摘要
人工湿地是生态修复受污染水体的重要手段,对氮的去除效果显著,但不同植物湿地对氮去除效果差异明显,因此不同类型植物组合后的湿地对氮去除效果及去除途径值得进一步研究.通过室内控制试验,以常见浮水植物绿狐尾藻(Myriophyllum elatinoides)和挺水植物梭鱼草(Pontederia cordata)为研究对象,利用15N稳定同位素示踪技术探究绿狐尾藻、梭鱼草及绿狐尾藻+梭鱼草植物组合对氨氮(NH4+-N)的去除途径.结果表明,绿狐尾藻+梭鱼草植物组合处理相较于单一绿狐尾藻和梭鱼草处理对氮的脱除量分别提高1.8％和3.6％.基于15N质量平衡,绿狐尾藻、梭鱼草和绿狐尾藻+梭鱼草植物组合处理氮去除途径的底泥吸附贡献分别为8.30％、7.96％和6.94％,植物吸收贡献分别为41.76％、35.34％和48.67％,微生物作用去除贡献分别为49.94％、56.71％和44.39％.微生物作用在湿地脱氮过程中占主导地位.相较于单一植物湿地,植物组合湿地对氮的去除效果得到提升,同时促进了植物对水体NH4+-N的吸收.该研究有助于更好地理解植物组合湿地对氨氮的脱除途径.
Abstract
Constructed wetlands(CWs)are an important method for the ecological remediation of polluted water bodies.Their removal efficiency of nitrogen(N)is remarkable.The N removal efficiency varies across CWs with different plant species.The questions are whether this efficiency is improved in CWs with plant combinations,and how is the removal path worthy of further study.In this study,Myriophyllum elatinoides and Pontederia cordata were used as research objects and examined through controlled laboratory experiments.The pathways for ammonia nitrogen(NH4+-N)removal were in-vestigated using the 15 N stable isotope tracer technique in M.elatinoides,P.cordata,and M.elatinoides+P.cordata com-bination.Compared with single plant,M.elatinoides and P.cordata,N removal amount of M.elatinoides+P.cordata combination was increased by 1.8％and 3.6％,respectively.According to the 15N mass balance,the contributions to N removal by M.elatinoides,P.cordata,and their combination through sediment adsorption were 8.30％,7.96％,and 6.94％,respectively,through plant uptake were 41.76％,35.34％,and 48.67％,respectively,and through microbial action were 49.94％,56.71％,and 44.39％,respectively.Microbial action played a dominant role in N removal process in CWs.Compared with single plant CWs,plant combination CWs could improve N removal efficiency and enhance NH4+-N uptake by plants from water.This study is helpful to better understand the removal pathway of NH4+-N in plant combina-tions CWs.
基金项目
国家自然科学基金(42077215)
湖南省自然科学基金(2021JJ30765)
郴州国家可持续发展议程创新示范区专项(2022sfq02)
国家科技期刊平台
NSTL
万方数据