Suppr超能文献

基于河流流量和溶解氧化亚氮浓度估算河流系统氧化亚氮排放量

[Estimation of Nitrous Oxide Emission from River System Based on Water Discharge and Dissolved Nitrous Oxide Concentration].

作者信息

Li Bing-Qing, Hu Min-Peng, Wang Ming-Feng, Zhang Yu-Fu, Wu Hao, Zhou Jia, Wu Kai-Bin, Dai Zhi-Zhou, Chen Ding-Jiang

机构信息

College of Environmental & Resource Sciences, Zhejiang University, Hangzhou 310058, China.

Ministry of Education Key Laboratory of Environment Remediation and Ecological Health, Zhejiang University, Hangzhou 310058, China.

出版信息

Huan Jing Ke Xue. 2022 Jan 8;43(1):369-376. doi: 10.13227/j.hjkx.202105005.

Abstract

Due to increasing active nitrogen pollution loads, river systems have become an important source of nitrous oxide (NO) in many areas. Due to the lack of monitoring data in many studies as well as the difficulty in estimating intermediate parameters and expressing temporal-spatial variability in current methods, a high level of uncertainty remains in the estimates of riverine NO emission quantity. Based on the monthly monitoring efforts conducted for 10 sampling sites across the Yonganxi River system in Zhejiang Province from June 2016 to July 2019, the temporal and spatial dynamics of riverine NO dissolved concentrations (NO), NO fluxes, and their influencing factors were addressed. A multiple regression model was then developed for predicating riverine NO emission flux to estimate annual NO emission quantity for the entire river system. The results indicated that observed riverine (NO) (0.03-2.14 μg·L) and the NO fluxes[1.32-82.79 μg·(m·h)] varied by 1-2 orders of magnitude of temporal-spatial variability. The temporal and spatial variability of (NO) were mainly influenced by the concentrations of nitrate, ammonia, and dissolved organic carbon, whereas the NO emission fluxes were mainly affected by river water discharges and (NO). A multiple regression model that incorporates variables of river water discharge and (NO) could explain 90% of the variability in riverine NO emission fluxes and has high accuracy. The model estimated NO emission quantity from the entire Yonganxi River system of 3.67 t·a, with 29% from the main stream and 71% from the tributaries. The IPCC default emission factor method might greatly overestimate and underestimate NO emission quantities for rivers impacted by low and high pressures of human activities, respectively. This study advances our quantitative understanding of NO emission for the entire river system and provides a reference method for estimating riverine NO emission with more accuracy.

摘要

由于活性氮污染负荷不断增加,在许多地区,河流系统已成为一氧化二氮(N₂O)的重要来源。由于许多研究缺乏监测数据,以及当前方法在估算中间参数和表达时空变异性方面存在困难,河流N₂O排放量的估算仍存在高度不确定性。基于2016年6月至2019年7月对浙江省永安溪河水系10个采样点进行的月度监测,研究了河流溶解态N₂O浓度(N₂O)、N₂O通量及其影响因素的时空动态。然后建立了多元回归模型来预测河流N₂O排放通量,以估算整个水系的年度N₂O排放量。结果表明,观测到的河流N₂O浓度(0.03 - 2.14 μg·L⁻¹)和N₂O通量[1.32 - 82.79 μg·(m²·h)⁻¹]在时空变异性上有1 - 2个数量级的差异。N₂O浓度的时空变异性主要受硝酸盐、氨和溶解有机碳浓度的影响,而N₂O排放通量主要受河水流量和N₂O浓度的影响。包含河水流量和N₂O浓度变量的多元回归模型可以解释河流N₂O排放通量90%的变异性,且具有较高的准确性。该模型估算出永安溪河水系的N₂O排放量为3.67 t·a⁻¹,其中干流占29%,支流占71%。IPCC默认排放因子法可能分别高估和低估受人类活动低压和高压影响河流的N₂O排放量。本研究推进了我们对整个水系N₂O排放的定量理解,并为更准确地估算河流N₂O排放提供了参考方法。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验