Characterization and optimization of an online system for the simultaneous measurement of atmospheric water-soluble constituents in the gas and particle phases
Bibliographic record
Abstract
In this work we present the results of extensive characterization and optimization of the Ambient Ion Monitor-Ion Chromatograph (AIM-IC) system, an instrument developed by URG Corp. and Dionex Inc. for simultaneous hourly measurements of the water-soluble chemical composition of atmospheric fine particulate matter (PM(2.5)) and associated precursor gases. The sampling assembly of the AIM-IC consists of an inertial particle size-selection assembly, a parallel-plate wet denuder (PPWD) for the collection of soluble gases, and a particle supersaturation chamber (PSSC) for collection of particles, in series. The analytical assembly of the AIM-IC consists of anion and cation IC units. The system detection limits were determined to be 41 ppt, 5 ppt, and 65 ppt for gas phase NH(3(g)), SO(2(g)), and HNO(3(g)) and 29 ng m(-3), 3 ng m(-3), and 45 ng m(-3) for particle phase NH(4)(+), SO(4)(2-), and NO(3)(-) respectively. From external trace gas calibrations with permeation sources, we determined that the AIM-IC is biased low for NH(3(g)) (11%), SO(2(g)) (19%), and HNO(3(g)) (12%). The collection efficiency of SO(2(g)) was found to strongly depend on the composition of the denuder solution and was found to be the most quantitative with 5 mM H(2)O(2) solution for mixing ratios as high as 107 ppb. Using a cellulose membrane in the PPWD, the system responded to changes in SO(2(g)) and HNO(3(g)) within an hour, however for NH(3(g)), the timescale can be closer to 20 h. With a nylon membrane, the instrument response time for NH(3(g)) was significantly improved, becoming comparable to the responses for SO(2(g)) and HNO(3(g)). Performance of the AIM-IC for collection and analysis of PM(2.5) was evaluated by generating known number concentrations of ammonium sulfate and ammonium nitrate particles (with an aerodynamic diameter of 300 nm) under laboratory conditions and by comparing AIM-IC measurements to measurements from a collocated Aerosol Mass Spectrometer (AMS) during a field-sampling campaign. On average, the AIM-IC and AMS measurements agreed well and captured rapid ambient concentration changes at the same time. In this work we also present a novel inlet configuration and plumbing for the AIM-IC which minimizes sampling inlet losses, reduces peak smearing due to sample carryover, and allows for tower-height sampling from the base of a research tower.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".