Fine particle pH and gas-particle partitioning during winter fog in Delhi, India
Bibliographic record
Abstract
Comprehensive measurements were conducted to simultaneously monitor the trace gases (HCl, HONO, HNO3, SO2, and NH3) and inorganic chemical constituents (Cl-, NO3-, SO42-, Na+, NH4+, K+, Ca2+, and Mg2+) of fine particulates (PM1 and PM2.5) at hourly resolution during the Winter Fog Experiment (WIFEX) field campaign, Delhi, India, for the winter period of 2017-2018. The measurements were performed using the instrument called Monitor for AeRosols and Gases in Ambient air (MARGA-2S) to study the role of chemical composition and gas-particle interplay chemistry in the life cycle of fog, i.e., formation, development, and dissipation phase. In the past, the variation of fine particle acidity (pH) and its impact on fog has not been studied explicitly and quantitatively over Delhi. The pH is a fundamental property of aerosol that plays a significant role in the chemical behavior and composition of particles, but it is very challenging and difficult to measure directly. Particulate water is also a significant component of aerosol and can serve as a medium for aqueous-phase reactions under foggy conditions. The pH depends on the particle water amount, as pH represents the concentration of H+ per liquid water volume (i.e., particulate water). Whereas, H+ concentration per unit volume of air is defined as the particulate proton loading. Using the measured gas-phase and particle-phase concentrations and meteorological observations (T, RH), the particulate water and pH were estimated from the thermodynamic model ISORROPIA-II. In this study, the gas phase NH3, HNO3, and HCl and particle-phase NH4+, NO3-, Cl-, and SO42- species were estimated using ISORROPIA-II, and model predictions of these species were validated by using the measured gas and particle-phase species. The predictions were confirmed by a good agreement between predicted and measured ammonia concentrations (r=0.94) and aerosol species concentrations ammonium (r=0.97) chloride (r=0.61), nitrate (r=0.61), and sulfate (r=0.74). The predicted PM2.5 pH ranged from 2.55 to 6.54, with mean pH of 4.55 ± 0.51. This was consistent with the findings of previous studies. It is concluded that high particle water content, higher acidic pH, and abundant ammonia concentrations can promote the gas-particle partitioning and formation of more secondary particles under foggy conditions. The scattering cross-section of these secondary fine hygroscopic particles increases under high humidity conditions due to water uptake, resulting in visibility degradation.
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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 | 0.001 |
| 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".