Combining Low-Cost Sensors with the New York State Mesonet for Continuous Fine-Scale Air Quality Monitoring in the New York City Metropolitan Area
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide A low-cost air quality sensor package to measure particulate matter, ozone, carbon monoxide, nitric oxide, and nitrogen dioxide was designed for integration with the New York State Mesonet (NYSM), an advanced weather monitoring network that has been continuously collecting observations since 2015. The low-cost sensors were calibrated using regulatory-grade instruments at the New York State Department of Environmental Conservation’s Queens College monitoring site. During spring, summer, and fall of 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 a 5-s sampling period and collected remotely in real time, with data retention rates exceeding 90% for all pollutants across the network. Calibrated and quality-controlled hourly data 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 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 | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".