Ртуть в атмосферном воздухе и осадках в 2022–2023 гг. на станции мониторинга Листвянка (Южное Прибайкалье)
Bibliographic record
Abstract
Газообразная элементарная ртуть (GEM) является преобладающей формой ртути в атмосфере. В результате осаждения она поступает в наземные и водные экосистемы, где в дальнейшем преобразуется в экотоксикант – метилртуть. Работа посвящена изучению GEM в атмосферном воздухе и общей ртути в атмосферных осадках в Южном Прибайкалье. Отбор проб проводился на станции мониторинга Листвянка (51,9° с.ш., 104,4° в.д.) в 2022–2023 гг. Концентрация ртути в воздухе измерялась газоанализатором РА-915АМ (Санкт-Петербург, Россия). Концентрация общей ртути в атмосферных осадках определялась по методике ПНД Ф 14.1:2:4.271-2012, метод А (перманганатная минерализация). За период исследования концентрация GEM в атмосферном воздухе составила в среднем 1,61 нг/м3; коэффициент парной корреляции между Hg0и SО2– 0,47; между Hg0и NО2 – 0,44; в 12 случаях отмечена сильная положительная корреляция (коэффициент > 0,9) между Hg0, SО2и NО2. Средневзвешенное содержание общей ртути в атмосферных осадках составило 44 нг/л, медиана – 29 нг/л, максимальное значение – 282 нг/л. Для каждого эпизода повышения концентрации ртути свыше 2,0 нг/м3рассчитаны обратные траектории движения воздушных масс с помощью модели HYSPLIT. Траекторный анализ также подтвердил наше предположение об едином типе источника ртути и малых газовых примесей. Дополнены существующие представления о содержании ртути в атмосфере Южного Прибайкалья. Установлено, что несмотря на значительное удаление от крупных городов, содержание ртути в атмосферных осадках на побережье оз. Байкал сопоставимо с результатами, полученными в городских агломерациях Непала, Канады, Кореи, Китая. Gaseous elemental mercury (GEM) is the predominant form of mercury in the atmosphere. As a result of deposition, it enters terrestrial and aquatic ecosystems, where it is further transformed into the ecotoxicant methylmercury. The work is devoted to the study of gaseous elemental mercury (GEM) in atmospheric air and total mercury in atmospheric precipitation in the Southern Baikal region. Sampling was carried out at the monitoring station Listvyanka (51.9° N, 104.4° E) in 2022–2023. Mercury concentration in air was measured by mercury gas analyzer RA-915AM (St. Petersburg, Russia). The concentration of total mercury in precipitation was determined by PND F 14.1:2:4.271-2012, method A (permanganate mineralization). Statistical analysis of data on mercury content in atmospheric air and precipitation is performed. During the period under study, the concentration of GEM in atmospheric air averaged 1.61 ng/m3. The analysis showed that the pair correlation coefficient throughout the period under study was 0.47 between Hg0and sulfur dioxide (SO2) and 0.44 between Hg0and nitrogen dioxide (NO2). In 12 cases, a strong positive correlation (> 0.9) between Hg0, SO2, and NO2was observed. For each episode of mercury concentration above 2.0 ng/m3, back trajectories of air masses were calculated using the HYSPLIT model. The trajectory analysis also confirmed our assumption of a common type of source for mercury and minor gas impurities. The weighted average content of total mercury in precipitation is 44 ng/L, the median value is 29 ng/L, and the maximum is 282 ng/L. We have supplemented the existing ideas about mercury content in the atmosphere of the Southern Baikal region. It was found that despite the significant distance from large cities, the mercury content in atmospheric precipitation on the shores of Lake Baikal is comparable to the results obtained in urban agglomerations of Nepal, Canada, Korea, and China.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".