Spatial and Temporal Variability of Perfluoroalkyl and Polyfluoroalkyl Substances in Major Rivers of New Mexico, USA
Bibliographic record
Abstract
Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are ubiquitous in the environment, but sources that contribute to temporal and spatial variability in surface waters are not well defined. Many states are assessing PFAS in water resources, and insight from these statewide assessments can help guide future sampling efforts. A statewide assessment of 28 PFAS was conducted in New Mexico starting in 2020, and subsequent follow-up sampling has improved understanding of PFAS occurrence and sources throughout the state. PFAS were present in all major rivers of New Mexico (Rio Grande, Pecos River, San Juan River, Animas River, Canadian River, Gila River, Rio Chama, and Rio Puerco) with 13 of 28 analyzed PFAS (PFBA, PFPeA, PFHxA, PFHpA, PFOA, PFNA, PFDA, PFBs, PFPeS, PFHxS, PFOS, PFOSA, 6:2 FTS) detected from at least one sample for samples collected between 2020 and 2024. This study found high temporal and spatial variability-PFAS concentrations ranged from below the laboratory detection level to 156 ng/L, with concentrations generally increasing downstream on the major rivers. PFBS was the most frequently detected and highest concentration PFAS in this study, ranging from 1 to 93 ng/L, followed by PFBA and PFPeA, ranging from 0.9 to 32 ng/L. The average of the sum of PFAS detected increased by an order of magnitude from 4 to 46 ng/L in the Rio Grande as it flows through Albuquerque, the largest urban area in New Mexico. PFAS concentration increased by 58% after a stormflow pulse flushed over Albuquerque and contributed water to the Rio Grande. The contribution of wastewater to surface water resources varied diurnally as well as seasonally. Sampling multiple locations on major rivers across multiple seasons, taking into account known anthropogenic inputs, would enhance characterization of temporal and spatial variability of PFAS concentrations. Increased sampling frequency at sites with wastewater contribution and focused investigations in areas with higher than expected PFAS could increase understanding of potential sources and variability of source contributions.
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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 | 0.002 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".