Atmospheric aerosols over two sites in a southeastern region of Texas
Bibliographic record
Abstract
Abstract Speciated samples of PM2.5 were collected at the Bayland Park and Orange sites in Southeastern Texas by US EPA (Environmental Protection Agency) from July 2003 to August 2005. A total of 256 samples for the Bayland Park site and 293 samples for the Orange site with 52 species were measured; however, 22 species were excluded because of too many below‐detection‐limit data. Among the 22 species excluded, 19 species are common to both sites. The two data sets were analyzed by positive matrix factorization (PMF) to infer the sources of PM observed at the two sites. The analysis identified ten common source‐related factors: sulphate‐rich secondary aerosol I, sulphate‐rich secondary aerosol II, cement/carbon‐rich, wood smoke, motor vehicle/road dust, nitrate‐rich secondary aerosol, metal processing, soil, sea salt, and chloride‐depleted marine aerosol. sulphate and nitrate mainly exist as ammonium salts. The two sulphate‐rich secondary aerosols account for about 59% and 54% of the PM2.5 mass concentration at the two sites, respectively. The factor containing highest concentrations of Cl and Na was attributed to sea salt due to the proximity of the monitoring sites to the Gulf of Mexico. The chloride‐depleted marine aerosol was related to the sea salt aerosol but was identified separately due to the chlorine replacement reactions. Basically, the factor of sulphate, nitrate, and soil at the two sites showed similar chemical composition profiles and seasonal variation that reflect the regional characteristics of these sources. The regional factors showed predominantly low frequency variations, however, the area‐related and local factors showed both high and low frequency variations. Motor vehicle/road dust, sea salt, and chloride‐depleted marine aerosol were likely to be area‐related factors. Cement/carbon‐rich, wood smoke, and metal processing factor were likely to be the local sources.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".