Studying pathogen attenuation and transport in freshwater systems using novel surrogate technology
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».