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Record W3167111050 · doi:10.5194/egusphere-egu21-14140

Fine particle pH and gas-particle partitioning during winter fog in Delhi, India

2021· article· en· W3167111050 on OpenAlexaff
Prodip Acharja, Sachin D. Ghude, Kaushar Ali, Ismail Gültepe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsChemistry

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.196
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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