农业面源污染时空分布及污染源解析——以安徽怀远县为例
投稿时间:2020-07-06    点此下载全文
引用本文:杜鹃,王乐宜,周皓媛,王本梧,李国学,张宝莉.农业面源污染时空分布及污染源解析——以安徽怀远县为例[J].中国农业大学学报,2021,26(2):139-149
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作者单位E-mail
杜鹃 中国农业大学 资源与环境学院, 北京 100193 dujuanjuan@cau.edu.cn 
王乐宜 中国农业大学 资源与环境学院, 北京 100193  
周皓媛 中国农业大学 资源与环境学院, 北京 100193  
王本梧 安徽省蚌埠市淮上区曹老集农技站, 安徽 蚌埠 233080  
李国学 中国农业大学 资源与环境学院, 北京 100193  
张宝莉 中国农业大学 资源与环境学院, 北京 100193 baoli@cau.edu.cn 
基金项目:全球环境基金(GEF)气候智慧型农业
中文摘要:为明确农业面源污染的来源与总量,制定相应控制措施,基于输出系数模型,以总氮和总磷的排放作为评价对象,研究安徽怀远县2014—2018年农业面源污染情况并分析面源污染来源及其时空分布特征。结果表明:2014—2018年该区域的总氮排放量分别为309.8、293.6、300.6、305.2、310.5 t,呈现先减少后增加的趋势;总磷排放量分别为21.7、21.9、22.4、23.0、22.8 t,整体呈现增加趋势;农业面源污染主要来自耕地源、人口源和畜禽源。各污染源对总氮排放量的贡献率为耕地源>人口源>畜禽源,对总磷排放量的贡献率为:人口源>畜禽源>耕地源;综合单位面积面源污染排放强度,将区域分为4 等级,时空分布具有明显变化。单位面积总氮排放强度多分布在2、3 等级,单位面积总磷排放强度分布多分布在1、2 等级;大部分村总氮、总磷排放强度分布一致,西北部余夏、找母和东部联合村排放强度较高,南部刘楼和镇南等村排放强度较低。因此,安徽怀远县主要面源污染物为总氮,可通过调整所施肥料的氮磷比,控制总氮排放量,同时根据各区域内各村不同的污染源构成,提出适宜对策。
中文关键词:农业面源污染  输出系数模型  总氮  总磷  时空分布
 
Spatial-temporal distribution of agricultural non-point source pollution: A case study of Huaiyuan, Anhui
Abstract:In order to better understand agricultural non-point source pollution(ANPSP)in Huaiyuan County, an output coefficient model was adopted to calculate the emissions of total nitrogen and total phosphorus from 2014 to 2018. The results showed that: The total nitrogen emissions were respectively 309. 8, 293. 6, 300. 6, 305. 2 and 310. 5 t from 2014 to 2018 showing a trend of decreasing initially and then increasing. Different from total nitrogen emissions, the total phosphorus emissions were respectively 21. 7、21. 9、22. 4、23. 0 and 22. 8 t from 2014 to 2018 showing a trend of increasing. The ANPSP mainly came from croplands, population and livestock, but they presented totally different contributions. The total nitrogen emission contribution rate was: croplands source>population source>livestock source. The total phosphorus emission contribution rate was: population source>livestock source>croplands source. Based on the intensity of ANPSP emission per unit area, the area was divided into four levels, with obvious spatial-temporal changes; The TN emission intensity were mainly located at level 2 and level 3, while the TP emission intensity were located at level 1 and level 2. Besides, there existed great differences among villages with larger emissions in Yuxia, Zhaomu and Lianhe, northwest and east in the investigated area. In conclusion, the total nitrogen emission was key agricultural non-point source pollution in Huaiyuan County. Application of suitable fertilizers could help to solve this problem. Meanwhile, reasonable countermeasures should also be put forward according to different pollution sources in different regions.
keywords:agriculture non-point pollution  export coefficient model  total nitrogen  total phosphorus  spatial-temporal distribution
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