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Titlebook: Stochastic Models of Air Pollutant Concentration; Jan Grandell Book 1985 Springer-Verlag Berlin Heidelberg 1985 Air.Markov model.Variance.

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樓主
發(fā)表于 2025-3-21 17:55:45 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Stochastic Models of Air Pollutant Concentration
編輯Jan Grandell
視頻videohttp://file.papertrans.cn/879/878028/878028.mp4
叢書(shū)名稱Lecture Notes in Statistics
圖書(shū)封面Titlebook: Stochastic Models of Air Pollutant Concentration;  Jan Grandell Book 1985 Springer-Verlag Berlin Heidelberg 1985 Air.Markov model.Variance.
描述About fifteen years ago Henning Rodhe and I disscussed the calculation of residence times, or lifetimes, of certain air pollutants for the first time. He was interested in pollutants which were mainly removed from the atmosphere by precipitation scavenging. His idea was to base the calculation on statistical models for the variation of the precipitation i~tensity and not only on the average precipitation intensity. In order to illustrate the importance of taking the variation into account we considered a simple model - here called the Markov model - for the precipitation intensity and computed the distribution of the residence time of an aerosol particle. Our expression for the average residence time - here formula (13- was rather much used by meteorologists. Certainly we were pleased, but while our ambition had been to provide an illustration, our work was merely understood as a proposal for a realistic model. Therefore we found it natural to search for more general models. The mathematical problems involved were the origin of my interest in this field. A brief outline of the background, purpose and content of this paper is given in section 1. It is a pleasure to thank Gunnar Engl
出版日期Book 1985
關(guān)鍵詞Air; Markov model; Variance; Variation; atmosphere
版次1
doihttps://doi.org/10.1007/978-1-4612-1094-8
isbn_softcover978-0-387-96197-2
isbn_ebook978-1-4612-1094-8Series ISSN 0930-0325 Series E-ISSN 2197-7186
issn_series 0930-0325
copyrightSpringer-Verlag Berlin Heidelberg 1985
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 23:06:59 | 只看該作者
Book 1985l for a realistic model. Therefore we found it natural to search for more general models. The mathematical problems involved were the origin of my interest in this field. A brief outline of the background, purpose and content of this paper is given in section 1. It is a pleasure to thank Gunnar Engl
板凳
發(fā)表于 2025-3-22 01:06:08 | 只看該作者
地板
發(fā)表于 2025-3-22 06:30:58 | 只看該作者
Lecture Notes in Statisticshttp://image.papertrans.cn/s/image/878028.jpg
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發(fā)表于 2025-3-22 10:47:50 | 只看該作者
https://doi.org/10.1007/978-1-4612-1094-8Air; Markov model; Variance; Variation; atmosphere
6#
發(fā)表于 2025-3-22 15:16:12 | 只看該作者
Some Basic Probability,Var(X) or All basic random variables, considered in this paper, will be non-negative, i.e. Pr{X < 0} =0. Assume now that X is non-negative. Its survivor function G. is defined by.and we have the relation
7#
發(fā)表于 2025-3-22 17:08:42 | 只看該作者
The Gibbs and Slinn Approximation,s section is to consider their approximation in some detail. Note that λ(t) and Q(t) are now allowed to be dependent. Recall that c(t), λ(t) and Q(t) are related by c’(t)=Q(t)-λ(t)c(t). From this relation we get
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發(fā)表于 2025-3-22 21:21:43 | 只看該作者
,Approximations for “Long-Lived” Particles, t}. We shall now state the precise formulations of the approximations holding when a → 0. In order to do this we need some technical assumptions. The parameter α is always choosen such that 1 < α ≤ 2.
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發(fā)表于 2025-3-23 03:31:37 | 只看該作者
10#
發(fā)表于 2025-3-23 09:16:50 | 只看該作者
Residence Times and Mean Concentrations,The residence time T of a single particle is the time spent in the atmosphere of that particle. From the definitions in section 3 it follows that
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