小流域面源污染负荷精细化估算与实证研究——以成都市阳化河流域为例
Refined Estimation and Empirical Study of Non-point Source Pollution Load in Small Watershed:A Case Study of Yanghua River Basin,Chengdu
夏玉超 1袁一斌 2詹琳 1何楚 3夏建新1
作者信息
- 1. 中央民族大学生命与环境科学学院,北京 100081
- 2. 成都市环境保护科学研究院水环境研究所,成都 610072
- 3. 北京市水务局南水北调环线管理处,北京 100176
- 折叠
摘要
精准估算沱江支流阳化河小流域面源污染负荷是制定水质达标方案的基础.运用改进的输出系数法估算阳化河流域面源污染负荷总量,并基于当月降雨量占年降雨量的比例对面源污染负荷年输出总量进行月份分配,获得精细化估算数据.结果表明:2020年流域内COD污染最严重,污染负荷总量为6 042.61 t,污染负荷主要分布在转龙镇、竹篙镇、禾丰镇、云龙镇和施家镇;污染物COD、氨氮和总磷主要来源于农村分散生活面源和规模化畜禽养殖;估算得到的面源污染负荷年内月份的分配结果与实测数据比较吻合,平均误差都＜10％.研究结果可为流域水环境精细化管理提供数据支持.
Abstract
Accurately estimating non-point source pollution load is crucial for developing a standard-meeting water quality plan for the Yanghua River.By employing an improved output coefficient method,we estimated the total non-point source pollution load of Yanghua River small watershed,which is a tributary of the Tuojiang River.In consideration of the proportion of monthly rainfall in annual rainfall,we can obtain the refined estimation data of monthly distribution of non-point source pollution load.Our findings indicate that COD pollution in the watershed was most severe in 2020,with a total pollution load of 6 042.61 t,primarily concentrated in Zhuanlong Town,Zhu-gao Town,Hefeng Town,Yunlong Town,and Shijia Town.Non-point sources such as rural dispersed areas and large-scale livestock and poultry farming contributed significantly to pollutants such as COD,ammonia nitrogen,and total phosphorus.Additionally,we successfully obtained the preliminary monthly distribution of non-point source pollution load within the year.These results demonstrated good agreement with measured data,with an aver-age error less than 10％.Overall,our research outcomes provide essential data support for the refined management of water environment in the basin.
关键词
面源污染估算/负荷分配/改进输出系数法/阳化河流域Key words
estimation of non-point source pollution/pollution load distribution/improved export coefficient method/Yanghua River Basin引用本文复制引用
基金项目
长江生态屏障建设成都市驻点研究项目(KY2021003)
出版年
2024
国家科技期刊平台
NSTL
万方数据