Measurements and modeling of the inorganic chemical composition of fine particulate matter and associated precursor gases in California's San Joaquin Valley during CalNex 2010
Bibliographic record
Abstract
Abstract A modified Ambient Ion Monitor‐Ion Chromatograph was utilized to monitor the composition of water‐soluble fine particulate matter (PM2.5) and precursor gases at the Bakersfield, CA, supersite during Research at the Nexus of Air Quality and Climate Change (CalNex) in May and June of 2010. The observations were used to investigate inorganic gas/particle partitioning, to derive an empirical relationship between ammonia emissions and temperature, and to assess the performance of the Community Multiscale Air Quality (CMAQ) model. The water‐soluble PM2.5 maximized in the morning and in the evening because of gas/particle partitioning and possibly regional transport. Among the PM2.5 constituents, pNO3− was the dominant chemical species with campaign average mass loading of 0.80 µg m−3, and the mass loadings of pNH4+ and pSO42− were 0.46 µg m−3 and 0.53 µg m−3, respectively. The observed HNO3 (g) levels had an average of 0.14 ppb. Sub‐ppb levels of SO2 (g) were measured, consistent with the absence of major emission sources in the region. Measured NH3 (g) had an average of 19.7 ppb over the campaign and demonstrated a strong relationship with temperature. Observations of ammonia were used to derive an empirical enthalpy for volatilization of 30.8 ± 2.1 kJ mol−1. The gas/particle partitioning of semivolatile PM2.5 composition was driven by meteorological factors and limited by total nitrate (TN) in this region. CMAQ model output exhibited significant biases in the predicted concentrations of pSO42−, NH3 (g), and TN. The largest model bias was in HNO3 (g), with an overprediction of an order of magnitude, which may be due to missing HNO3 (g) sinks such as reactive uptake on dust in the CMAQ framework.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".