Climate adaptation and resilience of biofiltration as a low-cost technological solution for water treatment – A critical review
Bibliographic record
Abstract
The water supply and sanitation sector has become vulnerable due to extreme weather events such as flooding, wildfires, and droughts. Following wildfires, the erosion of ashes and unburnt carbon into surface water bodies results in higher turbidity and total suspended solids in surface water, along with elevated concentrations of dissolved organic matter. This deterioration in water quality increases the difficulty of treating these waters for human consumption, highlighting the urgent need for adaptive water treatment methods. Amid these challenges, biofiltration has emerged as a sustainable, low-cost techno-ecological solution, recognized for its ability to enhance water quality while remaining environmentally friendly. The effectiveness of biofiltration stems from its utilization of microbial communities and natural processes, enabling it to adapt and recover from disruptions. Despite the threats posed by climate change, biofiltration systems have shown strong potential for resilience, although this resilience depends on a thorough understanding of the challenges brought on by climate change. This paper reviews the impacts of extreme weather events on water quality and the operations of water treatment plants. It highlights several conventional water treatment methods and discusses their insufficiencies in treating emerging contaminants. The mechanisms through which biofiltration removes contaminants as well as the key parameters that influence biofiltration such as biofilter media, types of microorganisms, temperature, pH, nutrients supply, etc and the dominant microbes present in biofilters were reviewed. The adaptation and resilience of biofiltration systems to challenges posed by climate change in water treatment was extensively discussed. The limitations and opportunities related to the adaptation and resilience of biofiltration were discussed, emphasizing the need for more proactive measures to optimize biofiltration systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".