Climatology and variability of smoke aerosols from MAIAC EPIC observations over North America (2016–2024)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study presents a comprehensive analysis of the monthly, seasonal, and interannual variability of smoke aerosol properties over North America from 2016 to 2024, using data retrieved from the MAIAC algorithm applied to NASA’s EPIC instrument aboard the DSCOVR spacecraft. The MAIAC EPIC data provide high-frequency, multi-year retrievals of key smoke properties, including aerosol optical depth (AOD), spectral absorption, aerosol layer height (ALH), and inferred black carbon (BC) and brown carbon (BrC) concentrations. The analysis reveals strong seasonal and regional variations, with peak smoke activity occurring in spring over Mexico and in summer over Canada and the western United States. Canadian and Alaskan smoke plumes frequently reach higher altitudes and exhibit elevated AOD, while smoke in Mexico tends to remain at lower altitudes with notably higher BC concentrations, likely influenced by smaller and lower-intensity fires and mixed biomass burning sources (agriculture and forest). The eastern United States, as a downwind region, shows increasing smoke influences, characterized by elevated ALH and rising levels of AOD and absorbing aerosols. Most study regions show a significant increase in smoke AOD (up to 5% per year in Canada), absorbing AOD, and BrC concentrations, highlighting the growing impact of wildfires on atmospheric composition and their potential implications for climate, air quality, and solar energy resources. These findings underscore the utility of MAIAC EPIC observations for monitoring multi-year smoke aerosol changes and for assessing their environmental consequences.
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.
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 | 0.001 |
| 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 it