Mobile monitoring of air pollution − a position paper on use cases, good practices, challenges, and opportunities
Bibliographic record
Abstract
Mobile monitoring has proven to be a very efficient tool to measure and feed into models of air pollution as it complements fixed air quality monitoring networks by adding spatiotemporal resolution. This paper explores best practices, opportunities and challenges related to mobile monitoring of air pollutants, focusing on three key application areas, namely source-, exposure-, and health-related use cases. Use cases are linked to users, ensuring mobile monitoring is effectively tailored to diverse research and policy needs. Tailoring mobile monitoring involves experimental design choices (platform, instrumentation, route planning and spatiotemporal coverage) and data processing choices (data-only vs modelling) optimized towards the envisaged use case. This position paper aims to guide researchers and air pollution stakeholders in generating high-quality mobile monitoring datasets. We identify best practices, discuss monitoring strategies, and highlight future research directions. Additionally, mobile monitoring supports public engagement and actionability, allowing communities to advocate for cleaner air and drive behavior change.
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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 | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".