Cyanotoxins in agricultural watersheds and their quantification in the soil-plant system
Bibliographic record
Abstract
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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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".