Yang Ping, Luo Liangjuan, Tang Kam W, Lai Derrick Y F, Tong Chuan, Hong Yan, Zhang Linhai
School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, PR China; Key Laboratory of Humid Subtropical Eco-geographical Process of Ministry of Education, Fujian Normal University, Fuzhou, 350007, PR China.
School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, PR China; Key Laboratory of Humid Subtropical Eco-geographical Process of Ministry of Education, Fujian Normal University, Fuzhou, 350007, PR China.
Environ Pollut. 2022 Feb 1;294:118568. doi: 10.1016/j.envpol.2021.118568. Epub 2021 Nov 24.
While Asia is projected to be one of the major nitrous oxide (NO) sources in the coming decades, a more accurate assessment of NO budget has been hampered by low data resolution and poorly constrained emission factor (EF). Since urbanized coastal reservoirs receive high nitrogen loads from diverse sources across a heterogeneous landscape, the use of a single fixed EF may lead to large errors in NO assessment. In this study, we conducted high spatial resolution sampling of dissolved NO, nitrate-nitrogen (NO-N) and other physico-chemical properties of surface water in Wenwusha Reservoir and other types of water bodies (river, drainage channels, and aquaculture ponds) in its catchment areas in southeastern China between November 2018 and June 2019. The empirically derived EF (calculated as NO-N:NO-N) for the reservoir showed considerable spatial variations, with a 10-fold difference ranging from 0.8 × 10 to 8.8 × 10. The average EF varied significantly among the four types of water bodies in the following descending order: aquaculture ponds > river > drainage channels > reservoir. Across all the water bodies, the mean EF in summer was 1.8-3.5 and 1.7-2.8 fold higher than that in autumn and spring, respectively, owing to the elevated water temperature. Overall, our derived EF deviated considerably from the IPCC default value, which implied that the use of default EF could result in over- or under-estimation of NO emissions by up to 42%. We developed a multiple regression model that could explain 82% of the variance in EF based on water temperature and the ratio between dissolved organic carbon and nitrate-nitrogen (p < 0.001), which could be used to improve the estimate of EF for assessing NO emission from coastal reservoirs and other similar environments.
虽然预计亚洲在未来几十年将成为一氧化二氮（N₂O）的主要来源之一，但由于数据分辨率低和排放因子（EF）约束不足，对N₂O预算的更准确评估受到了阻碍。由于城市化的沿海水库从异质景观中的不同来源接收高氮负荷，使用单一固定的排放因子可能会导致N₂O评估中的大误差。在本研究中，我们于2018年11月至2019年6月对中国东南部文坞沙水库及其集水区的其他类型水体（河流、排水渠道和养殖池塘）中的溶解态N₂O、硝态氮（NO₃-N）和其他理化性质进行了高空间分辨率采样。该水库根据经验得出的排放因子（计算为N₂O-N:NO₃-N）显示出相当大的空间变化，范围为0.8×10⁻³至8.8×10⁻³，相差10倍。四种类型水体的平均排放因子差异显著，按降序排列为：养殖池塘＞河流＞排水渠道＞水库。在所有水体中，由于水温升高，夏季的平均排放因子分别比秋季和春季高1.8 - 至3.5倍和1.7至2.8倍。总体而言，我们得出的排放因子与政府间气候变化专门委员会（IPCC）的默认值有很大偏差，这意味着使用默认排放因子可能导致N₂O排放量高估或低估高达42%。我们开发了一个多元回归模型，该模型基于水温以及溶解有机碳与硝态氮的比率，可以解释排放因子中82%的方差（p＜0.001），可用于改进对沿海水库和其他类似环境中N₂O排放评估的排放因子估计。
