Long-term monitoring of cloud water chemistry at Whiteface Mountain: the emergence of a new chemical regime
Bibliographic record
Abstract
Atmospheric aqueous chemistry can have profound effects on our environment. The importance of chemistry within the atmospheric aqueous phase started gaining widespread attention in the 1970s as there was growing concern over the negative impacts on ecosystem health from acid deposition. Research at mountaintop observatories including Whiteface Mountain (WFM) showed that gas phase sulfur dioxide emissions react in cloud droplets to form sulfuric acid, which also impacted air quality by increasing aerosol mass loadings. The current study updates the long-term trends in cloud water composition at WFM for the period 1994–2021, with special consideration given to samples that have traditionally been excluded from analysis due to inorganic charge imbalance. We emphasize three major findings: (1) a growing abundance of total organic carbon (TOC), with annual median concentrations more than doubling since measurements began in 2009, (2) a growing imbalance between the measured inorganic cations and anions, consistent with independent rain water observations, implying that a substantial fraction of anions are no longer being measured with the historical suite of measurements, and (3) a growing number of samples exhibiting greater ammonium concentrations than sulfate plus nitrate concentrations, which now routinely describes over one-third of samples. Organic acids are identified as the most likely candidates for the missing anions, since the measured inorganic ion imbalance correlates strongly with measured TOC concentrations. An “inferred cloud droplet pH” is introduced to estimate the pH of the vast majority of cloud droplets as they reside in the atmosphere using a simple method to account for the expected mixing state of calcium and magnesium containing particles. While the inferred cloud droplet pH closely matches the measured bulk cloud water pH during the early years of the cloud water monitoring program, a growing discrepancy is found over the latter half of the record. We interpret these observations as indicating a growing fraction of cloud droplet acidity that is no longer accounted for by the measured sulfate, nitrate and ammonium concentrations. Altogether, these observations indicate that the chemical system at WFM has shifted away from a system dominated by sulfate to a system controlled by base cations, reactive nitrogen species and organic compounds. Further research is required to understand the effects on air quality, climate and ecosystem health.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".