Water Quality Assessment Using Activated Carbon from Cocoshells in Lake Mainit, Philippines
Bibliographic record
Abstract
Mining activities pose environmental impacts especially when the operation is near bodies of water, thus, affecting water quality. As these environmental impacts are growing, there is a pressing need for increased intervention studies to improve water quality. This study aimed to evaluate the effectiveness of granulated activated carbon made from coconut shells in reducing heavy metal levels and enhancing the water quality of Lake Mainit located at Agusan del Norte and Surigao del Norte, Philippines. Silica sand, pumice stones, and white marble chips were added to a glass tank with the granulated activated carbon made from coconut shells. The water sample underwent various laboratory tests. The atomic absorption spectrometry flame technique was used to analyze the heavy metals lead and cadmium. Gravimetric method was employed in total suspended solids and total dissolved solids, and nephelometric method for turbidity. Pre-treated water sample analysis regarding lead, total dissolved solids, and turbidity are within the permissible limits, however, total suspended solids and cadmium concentration surpassed the allowable limits for Class A waters. Removal efficiency in terms of heavy metal concentration and the significant difference of parameters between the water sample before and after intervention were calculated. Results showed that after the intervention, activated carbon made from coconut shells were able to reduce the cadmium level present in the water sample. It also improved the quality of water within permissible limits. Hence, the activated carbon made from agricultural waste such as coconut shells has considerable potential to provide better water quality.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".