PM10-bound trace elements in the Great Lakes Basin (1988–2017) indicates effectiveness of regulatory actions, variations in sources and reduction in human health risks
Bibliographic record
Abstract
Limited studies focus on the effectiveness of regulatory actions on changes in sources and temporal trends of human health risks for trace elements in atmospheric particles < 10 μm (PM10). To address this knowledge gap, PM10 samples were collected at three stations in the Great Lakes Basin over a thirty-year time span and analyzed for 19 representative elements. Temporal trends of trace elements in PM10 were derived using the Digital Filtration Technique and sources of these elements were determined using multiple statistical techniques, namely enrichment factor analysis, positive matrix factorization (PMF) and potential source contribution function (PSCF). Non-carcinogenic and carcinogenic risks by chronic exposure were assessed using US EPA reference concentrations and inhalation unit risk. Our results showed a strong relationship between element concentrations and local populations, which suggested that the emissions of trace elements were anthropogenically-related and was confirmed by the enrichment factor analysis. The concentrations of most elements were significantly decreasing with halving times ranging from 10 to 48 years in response to national and international regulatory actions. Specific origins of atmospheric trace elements were from the copper refining industry, refuse incineration, coal combustion, vehicle emissions, oil/coal-fired power plants, and crustal/soil dust. Potential source region analysis indicates dominant sources south of the sampling sites in the US, associated with a higher population and more industrial and transportation activities. The possibility of non-cancer health effects due to inhalation were mostly within acceptable levels. However, potential cancer risk posed by inhalation of some elements cannot be ignored, with values approaching or higher than the acceptable level. Considering that the sampling locations are remote and regionally-representative, our finding emphasizes the importance of continued monitoring of metals in air to assess the effectiveness of control strategies.
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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 | 0.001 | 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".