Chemical characterization and source identification of PM2.5 at Baengnyeongdo Island, South korea: Three-year dynamics (2019–2021)
Bibliographic record
Abstract
Fine particulate matter (PM 2.5 ) remains a critical air pollutant with substantial public health risks, particularly in East Asia, where domestic emissions and transboundary transport contribute to elevated concentrations. This study examined the annual and seasonal variations of PM 2.5 and its chemical constituents at Baengnyeongdo Island, South Korea, from 2019 to 2021. The constituents analyzed included carbonaceous components (organic carbon [OC] and elemental carbon [EC]), major inorganic ions (sulfate [SO 4 2− ] and nitrate [NO 3 − ]), crustal elements (e.g., silicon [Si], calcium [Ca], iron [Fe], titanium [Ti]), and various other metallic species. The study also sought to identify potential sources of PM 2.5 , with particular emphasis on transboundary influences. Results showed a significant increase in PM 2.5 levels in 2021 (spring mean: 32.657 µgm −3 ), attributed to the resumption of industrial activities following the COVID-19 lockdowns, specific meteorological conditions, such as higher spring relative humidity (74.91%) and increased aerosol water content (32.16 µgm −3 ), and significant transboundary pollution, particularly from China. Seasonal analysis indicated that OC, EC, NO3-, and crustal elements (Si, Ca, Fe, Ti) were the dominant contributors. For example, OC and EC peaked in spring and winter, which was associated with biomass burning, heating, and industrial emissions, which were enhanced by low winter temperatures. NO 3 − also exhibited significant winter peaks (5.921 µgm −3 in 2021), driven by conditions favoring NH 4 NO 3 formation, while SO 4 2− levels, highest in 2019 (4.357 µgm −3 ), displayed a more moderate trend. Meteorological parameters, including aerosol water content, relative humidity, temperature, and wind patterns, play a major role in PM 2.5 formation, accumulation, and dispersion. Back-trajectory modeling consistently confirmed air mass transport from the heavily industrialized regions of China, Mongolia, and Russia during high-pollution episodes across all seasons. These findings underscore the complex interplay between local emissions, transboundary transport, and meteorological factors, highlighting the urgent need for coordinated international air quality management policies.
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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 | 0.001 |
| 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 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".