Sustainable AI-Based Prediction of Air Pollution Levels in London
Bibliographic record
Abstract
Air pollution exposure not only leads to respiratory and cardiovascular diseases, but is also detrimental to cognitive abilities, mental health, and prenatal development.Thus, cities worldwide have invested in sophisticated air pollution monitoring systems to assess and reduce air pollution and its consequences.When excessive build-up of air contaminants occurs, emergency measures must be enacted to reduce human exposure and decrease pollution levels.Predicting such situations a few hours in advance is critical to prevent human health from being compromised.While usage of deep neural networks has become very popular, standard machine learning approaches remain very attractive: they deliver competitive performance, they do not rely on specialised equipment, and their energy consumption is sustainable.Experiments conducted on London air quality data demonstrate that Linear Regression achieves state-of-the-art performance, with 1-hour and 24-hour predictions displaying, respectively, 0.2 and 3.2 mean absolute errors.Moreover, its power usage is a fraction of what is required by its deep learning competitor for both training and predicting, i.e., 1/2840th and 1/126th, respectively.This is significant as they demonstrate air pollution prediction can be sustainable and accurate without prohibitive hardware investments.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".