Biological treatment: Optimization of biological rapid sand filters for drinking water production
Bibliographic record
Abstract
Drinking water production from groundwater will often require removal of several compounds such as ammonia, manganese, ferrous iron, methane, sulphides or natural organic matter (NOM). In rapid sand filters this may be mediated through microbial processes. Sometimes the filters unfortunately fail to meet the design criteria, and a deeper insight in the underlying processes would provide a necessary platform to solve the problems. Efficient removal of potential microbial substrates is essential for production of biostable water which is required when the produced drinking water is stored and distributed without a disinfection residual as e.g. in Denmark. We have developed a toolbox including investigations of the presence of required microorganisms. To investigate the presence and density of various microbial fractions, qPCR-methods were established for quantification of ammonium oxidizing (AOB, AOA), nitrite oxidizing (NOB), iron oxidizing (IOB) and methane oxidizing (MeOB) microorganisms. In addition, pyrosequencing of the full microbiome of the sand filters revealed a high diversity, and especially the presence of a very large and dominating population of Nitrospira was surprizing. Nitrification was particularly investigated in full scale filters, and lab-scale CST-columns incubated with depth specific samples of filter material allowed for investigation of the depth specific kinetics and maximum removal capacity. Additionally, pilot scale column experiments allowed for investigation of e.g. increased load of ammonium due to increased hydraulic load versus increased concentration, physical space in the filter material and its surface qualities. A safe operational windows in terms of load was identified during short term up-shifts in the ammonium load to the different columns. This showed the importance of the total load no matter the increase was due to hydraulic load or concentration. Based on the obtained insight the functionality of the filters could be optimized. Addition of limiting micronutrients such as phosphorous or cupper (which specifically stimulating nitrification since cupper is an essential metal in the ammonium mono oxygenase) was able to increase nitrification rate, to overcome incomplete nitrification with accumulation of nitrite, and to reduce the startup time of the microbial processes in new filters. This presentation provides an overview of a number of research projects on biological rapid sand filters conducted during the last 5 years.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".