Applications of Discrete-time Markov Chains and Poisson Processes to Air Pollution Modeling and Studies

  • Eliane Regina Rodrigues
  • Jorge Alberto Achcar

Part of the SpringerBriefs in Mathematics book series (BRIEFSMATH)

Table of contents

  1. Front Matter
    Pages i-x
  2. Eliane Regina Rodrigues, Jorge Alberto Achcar
    Pages 1-10
  3. Eliane Regina Rodrigues, Jorge Alberto Achcar
    Pages 11-23
  4. Eliane Regina Rodrigues, Jorge Alberto Achcar
    Pages 25-64
  5. Eliane Regina Rodrigues, Jorge Alberto Achcar
    Pages 65-78
  6. Eliane Regina Rodrigues, Jorge Alberto Achcar
    Pages 79-89
  7. Eliane Regina Rodrigues, Jorge Alberto Achcar
    Pages 91-92
  8. Back Matter
    Pages 93-107

About this book

Introduction

​In this brief we consider some stochastic models that may be used to study problems related to environmental matters, in particular, air pollution.  The impact of exposure to air pollutants on people's health is a very clear and well documented subject. Therefore, it is very important to obtain ways to predict or explain the behaviour of pollutants in general. Depending on the type of question that one is interested in answering, there are several of ways studying that problem. Among them we may quote, analysis of the time series of the pollutants' measurements, analysis of the information obtained directly from the data, for instance, daily, weekly or monthly averages and standard deviations. Another way to study the behaviour of pollutants in general is through mathematical models. In the mathematical framework we may have for instance deterministic or stochastic models. The type of models that we are going to consider in this brief are the stochastic ones.​

Keywords

Bayesian inference Homogenous Poisson models Markov-chain models ozone air pollution

Authors and affiliations

  • Eliane Regina Rodrigues
    • 1
  • Jorge Alberto Achcar
    • 2
  1. 1.Area de la Investigación Científic, Instituto de MatemáticasUniversidad Nacional Autónoma de MéxicoMexico CityMexico
  2. 2.Universidade de São PauloSao PauloBrazil

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-4645-3
  • Copyright Information Springer Science+Business Media New York 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-4644-6
  • Online ISBN 978-1-4614-4645-3
  • Series Print ISSN 2191-8198
  • Series Online ISSN 2191-8201
  • About this book
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