Modeled Impacts of Drinking Water Pb Reduction Scenarios on Children’s Exposures and Blood Lead Levels
Bibliographic record
Abstract
In recent years, environmental lead (Pb) exposure through drinking water has resulted in community public health concerns. To understand potential impacts on blood Pb levels (BLLs) from drinking water Pb reduction actions (i.e., combinations of lead service lines [LSL] and corrosion control treatment [CCT] scenarios), EPA's Stochastic Human Exposure and Dose Simulation (SHEDS)-Multimedia/Integrated Exposure Uptake and Biokinetic (IEUBK) model was applied for U.S. children aged 0 to <6 years. The results utilizing a large drinking water sequential sampling data set from 15 cities to estimate model input concentration distributions demonstrated lowest predicted BLLs for the "no LSLs" with "combined CCT" scenario and highest predicted BLLs for the "yes LSLs" and "no CCT" scenario. Modeled contribution to BLLs from ingestion of residential drinking water ranged from ∼10 to 80%, with the highest estimated for formula-fed infants (age 0 to <1 year). Further analysis using a "bounding" data set spanning a range of realistic water Pb concentrations and variabilities showed BLL predictions consistent with the sequential sampling-derived inputs. Our study illustrates (1) effectiveness of LSL replacement coupled with CCT for reducing Pb in drinking water and children's BLLs, and (2) in some age groups, under realistic local and residential water use conditions, drinking water can be the dominant exposure pathway.
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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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".