship among demographic and socioeconomic variables in developing countries. Malaysia is used as a case study. This study, therefore, attempts to unravel the causality direction of demography and economic growth. We used annual data for the total fertility rate and infant mortality rate to represent demography, per capita gross domestic product and consumer price index to represent economic growth, and female labor participation along with female enrollment to secondary education percentage as links between demography and economic growth. Based on standard time series analysis technique, our findings tend to indicate the importance of female enrollment to education in finding a balance in the demography-growth nexus.
    [Show full text]
  • Math 636 - Mathematical Modeling Discrete Modeling 1 Discrete Modeling – Population of the United States U
    Discrete Modeling { Population of the United States Discrete Modeling { Population of the United States Variation in Growth Rate Variation in Growth Rate Autonomous Models Autonomous Models Outline Math 636 - Mathematical Modeling Discrete Modeling 1 Discrete Modeling { Population of the United States U. S. Population Malthusian Growth Model Programming Malthusian Growth Joseph M. Mahaffy, 2 Variation in Growth Rate [email protected] General Discrete Dynamical Population Model Linear Growth Rate U. S. Population Model Nonautonomous Malthusian Growth Model Department of Mathematics and Statistics Dynamical Systems Group Computational Sciences Research Center 3 Autonomous Models San Diego State University Logistic Growth Model San Diego, CA 92182-7720 Beverton-Holt Model http://jmahaffy.sdsu.edu Analysis of Autonomous Models Fall 2018 Discrete Modeling U. S. Population | Discrete Modeling U. S. Population | Joseph M. Mahaffy, [email protected] (1/34) Joseph M. Mahaffy, [email protected] (2/34) Discrete Modeling { Population of the United States Discrete Modeling { Population of the United States Malthusian Growth Model Malthusian Growth Model Variation in Growth Rate Variation in Growth Rate Programming Malthusian Growth Programming Malthusian Growth Autonomous Models Autonomous Models United States Census Census Data Census Data United States Census 1790 3,929,214 1870 39,818,449 1950 150,697,361 Constitution requires census every 10 years 1800 5,308,483 1880 50,189,209 1960 179,323,175 Census used for budgeting federal payments and representation
    [Show full text]
  • Simulation of Working Population Exposures to Carbon Monoxide Using EXPOLIS-Milan Microenvironment Concentration and Time-Activity Data
    Journal of Exposure Analysis and Environmental Epidemiology (2004) 14, 154–163 r 2004 Nature Publishing GroupAll rights reserved 1053-4245/04/$25.00 www.nature.com/jea Simulation of working population exposures to carbon monoxide using EXPOLIS-Milan microenvironment concentration and time-activity data YURI BRUINEN DE BRUIN,a,b,c OTTO HA¨ NNINEN,b PAOLO CARRER,a MARCO MARONI,a STYLIANOS KEPHALOPOULOS,c GRETA SCOTTO DI MARCOc AND MATTI JANTUNENb aDepartment of Occupational Health, University of Milan, Via San Barnaba 8, 20122 Milan, Italy bKTL, Department of Environmental Health, P.O. Box 95, FIN-70701, Kuopio, Finland cEC Joint Research Centre, Institute for Health and Consumer Protection, Via E. Fermi, 21020 (VA), Ispra, Italy Current air pollution levels have been shown to affect human health. Probabilistic modeling can be used to assess exposure distributions in selected target populations. Modeling can and should be used to compare exposures in alternative future scenarios to guide society development. Such models, however, must first be validated using existing data for a past situation. This study applied probabilistic modeling to carbon monoxide (CO) exposures using EXPOLIS-Milan data. In the current work, the model performance was evaluated by comparing modeled exposure distributions to observed ones. Model performance was studied in detail in two dimensions; (i) for different averaging times (1, 8 and 24 h) and (ii) using different detail in defining the microenvironments in the model (two, five and 11 microenvironments). (iii) The number of exposure events leading to 8-h guideline exceedance was estimated. Population time activity was modeled using a fractions-of-time approach assuming that some time is spent in each microenvironment used in the model.
    [Show full text]