Studying pathogen attenuation and transport in freshwater systems using novel surrogate technology
Bibliographic record
Abstract
By mimicking the physicochemical properties of important waterborne pathogens, we believe that synthetic particles can be used to predict water contamination risks in freshwaters and help to design improved water treatment systems and water-supply bore protections.We have developed two generations of synthetic pathogen surrogates for water quality applications. The first-generation was based on biomolecule-modifications of commercially available microspheres and nanoparticles to produce surrogates for the pathogens Cryptosporidium, rotavirus and adenovirus. The second-generation is based on biomolecule-modifications of microparticles and nanoparticles that we have made from food-grade natural biopolymers to produce surrogates for the pathogens Legionella, Cryptosporidium and rotavirus.The surrogates have closely mimicked the physicochemical properties (e.g., size, shape, surface charge, hydrophobicity) of the target pathogens. Experiments conducted have validated surrogates’ performance against the actual pathogens in different systems; the surrogates displayed the same order of magnitude removal as the target pathogens in different experimental conditions.The first-generation Cryptosporidium surrogates were used in pilot-scale studies to evaluate the efficacies of protozoan removal by drinking-water filtration systems commonly used in New Zealand under typical operating conditions. These included testing rapid sand filtration systems at a water treatment plant in collaboration with the Invercargill City Council and domestic point-of-use water filters in a domestic plumbing test rig. The experimental findings were incorporated into quantitative microbial risk assessments. Health-risk scenarios were identified and recommendations for improving water treatment performance were communicated to end-users. Our experimental results have also highlighted that turbidity, a key test of water clarity and a proxy for water quality used by water plant operators, may not be a reliable indicator of protozoan removal.Recently, we have advanced our pathogen surrogate technology by producing and testing a second-generation of surrogates that are more compatible with use in natural water systems. These surrogates, made from food-grade natural biopolymers, can be applied in operational water treatment systems and eco-sensitive freshwater environments. Our preliminary studies suggest that these new pathogen surrogates show great promise as new tools for water applications. We will conduct further validations.We can label the surrogates with unique synthetic DNA sequences for tracking and detection purposes. Degradation of the surrogates’ DNA was found to mimic pathogen’s DNA degradation to some degree. The DNA-tagged surrogates, even at very low concentrations, can be analysed sensitively and rapidly using qPCR. Working with ECAN and Waikato Regional Council, we have validated DNA encapsulated biopolymer particles (as pollution source tracers) in surface water, groundwater and soils, and they were readily trackable in a surface stream for at least 1 km.The surrogate technology approach has opened a new avenue for assessing pathogen removal and transport in water systems without the risk and expense that accompany work with actual pathogens. The research findings will facilitate improved management systems and engineering approaches to reduce waterborne infection risks and safeguard public health.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".