Network-calibrated low-cost air quality sensors integrated with the New York State Mesonet for continuous fine-scale monitoring in New York City
Bibliographic record
Abstract
A low-cost air quality sensor package to measure particulate matter (PM2.5), ozone (O3), carbon monoxide (CO), nitric oxide (NO), and nitrogen dioxide (NO2) was designed for integration with the New York State Mesonet (NYSM), an advanced weather monitoring network continuously collecting observations since 2015. The low-cost sensors were calibrated using regulatory-grade instruments at the New York State Department of Environmental Conservation Queens College monitoring site in Queens, NY. During spring and summer 2023, sensor packages were deployed at 38 NYSM sites within the New York City Metropolitan Area (NYCMA). From May 2023 through August 2024, air pollutants were measured at 5-s sampling period and collected remotely in real time, with data retention rates exceeding 90% for all pollutants across the network. We will present our Network Calibration Model, which prioritized long-term accuracy and stability of the low-cost sensors. Calibrated and quality-controlled hourly data will be used to demonstrate the capability of the network to characterize temporal pollutant patterns at daily, weekly, and seasonal time scales, to contrast pollutants in urban and rural environments, and to evaluate spatial correlations across the network. The study also highlights the network’s ability to resolve the impacts of episodic pollution events such as the 2023 Quebec, Canada wildfires on air quality across the NYCMA.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".