Nano Ranking Analysis: determining NPF event occurrence and intensity based on the concentration spectrum of formed (sub-5 nm) particles Article Swipe
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· 2023
· Open Access
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· DOI: https://doi.org/10.5194/ar-2023-5
Here we introduce a new method, termed “Nano Ranking Analysis,” for characterizing new particle formation (NPF) from atmospheric observations. Using daily variations of the particle number concentration at sizes immediately above the continuous mode of molecular clusters, here in practice 2.5–5 nm - ΔN2.5–5, we can determine the occurrence and estimate the strength of atmospheric NPF events. After determining the value of ΔN2.5–5 for all the days during a period under consideration, the next step of the analysis is to rank the days based on this simple metric. The analysis is completed by grouping the days either into a number of percentile intervals based on their ranking or into a few modes in the distribution of log(ΔN2.5–5) values. Using five years (2018–2022) of data from the SMEAR II station in Hyytiälä, Finland, we found that the days with higher (lower) ranking values had, on average, both higher (lower) probability of NPF events and higher (lower) particle formation rates. The new method provides probabilistic information about the occurrence and intensity of NPF events and is expected to serve as a valuable tool to define the origin of newly formed particles at many types of environments that are affected by multiple sources of aerosol precursors.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5194/ar-2023-5
- OA Status
- gold
- References
- 7
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4382398354
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4382398354Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/ar-2023-5Digital Object Identifier
- Title
-
Nano Ranking Analysis: determining NPF event occurrence and intensity based on the concentration spectrum of formed (sub-5 nm) particlesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-28Full publication date if available
- Authors
-
Diego Aliaga, Santeri Tuovinen, Tinghan Zhang, Janne Lampilahti, Xinyang Li, Lauri Ahonen, Tom Kokkonen, Tuomo Nieminen, Simo Hakala, Pauli Paasonen, Federico Bianchi, Doug R. Worsnop, Veli‐Matti Kerminen, Markku KulmalaList of authors in order
- Landing page
-
https://doi.org/10.5194/ar-2023-5Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5194/ar-2023-5Direct OA link when available
- Concepts
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Ranking (information retrieval), Percentile, Aerosol, Metric (unit), Event (particle physics), Particle (ecology), Intensity (physics), Statistics, Particle number, Environmental science, Rank (graph theory), Probabilistic logic, Mathematics, Atmospheric sciences, Meteorology, Physics, Computer science, Thermodynamics, Combinatorics, Astrophysics, Engineering, Optics, Geology, Information retrieval, Operations management, Oceanography, Volume (thermodynamics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
7Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.rates. | 158 |
| abstract_inverted_index.simple | 87 |
| abstract_inverted_index.termed | 7 |
| abstract_inverted_index.values | 142 |
| abstract_inverted_index.(lower) | 140, 148, 155 |
| abstract_inverted_index.2.5–5 | 41 |
| abstract_inverted_index.Ranking | 9 |
| abstract_inverted_index.aerosol | 202 |
| abstract_inverted_index.events. | 57 |
| abstract_inverted_index.method, | 6 |
| abstract_inverted_index.metric. | 88 |
| abstract_inverted_index.ranking | 107, 141 |
| abstract_inverted_index.sources | 200 |
| abstract_inverted_index.station | 129 |
| abstract_inverted_index.values. | 118 |
| abstract_inverted_index.“Nano | 8 |
| abstract_inverted_index.Finland, | 132 |
| abstract_inverted_index.affected | 197 |
| abstract_inverted_index.analysis | 78, 90 |
| abstract_inverted_index.average, | 145 |
| abstract_inverted_index.estimate | 51 |
| abstract_inverted_index.expected | 175 |
| abstract_inverted_index.grouping | 94 |
| abstract_inverted_index.multiple | 199 |
| abstract_inverted_index.particle | 14, 25, 156 |
| abstract_inverted_index.practice | 40 |
| abstract_inverted_index.provides | 162 |
| abstract_inverted_index.strength | 53 |
| abstract_inverted_index.valuable | 180 |
| abstract_inverted_index.Abstract. | 0 |
| abstract_inverted_index.clusters, | 37 |
| abstract_inverted_index.completed | 92 |
| abstract_inverted_index.determine | 47 |
| abstract_inverted_index.formation | 15, 157 |
| abstract_inverted_index.intensity | 169 |
| abstract_inverted_index.intervals | 103 |
| abstract_inverted_index.introduce | 3 |
| abstract_inverted_index.molecular | 36 |
| abstract_inverted_index.particles | 189 |
| abstract_inverted_index.continuous | 33 |
| abstract_inverted_index.occurrence | 49, 167 |
| abstract_inverted_index.percentile | 102 |
| abstract_inverted_index.variations | 22 |
| abstract_inverted_index.ΔN2.5–5 | 63 |
| abstract_inverted_index.Hyytiälä, | 131 |
| abstract_inverted_index.atmospheric | 18, 55 |
| abstract_inverted_index.determining | 59 |
| abstract_inverted_index.immediately | 30 |
| abstract_inverted_index.information | 164 |
| abstract_inverted_index.precursors. | 203 |
| abstract_inverted_index.probability | 149 |
| abstract_inverted_index.ΔN2.5–5, | 44 |
| abstract_inverted_index.Analysis,” | 10 |
| abstract_inverted_index.distribution | 115 |
| abstract_inverted_index.environments | 194 |
| abstract_inverted_index.(2018–2022) | 122 |
| abstract_inverted_index.concentration | 27 |
| abstract_inverted_index.observations. | 19 |
| abstract_inverted_index.probabilistic | 163 |
| abstract_inverted_index.characterizing | 12 |
| abstract_inverted_index.consideration, | 72 |
| abstract_inverted_index.log(ΔN2.5–5) | 117 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5087893407, https://openalex.org/A5030695012, https://openalex.org/A5075179945, https://openalex.org/A5030598605, https://openalex.org/A5113021422, https://openalex.org/A5061586279, https://openalex.org/A5063109766, https://openalex.org/A5000471665, https://openalex.org/A5037190463, https://openalex.org/A5015236692, https://openalex.org/A5054797720, https://openalex.org/A5100373651, https://openalex.org/A5054411581, https://openalex.org/A5075262199 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 14 |
| corresponding_institution_ids | https://openalex.org/I133731052, https://openalex.org/I2800229700, https://openalex.org/I4210111149, https://openalex.org/I75390827, https://openalex.org/I881766915 |
| citation_normalized_percentile.value | 0.12948377 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |