Understanding long-range transport mechanisms of perfluoroalkyl substances
Bibliographic record
Abstract
Perfluoroalkyl acids (PFAAs) are persistent, bioaccumulative compounds found \nubiquitously within the environment. They can be formed from the atmospheric oxidation \nof volatile precursor compounds and undergo long-range transport (LRT) through the \natmosphere and ocean to remote locations. Ice caps preserve a temporal record of PFAA \ndeposition making them useful in studying the atmospheric trends in LRT of PFAAs as \nwell as understanding major pollutant sources and production changes over time. \nA 15 m ice core representing 38 years of deposition (1977 – 2015) was collected \nfrom the Devon Ice Cap in Nunavut and analyzed for PFAAs. Samples were concentrated \nby solid phase extraction and analyzed by UPLC-MS/MS, IC, and ICP-OES. Both shortand \nlong-chain perfluorocarboxylic acids (PFCAs) and perfluorosulfonic acids (PFSAs) \nwere detected in the samples, with fluxes ranging from <LOD to 4.44×10⁴ ng m ⁻² yr⁻¹. \nIn this work I assess temporal trends in deposition, homologue profiles, ion \ntracers, air mass transport models, and production and regulation trends to characterize \nthe PFAA depositional profile on the Devon Ice Cap and to further understand the LRT \nmechanisms of these persistent pollutants. In Chapter 3 my results demonstrate that the \nPFCAs and perflurooctane sulfonate (PFOS) have continuous and increasing deposition \non Devon Ice Cap, despite recent North American regulations and phase-outs. I propose \nthat this is the result of on-going emission and use of these compounds, their precursors \nand other newly unidentified compounds in regions outside of North America. Through \nmodelling air mass transport densities, and comparing temporal trends in deposition with production changes of possible sources, I find that Eurasian sources, particularly from \nContinental Asia are large contributors to the global pollutants impacting Devon Ice Cap. \nBy comparing PFAAs to their precursors and correlating pairs of PFCAs, I determine that \ndeposition of PFAAs is dominated by atmospheric formation from volatile precursor \nsources, and major ion analysis provides new information regarding the transport of \nPFAAs, confirming that marine aerosol inputs are unimportant to the LRT mechanisms of \nthese compounds. In Chapter 4 my results from the Arctic ice core analysis show a tenfold \nincrease in short-chain PFCA (scPFCA) deposition between 1986 and 2014, which \ncoincides with increased production and atmospheric burden of chlorofluorocarbon (CFC)- \nreplacement compounds. This is the first multi-decadal temporal record of scPFCA \ndeposition and indicates that Montreal Protocol-mandated introduction of CFC-replacement \ncompounds for the heat-transfer industry is the dominant source of scPFCAs to remote \nregions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".