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Record W7036154944

Assessing Regional Sources of Atmospheric Polycyclic Aromatic Hydrocarbon Pollution and Associated Human Cancer Risk

2023· dissertation· en· W7036154944 on OpenAlexaboutno aff

Bibliographic record

VenueDSpace@MIT (Massachusetts Institute of Technology) · 2023
Typedissertation
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsnot available
Fundersnot available
KeywordsPollutionHuman healthArcticPolycyclic aromatic hydrocarbonThe arcticAir pollutionCarcinogenHealth risk assessment
DOInot available

Abstract

fetched live from OpenAlex

Atmospheric pollution from organic matter burning poses health risks to humans across the globe, making it crucial to assess and regulate such pollution’s sources. One class of atmospheric pollutants, toxic, carcinogenic polycyclic aromatic hydrocarbons (PAHs), are globally ubiquitous chemicals of interest to domestic and international policy decision-makers. Previous studies of PAHs use a single carcinogenic PAH, benzo[a]pyrene, to perform policy-relevant analyses of PAH sources and health effects. However, a new study from Kelly et al., (2021) suggests that BAP may not represent pollution and health effects of other carcinogenic PAHs and their degradation products. Kelly et al.’s findings suggest that previous BAP-based analyses may not capture the PAH emissions sources responsible for PAH concentrations and cancer risk in policy-relevant regions. This thesis uses Kelly et al.’s extended version of the global, three-dimensional GEOS-Chem atmospheric chemistry model to simulate concentrations of BAP and of 48 PAHs and degradation products in three policy-relevant regions: the Arctic Circle, the continental United States, and East Asia. This thesis performs source receptor analysis by simulating BAP (“the BAP simulation”) and simulating 48 PAHs (“the 48-PAH simulation”). In the Arctic, simulating 48 PAHs demonstrates that outside-Arctic sources, primarily from sub-Arctic Russia and continental Europe, contribute a higher percentage of Arctic PAH pollution and cancer risk than simulating BAP alone attributes. However, a high percentage of Arctic PAH concentrations come from within-Arctic sources in the BAP simulation and the 48-PAH simulation. In the United States, simulating 48 PAHs identifies single-digit contributions from outside-US sources, predominantly from sub-Arctic Canada and the Arctic, that are missed when simulating BAP alone. Geospatial mapping of these simulations indicates that Arctic wildfire emissions are likely responsible these sub-Arctic Canadian and Arctic contributions to US PAHs as well as of within-Arctic sources of Arctic PAHs. In East Asia, simulating BAP and simulating 48 PAHs show that within-East Asian sources contribute almost all of East Asian PAH concentrations and associated cancer risk. Overall, these source attribution findings demonstrate the saliency of multifactor PAH assessments, such as the 48-PAH simulation, to identifying outside-region sources of interest to stakeholders like the United Nations Economic Commission for Europe’s Convention on Long-Range Transboundary Air Pollution and the Arctic Council.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.014
GPT teacher head0.272
Teacher spread0.258 · 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.

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
Published2023
Admission routes1
Has abstractyes

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