Quantification of aerosol chemical composition using continuous single particle measurements
Bibliographic record
Abstract
Abstract. Mass concentrations of particulate matter (PM) chemical components were determined from data for 0.3 to 3.0 μm particles measured by an Aerosol Time-of-Flight Mass Spectrometer (ATOFMS) data at an urban and rural site. Hourly-averaged concentrations of nitrate, sulphate, ammonium, organic carbon, and elemental carbon, estimated based on scaled ATOFMS peak intensities of corresponding ion marker species, were compared with collocated chemical composition measurements by an Aerosol Mass Spectrometer (AMS), a Gas-Particle Ion Chromatograph (GPIC), and a Sunset Lab field OCEC analyzer. The highest correlation was found for nitrate, with correlation coefficients (Pearson r) of 0.89 and 0.85 at the urban and rural sites, respectively. ATOFMS mass calibration factors, determined for the urban site, were used to calculate mass concentrations of the major PM chemical components at the rural site. Mass reconstruction using this ATOFMS based composition data agreed very well with the total PM mass measured at the rural site. Size distributions of the ten main types of particles were resolved for the rural site and the mass composition of each particle type was determined in terms of sulphate, nitrate, ammonium, organic carbon and elemental carbon. This is the first study to estimate hourly mass concentrations of individual aerosol components and the mass composition of individual particle-types based on ATOFMS single particle measurements.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".