Anthropogenic influences on the physical state of submicron particulatematter over a tropical forest
Bibliographic record
Abstract
Abstract. The occurrence of non-liquid and liquid physical states of submicron atmospheric particulate matter (PM) downwind of an urban region in central Amazonia was investigated. Measurements were conducted during two Intensive Operating Periods (IOP1 and IOP2) that took place during the wet and dry seasons, respectively, of the GoAmazon2014/5 campaign. Air masses representing variable influences of background conditions, urban pollution, and regional and continental scale biomass burning passed over the research site. As the air masses varied, particle rebound fraction, which is an indicator of the mix of physical states in a sampled particle population, was measured in real time at ground level using an impactor apparatus. Micrographs collected by transmission electron microscopy confirmed that liquid particles adhered while non-liquid particles rebounded. Relative humidity (RH) was scanned to collect rebound curves. When the apparatus RH matched ambient RH, 95 % of the particles were liquid as a campaign average, although this percentage dropped to as low as 60 % during periods of anthropogenic influence. Secondary organic material, produced for the most part by the oxidation of volatile organic compounds emitted from the forest, was the largest source of liquid PM. Analyses of the mass spectra of the atmospheric PM by positive-matrix factorization (PMF) and of concentrations of carbon monoxide, total particle number, and oxides of nitrogen were used to identify time periods affected by anthropogenic influences, including both urban pollution and biomass burning. The occurrence of non-liquid PM correlated with these indicators of anthropogenic influence. A linear model having as output the rebound fraction and as input the PMF factor loadings explained up to 70 % of the variance in the observed rebound fractions. Anthropogenic influences appear to favor non-liquid PM by providing molecular species that increase viscosity when internally mixed with background PM, by contributing non-liquid particles in external mixtures of PM, and a by combination of these effects under real-world conditions.
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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".