Emission influences on air pollutant concentrations in New York State: I. ozone
Bibliographic record
Abstract
Relationships between regional air pollutant emissions and ambient concentration trends in New York State are studied. Large (∼50–85%) reductions in anthropogenic emissions occurred in the northeastern U.S. and eastern Canada between 1995 and 2015. Spatially and temporally aggregated ambient concentrations of O3 precursors declined steadily along with emission reductions: multi-site mean annual nitrogen dioxide (NO2), carbon monoxide (CO), and toluene concentrations tracked state and regional emissions of oxides of nitrogen (NOx, NO + NO2), CO, and volatile organic compounds (VOC), respectively (r2 = 0.97, 0.88, and 0.73), showing linear responses of average O3 precursor concentrations to both state and regional emission reductions. O3 exhibited complex spatial and temporal variations, indicating that O3 was influenced not only by the steady decline of regional emissions but also by subregional emission changes, chemical and physical processes, and day-specific variations in emissions and weather. The statewide multi-site mean annual 4th-highest daily 8-h O3 peak declined at the rate of 0.86 ± 0.14 ppbv y−1 (1.1% y−1). Influences on peak daily 8-h O3 were studied using a long-term data record from a research site at Pinnacle State Park (PSP), located near Addison, New York. A generalized additive model (GAM) was applied to separate the influences of local and regional emission changes, weather, and other factors. GAM results indicate that the reduction of summer O3 peak values and the overall trends in O3 at PSP were due to changes in ambient pollutant levels, rather than to trends in temperature or other meteorological factors. Whereas PSP summer O3 was NOx-sensitive, O3 was often VOC-sensitive during winter and spring days. The frequency of VOC-sensitive days decreased between 2001 – 2005 and 2012–2016, consistent with an expectation of increasing NOx sensitivity as ambient NOx concentrations decline.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".