e0tlcu

Property	Value
?:abstract	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 pollutant gases and hourly PM2.5 compositional data. Eight factors were resolved and interpreted as municipal incinerator, traffic nucleation, secondary inorganic aerosol (SIA), traffic emission, photonucleation, coal combustion, residential heating and festival emission. 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 emission 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.
is ?:annotates of	<https://research.tib.eu/covid-19/entity/52e0tlcu_hasAnnotation_C0009131> <https://research.tib.eu/covid-19/entity/52e0tlcu_hasAnnotation_C0027627> <https://research.tib.eu/covid-19/entity/52e0tlcu_hasAnnotation_C0220806> <https://research.tib.eu/covid-19/entity/52e0tlcu_hasAnnotation_C0428981> <https://research.tib.eu/covid-19/entity/52e0tlcu_hasAnnotation_C1824370>
?:creator	<https://research.tib.eu/covid-19/entity/Dai%2C_Qili%3B_Ding%2C_Jing%3B_Song%2C_Congbo%3B_Liu%2C_Baoshuang%3B_Bi%2C_Xiaohui%3B_Wu%2C_Jianhui%3B_Zhang%2C_Yufen%3B_Feng%2C_Yinchang%3B_Hopke%2C_Philip_K.>
?:doi	10.1016/j.scitotenv.2020.143548
?:doi	<https://research.tib.eu/covid-19/entity/10.1016/j.scitotenv.2020.143548>
?:journal	Sci_Total_Environ
?:license	els-covid
?:pdf_json_files	document_parses/pdf_json/8041d4d12bde77dfdfc2c44454a19ad89da48e7e.json
?:pmcid	<https://research.tib.eu/covid-19/entity/PMC7647391>
?:pmid	<https://research.tib.eu/covid-19/entity/33189385.0>
?:pmid	33189385.0
?:publication_isRelatedTo_Disease	<https://research.tib.eu/covid-19/entity/COVID-19>
?:sha_id	<https://research.tib.eu/covid-19/entity/8041d4d12bde77dfdfc2c44454a19ad89da48e7e>
?:source	Elsevier; Medline; PMC
?:title	Changes in source contributions to particle number concentrations after the COVID-19 outbreak: Insights from a dispersion normalized PMF
?:type	<https://research.tib.eu/covid-19/vocab/Publication>
?:year	2020-11-06

Property

Value

?:abstract

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 pollutant gases and hourly PM2.5 compositional data. Eight factors were resolved and interpreted as municipal incinerator, traffic nucleation, secondary inorganic aerosol (SIA), traffic emission, photonucleation, coal combustion, residential heating and festival emission. 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 emission 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.

is ?:annotates of