PM2.5 Source Apportionment Using a Hybrid Environmental Receptor Model

L. W. Antony Chen; Xiaoliang Wang; Judith C. Chow; John G. Watson; Junji Cao

PM2.5 Source Apportionment Using a Hybrid Environmental Receptor Model

L. W. Antony Chen
, Xiaoliang Wang
, Judith C. Chow
, John G. Watson
, Junji Cao

School of Human Settlements and Civil Engineering

Research output: Contribution to journal › Conference article › peer-review

Abstract

The development of the Hybrid Environmental Receptor Model (HERM), which takes into account both source profiles and uncertainties, was evaluated. Unlike Effective-Variance Chemical Mass Balance (EV-CMB), which solves the CMB equation using effective-variance regression, HERM solves the CMB equation using an iterative non-negative algorithm similar to Positive Matrix Factorization (PMF). PM_2.5 were monitored in the Shing Mun tunnel (SMT) in Hong Kong during the winter of 2015 to quantify emission factors of diesel, gasoline, and LPG vehicles. The major sources contributing to SMT samples are vehicle exhaust, road dust, and background air. For the simulated data, HERM yielded identical SCEs as the EV-CMB when all 5 source profiles were used. On the other hand, PMF results are biased from the EV-CMB values and show more fluctuations.

Original language	English
Journal	Proceedings of the Air and Waste Management Association's Annual Conference and Exhibition, AWMA
State	Published - 2017
Event	Air and Waste Management Association's 110th Annual Conference and Exhibition: Bridging Environment, Energy and Health - Pittsburgh, United States Duration: 5 Jun 2017 → 8 Jun 2017

Keywords

Chemical mass balance
PM source apportionment
PMF
Receptor model

Cite this

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title = "PM2.5 Source Apportionment Using a Hybrid Environmental Receptor Model",

abstract = "The development of the Hybrid Environmental Receptor Model (HERM), which takes into account both source profiles and uncertainties, was evaluated. Unlike Effective-Variance Chemical Mass Balance (EV-CMB), which solves the CMB equation using effective-variance regression, HERM solves the CMB equation using an iterative non-negative algorithm similar to Positive Matrix Factorization (PMF). PM2.5 were monitored in the Shing Mun tunnel (SMT) in Hong Kong during the winter of 2015 to quantify emission factors of diesel, gasoline, and LPG vehicles. The major sources contributing to SMT samples are vehicle exhaust, road dust, and background air. For the simulated data, HERM yielded identical SCEs as the EV-CMB when all 5 source profiles were used. On the other hand, PMF results are biased from the EV-CMB values and show more fluctuations.",

keywords = "Chemical mass balance, PM source apportionment, PMF, Receptor model",

author = "\{Antony Chen\}, \{L. W.\} and Xiaoliang Wang and Chow, \{Judith C.\} and Watson, \{John G.\} and Junji Cao",

year = "2017",

language = "英语",

journal = "Proceedings of the Air and Waste Management Association's Annual Conference and Exhibition, AWMA",

issn = "1052-6102",

note = "Air and Waste Management Association's 110th Annual Conference and Exhibition: Bridging Environment, Energy and Health ; Conference date: 05-06-2017 Through 08-06-2017",

}

TY - JOUR

T1 - PM2.5 Source Apportionment Using a Hybrid Environmental Receptor Model

AU - Antony Chen, L. W.

AU - Wang, Xiaoliang

AU - Chow, Judith C.

AU - Watson, John G.

AU - Cao, Junji

PY - 2017

Y1 - 2017

N2 - The development of the Hybrid Environmental Receptor Model (HERM), which takes into account both source profiles and uncertainties, was evaluated. Unlike Effective-Variance Chemical Mass Balance (EV-CMB), which solves the CMB equation using effective-variance regression, HERM solves the CMB equation using an iterative non-negative algorithm similar to Positive Matrix Factorization (PMF). PM2.5 were monitored in the Shing Mun tunnel (SMT) in Hong Kong during the winter of 2015 to quantify emission factors of diesel, gasoline, and LPG vehicles. The major sources contributing to SMT samples are vehicle exhaust, road dust, and background air. For the simulated data, HERM yielded identical SCEs as the EV-CMB when all 5 source profiles were used. On the other hand, PMF results are biased from the EV-CMB values and show more fluctuations.

AB - The development of the Hybrid Environmental Receptor Model (HERM), which takes into account both source profiles and uncertainties, was evaluated. Unlike Effective-Variance Chemical Mass Balance (EV-CMB), which solves the CMB equation using effective-variance regression, HERM solves the CMB equation using an iterative non-negative algorithm similar to Positive Matrix Factorization (PMF). PM2.5 were monitored in the Shing Mun tunnel (SMT) in Hong Kong during the winter of 2015 to quantify emission factors of diesel, gasoline, and LPG vehicles. The major sources contributing to SMT samples are vehicle exhaust, road dust, and background air. For the simulated data, HERM yielded identical SCEs as the EV-CMB when all 5 source profiles were used. On the other hand, PMF results are biased from the EV-CMB values and show more fluctuations.

KW - Chemical mass balance

KW - PM source apportionment

KW - PMF

KW - Receptor model

UR - https://www.scopus.com/pages/publications/85039153172

M3 - 会议文章

AN - SCOPUS:85039153172

SN - 1052-6102

JO - Proceedings of the Air and Waste Management Association's Annual Conference and Exhibition, AWMA

JF - Proceedings of the Air and Waste Management Association's Annual Conference and Exhibition, AWMA

T2 - Air and Waste Management Association's 110th Annual Conference and Exhibition: Bridging Environment, Energy and Health

Y2 - 5 June 2017 through 8 June 2017

ER -

PM2.5 Source Apportionment Using a Hybrid Environmental Receptor Model

Abstract

Keywords

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