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Evaluation of variation in background nitrous oxide emissions: A new global synthesis integrating the impacts of climate, soil, and management conditions.

Yulong YinZihan WangXingshuai TianYingcheng WangJiahui CongZhenling Cui
Published in: Global change biology (2021)
Robust global simulation of soil background N2 O emissions (BNEs) is a challenge due to the lack of a comprehensive system for quantification of the variations in their magnitude and location. We mapped global BNEs based on 1353 field observations from globally distributed sites and high-resolution climate and soil data. We then calculated global and national total BNE budgets and compared them to the IPCC-estimated values. The average BNE was 1.10, 0.92, and 0.84 kg N ha-1  year-1 with variations from 0.18 to 3.47 (5th-95th percentile, hereafter), 0.20 to 3.44, and -1.16 to 3.70 kg N ha-1  year-1 for cropland, forestland, and grassland, respectively. Soil pH, soil N mineralization, atmospheric N deposition, soil volumetric water content, and soil temperature were the principle significant drivers of BNEs. The total BNEs of three land use types was lower than IPCC-estimated total BNEs by 0.83 Tg (1012  g) N year-1 , ranging from -47% to 94% across countries. The estimated BNE with cropland values were slightly higher than the IPCC estimates by 0.11 Tg N year-1 , and forestland and grassland lower than the IPCC estimates by 0.4 and 0.54 Tg N year-1 , respectively. Our study underlined the necessity for detailed estimation of the spatial distribution of BNEs to improve the estimates of global N2 O emissions and enable the establishment of more realistic and effective mitigation measures.
Keyphrases
  • high resolution
  • plant growth
  • climate change
  • machine learning
  • municipal solid waste
  • particulate matter
  • big data
  • artificial intelligence
  • deep learning
  • heavy metals
  • sewage sludge