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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
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

Rodrigues, Eliane Regina
Achcar, Jorge Alberto

Paperback

Series: Springerbriefs in Mathematics, Book 0

Environmental StudiesProbability & Statistics

ISBN10: 1461446449
ISBN13: 9781461446446
Publisher: Springer Nature
Published: Sep 1 2012
Pages: 107
Weight: 0.39
Height: 0.25 Width: 6.14 Depth: 9.21
Language: English
​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.​

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