Monitoring contaminants of emerging concern in aquatic systems through the lens of citizen science
Bibliographic record
Abstract
Global urbanization trends have led to the widespread increasing occurrence of contaminants of emerging concern (CECs) such as pharmaceuticals, personal care products, pesticides, and micro- and nano-plastics in aquatic systems. Even at low concentrations, these contaminants pose a threat to aquatic ecosystems. To better understand the effects of CECs on aquatic ecosystems, it is important to measure concentrations of these contaminants present in these systems. Currently, there is an imbalance in CEC monitoring, with more attention to some categories of CECs, and a lack of data about environmental concentrations of other types of CECs. Citizen science is a potential tool for improving CEC monitoring and to establish their environmental concentrations. However, incorporating citizen participation in the monitoring of CECs poses some challenges and questions. In this literature review, we explore the landscape of citizen science and community science projects which monitor different groups of CECs in freshwater and marine ecosystems. We also identify the benefits and drawbacks of using citizen science to monitor CECs to provide recommendations for sampling and analytical methods. Our results highlight an existing disparity in frequency of monitoring different groups of CECs with implementing citizen science. Specifically, volunteer participation in microplastic monitoring programs is higher than volunteer participation in pharmaceutical, pesticide, and personal care product programs. These differences, however, do not necessarily imply that fewer sampling and analytical methods are available. Finally, our proposed roadmap provides guidance on which methods can be used to improve monitoring of all groups of CECs through citizen science.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".