Evidence map›Paper›PMID 39130798›Full record

ArticleEnvironmental science: atmospheres2024

Towards automated inclusion of autoxidation chemistry in models: from precursors to atmospheric implications.

Lukas Pichelstorfer, Pontus Roldin, Matti Rissanen, Noora Hyttinen, Olga Garmash, Carlton Xavier, Putian Zhou, Petri Clusius, Benjamin Foreback, Thomas Golin Almeida and 4 more

Abstract read
In one paragraph

Article in Environmental science: atmospheres, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Lukas Pichelstorferpi-numerics Neumarkt amW. 5202 Austria office@pi-numerics.com.ORCID https://orcid.org/0000-0001-9470-257X
Pontus RoldinDivision of Nuclear Physics, Department of Physics, Lund University P. O. Box 118 221 00 Lund Sweden.
Matti RissanenAerosol Physics Laboratory, Tampere University 33720 Tampere Finland.
Noora HyttinenDepartment of Chemistry, Nanoscience Center, University of Jyväskylä FI-40014 Jyväskylä Finland.ORCID https://orcid.org/0000-0002-6025-5959
Olga GarmashAerosol Physics Laboratory, Tampere University 33720 Tampere Finland.
Carlton XavierInstitute for Atmospheric and Earth System Research/Physics, University of Helsinki 00560 Helsinki Finland.
Putian ZhouInstitute for Atmospheric and Earth System Research/Physics, University of Helsinki 00560 Helsinki Finland.
Petri ClusiusInstitute for Atmospheric and Earth System Research/Physics, University of Helsinki 00560 Helsinki Finland.
Benjamin ForebackInstitute for Atmospheric and Earth System Research/Physics, University of Helsinki 00560 Helsinki Finland.
Thomas Golin AlmeidaInstitute for Atmospheric and Earth System Research/Physics, University of Helsinki 00560 Helsinki Finland.ORCID https://orcid.org/0000-0002-1794-1507
Chenjuan DengState Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University 100084 Beijing China.
Metin BaykaraInstitute for Atmospheric and Earth System Research/Physics, University of Helsinki 00560 Helsinki Finland.
Theo KurtenDepartment of Chemistry, University of Helsinki 00014 Helsinki Finland.
Michael BoyInstitute for Atmospheric and Earth System Research/Physics, University of Helsinki 00560 Helsinki Finland.ORCID https://orcid.org/0000-0002-8107-4524

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the last few decades, atmospheric formation of secondary organic aerosols (SOA) has gained increasing attention due to their impact on air quality and climate. However, methods to predict their abundance are mainly empirical and may fail under real atmospheric conditions. In this work, a close-to-mechanistic approach allowing SOA quantification is presented, with a focus on a chain-like chemical reaction called "autoxidation". A novel framework is employed to (a) describe the gas-phase chemistry, (b) predict the products' molecular structures and (c) explore the contribution of autoxidation chemistry on SOA formation under various conditions. As a proof of concept, the method is applied to benzene, an important anthropogenic SOA precursor. Our results suggest autoxidation to explain up to 100% of the benzene-SOA formed under low-NO

Identifiers

PMID39130798
PMCPMC11307592

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.