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Record W2755590865 · doi:10.3390/environments4030066

Air Quality Impacts of Petroleum Refining and Petrochemical Industries

2017· article· en· W2755590865 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnvironments · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOil refineryPetrochemicalAir quality indexPollutantEnvironmental scienceRefining (metallurgy)IndustrialisationWaste managementNatural resource economicsQuality (philosophy)Greenhouse gasAir pollutionParticulatesEnvironmental engineeringEnvironmental protectionBusinessEnvironmental planningEngineeringGeographyChemistry

Abstract

fetched live from OpenAlex

Though refineries and petrochemical industries meet society’s energy demands and produce a range of useful chemicals, they can also affect air quality. The World Health Organization (WHO) has identified polluted air as the single largest environmental risk, and hence it is necessary to strive for and maintain good air quality. To manage potential health impacts, it is important to implement proper air quality management by understanding the link between specific pollutant sources and resulting population exposures. These industries release pollutants such as Volatile Organic Compounds, greenhouse gases and particulate matter, from various parts of their operations. Air quality should be monitored and controlled more meticulously in developing nations where increased energy demands, industrialization and overpopulation has led to more emissions and lower air quality. This paper presents a review of findings and highlights from various studies on air quality impacts of petroleum refining and petrochemical plants in many regions in the world.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.055
GPT teacher head0.334
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it