Role of BP-ANN in simulating greenhouse gas emissions from global aquatic ecosystems via carbon component-environmental factor coupling.

ABSTRACT

Inland waters (IW), estuarine areas (EA), and offshore areas (OA) function as aquatic systems in which the transport of carbon components results in the release of greenhouse gases (GHGs). Interconnected subsystems exhibit a greater greenhouse effect than individual systems. Despite this, there is a lack of research on how carbon loading and its components impact GHG emissions in various aquatic systems. In this study, we analyzed 430 aquatic sites to explore trade-off mechanisms among dissolved organic carbon (DOC), particulate organic carbon, dissolved inorganic carbon (DIC), and GHGs. The results revealed that IW emerged as the most significant GHG source, possessing a comprehensive global warming potential (GWP) of 0.78 ± 0.08 (10-2 Pg CO2-ep ha-1 year-1) for combined carbon dioxide, methane, and nitrous oxide. This surpassed the cumulative potentials of EA and OA (0.35 ± 0.05 (10-2 Pg CO2-ep ha-1 year-1)). Additionally, structural equation modeling indicated that GHG emissions resulted from a combination of carbon component loading and environmental factors. DOC exhibited a positive correlation with GWPs when influenced by biodegradable DOC. Total alkalinity and pH influenced DIC, leading to elevated pCO2 in aquatic systems, thereby enhancing GWPs. Predictive modeling using backpropagation artificial neural networks (BP-ANN) for GWPs, incorporating carbon components and environmental factors, demonstrated a good fit (R2 = 0.6078, RMSEaverage = 0.069, p > 0.05) between observed and predicted values. Enhancing the estimation of aquatic region feedback to GHG changes was achieved by incorporating corresponding water quality parameters. In summary, this study underscores the pivotal role of carbon components and environmental factors in aquatic regions for GHG emissions. The application of BP-ANN to estimate greenhouse effects from aquatic regions is highlighted, providing theoretical and experimental support for future advancements in monitoring and developing policies concerning the influence of water quality on GHG emissions.