Evaluation of River Water Pollution Level in Yogyakarta City Using CCME Method and Biodegradability Index
Bibliographic record
Abstract
River water quality in urban areas, particularly in Yogyakarta, has declined due to pollution from domestic, industrial, and agricultural activities. Communal wastewater treatment plants (CWWTPs) were established to address this issue; however, they have not been operating optimally, limiting their impact on improving water quality. Therefore, this study aims to 1) analyze the condition of water quality temporally and spatially in river sections in Yogyakarta City, 2) determine river water quality index temporally and spatially using Canadian Council of Ministers of the Environmental (CCME) method and Biodegradability Index (BI), 3) evaluate the level of water pollution between CCME method and BI, and 4) analyze water quality parameters influencing the pollution level. The study procedures were carried out using the institutional survey method, and data were obtained from temporal water quality monitoring by Yogyakarta City Environmental Service. Water quality assessment was based on standards according to Governor Regulation No. 20 of 2008. Evaluation of pollution levels was carried out using water quality index with CCME method and BI. The influence of dominant parameters was statistically tested using Principal Component Analysis (PCA). The results showed that water quality in Yogyakarta City based on CCME method and BI was dominated by the poor and non-biodegradable categories. Between 2020 and 2023, the CCME and BI index values of rivers showed an increasing trend, indicating a reduction in pollution. The primary factors affecting water quality include NO₂, TDS, temperature, DO, NO₃, and total phosphate, originating from domestic and agricultural activities. In contrast, Cu, Zn, and Cd are primarily sourced from industrial activities.
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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 | 0.002 | 0.002 |
| 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) | 0.000 | 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 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".