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Record W4376114874 · doi:10.1016/j.envpol.2023.121805

Investigation of four-year chemical composition and organic aerosol sources of submicron particles at the ATOLL site in northern France

2023· article· en· W4376114874 on OpenAlexfundno aff
Hasna Chebaicheb, Joël Brito, Gang Chen, Emmanuel Tison, Caroline Marchand, Andrê S. H. Prévôt, Olivier Favez, Véronique Riffault

Bibliographic record

VenueEnvironmental Pollution · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
FundersMedical Research CouncilEuropean Regional Development FundUniversity of TorontoMinistry of EnvironmentAgence Nationale de la Recherche
KeywordsAerosolEnvironmental scienceEnvironmental chemistryNitratePollutionSeasonalityAmmoniumTotal organic carbonBiomass burningAir pollutionCombustionChemistryEcology

Abstract

fetched live from OpenAlex

This study presents the first long-term online measurements of submicron (PM1) particles at the ATOLL (ATmospheric Observations in liLLe) platform, in northern France. The ongoing measurements using an Aerosol Chemical Speciation Monitor (ACSM) started at the end of 2016 and the analysis presented here spans through December 2020. At this site, the mean PM1 concentration is 10.6 μg m−3, dominated by organic aerosols (OA, 42.3%) and followed by nitrate (28.9%), ammonium (12.3%), sulfate (8.6%), and black carbon (BC, 8.0%). Large seasonal variations of PM1 concentrations are observed, with high concentrations during cold seasons, associated with pollution episodes (e.g. over 100 μg m−3 in January 2017). To study OA origins over this multiannual dataset we performed source apportionment analysis using rolling positive matrix factorization (PMF), yielding two primary OA factors, a traffic-related hydrocarbon-like OA (HOA) and biomass-burning OA (BBOA), and two oxygenated OA (OOA) factors. HOA showed a homogeneous contribution to OA throughout the seasons (11.8%), while BBOA varied from 8.1% (summer) to 18.5% (winter), the latter associated with residential wood combustion. The OOA factors were distinguished between their less and more oxidized fractions (LO-OOA and MO-OOA, on average contributing 32% and 42%, respectively). During winter, LO-OOA is identified as aged biomass burning, so at least half of OA is associated with wood combustion during this season. Furthermore, ammonium nitrate is also a predominant aerosol component during cold-weather pollution episodes – associated with fertilizer usage and traffic emissions. This study provides a comprehensive analysis of submicron aerosol sources at the recently established ATOLL site in northern France from multiannual observations, depicting a complex interaction between anthropogenic and natural sources, leading to different mechanisms of air quality degradation in the region across different seasons.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.457

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.161
Teacher spread0.153 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2023
Admission routes1
Has abstractyes

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