Characterisation of Airborne Particulate Matter in a City Environment
Bibliographic record
Abstract
Airborne particulate matter contains a mixture of pollutants. Identifying the source of these particles, their composition and physical/chemical properties would help to provide a clear connection between their impacts on the environment and the human health. Individual particles have a different chemical morphology and this data could provide information on the formation and reaction mechanism of these particles. It also helps to identify the source they originate from as well as their atmospheric history. Over the years, numerous studies have been conducted to characterise PM10 and little work has been carried out on PM2.5. However, there is an emerging interest in identifying the effects of very fine particles such as nano-particles. The main objective of this research project was to carry out a comprehensive characterisation study of nano-particles collected from a city environment. Environmental monitoring samples from a local authority monitoring site were collected over a period of 7 months using a tapered element oscillating microbalance technique (TEOM). The sample filters were then analysed for their morphology and elemental compositions using SEM/EDS and LA-ICP-MS. SEM/EDS analysis was able to detect several heavy metal particulate matter while the LA-ICP-MS showed that there were more heavy metals present in the filter samples especially the heavier metals. Some of these heavier elements could have been inhibited by organic or higher amounts of the more common metals found in the EDS such as Fe, Zn, Si and Al. Nano-particles originated from high temperature sources, biological, carbonaceous and road transport were also detected in the samples. It was also found that particles containing more metallic elements tended to have a more defined shape while carbonaceous materials typically had amorphous structures. Tests showed that particles with environmental dust compositions of Ca, Al and Si were abundant. It was observed that the biological particles had very fine sizes.
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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 | 0.001 | 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 | 0.001 |
| 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 | 0.001 |
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".