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Record W4391885374 · doi:10.32920/25234654.v1

Characterization of Alkylated Polycyclic Aromatic Hydrocarbons in Urban and Industrial Settings

2024· preprint· en· W4391885374 on OpenAlexaffabout
Maryam Moradi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEnvironmental chemistryAnthraceneAlkylationPyreneChemistryPolycyclic aromatic hydrocarbonPollutantEnvironmental scienceParticulatesOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Polycyclic aromatic compounds (PACs) are a large class of toxic pollutants that include unsubstituted polycyclic aromatic hydrocarbons (PAHs), alkylated PAHs (alk-PAHs), dibenzothiophenes (DBTs), and heteroatoms (contain N, S, O-atoms). The majority of research and monitoring data has historically focused on 16 unsubstituted priority PAHs. Recently, concern has been raised regarding alk-PAHs, as some are more toxic than their unsubstituted analogues, and research is needed to quantify alk-PAHs under different environmental settings. This thesis consists of three measurement campaigns of PACs in air to serve the objectives of this thesis. For the first time in Canada, twenty-two alk-PAHs and five unsubstituted priority PAHs were measured individually at two urban and semi-urban locations in Toronto using three types of samplers (high volume air sampler (HiVol), and two Integrated Organic GAs and Particle (IOGAP) systems with stainless steel (SS) and glass (GL) denuders, separately. In general, PACs are more abundant at the urban site (>3 times) than in the semi-urban area. Regardless of the site environment, alk-PAHs are more prevalent (>3 times) than unsubstituted PAHs. Alk-PAHs contributed 87% (urban) and 55% (semi-urban) of the mean toxic equivalences (TEQ) in air samples. Some alk-PAHs (e.g., 7,12-dimethylbenz[a]anthracene) had a significant impact on toxicity in urban air samples (63 % of TEQ). The toxic effect of alkylated and gaseous PAHs, which are not routinely included in many air-monitoring programs, were significant and should not be neglected. Total concentrations and particle/gas partitioning measured with IOGAP-SS compared well with IOGAP-GL, previously shown to be efficient in capturing gas and particle-phase PAHs. HiVol agrees well with IOGAP-SS when measuring less volatile PACs. Incorporating XAD-resin (divinylbenzene–styrene copolymer) in the HiVol sampling train increased sampling efficiency for highly volatile compounds and is recommended for routine monitoring purposes due to its ease of use. An industrial site with a tailings pond (Suncor Tailings Pond) in Alberta was monitored and modelled. Fluxes of 5 parent and 22 alk-PAHs were estimated, based on the measured co-located air and water concentrations using a two-film fugacity-based model (FUG), an inverse dispersion model (DISP), and a simple box model (BOX). Correlation between the estimated flux results of BOX and DISP model was statistically significant (r = 0.99 and p < 0.05) and Pearson correlation coefficient between FUG and DISP results ranged from 0.54 to 0.85. In this first-ever assessment of PAC fluxes from this tailings pond in Canada, the three models confirmed volatilization fluxes of PACs, indicating the selected Suncor tailings pond is a source of PAC emissions to the atmosphere. The finding addressed a critical data gap identified in the Joint Oil Sands Monitoring Emissions Inventory Compilation Report (Government of Alberta and Canada, 2016), which lacks consistent real-world monitoring of tailings pond fugitive emission of organic chemicals.

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 score0.195
Threshold uncertainty score0.388

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.223
Teacher spread0.211 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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