Atmospheric new particle formation identifier using longitudinal global particle number size distribution data
Kecorius, Simonas ; Bycenkiene, Steigvile and Kristensson, Adam LU
(2024)
In Scientific Data
11.
- Abstract
- 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.... (More)
- 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. The discussed data set can be used in future studies related to regional NPF. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/7d5691f4-e393-4964-a908-e20b195c6ba3
- author
- Kecorius, Simonas
; Bycenkiene, Steigvile
and Kristensson, Adam
LU
- author collaboration
- organization
- publishing date
- 2024-11-16
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Scientific Data
- volume
- 11
- article number
- 1239
- pages
- 10 pages
- publisher
- Nature Publishing Group
- external identifiers
-
- scopus:85209196638
- pmid:39550387
- ISSN
- 2052-4463
- DOI
- 10.1038/s41597-024-04079-1
- language
- English
- LU publication?
- yes
- id
- 7d5691f4-e393-4964-a908-e20b195c6ba3
- date added to LUP
- 2024-11-20 14:50:19
- date last changed
- 2025-06-19 03:00:08
@article{7d5691f4-e393-4964-a908-e20b195c6ba3,
abstract = {{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. The discussed data set can be used in future studies related to regional NPF.}},
author = {{Kecorius, Simonas and Bycenkiene, Steigvile and Kristensson, Adam}},
issn = {{2052-4463}},
language = {{eng}},
month = {{11}},
publisher = {{Nature Publishing Group}},
series = {{Scientific Data}},
title = {{Atmospheric new particle formation identifier using longitudinal global particle number size distribution data}},
url = {{http://dx.doi.org/10.1038/s41597-024-04079-1}},
doi = {{10.1038/s41597-024-04079-1}},
volume = {{11}},
year = {{2024}},
}