Spatiotemporal Patterns of Methane and Nitrous Oxide Emissions in China’s Inland Waters Identified by Machine Learning Technique
Cheng Yang, Wen Jie Du, Ru‐Li He, Yi-Rong Hu, Houqi Liu, Tianyin Huang, Wen‐Wei Li
Abstract
The fugitive emissions of greenhouse gases, primarily methane (CH 4 ) and nitrous oxide (N 2 O), from water environments have aroused global concern. However, there are currently limited information about national-scale data of CH 4 and N 2 O emissions from inland waters, such as lakes, rivers, and reservoirs, particularly in developing countries. This study employed machine learning techniques, based on the literature data and national water quality monitoring data, to reveal the CH 4 and N 2 O emission patterns of China’s inland waters at the third-level basin and daily resolution. Our results show significant seasonal variations in CH 4 emissions, which were influenced by total nitrogen and chemical oxygen demand concentrations. Northern watersheds were identified as hotspots of CH 4 emissions, with 57% higher CH 4 flux than the other watersheds. In contrast, N 2 O had a relatively lower contribution to total carbon emissions and showed smaller temporal and spatial variations. The estimated total emissions of CH 4 and N 2 O in China’s inland waters in 2021 amounted to 80.22 Tg of carbon dioxide equivalent, accounting for 9–11% of China’s terrestrial carbon sinks. This research provides valuable insights to guide the counting and control of greenhouse gas emissions from environmental water bodies.