On The Importance of Airborne Nano-Size Particles: Air Quality, Health, Sustainability, and Climate Change
Bibliographic record
Abstract
Particles, nano, micro and macro-particles, are ubiquitous on Earth. They are chemically, physically, and biologically diverse. They are naturally produced or increasingly through numerous anthropogenic activities, namely medicine-health, chemical industries, materials, construction, transport, communication, aerospace, agriculture, and energy sectors. Air pollution, particularly airborne nano-size particles, is identified as the cause of ~ six million people per year worldwide (WHO, 2020). Aerosols are also significant in climate change and Earth's energy processes. They play a role in radiation, ice nucleation and precipitation events (IPCC, 2018). The identified gap of knowledge by both the IPCC and the WHO are converging, and it becomes clear that they are related to the physicochemical characteristics of particles. Air and water are in motion, as are the particles in air and water. We should be able to observe, track, characterize and remediate in-situ and real-time in 4D (3 dimensions and time). In this talk, we provide an overview of the recent advances in this lab to help to fill the gap identified by the IPCC and the WHO in the age of climate change and COVID-19. We discuss the development of novel promising technologies for fast in-situ and real-time observation of aerosols and waterborne viruses and physicochemical transformations and ice nucleation of anthropogenic emerging nanoparticles (e.g., nano-plastics in air/water). We explore some links between fundamental studies that provide advances in designing zero-net energy and recyclable technology using natural particles in air and soil to remove gaseous and particulate matter in the hydrosphere, cryosphere, and atmosphere.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".