Underestimation of sulfate concentration in PM2.5 using a semi-continuous particle instrument based on ion chromatography
Bibliographic record
Abstract
Recently, a variety of ion-chromatography-based semi-continuous particle instruments such as the Dionex gas particle ion chromatograph (GP-IC), wet-annular denuder/steam-jet aerosol collector (WAD/SJAC), particle-into-liquid sampler with ion chromatograph (PILS-IC), gas and aerosol monitoring system (GAMS) have been introduced for measuring particle chemical components in the atmosphere. It has been reported that sulfate concentrations in PM2.5 measured by these semi-continuous particle instruments correlate well with those measured by other semi-continuous instruments such as the Aerodyne aerosol mass spectrometer (AMS), R&P 8400S and R&P 8400N analyzers, and Thermo model 5020 sulfate particle analyzer and at times exhibit a unity slope. However, the sulfate concentration measured by some of these semi-continuous instruments has been reported to be underestimated by 20-50%, as compared to that in PM2.5 filter samples. In this study, numerous potential causes for underestimation of the sulfate concentrations by the GP-IC were investigated. We found a 30-40% negative artifact arising from an improper calibration procedure inherent within the instrument design. An improved calibration procedure was developed and it substantially reduced the sulfate concentration difference between the GP-IC measurements and PM2.5 filter samples from 36% to 5%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".