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Changes in source contributions to particle number concentrations after the COVID-19 outbreak: Insights from a dispersion normalized PMF

Factor analysis models use the covariance of measured variables to identify and apportion sources. These models, particularly positive matrix factorization (PMF), have been extensively used for analyzing particle number concentrations (PNCs) datasets. However, the variation of observed PNCs and part...

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Autores principales: Dai, Qili, Ding, Jing, Song, Congbo, Liu, Baoshuang, Bi, Xiaohui, Wu, Jianhui, Zhang, Yufen, Feng, Yinchang, Hopke, Philip K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647391/
https://www.ncbi.nlm.nih.gov/pubmed/33189385
http://dx.doi.org/10.1016/j.scitotenv.2020.143548
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author Dai, Qili
Ding, Jing
Song, Congbo
Liu, Baoshuang
Bi, Xiaohui
Wu, Jianhui
Zhang, Yufen
Feng, Yinchang
Hopke, Philip K.
author_facet Dai, Qili
Ding, Jing
Song, Congbo
Liu, Baoshuang
Bi, Xiaohui
Wu, Jianhui
Zhang, Yufen
Feng, Yinchang
Hopke, Philip K.
author_sort Dai, Qili
collection PubMed
description Factor analysis models use the covariance of measured variables to identify and apportion sources. These models, particularly positive matrix factorization (PMF), have been extensively used for analyzing particle number concentrations (PNCs) datasets. However, the variation of observed PNCs and particle size distribution are driven by both the source emission rates and atmospheric dispersion as well as chemical and physical transformation processes. This variation in the observation data caused by meteorologically induced dilution reduces the ability to obtain accurate source apportionment results. To reduce the influence of dilution on quantitative source estimates, a methodology for improving the accuracy of source apportionment results by incorporating a measure of dispersion, the ventilation coefficient, into the PMF analysis (called dispersion normalized PMF, DN-PMF) was applied to a PNC dataset measured from a field campaign that includes the Spring Festival event and the start of the COVID-19 lockdown in Tianjin, China. The data also included gaseous pollutants and hourly PM(2.5) compositional data. Eight factors were resolved and interpreted as municipal incinerator, traffic nucleation, secondary inorganic aerosol (SIA), traffic emissions, photonucleation, coal combustion, residential heating and festival emissions. The DN-PMF enhanced the diel patterns of photonucleation and the two traffic factors by enlarging the differences between daytime peak values and nighttime concentrations. The municipal incinerator plant, traffic emissions, and coal combustion have cleaner and more clearly defined directionalities after dispersion normalization. Thus, dispersion normalized PMF is capable of enhancing the source emission patterns. After the COVID-19 lockdown began, PNC of traffic nucleation and traffic emissions decreased by 41% and 44%, respectively, while photonucleation produced more particles likely due to the reduction in the condensation sink. The significant changes in source emissions indicate a substantially reduced traffic volume after the implement of lockdown measures.
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spelling pubmed-76473912020-11-09 Changes in source contributions to particle number concentrations after the COVID-19 outbreak: Insights from a dispersion normalized PMF Dai, Qili Ding, Jing Song, Congbo Liu, Baoshuang Bi, Xiaohui Wu, Jianhui Zhang, Yufen Feng, Yinchang Hopke, Philip K. Sci Total Environ Article Factor analysis models use the covariance of measured variables to identify and apportion sources. These models, particularly positive matrix factorization (PMF), have been extensively used for analyzing particle number concentrations (PNCs) datasets. However, the variation of observed PNCs and particle size distribution are driven by both the source emission rates and atmospheric dispersion as well as chemical and physical transformation processes. This variation in the observation data caused by meteorologically induced dilution reduces the ability to obtain accurate source apportionment results. To reduce the influence of dilution on quantitative source estimates, a methodology for improving the accuracy of source apportionment results by incorporating a measure of dispersion, the ventilation coefficient, into the PMF analysis (called dispersion normalized PMF, DN-PMF) was applied to a PNC dataset measured from a field campaign that includes the Spring Festival event and the start of the COVID-19 lockdown in Tianjin, China. The data also included gaseous pollutants and hourly PM(2.5) compositional data. Eight factors were resolved and interpreted as municipal incinerator, traffic nucleation, secondary inorganic aerosol (SIA), traffic emissions, photonucleation, coal combustion, residential heating and festival emissions. The DN-PMF enhanced the diel patterns of photonucleation and the two traffic factors by enlarging the differences between daytime peak values and nighttime concentrations. The municipal incinerator plant, traffic emissions, and coal combustion have cleaner and more clearly defined directionalities after dispersion normalization. Thus, dispersion normalized PMF is capable of enhancing the source emission patterns. After the COVID-19 lockdown began, PNC of traffic nucleation and traffic emissions decreased by 41% and 44%, respectively, while photonucleation produced more particles likely due to the reduction in the condensation sink. The significant changes in source emissions indicate a substantially reduced traffic volume after the implement of lockdown measures. Elsevier B.V. 2021-03-10 2020-11-06 /pmc/articles/PMC7647391/ /pubmed/33189385 http://dx.doi.org/10.1016/j.scitotenv.2020.143548 Text en © 2020 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Dai, Qili
Ding, Jing
Song, Congbo
Liu, Baoshuang
Bi, Xiaohui
Wu, Jianhui
Zhang, Yufen
Feng, Yinchang
Hopke, Philip K.
Changes in source contributions to particle number concentrations after the COVID-19 outbreak: Insights from a dispersion normalized PMF
title Changes in source contributions to particle number concentrations after the COVID-19 outbreak: Insights from a dispersion normalized PMF
title_full Changes in source contributions to particle number concentrations after the COVID-19 outbreak: Insights from a dispersion normalized PMF
title_fullStr Changes in source contributions to particle number concentrations after the COVID-19 outbreak: Insights from a dispersion normalized PMF
title_full_unstemmed Changes in source contributions to particle number concentrations after the COVID-19 outbreak: Insights from a dispersion normalized PMF
title_short Changes in source contributions to particle number concentrations after the COVID-19 outbreak: Insights from a dispersion normalized PMF
title_sort changes in source contributions to particle number concentrations after the covid-19 outbreak: insights from a dispersion normalized pmf
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647391/
https://www.ncbi.nlm.nih.gov/pubmed/33189385
http://dx.doi.org/10.1016/j.scitotenv.2020.143548
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