有机质
曲面(拓扑)
环境科学
化学
数学
有机化学
几何学
作者
Amavi N. Silva,Surandokht Nikzad,Theresa Barthelmeß,Anja Engel,Hartmut Herrmann,Manuela van Pinxteren,Kai Wirtz,Oliver Wurl,Markus Schartau
标识
DOI:10.5194/egusphere-2025-4050
摘要
Abstract. The surface microlayer (SML), the uppermost ~1 mm water layer at the air-water interface, plays a critical role in mediating Earth system processes, yet current knowledge of its composition and organic matter enrichment remains scattered across disciplines. Here, we present the first known meta-analysis of SML studies that quantitatively assesses the distributional characteristics of selected organic compounds, including organic carbon and nitrogen, amino acids, fatty acids, transparent exopolymer particles, carbohydrates, lipids and proteins, through probability density estimates, central tendency metrics and correlations analyses. Our results confirm a preferential enrichment of nitrogen-enriched, particulate organic matter in the SML, highlighting the significance of compound-specific accumulation and selective enrichment patterns. We also observe that the enrichment of a given compound may exhibit notable variability that depends on distinct internal and external conditions. Our evaluation of enrichment factors (EFs) of various measurable compounds provides updated estimates for their typical values and ranges. While delving into the ability of EFs to reflect the partitioning of organic matter within the SML, we also critically examine their limitation in capturing trophic conditions. Based on these findings, we propose that future SML research should incorporate both absolute concentration changes and enrichment capacities in the SML, alongside their relative changes (as denoted by EFs), to more accurately interpret ecological implications. Additionally, our meta-analysis demonstrates the value of logarithmic data transformations and robust central tendency estimates, as essential tools for improving the statistical reliability, comparability, and representation of SML enrichment patterns.
科研通智能强力驱动
Strongly Powered by AbleSci AI