Bayesian and Positive Matrix Factorization approaches to pollution source apportionment

Jeff William Lingwall

Bayesian and Positive Matrix Factorization approaches to pollution source apportionment

Jeff William Lingwall

Brigham Young University - Provo

Research output: Types of Thesis › Doctoral thesis

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Abstract

The use of Positive Matrix Factorization (PMF) in pollution source apportionment (PSA) is examined and illustrated. A study of its settings is conducted in order to optimize them in the context of PSA. The use of a priori information in PMF is examined, in the form of target factor profiles and pulling profile elements to zero. A Bayesian model using lognormal prior distributions for source profiles and source contributions is fit and examined.

Original language	American English
Qualification	Doctor of Philosophy
State	Published - 2 May 2006
Externally published	Yes

Keywords

air pollution
pollution source apportionment
source attribution
statistics

EGS Disciplines

Statistics and Probability

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Bayesian and Positive Matrix Factorization approaches to pollutioFinal published version, 2.95 MBLicense: Unspecified

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title = "Bayesian and Positive Matrix Factorization approaches to pollution source apportionment",

abstract = " The use of Positive Matrix Factorization (PMF) in pollution source apportionment (PSA) is examined and illustrated. A study of its settings is conducted in order to optimize them in the context of PSA. The use of a priori information in PMF is examined, in the form of target factor profiles and pulling profile elements to zero. A Bayesian model using lognormal prior distributions for source profiles and source contributions is fit and examined.",

keywords = "air pollution, pollution source apportionment, source attribution, statistics",

author = "Lingwall, \{Jeff William\}",

year = "2006",

month = may,

day = "2",

language = "American English",

}

TY - BOOK

T1 - Bayesian and Positive Matrix Factorization approaches to pollution source apportionment

AU - Lingwall, Jeff William

PY - 2006/5/2

Y1 - 2006/5/2

N2 - The use of Positive Matrix Factorization (PMF) in pollution source apportionment (PSA) is examined and illustrated. A study of its settings is conducted in order to optimize them in the context of PSA. The use of a priori information in PMF is examined, in the form of target factor profiles and pulling profile elements to zero. A Bayesian model using lognormal prior distributions for source profiles and source contributions is fit and examined.

AB - The use of Positive Matrix Factorization (PMF) in pollution source apportionment (PSA) is examined and illustrated. A study of its settings is conducted in order to optimize them in the context of PSA. The use of a priori information in PMF is examined, in the form of target factor profiles and pulling profile elements to zero. A Bayesian model using lognormal prior distributions for source profiles and source contributions is fit and examined.

KW - air pollution

KW - pollution source apportionment

KW - source attribution

KW - statistics

UR - https://scholarsarchive.byu.edu/etd/430

M3 - Doctoral thesis

ER -

Bayesian and Positive Matrix Factorization approaches to pollution source apportionment

Abstract

Keywords

EGS Disciplines

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