Paper Push: 2026-06-19
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每日论文推送：BGC-Argo、海色/海洋光学、海洋热浪与碳泵Daily Paper Push: BGC-Argo, ocean colour/ocean optics, marine heatwaves and carbon pump
本期由 GitHub Actions 自动检索生成：Nature/Science 系列优先，其次是用户指定重点期刊，再补充重点关注团队的新论文，最后纳入其他相关期刊；历史去重后保留 4 篇，不超过每日 50 篇上限。 This issue was generated automatically by GitHub Actions: Nature and Science series first, then the user-defined priority journals, then new papers from the focused team, followed by other relevant journals as topical supplements. After deduplication, 4 papers remain, below the daily limit of 50.
Download Word summary
无 mechanism sketch 图。今天的意大利语卡片： No mechanism sketch figure today. Daily Italian card:
每日一句意大利语Daily Italian
Per me si va ne la città dolente.
Dante, Commedia, Inferno III, 1; Italian original from Kalliope
这是地狱门铭的开头，意为“由我进入痛苦之城”。它比后面的“放弃希望”更像一段严峻旅程的入口。
This is the opening of the inscription over Hell's gate: through me one enters the city of sorrow. It marks the threshold of a hard journey.
趋势总结Trend Summary
本期重点关注 BGC-Argo、海色遥感/海洋光学、海洋热浪、浮游植物垂向结构和碳泵过程。筛选逻辑不再只限于重点期刊；当高影响力期刊当天新增较少时，会额外检索重点关注团队作者的新论文，并用海洋、海色/光学和碳循环关键词过滤，再从其他相关期刊补充候选论文。
This issue focuses on BGC-Argo, ocean-colour remote sensing, ocean optics, marine heatwaves, vertical phytoplankton structure and carbon-pump processes. The selection is no longer limited to priority journals; when few high-impact papers are newly available, the workflow also checks focused-team authors and filters those papers with ocean, ocean-colour/optics, and carbon-cycle keywords before adding other relevant journals as supplements.
重点关注团队Focused team
1. Hotspots of Arctic and sub-Arctic marine sediment organic carbon are dominated by the Baltic, Barents and Chukchi Seas
作者Authors: B. Langley; H. L. Burdett; K. Cameron; T. Juul-Pedersen; A. Rouillard; C. Slaymark; N. A. Kamenos
发表月份Publication month: 2026-06 2026-06
Communications Earth & Environment · DOI: 10.1038/s43247-026-03720-8
关键词Tags: ocean biogeochemistry ocean biogeochemistry
摘要：北极和亚北极地区的变暖速度至少是全球平均水平的三倍，改变了陆地碳向海洋的输送和海洋碳循环。将此类碳封存在海洋沉积物中是气候调节的关键因素。尽管如此，人们对高纬度地区有机碳热点的位置知之甚少，这阻碍了我们识别其对环境变化敏感性的能力。利用包含超过 13,000 个沉积物样本的分位数回归森林，我们将波罗的海、巴伦支海和楚科奇海确定为主要的高纬度海洋有机碳热点，在面积归一化的全球沉积碳储量中发挥着不成比例的重要作用。浅层陆架环境和海岸线的有机碳积累率升高，特别是在北极河流附近。 有机碳热点的发展反映了本地和外部过程，包括盐度、混合层深度、初级生产和沉积，证明了陆地-海洋耦合过程的重要性。因此，有机碳循环和运输途径（包括河流运输）未来变化的不确定性是海洋沉积物碳储存稳定性的一个新兴风险因素。更重要的是，目前只有 10. 19% 的表层沉积物有机碳储量位于海洋保护区内，这使得 >17 Pg 面临更高的人为干扰风险。
Abstract: The Arctic and sub-Arctic are warming at least three times faster than the global average, altering terrestrial carbon delivery to the oceans and marine carbon cycling. The sequestration of such carbon into marine sediments is a key contributor to climate regulation. Despite this, the location of organic carbon hotspots at high latitudes are poorly understood, hindering our ability to identify their sensitivity to environmental change. Using quantile regression forests with >13,000 sediment samples, we identify the Baltic, Barents and Chukchi Seas as the dominant high-latitude marine organic carbon hotspots, playing a disproportionately large role in the area-normalised global sedimentary carbon stock. Organic carbon accumulation rates are elevated across shallow shelf environments and coastlines, particularly in proximity to Arctic rivers. Development of organic carbon hotspots reflects both local and external processes, including salinity, mixed layer depth, primary production and sedimentation, demonstrating the importance of coupled land-ocean processes. Uncertainty in future changes to organic carbon cycling and transit pathways, including river transport, is therefore an emerging risk factor for the stability of marine sediment carbon stores. Compounding this, only 10.19% of the surface sediment organic carbon stock is currently within marine protected areas, placing >17 Pg at higher risk of anthropogenic disturbance.
2. Biotransformation of Microcystin-LR in Marine Sediments: Mechanism and Global Potential
作者Authors: Arbaz Rehman; Xiangzhi Wang; Mariam Yousaf; Jing Wang; Zelong Li
发表月份Publication month: 2026-06 2026-06
Journal of Hazardous Materials · DOI: 10.1016/j.jhazmat.2026.142754
关键词Tags: ocean biogeochemistry ocean biogeochemistry
摘要：Crossref 未提供该 DOI 的摘要。
Abstract: Crossref did not provide an abstract for this DOI.
其他相关期刊：按主题相关性补充Other relevant journals: topical supplements
3. Marine and terrestrial heatwaves and their impacts on coastal environments in southern coastal java, Indonesia
作者Authors: Halimurrahman Halimurrahman; Ginaldi Ari Nugroho; Asif Awaludin; Ibnu Fathrio; Mochamad Furqon Azis Ismail; Zahidah Hasan; Iskandar Iskandar; Asri Indrawati; et al.
发表月份Publication month: 2026-06 2026-06
Discover Applied Sciences · DOI: 10.1007/s42452-026-08996-1
关键词Tags: marine heatwaves marine heatwaves
摘要：热浪会严重影响沿海农业系统，但在印度尼西亚爪哇南部海岸，热浪的特征和气候驱动因素仍然相对不足。本研究使用 23 年数据集（2000-2022 年）研究陆地热浪 (THW)、海洋热浪 (MHW) 及其在 Pameungpeuk 的同时发生。 THW 的定义是每日最高表面气温 (SAT) 至少连续三天超过 85%，而 MHW 的定义是海面温度 (SST) 至少连续 5 天超过 90%。同时发生的事件的特点是 THW 和 MHW 之间的时间重叠。使用源自 Sentinel-2 数据的归一化植被指数 (NDVI) 以及土地利用信息来评估植被响应。 为了检查大规模气候驱动因素的影响，采用了统计和交叉小波分析。结果表明，THW 比 MHW 更频繁，主要发生在湿向干过渡期间（3 月至 5 月）。这些热浪的特点是 SAT 正异常超过 1 °C、相对湿度降低和风速适中。这些特征与当地报道的安金拉达现象有部分相似之处。重灾区频率较低，但往往持续时间较长，同时发生的事件则显示出不同的大气特征。 NDVI 下降 21-46。热浪期间稻田上空 5% 表明潜在的植被压力。 2010 年持续时间较长的 THW 事件凸显了大规模气候变化可能产生的影响。 然而，这些气候驱动因素相互作用产生复合热浪事件的程度仍不清楚，需要进一步调查。
Abstract: Heatwaves can severely affect coastal agricultural systems, yet their characteristics and climate drivers remain relatively underexplored along the southern coast of Java, Indonesia. This study examines terrestrial heatwaves (THWs), marine heatwaves (MHWs), and their co-occurrence in Pameungpeuk using a 23-year dataset (2000–2022). THWs are defined as daily maximum surface air temperature (SAT) exceeding the 85th percentile for at least three consecutive days, while MHWs are identified as sea surface temperature (SST) exceeding the 90th percentile for at least five consecutive days. Co-occurring events are characterised by the temporal overlap between THWs and MHWs. The vegetation response is assessed using the Normalised Difference Vegetation Index (NDVI) derived from Sentinel-2 data, along with land-use information. To examine the influence of large-scale climate drivers, statistical and cross-wavelet analyses are employed. Results indicate that THWs are more frequent than MHWs, primarily occurring during the wet-to-dry transition period (March–May). These heatwaves are characterised by positive SAT anomalies exceeding 1 °C, reduced relative humidity, and moderate wind speeds. These features show partial similarities to the locally reported Angin Lada phenomenon. MHWs are less frequent but tend to persist longer, while co-occurring events display distinct atmospheric characteristics. NDVI declines of 21–46.5% over rice fields during heatwave periods suggest potential vegetation stress. A prolonged THW event in 2010 highlights the possible influence of large-scale climate variability. However, the extent to which these climate drivers interact to produce compound heatwave events remains unclear and warrants further investigation.
4. Research on the Application of CYGNSS Data and Machine Learning for Monitoring and Predicting Soil Salinity in the Ben Tre Area, Vinh Long Province
作者Authors: Phan Minh Quan; Hoang Tich Phuc; Vu Phuong Lan; Nguyen Huu Duy; Ha Minh Cuong
发表月份Publication month: 2026-06 2026-06
VNU Journal of Science: Earth and Environmental Sciences · DOI: 10.25073/2588-1094/vnuees.5462
关键词Tags: ocean colour ocean colour
摘要：土壤盐分是一个严重的环境问题，直接影响作物生产力和农业可持续发展，特别是在气候变化背景下。本研究开发了一种低成本方法来绘制土壤电导率 (EC)，该方法基于机器学习与越南湄公河三角洲沿海平原地区永隆省槟椥地区的多源遥感数据。使用三种机器学习模型（CatBoost (CB)、随机森林 (RF)、XGBoost (XGB)）以及从 MODIS 图像、辅助数据和 CYGNSS 卫星的 GNSS-R 反射率数据中提取的 17 个输入变量。特征重要性分析表明，源自 CYGNSS 的表面反射率 (SR) 是三个最重要的预测因子之一。 性能比较显示了模型的学习能力和泛化性能之间的区别。在训练集上，CatBoost 表现出了卓越的学习能力 (R = 0. 89)，其次是 XGB (R = 0. 84) 和 RF (R = 0. 83)。然而，在验证集上，RF模型表现出最好的泛化性能，达到R = 0. 71。由此产生的盐度分布图显示出明显的空间趋势：盐度从西部内陆地区向东部沿海地区增加。研究结果证实了整合机器学习和 GNSS-R 数据进行土壤盐分监测的可行性。
Abstract: Soil salinity is a serious environmental problem, directly affecting crop productivity and sustainable agricultural development, especially in the context of climate change. This study develops a low-cost method for mapping soil electrical conductivity (EC), based on machine learning combined with multi-source remote sensing data for the Ben Tre area, Vinh Long province, a coastal plain region of the Mekong Delta, Vietnam. Three machine learning models (CatBoost (CB), Random Forest (RF), XGBoost (XGB)) were used with 17 input variables extracted from MODIS imagery, ancillary data, and GNSS-R reflectivity data from the CYGNSS satellite. Feature importance analysis revealed that Surface Reflectivity (SR) derived from CYGNSS was one of the three most important predictors. The performance comparison showed a distinction between the models' learning capabilities and their generalization performance. On the training set, CatBoost demonstrated superior learning capability (R = 0.89), followed by XGB (R = 0.84) and RF (R = 0.83). However, on the validation set, RF model showed the best generalization performance, achieving R = 0.71. The resulting salinity distribution map indicates a clear spatial trend: salinity increases from the inland Western areas to the coastal Eastern areas. The research results confirm the feasibility of integrating machine learning and GNSS-R data for soil salinity monitoring.
