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Record W3119569241 · doi:10.1016/j.envint.2020.106343

The influence of chemical composition, aerosol acidity, and metal dissolution on the oxidative potential of fine particulate matter and redox potential of the lung lining fluid

2021· article· en· W3119569241 on OpenAlexafffundabout
Pourya Shahpoury, Zheng Wei Zhang, Andrea M. Arangio, Valbona Celo, Ewa Dąbek-Złotorzyńska, Tom Harner, Athanasios Nenes

Bibliographic record

VenueEnvironment International · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsEnvironment and Climate Change Canada
FundersEuropean Research CouncilHORIZON EUROPE Excellent ScienceEnvironment and Climate Change Canada
KeywordsParticulatesEnvironmental chemistryAscorbic acidChemistryAerosolGlutathioneDissolutionOrganic matterBiochemistryFood scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Air pollution is a major environmental health risk and it contributes to respiratory and cardiovascular diseases and excess mortality worldwide. The adverse health effects have been associated with the inhalation of fine particulate matter (PM2.5) and induction of respiratory oxidative stress. In this work, we quantified the oxidative potential (OP) of PM2.5 from several Canadian cities (Toronto, Hamilton, Montreal, Vancouver) using a recently developed bioanalytical method which measures the oxidation of lung antioxidants, glutathione, cysteine, and ascorbic acid, the formation of glutathione disulfide and cystine, and the related redox potential (RP) in a simulated epithelial lining fluid (SELF). We evaluated the application of empirical SELF RP as a new metric for aerosol OP. We further investigated how PM2.5 chemical composition and OP are related across various emission source sectors and whether these features are linked to specific properties of aerosol aqueous phase, such as pH and metal-ligand complexation. The OP indicators including SELF RP were strongly correlated among each other, indicating that the empirical RP could be used as a reliable metric in future studies. OP based on ascorbic acid showed dependency on the emission source sectors, most likely due to variation in the solubility of Fe. Traffic emissions resulted in the highest OP, followed by industrial emissions and resuspended crustal matter. OP presented low correlation with PM2.5 concentrations, low-moderate correlation with the aerosol organic matter, and moderate-strong association with black carbon and transition metals across the sites. We did not find strong association between the concentration of biomass burning tracers and OP. Copper was the only metal that showed high association with OP across all sites, whereas the correlation with other metals, such as iron, manganese, and titanium, showed clear dependency on the source sectors. The aerosol pH correlated negatively with ambient temperature and positively with biomass burning tracers and the levels of nitrate, ammonium, and aerosol liquid water content. The solubility of Fe was associated with sulfate and aerosol pH at most sites, suggesting the involvement of proton-mediated dissolution pathway, while this was not visible at the site influenced by industrial emission, most likely due to the abundance of pyrogenic Fe. The effect of metal-ligand complexation on the solubility of transition metals, in particular Fe, was clearly observed at all sites, whereas a combined effect with aerosol pH, and a subsequent impact on OP, was only seen at the traffic site in Toronto. The enhanced solubility of Fe due to proton- and ligand-mediated dissolution pathways and subsequent formation of reactive oxygen species may in part explain the health effects of PM2.5 seen in previous epidemiological studies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.251
Teacher spread0.240 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations102
Published2021
Admission routes3
Has abstractyes

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