Cost Effective Off-Grid Automatic Precipitation Samplers for Pollutant and Biogeochemical Atmospheric Deposition
Bibliographic record
Abstract
Abstract. An important transport process for particles and gases from the atmosphere to aquatic and terrestrial environments is through dry and wet deposition. An open-source, modular, off-grid, and affordable instrument that can automatically collect wet deposition samples allows for more extensive deployment of deposition samplers in fieldwork and would enable more comprehensive monitoring of remote locations. Precipitation events selectively sampled using a conductivity sensor powered by a battery-based supply are central to off-grid capabilities. The prevalence of conductive precipitation, initially containing high solute levels and progressing through trace level concentrations to ultrapure water in full atmospheric washout, depends on the sampling location but is ubiquitous. This property is exploited here to trigger an electric motor via limit switches to open and close a lid resting over a funnel opening. The motors are operated via a custom-built and modular digital logic control board, which have low energy demands. All components, their design and rationale, and assembly are provided for community use. The modularity of the control board allows operation of up to six independent wet deposition units, such that replicate measurements (e.g., canopy throughfall) or different collection materials for various targeted pollutants can be implemented as necessary. We demonstrate that these platforms are capable of continuous operation off-grid for integrated monthly and bimonthly collections performed across the Newfoundland and Labrador Boreal Ecosystem Latitudinal Transect (47° to 53° N) during the growing seasons of 2015 and 2016. System performance was assessed through measured power consumption from 115 volts of alternating current (VAC; grid power) or 12 volts of direct current from battery supplies during operation under both standby (40 or 230 mA, respectively) and in-use (78 or 300 mA, respectively) conditions. In the field, one set of triplicate samplers was deployed in the open to collect incident precipitation (open fall) while another set was deployed under the experimental forest canopy (throughfall). The proof-of-concept systems were validated with basic measurements of rainwater chemistry including: i) pH ranging from 4.14 to 5.71 in incident open fall rainwater; ii) conductivity ranging from 21 to 166 uS/cm; and iii) dissolved organic carbon (DOC) concentrations in open fall and canopy throughfall of 16 ± 10 mg/L and 22 ± 12 mg/L, respectively; with incident fluxes spanning 600 to 4200 mg C m-2 a-1 across the transect. Ultimately, this demonstrates that the customized precipitation sampling design of this new platform enables more universal accessibility of deposition samples to the atmospheric observation community – for example, those who have made community calls for targeting biogeochemical budgets and/or contaminants of emerging concern in sensitive and remote regions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".