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Record W4306964373 · doi:10.18280/ijsdp.170613

Monitoring Study of the Effect of Atyrau Evaporation Fields on the Content of Hydrogen Sulfide in the Air

2022· article· en· W4306964373 on OpenAlexvenueno aff
Damilya Ryskalieva, Mansiya Yessenamanova, Е. Г. Королева, Zhanar Yessenamanova, Anar Tlepbergenova, Samal Amanzholkyzy, Rimma Turekeldiyeva

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and environmental studies
Canadian institutionsnot available
Fundersnot available
KeywordsHydrogen sulfideRefineryEnvironmental scienceOil refineryAtmosphere (unit)HydrogenSulfideChemistryEnvironmental engineeringMeteorologyMetallurgyMaterials scienceSulfurGeography

Abstract

fetched live from OpenAlex

In this article, a monitoring study was conducted on the content of hydrogen sulfide in the air of the city of Atyrau, the exceeded content of which is associated with reclamation work in the evaporation fields, where industrial wastewater from the Atyrau oil refinery and household water from all over the city have been drained for decades. Monitoring was carried out for the period from September 2021 to June 2022 on 15 observation points recorded by the Republican State Enterprise "Kazhydromet" through the mobile application “Air Kz". The measurements were carried out by the GANK-4AR gas analyzer. The data were analyzed taking into account the maximum permissible concentrations, the repeatability of concentrations of impurities in the atmosphere, the standard index, taking into account the average sample values of hydrogen sulfide content at 10 points out of 15, where increased hydrogen sulfide content was noted. Of the remaining these 10 points, the highest value was noted in the Drag and drop AOR (Atyrau Oil Refinery) point, the hydrogen sulfide content in which, according to the average maximum values, exceeded more than 36.5 MPC (maximum permissible concentration). In general, by month, we note that high values for the average values of the maximum hydrogen sulfide indicators were noted in September, April and June months. Especially strong increase is characterized during warm periods in industrial areas and residential areas as NCOC No. 109 (Vostok), NCOC No. 110 (Privokzalny), NCOC No. 112 (Akimat), NCOC No. 111 (Zhilgorodok), NCOC No. 113 (Avangard) and NCOC No. 114 (Zagorodnaya) points.

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.

How this classification was reachedexpand

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 score0.076
Threshold uncertainty score0.096

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.227
Teacher spread0.205 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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