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March 5, 2026Biogeosciences2 citationsOpen Access

Meta-analytical insights into organic matter enrichment in the surface microlayer

ASAmavi N. SilvaSNSurandokht NikzadTBTheresa Barthelmeß

Key Points

  • To quantitatively assess the distribution and enrichment of organic compounds in the surface microlayer (SML).
  • Conducted a meta-analysis of existing studies on SML
  • Analyzed selected organic compounds using probability density estimates and central tendency metrics
  • Examined the impact of environmental and methodological conditions on enrichment patterns
  • Confirmed enrichment of nitrogen-enriched particulate organic matter in the SML
  • Identified variability in enrichment patterns based on surfactant-specific factors
  • Logarithmic transformations and robust central tendency estimates yielded more accurate results compared to linear methods

Abstract

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 correlation analyses. Our results confirm a preferential enrichment of nitrogen-enriched, particulate organic matter in the SML, while also highlighting the significance of surfactant-specific factors that govern selective enrichment in the SML. We find that enrichment patterns can vary systematically with environmental and methodological conditions, underscoring the need to account for such influences when interpreting observations and developing SML-based models. We provide the full range of typical EF values for the studied compounds, offering a clear reference for assessing whether new measurements are typical or extreme. While delving into the ability of EFs to reflect organic matter partitioning in the SML, we also critically examine their limitations in capturing trophic variability and suggest that EF-based assessments be complemented with metrics that remove background variability from underlying water concentrations, enabling more accurate interpretations of true SML enrichment and informing future modelling efforts. Additionally, our meta-analysis demonstrates that logarithmic data transformations and robust central tendency estimates outperform traditional linear-scale approaches, providing more accurate and reliable SML enrichment estimates.

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Cite This Study

Silva et al. (2026) studied this question.

synapsesocial.com/papers/69a91df9d6127c7a504c1633https://doi.org/10.5194/bg-23-1697-2026
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