Evaluation of discrepancy between measured and modelled oxidized mercury species
Bibliographic record
Abstract
Abstract. L. Zhang et al. (2012), in a recent report, compared model estimates with new observations of oxidized and particulate mercury species (Hg2+ and Hgp) in the Great Lakes region and found that the sum of Hg2+ and Hgp varied between a factor of 2 to 10 between measurements and model. They suggested too high emission inputs as Hg2+ and too fast oxidative conversion of Hg0 to Hg2+ and Hgp as possible causes. This study quantitatively explores measurement uncertainties in detail. These include sampling efficiency, composition of sample, interfering species and calibration errors. Model (Global/Regional Atmospheric Heavy Metals Model – GRAHM) sensitivity experiments are used to examine the consistency between various Hg measurements and speciation of Hg near emission sources to better understand the discrepancies between modelled and measured concentrations of Hg2+ and Hgp. We find that the ratio of Hg0, Hg2+ and Hgp in the emission inventories, measurements of surface air concentrations of oxidized Hg and measurements of wet deposition are currently inconsistent with each other in the vicinity of emission sources. Current speciation of Hg emissions suggests higher concentrations of Hg2+ in air and in precipitation near emission sources; however, measured air concentrations of Hg2+ and measured concentrations of Hg in precipitation are not found to be significantly elevated near emission sources compared to the remote regions. The averaged unbiased root mean square error (RMSE) between simulated and observed concentrations of Hg2+ is found to be reduced by 42% and for Hgp reduced by 40% for 21 North American sites investigated, when a ratio for Hg0 : Hg2+ : Hgp in the emissions is changed from 50 : 40 : 10 (as specified in the original inventories) to 90 : 8 : 2. Unbiased RMSE reductions near emissions sources in the eastern United States and Canada are found to be reduced by up to 58% for Hg2+. Significant improvement in the model simulated spatial distribution of wet deposition of mercury in North America is noticed with the modified Hg emission speciation. Measurement-related uncertainties leading to lower estimation of Hg2+ concentrations are 86%. Uncertainties yielding either to higher or lower Hg2+ concentrations are found to be 36%. Finally, anthropogenic emission uncertainties are 106% for Hg2+. Thus it appears that the identified uncertainties for model estimates related to mercury speciation near sources, uncertainties in measurement methodology and uncertainties in emissions can close the gap between modelled and observed estimates of oxidized mercury found in L. Zhang et al. (2012). Model sensitivity simulations show that the measured concentrations of oxidized mercury, in general, are too low to be consistent with measured wet deposition fluxes in North America. Better emission inventories (with respect to speciation), better techniques for measurements of oxidized species and knowledge of mercury reduction reactions in different environments (including in-plume) in all phases are needed for improving the mercury models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".