A modelling investigation of foliage pH and its impact on the dry deposition flux of SO2 and NO2
Bibliographic record
Abstract
The dry deposition of atmospheric pollutants (i.e., particulate matter, gases) has profound impacts on human and ecosystem health.  In regions of vegetation, a large fraction of particulate matter is deposited onto plant foliage, where it may undergo deliquescence to form a thin water layer on the leaf surface, altering the leaf surface pH.  Presently, gas-phase deposition algorithms used in air-quality models tend to ignore variations in the foliage pH and assume a constant neutral leaf of pH 6.68.  This constant pH is then used to determine the effective Henry’s law constants, which in turn influences the deposition velocity of atmospheric gases such as SO2 and NO2.  We use the GEM-MACH air-quality model to investigate and contrast the use of a neutral foliage pH versus a ‘dynamic’ foliage pH on the dry deposition of SO2 and NO2 in the Athabasca Oil Sands region.  In this work, the surface foliage pH in GEM-MACH is dynamically determined using HETP (Miller et al., 2023) by considering the accumulated deposition of anion and cation species to leaf surfaces (i.e., deposited precursor species such as sulfate, nitrate, ammonium, sodium, chloride, potassium, calcium and magnesium).  Processes such as precipitation, nutrient leeching, and epicuticular wax encapsulation are also considered in these simulations since these processes may impact the leaf surface chemistry after dry deposition has occurred. The results show that near the Athabasca Oil Sands sources, the large amount of base cation deposition has a profound impact on the predicted foliage surface pH where it often exceeds 7.0 (in contrast to the pH within the plant cells, for example).  The result of this elevated foliage surface pH is an increase in the dry deposition flux of SO2 and NO2 by a factor of 2 to 10 close to the sources, relative to using a foliage pH of 6.68.  Downwind of the sources, the foliage pH is often near neutral to moderately acidic, leading to decreased dry deposition of SO2 and NO2..  Together, these changes in pH result in an exponential decrease in the dry deposition fluxes with increasing distance from the Oil Sands sources.References Miller, S. J., Makar, P. A., and Lee, C. J.: HETerogeneous vectorized or Parallel (HETPv1.0): An updated inorganic heterogeneous chemistry solver for metastable state NH4+–Na+–Ca2+–K+–Mg2+–SO42––NO3––Cl– based on ISORROPIA II, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2023-159, in review, 2023.
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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) | 0.000 | 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 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".