An AI-Driven Framework for Real-Time Fenceline Monitoring to Proactively Detect and Mitigate Hazardous Air Pollutants (HAPs)
Bibliographic record
Abstract
Fenceline monitoring plays a pivotal role in detecting and mitigating hazardous air pollutants (HAPs) in industrial regions.This paper presents a comprehensive framework for deploying an artificial intelligence (AI) enabled low latency fenceline monitoring system designed for volatile organic compounds (VOCs) and HAPs, including carcinogens like benzene and toluene.By combining a distributed network of low-cost sensors, edge-based processing, and advanced deep learning classification models, the system enhances pollutant detection accuracy and provides sub-five-minute early warnings for emission events.Results based on a simulated study utilizing open-source data from the US Environmental Protection Agency (EPA) and National Oceanic and Atmospheric Administration (NOAA) demonstrate a classification accuracy of 92% and a reduction in response time from 48 hours to less than 5 minutes, with profound implications for regulatory compliance, community transparency, and proactive environmental management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".