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

Applications of Discrete-time Markov Chains and Poisson Processes to Air Pollution Modeling and Studies by Eliane Regina Rodrigues
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​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.​
Springer New York; September 2012
115 pages; ISBN 9781461446453
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Title: Applications of Discrete-time Markov Chains and Poisson Processes to Air Pollution Modeling and Studies
Author: Eliane Regina Rodrigues; Jorge Alberto Achcar
 
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ISBNs
1461446457
9781461446446
9781461446453