Increase in outdoor carbon dioxide and its effects on the environment and human health in Kuje FCT Nigeria
Bibliographic record
Abstract
Globally there are concerns about outdoor air pollution and its effects on the environment and human health. Researchers are concerned with the negative effects of and best mitigation strategies for air pollution. Climate change and human health are a common phenomenon associated with air pollution, as carbon dioxide (CO2) is absorbed and emitted naturally as part of the carbon cycle. This study was aimed at assessing the emission level of CO2 in Kuje Area Council in the Federal Capital Territory, Nigeria and its effect on the environment and human health. Fifty specific residential and commercial locations were considered, and over 1200 observations of CO2 field data were collected and analysed during two seasons–dry and wet. The land-use settlement pattern was taken into consideration. The data were collected weekly at different time bands (2-, 4-, 6-, and 8-hour time intervals) using the AMPROBE CO2-100, CO2 meter gas detection instrument with self-calibration capability of ±30 parts per million (ppm), ±5% reading (0–5000) accuracy. A Garmin CX60 global positioning system was used to obtain the point locations Universal Transvers Mercator coordinates. The results showed higher mean CO2 emission values of >541 ppm and <713 ppm during the dry and wet seasons, respectively. It was also observed that these values were contrary to and exceeded the American Society of Heating, Refrigerating and Air-Conditioning Engineers and Occupational Safety and Health Administration normal outdoor level standards of 350–450 ppm. These higher CO2 values were found in the residential and commercial districts, and if not monitored and controlled they will have adverse effects on human health and climate change effects. Quick interventions would be to plant trees to sequestrate the CO2 and to regulate the transportation system within the area due to continuous carbon emission.
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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 | 0.001 |
| 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".