Spatial Patterns of Heavy Metals in the Sediments of a Municipal Wastewater Treatment Pond System and Receiving Waterbody, Cha am, Thailand
Bibliographic record
Abstract
Cha am, a popular beach destination in Thailand, uses an aerated lagoon system with four ponds in series to treat its municipal wastewater. This study investigated the spatial pattern of heavy metal concentrations in the sediment deposited at the bottom of the four ponds and along the river receiving the treated wastewater discharge. Using a stratified random sampling scheme, between 11 and 14 surface grab samples were collected from each of the four ponds on two different dates in September and October 2016 (94 samples in total). An additional 17 samples were collected in December 2016 along the 1.8 km river section connecting the ponds to the ocean. A Bruker S1 Titan 600 X-ray fluorescence (XRF) analyser was used to determine metal concentrations in the air dried sediment samples. Ordinary kriging in ArcGIS10.1 indicated that while metal concentrations were greater in the middle areas of each pond, from pond to pond the metal concentrations exhibited different spatial trends. The ponds provide treatment for most of the metals analysed, with Student t-tests showing that mean concentrations of arsenic, chlorine and zinc decreased significantly from the first pond to the third pond but increased significantly in the fourth pond. Chromium concentration changed insignificantly between ponds; lead concentration decreased significantly from the first to the second pond, but there were insignificant changes in mean lead concentration thereafter. Concentrations of cadmium, cobalt, mercury and selenium were below the XRF limit of detection, but the mean levels of arsenic, chromium, copper, lead and manganese in each of the four ponds frequently exceeded Ontario Ministry of the Environment and Climate Change lowest effect level (LEL) guidelines for sediment. Metal levels in the upper reach of the river, closest to the pond discharge, were similar to the pond levels and generally decreased downstream. With the exception of zinc, metal levels detected in the river sediment frequently exceeded the LEL guidelines.
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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 | 0.002 | 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 | 0.000 | 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".