Fine particle pH and gas–particle phase partitioning of inorganic species in Pasadena, California, during the 2010 CalNex campaign
Bibliographic record
Abstract
Abstract. pH is a fundamental aerosol property that affects ambient particle concentration and composition, linking pH to all aerosol environmental impacts. Here, PM1 and PM2. 5 pH are calculated based on data from measurements during the California Research at the Nexus of Air Quality and Climate Change (CalNex) study from 15 May to 15 June 2010 in Pasadena, CA. Particle pH and water were predicted with the ISORROPIA-II thermodynamic model and validated by comparing predicted to measured gas–particle partitioning of inorganic nitrate, ammonium, and chloride. The study mean ± standard deviation PM1 pH was 1.9 ± 0.5 for the SO42−–NO3−–NH4+–HNO3–NH3 system. For PM2. 5, internal mixing of sea salt components (SO42−–NO3−–NH4+–Na+–Cl−–K+–HNO3–NH3–HCl system) raised the bulk pH to 2.7 ± 0.3 and improved predicted nitric acid partitioning with PM2. 5 components. The results show little effect of sea salt on PM1 pH, but significant effects on PM2. 5 pH. A mean PM1 pH of 1.9 at Pasadena was approximately one unit higher than what we have reported in the southeastern US, despite similar temperature, relative humidity, and sulfate ranges, and is due to higher total nitrate concentrations (nitric acid plus nitrate) relative to sulfate, a situation where particle water is affected by semi-volatile nitrate concentrations. Under these conditions nitric acid partitioning can further promote nitrate formation by increasing aerosol water, which raises pH by dilution, further increasing nitric acid partitioning and resulting in a significant increase in fine particle nitrate and pH. This study provides insights into the complex interactions between particle pH and nitrate in a summertime coastal environment and a contrast to recently reported pH in the eastern US in summer and winter and the eastern Mediterranean. All studies have consistently found highly acidic PM1 with pH generally below 3.
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 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".