Environmental drivers of dissolved organic matter composition across central European aquatic systems: A novel correlation-based machine learning and FT-ICR MS approach

Dissolved organic matter (DOM) present in surface aquatic systems is a heterogeneous mixture of organic compounds reflecting its allochthonous and autochthonous organic matter (OM) sources. The composition of DOM is determined by environmental factors like land use, water chemistry, and climate, which influence its release, movement, and turnover in the ecosystem. However, studying the impact of these environmental factors on DOM composition is challenging due to the dynamic nature of the system and the complex interactions of multiple environmental factors involved. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) enables detailed molecular-level analysis of DOM, allowing the identification of thousands of individual molecular formulas potentially representing unique markers for its "molecular history". The combination of FT-ICR MS with machine-learning techniques is promising to unravel DOM-environment interactions owing to their capacity to capture complex non-linear relationships. We present a novel unsupervised multi-variant machine-learning approach, aiming to model correlation coefficients as robust indicators of how changes in environmental factors (e.g., the concentration of nutrients or the land use) result in changes in the molecular formula descriptors of DOM (i.e., aromaticity index or hydrogen to carbon ratio). We applied this approach to an environmental data set collected from 84 sites across central Europe exhibiting a broad range of water chemistry and land uses. Our model revealed an increase in molecular mass and aromaticity of DOM in densely forested regions as compared to open urban areas, where DOM was characterized by higher concentrations of dissolved ions and increased microbial degradation, leading to smaller and more aliphatic DOM. Our findings highlight the substantial human impact on climate change, as evidenced by the accelerated photochemical and microbial degradation of DOM, which consequently enhances greenhouse gas emissions and exacerbates global warming.