Multipollutant Air Quality Management
Bibliographic record
Abstract
The 40th annual A&WMA critical review 1,2 by Hidy and Pennell provided a conceptual framework for multipollutant air quality management (AQM) and the assessment of accountability.3 The critical review 1 summarized and elaborated on the NARSTO 4 integrated assessment 5 dealing with the same topics, proposing that a multipollutant risk analysis methodology be developed that would relate multiple concentrations to multiple effects, with a primary emphasis on adverse health effects.Although straightforward in theory, the critical review 1 identified substantial deficiencies in current knowledge to fully implement such an approach.It noted the emphasis on criteria pollutants, at the expense of other hazardous air pollutants (HAPs), 6 in current AQM.It also recognized the limitations of current monitoring networks, which focus on compliance with criteria pollutant standards, 7,8 for determining exposures and relationships of these exposures with adverse effects.This discussion was compiled from written submissions and presentation transcripts that were revised for conciseness and to minimize redundancy.Substantial deviations from the intent of a discussant are unintentional and can be addressed in a follow-up letter to the journal.Invited discussants are as follows:• John D. Bachmann, Primary for Vision Air Consulting, where he advises on environmental policy issues.He was formerly Associate Director for Science/Policy and New Program Initiatives at the U.S. Environmental Protection Agency (EPA)'s Office of Air Quality Planning and Standards (OAQPS), where he worked since 1974 developing and applying the U.S. National Ambient Air Quality Standards (NAAQS) review process.He authored the 2007 critical review on AQM. 9 • Mr. John D. Kinsman, Senior Director of Environment at the Edison Electric Institute, where he has been employed for more than 20 years.His work focuses on air quality regulation and legislation relevant to U.S. shareholder-owned electric companies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".