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Record W58026515

Mercury Transport and Fluxes in the Lake Ontario Basin

2014· article· en· W58026515 on OpenAlexaboutno aff
Joseph S. Denkenberger

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
FundersSyracuse UniversityU.S. Environmental Protection Agency
KeywordsMercury (programming language)Structural basinEnvironmental scienceHydrology (agriculture)MeteorologyOceanographyGeologyGeographyGeomorphology
DOInot available

Abstract

fetched live from OpenAlex

In this dissertation I assess mercury dynamics in the Lake Ontario Basin. In four interrelated phases, ecosystem mercury concentrations and fluxes were analyzed at increasing scales. Each phase is presented in order of increasing scale, starting with a reach-by-reach analysis in the Seneca River in New York, then moving up to an analysis of the Lake Ontario watershed, followed by an assessment of Lake Ontario, and culminating with an estimate of atmospheric mercury exchange for the entire Great Lakes Basin. Phase 1 of my research is a multi-year study exploring mercury (Hg) dynamics in the Three Rivers system, with particular emphasis on the Seneca River in Central New York. In addition to bi-weekly water sampling, additional field investigations were conducted to estimate elemental Hg (Hg0) volatilization rates from the river, and to assess the impacts of zebra mussel metabolism on Hg0 volatilization. Elemental Hg volatilization was estimated at 1.3 ng m-2 hr-1 for the field season, and was positively correlated to incident solar radiation. It appears that the clearing of the water column caused by zebra mussels may increase Hg0 volatilization rates due to subsequent increased penetration of incident solar radiation, though additional limiting factors are apparent. Reach-by-reach Hg mass balances were developed for three river reaches to assess the effects of an intervening hypereutrophic lake, a zone of intense zebra mussel infestation, and contributions from Onondaga Lake which is contaminated with elevated levels of Hg. It was determined that particulate Hg (THgP) is the dominant form of Hg in the Seneca River, and Hg flux in the ecosystem is governed by flow characteristics. Intervening Cross Lake serves an important role for Hg transport in the Seneca River, reducing total Hg (THg) flux in the river by over 65% due to deposition of THgP. Methylmercury (MeHg) concentration increased over the reach of intense zebra mussel infestation, possibly due to support of anaerobic respiration as a result of the zebra mussel oxygen demand. The river reach receiving input from Onondaga Lake shows a 15% increase in THg flux. Net atmospheric Hg exchange is in the direction of deposition. However, direct atmospheric Hg deposition to the water surface plays a minimal role in Seneca River reaches, representing less than 1% of the fluvial Hg flux of the river. Overall, the Seneca River watershed is a sink for inputs of atmospheric Hg at a rate of 42 kg yr-1, and the watershed efficiently retains >85% of this Hg, exporting approximately 5 kg yr-1 to the Three Rivers confluence. For Phase 2, Hg speciation and concentrations were measured near the mouths of nine tributaries to Lake Ontario during two independent field-sampling programs. Among the study tributaries, mean THg ranged from 0.9 to 2.6 ng L-1; mean dissolved Hg (THgD) ranged from 0.5 to 1.5 ng L-1; mean THgP ranged from 0.3 to 2.0 ng L-1; and mean MeHg ranged from 0.05 to 0.14 ng L-1. Watershed land-cover, total suspended solids (TSS), and dissolved organic carbon (DOC) were evaluated as potential controls of Hg. Significant relationships between THgD and DOC were limited, whereas significant relationships between THgP and TSS were common. Methylmercury was largely associated with the aqueous phase, and MeHg as a fraction of THg was positively correlated to open water land-cover. Wetland cover was positively correlated to THg and MeHg particle associations. A relation was evident between dense urban land-cover and higher THgP fractions. Phase 3 utilized the results of Phase 2 to estimate Hg flux for ten inflowing tributaries and the outlet for Lake Ontario. Total Hg flux for nine study watersheds that directly drain into the lake ranged from 0.2 kg yr-1 to 13 kg yr-1, with the dominant fluvial THg load from the Niagara River at 154 kg yr-1. Total Hg loss at the outlet (St. Lawrence River) was 68 kg yr-1. Fluvial Hg inputs largely (62%) occur in the dissolved fraction and are similar to estimates of atmospheric Hg inputs. Fluvial mass balances suggest strong in-lake retention of THgP inputs (99%), compared to THgD (45%) and MeHg (22%) fractions. Wetland land-cover is a good predictor of MeHg yield for Lake Ontario watersheds. Sediment deposition studies and coupled atmospheric and fluvial Hg fluxes indicate that Lake Ontario is a net sink of Hg inputs and not at steady-state likely due to recent decreases in point source inputs and atmospheric Hg deposition. In Phase 4, rates of surface-air Hg0 fluxes in the literature were synthesized for the Great Lakes Basin (GLB). For the majority of surfaces, fluxes were net positive (evasion). Annual Hg0 evasion for the GLB was estimated at 7.7 Mg yr-1, whereas Hg deposition to the area is estimated at 15.9 Mg yr-1. This analysis therefore suggests the GLB is a net sink for atmospheric Hg. Land-cover types that contributed most to the annual Hg0 evasion for the GLB are agriculture (~55%) and forest (~25%), and the open water of the Great Lakes (~15%). Most land-cover types displayed similar areal evasion rates, with a range of 7.0 to 21.0 µg m-2 yr-1. The highest rates were associated with urban (12.6 µg m-2 yr-1) and agricultural (21.0 µg m-2 yr-1) lands. Considerable uncertainty was noted in estimates of Hg0 evasion. Methods used to estimate Hg0 evasion vary, and results are affected based on the methods applied. A unified methodological approach could partially remedy uncertainty in estimating Hg0 fluxes.

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 score0.131
Threshold uncertainty score0.264

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 head0.013
GPT teacher head0.222
Teacher spread0.209 · 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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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