Assessment of the Spatial Relationship between Air Pollutants in Kaduna Metropolis, Nigeria
Bibliographic record
Abstract
This study analyzes the varying spatial relationship between air pollutants [Carbon monoxide (CO), Sulphur dioxide (SO2), Carbon dioxide (CO2) and Particulate Matter (PM2.5 and PM10)] in Kaduna metropolis. This was aimed at having more insight into be characteristics of air pollutants in the study area, which was geared towards efficient environmental management of the city. Validated portable pollutant monitors (MSA Altair 5x Gas Detectora, CO2 meter Model AZ-0002-DL and CW-HAT200 Particulate Counter) were utilized for data collection, on the concentration of air pollutants CO and SO2; CO2 and particulate matters (PM2.5 and PM10) respectively. Pearson correlation and Geographical Weighed Regression (GWR) were used for data analysis, whereas, maps and tables were utilized for data presentation. The findings revealed that a non - perfect positive linear relationship exists between the air pollutants. The GWR model revealed that the distribution of the local R2 values vary spatially across Kaduna Metropolis, indicating that there is a spatially varying relationship between the air pollutants. Higher R2 values were mostly in the central and southern part of the metropolis except for PM2.5 and PM10 in the Northern part of the metropolis. The finding further reveals that CO has the strongest association with SO2, SO2 has the strongest association with PM2.5 and PM10, CO2 with SO2, PM10 has the highest association with PM2.5, likewise PM2.5 with PM10. The use of GWR to establish the existence of locally varying relationship between the air pollutants was successful. Hence, this study recommends the use of GWR to model air pollutants in cities with limited monitoring capability.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".