Machine Learning Provides Opportunities to Recognize Greenhouse Gas Emissions from Water at a Large Scale

Abstract

Water environments (e.g., oceans, lakes, and rivers) are important carbon sinks and sources and contribute to the carbon cycle of the earth's ecosystem. Machine learning provides a potential solution for recognizing greenhouse gas (GHG) emissions from water based on big data analysis. Data-driven machine learning can comprehensively recognize the key environmental drivers that affect GHG emissions. However, several urgent issues should be addressed to guarantee machine learning recognition of GHG emissions from water. For example, matching in situ data and databases is the greatest challenge in conducting large-scale machine learning research. It is imperative to unify in situ data collection methods and improve database quality (e.g., spatiotemporal matching and high resolution). Quantifying the contributions of human activity and climate change to GHG emissions from water is urgently needed to resolve future challenges. Beyond providing recognition and prediction, machine learning, due to its interpretability, can optimize the carbon cycle model; thus, empirical formulas for machine learning deserve attention. Overall, machine learning can manage large-scale and complicated data for climate change regarding GHG emissions from water.

Carbon Emissions From Chinese Inland Waters: Current Progress and Future Challenges

“…Recent work on C emissions from Tibetan Plateau streams and lakes have proved the effectiveness of these techniques (for example, Mu et al., 2023; Zhang, Xia, et al., 2020). Furthermore, artificial intelligence techniques (e.g., data‐driven machine learning) are a promising tool to resolve complex and multi‐dimensional relationships that often underlie inland water C cycling processes, and to identify the major determinants of GHG emissions (Deng et al., 2023). These techniques will help constrain the spatiotemporal variability in large‐scale flux estimates and enable more accurate prediction of future C emissions from Chinese inland waters, thereby contributing to China's national C emission accounting toward its carbon neutrality goals.…”

Applications of Artificial Intelligence, Machine Learning, and Data Analytics in Water Environments

“…The special issue includes several review articles encompassing a wide spectrum, ranging from a historical perspective of water data to computational modeling in wastewater treatment to ML modeling of environmental chemical reactions, environmental toxicology, heavy metal removal, and cyanobacterial harmful algal blooms (HABs) . One significant application of these innovative tools is ML-assisted environmental monitoring, which can address diverse problems, such as predicting effluent nutrients or influent flow rates and nutrient loads at wastewater treatment plants, , formation of disinfection byproducts, drivers of the accumulation of potentially toxic elements in sediments, greenhouse gas emissions, , occurrence of PFAS, water quality assessment, microplastics, microcystins, and differentiation of landfill leachate and domestic sludge . ML has also been extensively employed to model environmental chemical reactions and processes, including adsorption onto various materials, , biodegradation, photodegradation, and the physicochemical and meteorological variables that affect the seasonal growth and decline of HABs .…”

“…ML has also been extensively employed to model environmental chemical reactions and processes, including adsorption onto various materials, , biodegradation, photodegradation, and the physicochemical and meteorological variables that affect the seasonal growth and decline of HABs . Several articles in this special issue underscore the importance of building more trustworthy predictive models by integrating mechanistic information about the studied systems. ,, …”