Atmospheric new particle formation identifier using longitudinal global particle number size distribution data
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985858%3A_____%2F24%3A00605271" target="_blank" >RIV/67985858:_____/24:00605271 - isvavai.cz</a>
Výsledek na webu
<a href="https://www.nature.com/articles/s41597-024-04079-1" target="_blank" >https://www.nature.com/articles/s41597-024-04079-1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41597-024-04079-1" target="_blank" >10.1038/s41597-024-04079-1</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Atmospheric new particle formation identifier using longitudinal global particle number size distribution data
Popis výsledku v původním jazyce
Atmospheric new particle formation (NPF) is a naturally occurring phenomenon, during which high concentrations of sub-10 nm particles are created through gas to particle conversion. The NPF is observed in multiple environments around the world. Although it has observable influence onto annual total and ultrafine particle number concentrations (PNC and UFP, respectively), only limited epidemiological studies have investigated whether these particles are associated with adverse health effects. One plausible reason for this limitation may be related to the absence of NPF identifiers available in UFP and PNC data sets. Until recently, the regional NPF events were usually identified manually from particle number size distribution contour plots. Identification of NPF across multi-annual and multiple station data sets remained a tedious task. In this work, we introduce a regional NPF identifier, created using an automated, machine learning based algorithm. The regional NPF event tag was created for 65 measurement sites globally, covering the period from 1996 to 2023. Thendiscussed data set can be used in future studies related to regional NPF.
Název v anglickém jazyce
Atmospheric new particle formation identifier using longitudinal global particle number size distribution data
Popis výsledku anglicky
Atmospheric new particle formation (NPF) is a naturally occurring phenomenon, during which high concentrations of sub-10 nm particles are created through gas to particle conversion. The NPF is observed in multiple environments around the world. Although it has observable influence onto annual total and ultrafine particle number concentrations (PNC and UFP, respectively), only limited epidemiological studies have investigated whether these particles are associated with adverse health effects. One plausible reason for this limitation may be related to the absence of NPF identifiers available in UFP and PNC data sets. Until recently, the regional NPF events were usually identified manually from particle number size distribution contour plots. Identification of NPF across multi-annual and multiple station data sets remained a tedious task. In this work, we introduce a regional NPF identifier, created using an automated, machine learning based algorithm. The regional NPF event tag was created for 65 measurement sites globally, covering the period from 1996 to 2023. Thendiscussed data set can be used in future studies related to regional NPF.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10509 - Meteorology and atmospheric sciences
Návaznosti výsledku
Projekt
<a href="/cs/project/LM2023030" target="_blank" >LM2023030: ACTRIS – účast ČR</a><br>
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2024
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Scientific Data
ISSN
2052-4463
e-ISSN
2052-4463
Svazek periodika
11
Číslo periodika v rámci svazku
1
Stát vydavatele periodika
GB - Spojené království Velké Británie a Severního Irska
Počet stran výsledku
10
Strana od-do
1239
Kód UT WoS článku
001376593400001
EID výsledku v databázi Scopus
2-s2.0-85209196638