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10.1021/acs.est.0c02776 http://scihub22266oqcxt.onion/10.1021/acs.est.0c02776 32672453!ä!32672453 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Environ+Sci+Technol 2020 ; 54 (16): 9917-9927 Nephropedia Template TP {{tp|p=32672453|t=2020. Dispersion Normalized PMF Provides Insights into the Significant Changes in Source Contributions to PM(2 5) after the COVID-19 Outbreak |pdf=|usr=}}{{32672453}} gab.com Text Dispersion Normalized PMF Provides Insights into the Significant Changes in Source Contributions to PM(2 5) after the COVID-19 Outbreak JO: Environ Sci Technol http://www.kidney.de/pget.php?&tw=1&pmid=32672453 #FOAvip Factor analysis utilizes the covariance of compositional variables to separate sources of ambient pollutants like particulate matter (PM). However, meteorology causes concentration variations in addition to emission rate changes. Conventional positive matrix factorization (PMF) loses information from the data because of these dilution variations. By incorporating the ventilation coefficient, dispersion normalized PMF (DN-PMF) reduces the dilution effects. DN-PMF was applied to hourly speciated particulate composition data from a field campaign that included the start of the COVID-19 outbreak. DN-PMF sharpened the morning coal combustion and rush hour traffic peaks and lowered the daytime soil, aged sea salt, and waste incinerator contributions that better reflect the actual emissions. These results identified significant changes in source contributions after the COVID-19 outbreak in China. During this pandemic, secondary inorganic aerosol became the predominant PM(2.5) source representing 50.5% of the mean mass. Fireworks and residential burning (32.0%), primary coal combustion emissions (13.3%), primary traffic emissions (2.1%), soil and aged sea salt (1.2%), and incinerator (0.9%) represent the other contributors. Traffic decreased dramatically (70%) compared to other sources. Soil and aged sea salt also decreased by 68%, likely from decreased traffic. Twit Text FOAVip Dispersion Normalized PMF Provides Insights into the Significant Changes in Source Contributions to PM(2 5) after the COVID-19 Outbreak ????Environ Sci Technol http://www.kidney.de/pget.php?&tw=1&pmid=32672453 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32672453 #FOAvip English Wikipedia <ref>{{Cite pmid|32672453}}</ref> Dispersion Normalized PMF Provides Insights into the Significant Changes in Source Contributions to PM(2 5) after the COVID-19 Outbreak #MMPMID32672453 Dai Q; Liu B; Bi X; Wu J; Liang D; Zhang Y; Feng Y; Hopke PK Environ Sci Technol 2020[Aug]; 54 (16): 9917-9927 PMID32672453show ga Factor analysis utilizes the covariance of compositional variables to separate sources of ambient pollutants like particulate matter (PM). However, meteorology causes concentration variations in addition to emission rate changes. Conventional positive matrix factorization (PMF) loses information from the data because of these dilution variations. By incorporating the ventilation coefficient, dispersion normalized PMF (DN-PMF) reduces the dilution effects. DN-PMF was applied to hourly speciated particulate composition data from a field campaign that included the start of the COVID-19 outbreak. DN-PMF sharpened the morning coal combustion and rush hour traffic peaks and lowered the daytime soil, aged sea salt, and waste incinerator contributions that better reflect the actual emissions. These results identified significant changes in source contributions after the COVID-19 outbreak in China. During this pandemic, secondary inorganic aerosol became the predominant PM(2.5) source representing 50.5% of the mean mass. Fireworks and residential burning (32.0%), primary coal combustion emissions (13.3%), primary traffic emissions (2.1%), soil and aged sea salt (1.2%), and incinerator (0.9%) represent the other contributors. Traffic decreased dramatically (70%) compared to other sources. Soil and aged sea salt also decreased by 68%, likely from decreased traffic. |*Coronavirus Infections[MESH] |*Pandemics[MESH] |*Pneumonia, Viral[MESH] |Air Pollutants/*analysis[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |China[MESH] |Environmental Monitoring[MESH] |Humans[MESH] |Particulate Matter/*analysis[MESH]Dispersion Normalized PMF Provides Insights into the Significant Chang(epmc).pdf DeepDyve Pubget Overpricing