Applications of Discrete-time Markov Chains and Poisson Processes to Air Pollution Modeling and Studies [electronic resource] /
Rodrigues, Eliane Regina.author.
Achcar, Jorge Alberto.author.
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.
Description based upon print version of record.
Applications of Discrete-time Markov Chains and Poisson Processes to Air Pollution Modeling and Studies; Acknowledgements; Contents; Chapter1 Introduction; Chapter2 Markov Chain Models; 2.1 Introduction; 2.2 Description of the Mathematical Model; 2.3 Bayesian Formulation; 2.4 Application to Ozone Air Pollution; Chapter3 Poisson Models and Their Application to Ozone Data; 3.1 Introduction; 3.2 Homogeneous Poisson Models; 3.3 Non-homogeneous Poisson Models; 3.4 Models with the Presence of Change-Points; Chapter4 Modeling the Time Between Ozone Exceedances; 4.1 Introduction
4.2 The Mathematical Models4.3 An Application to Ozone Data; Chapter5 Some Counting Processes and Ozone Air Pollution; 5.1 Introduction; 5.2 Description of the Independent and Bivariate Models; 5.3 A Copula Model; Chapter6 Comments; References; Appendix: Program Code; A.1 R Code for the Non-homogeneous Poisson Models with No Change-Points; A.1.1 Weibull Rate Function; A.1.2 Generalized Goel-Okumoto Rate Function; A.1.3 Musa-Okumoto Rate Function; A.2 WinBugs Code; A.2.1 WinBugs Code for the Non-homogeneous Models with One Change-Point; A.2.2 WinBugs Code for the Times Between Exceedances
A.2.2.1 Model IA.2.2.2 Model II; A.2.2.3 Model III; A.2.2.4 Model IV; A.2.2.5 Multiple Change-Points; Index
Includes bibliographical references and index.
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.
Probabilities.
Environmental monitoring.
Air pollution.
Probability Theory and Stochastic Processes.
Monitoring/Environmental Analysis.
Atmospheric Protection/Air Quality Control/Air Pollution.
SpringerBriefs in Mathematics