Cyanotoxins in agricultural watersheds and their quantification in the soil-plant system
Notice bibliographique
Résumé
Cyanobacteria and their cyanotoxins are present in many regions and ecosystems worldwide. Eutrophic freshwater lakes that receive regular inputs of nutrient-rich runoff from agricultural land are an ideal environment for cyanobacteria blooms, which are associated with cyanotoxin production, but cyanotoxins could also be present in, or originate from, the soils, groundwater and the vadose zone of agricultural watersheds. Humans will be exposed to these toxins when they consume cyanobacteria-contaminated groundwater and crops grown in contaminated soils. However, we do not understand fully how agricultural nutrient loading may trigger cyanotoxin production and the occurrence of cyanotoxins in ecosystem compartments besides eutrophic waterbodies. One of the major challenges is to develop quantitative methods that can detect the biologically significant cyanotoxins at low concentrations, and another challenge is to sensitize the agricultural community to the risk of cyanotoxins to agricultural plants, which has consequences for the health of animals and humans that consume those plants. Therefore, my thesis aimed to address these knowledge gaps. First, I completed a critical review, proposing that nutrient loading from agricultural runoff may trigger beta-N-methylamino-L-alanine production in cyanobacterial blooms, based on evidence from Lake Winnipeg, Canada. I provide evidence that allochthonous agricultural nutrients may trigger beta-N-methylamino-L-alanine production by influencing the composition of potentially toxic cyanobacteria species and the nitrogen availability. Then, I confirmed that cyanotoxins are present in ecosystem compartments beside eutrophic waterbodies, based on the detection of microcystins in agricultural soils and subsurface water (drainage water, well water and municipal drinking water) in agricultural watersheds in Quebec, Canada. However, the semi-quantitative method in this study was not conclusive and led me to develop a method to extract and quantify the cyanotoxins in soil. My new method can efficiently detect 15 cyanotoxins in soils. Finally, I evaluated the phytotoxicity of microcystins in soil on agricultural plants and determined the human health risk from consuming microcystins-contaminated plants. Based on my meta-analysis, microcystins are potentially most phytotoxic to potato but leafy vegetables such as dill, parsley and cabbage could bioconcentrate ~3 times more microcystins than other agricultural plants. Consuming leafy vegetables containing microcystins could be risky to adults and children because the estimated daily intake values (> 0.2 µg kg-1d-1) exceed the WHO guidelines (0.04 µg kg-1d-1). In conclusion, my research showed that cyanotoxins are detectable in terrestrial and subterranean environments where they are seldom studied, and reveals a public health risk associated with microcystins in the edible components of agricultural plants. My findings support the One Health approach to manage the risk of public exposure to toxic substances, and indicate that cyanotoxins warrant investigation in a greater number of environments than are monitored at present
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».