Source apportionment of mercury in ambient air and in precipitation at New England region using hybrid receptor model.
Bibliographic record
Abstract
The primary goal of this research was to quantify the contributions of specific mercury emission sources using multivariate and hybrid receptor modeling methods. Measurements of mercury and trace elements collected in both the ambient air and wet deposition were made at five New England sites during the study period, 1997--2003. Positive Matrix Factorization (PMF) was used to identify seven sources contributing to the aerosol mercury with specific chemical composition profiles. The aerosol As/Se and Sb/Pb ratios were found as useful regional signatures, as well as used to identify source profiles for individual source categories, and characterize their apparent regional characteristics. PMF identified six sources using the measured precipitation data. A mixed coal combustion and waste incineration source was the largest contributing source to wet deposited mercury. Quantitative Transport Bias Analysis (QTBA) revealed that source areas located within Great Lakes, Midwest, and East Coast regions had the greatest impact on the vapor-phase mercury concentrations recorded at the sampling sites. Source areas located within Great Lakes, Ontario, and New England regions contributed to the elevated particle-phase mercury concentrations. Trajectory cluster analysis using 48-hour backward trajectories was used to understand source-receptor relationship of precipitated mercury. Trajectory clusters with air masses transported from the East Coast or the Midwest displayed relatively higher precipitated mercury levels. Finally, the results of this research suggest that emissions from fuel combustion (coal and oil) and waste incineration sources located mainly in Midwest and along the East Coast region are the primary contributors to the ambient and precipitated mercury measured at the New England sites. The combination of multivariate receptor and hybrid-receptor models applied jointly provided a better understanding of source-receptor relationships for aerosol mercury concentrations in the New England region.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".