The abiologically and biologically driving effects on organic matter in marginal seas revealed by deep learning-assisted model analysis

Ting Wang; Jialin Li; Saralees Nadarajah; Meng Gao; Jingyuan Chen; Song Qin

doi:10.1016/j.scitotenv.2024.178251

The abiologically and biologically driving effects on organic matter in marginal seas revealed by deep learning-assisted model analysis

Sci Total Environ. 2025 Jan 2:959:178251. doi: 10.1016/j.scitotenv.2024.178251. Online ahead of print.

Authors

Ting Wang¹, Jialin Li², Saralees Nadarajah³, Meng Gao⁴, Jingyuan Chen⁵, Song Qin⁶

Affiliations

¹ Key Laboratory of Coastal Biology and Biological Resource Utilization, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China; University of Chinese Academy of Sciences, Beijing 101408, China.
² Key Laboratory of Coastal Biology and Biological Resource Utilization, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China. Electronic address: jlli@yic.ac.cn.
³ Department of Mathematics, University of Manchester, Manchester M13 9PL, UK.
⁴ School of Mathematics and Information Sciences, Yantai University, Yantai 264005, China.
⁵ Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), Zhejiang University School of Medicine, Zhejiang University, Haining 314400, China.
⁶ Key Laboratory of Coastal Biology and Biological Resource Utilization, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China; University of Chinese Academy of Sciences, Beijing 101408, China. Electronic address: sqin@yic.ac.cn.

PMID: 39754949
DOI: 10.1016/j.scitotenv.2024.178251

Abstract

The biogeochemical processes of organic matter exhibit notable variability and unpredictability in marginal seas. In this study, the abiologically and biologically driving effects on particulate organic matter (POM) and dissolved organic matter (DOM) were investigated in the Yellow Sea and Bohai Sea of China, by introducing the cutting-edge network inference tool of deep learning. The concentration of particulate organic carbon (POC) was determined to characterize the status of POM, and the fractions and fluorescent properties of DOM were identified through 3D excitation-emission-matrix spectra (3D-EEM) combined parallel factor analysis (PARAFAC). The results indicated that the distribution of POM and DOM exhibited regional disparity across the studied sea regions. POM demonstrated greater heterogeneity in the South Yellow Sea (p < 0.05), and in contrast, all three fluorescent components of DOM displayed a higher degree of heterogeneity in the Bohai Sea (p < 0.05). To delve into the drivers of the discrepancy, artificial neural network (ANN) models were constructed, incorporating 15 extra abiotic and biotic parameters. Under optimal parameter setting, ANNs achieved a maximum Pearson correlation coefficient (PCC) of 0.87 and a minimum Root Mean Squared Error (RMSE) of 0.23. The model identified turbidity and temperature as the most influential factors, accounting for the variation in the heterogeneity of POM and DOM across different sea regions, respectively. Additionally, the result highlighted the significant role of pico-size photosynthetic organisms among biological predictors, which may suggest their pivotal, yet often underappreciated, role in blue carbon cycles. In conclusion, this research introduces advanced deep-learning modeling techniques, providing novel insights into the biogeochemical processes of organic matter in marginal seas.

Keywords: Biogeochemical cycle; Biological pump; Machine learning; Marginal sea; Organic carbon.