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Record W4237465224 · doi:10.22215/etd/2015-10930

Urban/rural source apportionment and intraurban source-based spatial analysis of Polycyclic Aromatic Hydrocarbons (PAH) and associated toxicity

2015· dissertation· en· W4237465224 on OpenAlexaboutno aff
Angelos Anastassopoulos

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental chemistryCoal combustion productsEnvironmental sciencePollutantCombustionParticulatesUltrafine particlePyreneApportionmentChemistry

Abstract

fetched live from OpenAlex

This research identified and quantified source types contributing to ambient PAH and associated toxicity at the urban and intraurban scales, reflecting awareness of the variability in exposure toxicity and in source toxicity for PM-associated toxic pollutants. Source apportionment analysed vapour+particle PAH time-series data (2001-2010) from central site monitoring stations at urban (Hamilton, Toronto) and rural background (Egbert) sites in Southern Ontario, Canada. Receptor modeling by Positive Matrix Factorization (PMF) identified four source types: volatilized PAH/long-range transported coal combustion, vehicle traffic exhaust, space heating, biomass combustion. At Hamilton, local industry emissions were also identified, associated with iron/steel manufacturing. Apportionment of PAH toxicity using Benzo(a)Pyrene-toxicity equivalency factors identified traffic exhaust and local industry as ‘more toxic’ source types, contributing comparably little to ambient PAH yet disproportionately to PAH-associated toxicity. Intraurban investigation of PAH sources sampled vapour+particle PAH and PM2.5 from a dense network of >30 Hamilton sites over a two-week period in June-July and December 2009. Ambient PAH exhibited substantially greater spatial variability than PM2.5 and ‘hot spots’ of elevated pollutant levels were observed near/downwind of the business district and harbour-front. A combined PMF-Chemical Mass Balance (CMB) receptor modeling approach applied factors derived from the PMF model of Hamilton central site time-series data as ‘local source profiles’ in a CMB model of spatial field sampling data, explaining spatial variability observed for PAH and PAH toxicity in terms of sources. Contributions by space heating, volatilized PAH/transported coal combustion, wood combustion showed low intraurban variability, while vehicle traffic exhaust showed moderate variability, and local industry emissions contributed significantly only near the industrial harbour-front. Vehicle traffic exhaust contributed majority of PAH toxicity at all sites, even where ambient PAH concentrations were comparably low, and local industry emissions contributed significantly only near the industrial zone, explaining ‘toxicity hot spots’ as high contributions of local industry in addition to vehicle traffic. Findings recommend that efforts to reduce PAH exposures prioritize ‘more toxic’ source types such as vehicle traffic and local industry. PMF-CMB receptor modeling using local time-series PAH data to interpret intraurban variability in ambient PAH demonstrated a viable analysis method for other urban locations.

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)0.001
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 score0.102
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.007
GPT teacher head0.224
Teacher spread0.217 · 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
Published2015
Admission routes1
Has abstractyes

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