曝气灌溉条件下土壤N2O排放特征及影响因子分析

为了明确曝气灌溉下土壤N₂O排放特征及主要影响因子，实验设置了2个灌水量（70%和90%田间持水量）和2个增氧水平（5，40mg/L），采用静态箱法和qPCR技术对土壤N₂O通量及土壤关键功能基因进行测定，研究不同灌水量和增氧水平对土壤充水孔隙度、溶解氧、氧化还原电位（Eh）、矿质氮及氨氧化古菌（AOA）、氨氧化细菌（AOB）和反硝化基因（narG和nosZ）的影响.结果表明：培养过程中，各处理N₂O排放通量均呈现先增加后降低的趋势，于灌溉后1d达到峰值；曝气量和灌水量的增加可显著增加土壤N₂O的排放通量和排放峰值.灌溉造成土壤含水量增加的同时，降低了土壤溶解氧和Eh；曝气可提高土壤溶解氧和Eh，改善土壤通气性（P<0.05），而对土壤充水孔隙度无显著影响.土壤充水孔隙度、Eh、NO₃^--N含量是曝气灌溉下驱动土壤N₂O排放的主要理化因子.曝气显著增加了AOA的基因拷贝数，且N₂O排放与AOA的基因拷贝数呈显著正相关关系（P<0.05）.研究结果为进一步明确曝气灌溉对土壤N₂O排放的影响机制和曝气灌溉模式下农田N₂O排放管理提供支撑.

Characteristics and influencing factors of N2O emission from incubated soil under aerated irrigation

To clarify the characteristics of soil N₂O emission and identify the main factors under aerated irrigation (AI), the soil culture experiments were conducted at 2 irrigation levels with upper soil moisture limit as 70% and 90% of field capacity and 2 dissolved oxygen (DO) levels at 5 and 40mg/L. Soil N₂O emission fluxes were monitored using static chamber-gas chromatography method and the copy number of nitrification and denitrification gene were determined using the real-time quantitative polymerase chain reaction (qPCR) technique. In addition, the main factors on soil N₂O fluxes were analyzed, including soil water filling porosity (WFPS), DO, redox potential (Eh), mineral nitrogen content (NO₃^--N and NH₄⁺-N), as well as the abundance of soil ammonia-oxidizing bacterial (AOB) and ammonia-oxidizing archaea (AOA) and denitrifier genes (narG and nosZ). Results showed that soil N₂O flux increased from the beginning, peaked at 1d after irrigation, dropped in the following 3days, and then stabilized. An increase of aeration and irrigation amount resulted in the increase of average values and peak values of soil N₂O fluxes. Irrigation caused an increase of WFPS, while a decrease of soil DO and Eh. Aeration treatments increased soil DO concentration and Eh (P<0.05), improved soil aeration. However, aeration treatments showed no significant impact on WFPS. The WFPS, Eh and NO₃^--N content were the main physical, chemical influencing factors driving soil N₂O emission under AI. The AI significantly affected AOA copy numbers. In addition, soil N₂O fluxes were significantly correlated with AOA copy number (P<0.05). The results could provide scientific support for the influential mechanism of AI on soil N₂O and the farmland N₂O emission management.