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Record W2948793607 · doi:10.5539/jgg.v11n2p35

Impacts of Anthropogenic Factors on Urban Air Quality in Lagos Metropolis

2019· article· en· W2948793607 on OpenAlexvenueno aff
Peter Nkashi Agan

Bibliographic record

VenueJournal of Geography and Geology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsPollutantEnvironmental scienceAir quality indexMegacityAir pollutionLand useEnvironmental engineeringEnvironmental protectionStovePollutionState of the EnvironmentVegetation (pathology)GeographyEnvironmental resource managementEcologyCivil engineeringEngineeringMeteorology

Abstract

fetched live from OpenAlex

Land use is the utilization and reordering of land cover for human comfort. This process disrupts the pristine state of the environment reducing the quality of environmental receptors like water, air, vegetation etc. Air pollution is introduced into the environment as a result of anthropogenic activities from commercial, industrial and residential areas. These activities are burning of fossil fuels for power generation, transport of goods and services, valorization of raw materials into finished products, bush burning, use of gas cookers, generators and electric stove etc. The introduction of pollutants into the planetary layer of the atmosphere has impacted negatively on the quality of the environment posing threat to humans and the survival of the ecosystem. In Lagos metropolis, commercial activities and high population densities have caused elevated levels of pollution in the city. This study aimed to investigate the spatial distribution of pollutant in Lagos metropolis with a view to revealing the marked spatial/temporal difference in pollutants levels over residential, commercial and industrial land uses. Commercial and industrial land uses revealed higher levels of pollutants than the residential areas. Pearson product moment correlation coefficients revealed strong positive relationship between land use and air quality in the city.

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 score0.003
Threshold uncertainty score0.987

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)0.0010.000

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 head0.020
GPT teacher head0.315
Teacher spread0.294 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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