scholarly journals Using a combined power law and log-normal distribution model to simulate particle formation and growth in a mobile aerosol chamber

2016 ◽  
Author(s):  
M. Olin ◽  
T. Anttila ◽  
M. Dal Maso
Keyword(s):  
Normal Distribution ◽  
Power Law ◽  
Particle Formation ◽  
Distribution Model ◽  
Aerosol Formation ◽  
Aerosol Chamber ◽  
Relative Errors ◽  
Log Normal ◽  
Log Normal Distribution ◽  
Particle Formation And Growth

Abstract. We present the combined power law and log-normal distribution (PL+LN) model, a computationally efficient model to be used in simulations where the particle size distribution cannot be accurately represented by log-normal distributions, such as in simulations involving the initial steps of aerosol formation, where new particle formation and growth occur simultaneously, or in the case of inverse modelling. The model was validated against highly accurate sectional models using input parameter values that reflect conditions typical to particle formation occurring in the atmosphere and in vehicle exhaust, and tested in the simulation of a particle formation event performed in a mobile aerosol chamber at Mäkelänkatu street canyon measurement site in Helsinki, Finland. The number, surface area, and mass concentrations in the chamber simulation were conserved with the relative errors lower than 2 % using the PL+LN model, whereas a moment-based log-normal model and sectional models with the same computing time as with the PL+LN model caused relative errors up to 10 % and 135 %, respectively.

scholarly journals Using a combined power law and log-normal distribution model to simulate particle formation and growth in a mobile aerosol chamber

2016 ◽  
Vol 16 (11) ◽  
pp. 7067-7090 ◽  
Author(s):  
Miska Olin ◽  
Tatu Anttila ◽  
Miikka Dal Maso
Keyword(s):  
Normal Distribution ◽  
Power Law ◽  
Particle Formation ◽  
Distribution Model ◽  
Aerosol Formation ◽  
Aerosol Chamber ◽  
Relative Errors ◽  
Log Normal ◽  
Log Normal Distribution ◽  
Particle Formation And Growth

Abstract. We present the combined power law and log-normal distribution (PL+LN) model, a computationally efficient model to be used in simulations where the particle size distribution cannot be accurately represented by log-normal distributions, such as in simulations involving the initial steps of aerosol formation, where new particle formation and growth occur simultaneously, or in the case of inverse modeling. The model was evaluated against highly accurate sectional models using input parameter values that reflect conditions typical to particle formation occurring in the atmosphere and in vehicle exhaust. The model was tested in the simulation of a particle formation event performed in a mobile aerosol chamber at Mäkelänkatu street canyon measurement site in Helsinki, Finland. The number, surface area, and mass concentrations in the chamber simulation were conserved with the relative errors lower than 2 % using the PL+LN model, whereas a moment-based log-normal model and sectional models with the same computing time as with the PL+LN model caused relative errors up to 17 and 79 %, respectively.

A Log-Normal Distribution Model for the Molecular Weight of Aquatic Fulvic Acids

2000 ◽  
Vol 34 (6) ◽  
pp. 1103-1109 ◽  
Author(s):  
Stephen E. Cabaniss ◽  
Qunhui Zhou ◽  
Patricia A. Maurice ◽  
Yu-Ping Chin ◽  
George R. Aiken
Keyword(s):  
Molecular Weight ◽  
Normal Distribution ◽  
Fulvic Acids ◽  
Distribution Model ◽  
Log Normal ◽  
Log Normal Distribution ◽  
Normal Distribution Model

Comprehensive Source Identification and Apportionment Analysis of five Heavy Metals in Soils in Wenzhou City, China

2020 ◽  
Author(s):  
Shuai Shao ◽  
Bifeng Hu ◽  
Yin Zhou ◽  
Zhou Shi
Keyword(s):  
Heavy Metals ◽  
Normal Distribution ◽  
Source Identification ◽  
Mixture Distribution ◽  
Anthropogenic Sources ◽  
Distribution Model ◽  
Auxiliary Variables ◽  
Industrial Emissions ◽  
Log Normal ◽  
Log Normal Distribution

<p>Source identification and apportionment of heavy metals (HMs) has been a vital issue of soil contamination restoration. In this study, qualitive approaches (Finite mixture distribution model (FMDM) and raster based principal components analysis (RB-PCA)) as well as quantitative approach (positive matrix factorization (PMF)) were composed to identify and apportion sources of five HMs (Cd, Hg, As, Pb, Cr) with the help of several crucial auxiliary variables in Wenzhou City, China. The result of FMDM showed Cd, and Pb fitted for single log-normal distribution, while Hg fitted for double log-normal mixed distribution, and As, Cr presented triple log-normal distribution. Each element was identified and separated from natural or anthropogenic sources. An improved score interpolation map of PCA attached with corresponded auxiliary variables analysis suggested three main contribution sources including parental materials, mines exploiting and industrial emissions contributes most in the whole study area. Each element was further discussed for quantitative contributions through PMF model. Parental materials contributed to all elements (Cd, Hg, As, Pb, Cr) as 89.22%, 84.81%, 7.31%, 35.84%, 27.42%. Industrial emissions had a contribution as 2.94%, 80.77%, 15.93%, 4.79%, 25.63% for each element respectively. While Mine exploiting mixed with fertilizers inputs has dedicated for such five HMs as 7.84%,11.92%, 48.23%, 10.40% and 46.95%. Such results could efficiently be devoted to scientific decisions and strategies making regarding HMs pollution regulation in soils.</p>

FROM LOGNORMAL DISTRIBUTION TO POWER LAW: A NEW CLASSIFICATION OF THE SIZE DISTRIBUTIONS

2006 ◽  
Vol 17 (10) ◽  
pp. 1429-1436 ◽  
Author(s):  
LUCIEN BENGUIGUI ◽  
EFRAT BLUMENFELD-LIEBERTHAL
Keyword(s):  
Normal Distribution ◽  
Power Law ◽  
Lognormal Distribution ◽  
Size Distributions ◽  
New Classification ◽  
Multiplicative Process ◽  
Random Multiplicative Process ◽  
Log Normal ◽  
Log Normal Distribution

We propose a new classification of the size distributions of entities based on an exponent α defined from the shape of the log–log Rank Size plot. From an inspection of a large number of cases in different fields, one finds three possibilities: α = 1 giving a power law, α > 1 (parabola like curve) and 0 < α < 1 (analogous to a log normal distribution). A fourth possibility that can be defined when α < 0 was never observed. We present a modified version of models based on a random multiplicative process and an introduction of new entities during the growth. We recover all three kinds of distributions and show that the type of a distribution is conditioned by the rate of the introduction of new entities.

scholarly journals Quasi-Static Variation of Power-Law and Log-Normal Distributions of Urban Population

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 908
Author(s):  
Atushi Ishikawa ◽  
Shouji Fujimoto ◽  
Arturo Ramos ◽  
Takayuki Mizuno
Keyword(s):  
Normal Distribution ◽  
Power Law ◽  
Time Variation ◽  
Time Reversal ◽  
Urban Population ◽  
Large Scale ◽  
Time Reversal Symmetry ◽  
Scale Range ◽  
Log Normal ◽  
Log Normal Distribution

We analytically derived and confirmed by empirical data the following three relations from the quasi-time-reversal symmetry, Gibrat’s law, and the non-Gibrat’s property observed in the urban population data of France. The first is the relation between the time variation of the power law and the quasi-time-reversal symmetry in the large-scale range of a system that changes quasi-statically. The second is the relation between the time variation of the log-normal distribution and the quasi-time-reversal symmetry in the mid-scale range. The third is the relation among the parameters of log-normal distribution, non-Gibrat’s property, and quasi-time-reversal symmetry.

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