Vehicle Underway Mobile Monitoring of Volatile Organic Compounds Using Online Mass Spectrometry: Application in an Industrial Park
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The environmental problem of volatile organic compounds (VOCs) pollution in industrial parks is becoming increasingly serious. Rapid and large-scale monitoring of the overall pollution in the area and identification of pollution sources are keys to efficient pollution control. However, traditional detection methods cannot achieve rapid detection, let alone accurately pinpoint the source of pollution. In this study, an online VOC mobile monitoring system based on time-of-flight mass spectrometry was used to monitor an industrial park and successfully obtain the VOC distribution image of the industrial park. Two polluted areas (A and B) were pinpointed by examining the image, and the ΣVOCs concentration ranges were 74–421 μg/m3 and 51–577 μg/m3. Eight abnormal sites with concentration peaks exceeding the standard were found, and the pollution source of one abnormal site was analyzed. The correlation between the pollution source factor of the abnormal site and the emission enterprise was as high as 0.91, and the pollution source of the abnormal site was identified. To explore whether it was affected by other surrounding enterprises, a positive matrix factorization model was further used to analyze the receptor data of abnormal site. Three pollution sources were analyzed and compared with enterprise emission sources. Combined with the location of the abnormal site and emission sources, we found that the abnormal sites were not only affected by the emission sources of surrounding enterprises but also by the emission sources of enterprises in the northeast. This approach offers rapid and large-scale monitoring of the overall pollution in an area and quickly pinpointed the location of the pollution sources. This method solves the shortcomings of traditional methods that cannot identify the location of pollution source and provides an effective technical means for the rapid detection of the pollution distribution in industrial parks for efficient screening of pollution sources.
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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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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 it