Indoor Air Pollution in Rumuewhera Community in Obio-Akpor Local Government Area of Rivers State, Nigeria
Bibliographic record
Abstract
Indoor air pollution arising from the use of biomass fuel for cooking is a serious health issue in Nigeria especially in rural communities. This study investigated the levels of Carbon monoxide (CO), PM2.5 and PM10 released during morning and evening cooking sessions in 17 households in Rumuewhara community in Obio/Akpor LGA, Rivers State Nigeria. This was to ascertain indoor air pollution concentrations in rural households categorized in the terms of fuel type (Firewood, Kerosene and LPG) and kitchen configuration. In the morning cooking session, mean and standard deviation of CO, PM2.5 and PM10 concentration levels from households using LPG (8.78 + 5.20 ppm, 25.5 + 6.65 µg/m3 and 39.38 + 13.28 µg/m3) were observed as lower than those from other households using biomass fuels (36.78 + 19.44 ppm, 270.16 + 159.44 µg/m3 and 419.82 + 247.29 µg/m3 for firewood). The mean concentrations of CO, PM2.5 and PM10 during cooking sessions in firewood kitchens are clearly higher than the standard limits of WHO and Health Canada due to the fuel type, kitchen configuration and ventilation habit. With correlation coefficients, r = -0.537, P=.03; r = -0.583, P=.01 and r = -0.566, p=0.02; there is a statistically significant and strong negative correlation between Relative Humidity vs CO, PM2.5 and PM10 respectively. The use of biomass fuels for household cooking should be discouraged in favour of LPG or kerosene due to the high concentration of indoor air pollutants it generates. To reduce the effects of biomass fuels, well-positioned Chimneys should be incorporated into houses to limit the accumulation of indoor air pollutants in the cooking area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".