Multi-Year Methane and Carbon Dioxide Concentrations from Mobile Surveys in Alberta, Canada (2021–2024): A Greenhouse Gas Emission Dataset
Bibliographic record
Abstract
This dataset contains Greenhouse Gas (GHG) emission mixing ratios collected during mobile surveys conducted by FluxLab between November 5, 2021, and May 20, 2024. Measurements were taken from various regions in Alberta, Canada, using offsite transect techniques from public roads. The dataset includes time series data for downwind offsite transects, featuring methane (CH₄) and carbon dioxide (CO₂) concentrations, wind speed and direction, and geographical locations (WGS84) of the survey vehicle. These measurements span four consecutive years, from 2021 to 2024. This project was funded by the Natural Sciences and Engineering Research Council (NSERC) and various other initiatives, with the aim of providing insights into emission sources, particularly from oil and gas infrastructures in Alberta, Canada. Instrumentation and Methodology • 2021 Campaign: During the 2021 campaign, a truck was equipped with a Picarro G2210i cavity ring-down spectrometer (in concentration mode) to measure CH₄ and CO₂. The gas inlet was mounted on a mast at a height of 2.01 meters above the truck roof. Additional equipment included a digital compass and a high-accuracy GPS unit. • 2022–2024 Campaigns: In subsequent campaigns, the Picarro G2210i was replaced with either a Los Gatos Research UGGA gas analyzer (LGR_UGGA) or a Los Gatos Research -ICOS (MicroLGR_GGA) capable of measuring CH₄ and CO₂ at 1–2 Hz. The mast height was increased to 2.5–2.7 meters and equipped with additional sensors, including a Gill ultrasonic anemometer for wind speed and direction, a digital compass, and a high-accuracy GPS unit. Calibration and Quality Control To maintain accurate positioning, GPS calibration was conducted daily by driving the vehicle in all four cardinal directions—north, south, east, and west. This process helped evaluate GPS precision and make necessary adjustments to ensure reliable positional data. All sensor readings were recorded using a CR1000X Campbell Scientific data logger. To ensure the accuracy of gas measurements, benchmarking gases were used to assess the performance of the analyzers. These evaluations were carried out both in the laboratory, before and after the campaigns, using traceable gases, and daily during the campaigns with compressed air cylinders of known concentrations. Additionally, breath tests were performed at the gas inlet before each survey to identify and correct time delays introduced by the pump, tubing, or analyzer. Sampling Strategy Sampling was conducted by driving slowly (approximately 15–20 km/h) from ambient conditions toward areas with elevated CH₄ concentrations and then returning to ambient conditions to sample the full crosswind section of the plume. Public road access often determined the number and location of the offsite transects. This dataset provides valuable insights into GHG emissions, particularly CH₄ and CO₂, over multiple years, offering a robust resource for understanding emissions from Alberta's oil and gas infrastructure.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".