Passive sampling to understand and predict sources of wastewater and agricultural contamination in rural watersheds
Bibliographic record
Abstract
Passive sampling techniques have emerged as valuable tools in environmental monitoring, offering distinct advantages over traditional discrete sampling methods. Discrete sampling techniques are only representative of the time of sampling, often over or underestimating concentrations of constituents in flowing surface waters. Passive samplers contain a collecting medium which accumulates and concentrates constituents to a higher level of detection, and reduces variability associated with discrete sampling. To identify and differentiate non-point sources in rural catchments, this study used passive samplers, including the diffusive gradient in thin-films (DGT) and polar organic chemical integrative sampler (POCIS), along with discrete sampling to detect a range of anthropogenic and agricultural tracers and nutrients. \nPassive samplers were deployed for select compounds including nutrients (PO4-P, NO3-N, NH4-N), anthropogenic tracers (caffeine, carbamazepine, gemfibrozil, ibuprofen, naproxen, sulfamethoxazole, acesulfame potassium, sucralose), and agricultural tracers (glyphosate, aminomethylphosphonic acid (AMPA)). Among the passive samplers deployed, the Metsorb®-DGT, selective for phosphate-bearing compounds, was evaluated for competition impacts between PO4-P and glyphosate. Competition between analytes can occur for sorbents selective for a range of compounds with similar properties. A new sampler using a weak anion exchange (WAX) resin was developed for the uptake of artificial sweeteners acesulfame potassium (ACE-K) and sucralose (SUC). \nPassive samplers were deployed at three distinct locations (Hamlet, Agricultural, and Mixed-land Sites), for 3 to 14 days, while discrete samples were collected every 3 days over 2 weeks in Fall of 2021 and Spring of 2022. The effect of potentially competing ions between phosphate-bearing compounds was evaluated by deploying triplicate Metsorb®-DGT sampler in a solution of high PO4-P and low glyphosate and AMPA. Strata-DGT samplers were developed as a potential sampler for the uptake of ACE-K and SUC and deployed in the Spring 2022 sampling event. The goals of this thesis were to (1) use DGT and POCIS to delineate anthropogenic contamination from agricultural activities to surface waters in rural communities, and to compare their effectiveness to discrete samples and (2) further develop and evaluate DGT samplers. \nDGT and POCIS had comparable concentrations with 64% of compounds not statistically different (ρ > 0.05) from discrete samples. Diffusive boundary layer (DBL) corrections were applied to Spring 2022 DGT samples, increasing the mean absolute error between DGT concentrations (CDGT) and discrete concentrations (CACTIVE) by 32% for all compounds. Phosphate was elevated at the Hamlet Site (74 ± 8.2 µg L-1) in discrete samples compared to the Agriculture Site during the Fall 2021 sampling season. The Hamlet Site land use was 40% urban and 60% agriculture, while the Agriculture and Mixed-land Sites were 94% and 88% agricultural land use, respectively. Tracers consistently showed elevated concentrations of ACE-K (412 ± 202 µg L-1) and SUC (17,089 ± 5370 µg L-1) in discrete samples across both sampling seasons as well as all other anthropogenic tracers at the Hamlet Site, while glyphosate and AMPA were elevated at the Agriculture Site (0.86 ± 1.66 µg L-1 and 1.41 ± 0.85 µg L-1, respectively). Principal component analysis (PCA) and correlation coefficients indicated a positive relationship between phosphate and anthropogenic tracers at the Hamlet Site. Biological markers (Human-specific Bacteroides), specific to human fecal contamination, were also elevated at the Hamlet Site compared to the other sites. The results of this study clearly show septic system contamination in a headwater stream and suggest impacts of septic systems to these surface water bodies may be an underappreciated nonpoint source of nutrients. \nThere was no apparent competition on the Metsorb®-DGT sampler at initial concentrations of 104 µg L-1 of PO4-P, 0.372 µg L-1 of glyphosate, and 2.14 µg L-1 of AMPA as uptake was mostly linear after 6 days of deployment. Strata X-AW resin was not a suitable binding medium for artificial sweeteners, resulting in concentrations only 39% of discrete samples for ACE-K at the Hamlet Site. \nOverall, an array of passive samplers was helpful to distinguish nonpoint sources of septic system from agricultural contamination in rural head-water streams. Passive samplers generally showed comparable results to discrete samples, with the exception of ACE-K, and reduced the variability and additional sampling and analysis associated with discrete sample collection.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".