Assessing Source Contributions to Air Quality and Noise in Unconventional Oil Shale Plays.
Bibliographic record
Abstract
Introduction: ) has enabled the exploration of previously inaccessible or uneconomic oil and gas resources in shale rock, resulting in thousands of extraction sites across the landscape, many near people's homes. Human exposure to air pollution and noise related to these activities poses a health risk. This study focused on characterizing air pollutants, greenhouse gas emissions, airborne radioactivity, and noise associated with UOGD in two shale production basins. Methods: ), sulfur dioxide, hydrogen sulfide, 20 speciated volatile organic compounds (VOCs) in the ethane to octane volatility range, and noise. Airborne radioactivity was measured in the gas and particle phases. Source apportionment was performed using nonnegative matrix factorization (NMF). To disentangle sound frequencies, we developed spectrograms and conducted machine learning regression to analyze sound sources and relationships to air pollutants. A network of passive hydrocarbon samplers that collected weekly measurements of 15 hydrocarbons was established throughout populated regions of both the PB and Eagle Ford Shale (EFS) areas to measure regional pollutant concentrations and their spatial gradients around UOGD. Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire (VNF) data were acquired to quantify gas flaring activity throughout the region during our field measurement period. VNF flares and estimated flare gas volume were linked to the stationary air quality measurements to examine associations. Results: . The passive sampler network identified northwest-to-southeast increases in ambient hydrocarbon concentrations, in line with the identified UOGD and traffic density in the area. Benzene levels across the network at times exceeded health-based reference values and were significantly higher than benzene levels recorded during air monitoring in large Texas metropolitan areas. Average hydrocarbon levels were significantly correlated with the well density surrounding each site in both shale basins. Conclusions: Our extensive air quality research in the Permian-Delaware Basin revealed, at times, extraordinarily high levels of air pollution, including air toxics such as benzene, and frequent high ozone days in violation of the ozone NAAQS. Our analyses show that the overwhelming amount of this pollution is due to UOGD activities, including emissions from production and storage, gas flaring, and truck traffic.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".