基于DPeRS模型的西安市农业空间面源污染特征解析

张秀红; 李任革; 赵月帅; 高琳婷; 宁博

doi:10.13205/j.hjgc.202312017

基于DPeRS模型的西安市农业空间面源污染特征解析

doi: 10.13205/j.hjgc.202312017

1. 陕西华地勘察设计咨询有限公司, 西安 710018;
2. 西安市自然资源和规划局, 西安 710018

基金项目:

西安市自然资源和规划局委托项目"西安市国土空间生态修复调查和规划编制"(HMZB-21044)

详细信息

作者简介:
张秀红(1993-),女,硕士研究生,主要研究方向为国土空间综合整治与生态修复。zxh_xiuhong@163.com

通讯作者:
赵月帅(1990-),男,硕士研究生,主要研究方向为国土空间综合整治与生态修复。575608180@qq.com

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出版历程
- 收稿日期: 2023-03-07
- 网络出版日期: 2024-03-08

ANALYSIS OF DIFFUSE POLLUTION CHARACTERISTICS IN AGRICULTURAL SPACE IN XI'AN BASED ON DPeRS MODEL

1. Shaanxi Huadi Survey and Design Consulting Co., Ltd., Xi'an 710018, China;
2. Xi'an Bureau of Natural Resources and Planning, Xi'an 710018, China

摘要

摘要: 研究采用遥感分布式面源污染评估(DPeRS)模型,量化西安市2020年农业空间面源污染负荷排放量和入河量,对面源污染空间分布特征进行了解析,识别出西安市农业空间主要面源污染类型,提出了针对性的面源污染防治建议。结果表明:西安市农业空间农田氮磷盈余量较大且分布范围广;面源污染排放量空间分布差异明显,高值区主要分布在阎良区东部、高陵区东部、周至县和长安区平原地区、临潼区和蓝田县骊山丘陵地区,氮面源污染受农村生活影响较大,磷面源污染受水土流失影响较大;农业空间氮、磷面源污染入河量空间分布规律相似,但含量相差较大,高值区主要位于阎良区东部、临潼区中部和北部、蓝田县骊山丘陵地区、周至县中东部平原地区、秦岭浅山地区及部分峪道两侧。
- 面源污染 /
- DPeRS模型 /
- 农业空间 /
- 西安市
Abstract: The diffuse pollution estimation with remote sensing (DPeRS) model was conducted to quantify the agricultural spatial diffuse pollution load discharge and river inflow in Xi'an in 2020. The spatial distribution characteristics of surface source pollution were analyzed, the main types of non-point source pollution in the agricultural space of Xi'an were identified, and the targeted prevention and control suggestions for non-point source pollution were also provided. The results showed that the surplus of nitrogen and phosphorus in farmland of agricultural space in Xi'an was large and widely distributed. There were significant differences in the spatial distribution of non-point source pollution emissions. High-value areas were distributed in the east part of Yanliang District, the east part of Gaoling District, the plain area of Zhouzhi County and Chang'an District, and the Lishan hilly area in Lintong District and Lantian County. The nitrogen non-point source pollution is main affected by rural life, and the phosphorus non-point source pollution is mainly affected by soil erosion. The spatial distribution of nitrogen and phosphorus non-point source pollution into the river in agricultural space is similar, but the content is quite different. The high-value areas are located in the east part of Yanliang District, the middle and north part of Lintong District, the Lishan Mountain area of Lantian County, the central and eastern plain area of Zhouzhi County, the shallow mountain area of Qinling Mountains and both sides of some valley roads.
- diffuse pollution /
- DPeRS model /
- agricultural space /
- Xi'an

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参考文献(28)

[1]	高荣伟. 我国水资源的污染现状及对策分析[J]. 资源与人居环境, 2018, 239(11): 44-51.
[2]	王一格, 王海燕, 郑永林, 等. 农业面源污染研究方法与控制技术研究进展[J]. 中国农业资源与区划, 2021,42(1):25-33.
[3]	黄瑜, 刘佩诗, 甘曼琴,等. GIS技术在农业面源污染研究中的应用[J]. 中国土壤与肥料, 2020, 290(6):279-285.
[4]	中华人民共和国生态环境部, 国家统计局, 中华人民共和国农业农村部. 第二次全国污染源普查公报[R]. 2020.
[5]	王雪蕾. 遥感分布式面源污染评估模型:理论方法与应用[M]. 北京: 科学出版社, 2015.
[6]	USEPA(United States Environmental Protection Agency). Methods for Identifying and Evaluating the Nature and Extent of Non-point Sources of Pollutants[R].Washington, D.C., U.S. Environmental Protection Agency,1973.
[7]	冯爱萍, 黄莉, 王雪蕾, 等. 浦阳江流域(浦江县段)面源污染模型估算及河流生态缓冲带重点区域识别[J]. 环境工程学报,2022, 16(1): 73-84.
[8]	BORAH D K, YAGOW G, SALEH A, et al. Sediment and nutrient modeling for TMDL development and implementation[J]. Transactions of the ASABE, 2006, 49(4): 967-986.
[9]	张玉珍. 九龙江上游五川流域农业非点源污染研究[D]. 厦门: 厦门大学, 2003.
[10]	吴在兴, 王晓燕. 流域空间统计模型SPARROW及其研究进展[J]. 环境科学与技术, 2010, 33(9): 87-90 ,139.
[11]	谢经朝, 赵秀兰, 何丙辉,等. 汉丰湖流域农业面源污染氮磷排放特征分析[J]. 环境科学, 2019, 40(4): 1760-1769.
[12]	王玉, 王雪蕾, 张亚群,等. 基于DPeRS模型的渭河典型断面汇水区面源污染评估及污染成因分析[J]. 环境监控与预警, 2022, 14(6):8-16.
[13]	ABATZOGLOU J T, DOBROWSKI S Z, PARKS S A, et al. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958—2015[DB/OL]. Sci. Data 2018, 5, 170191. [2023-1-4]. https://developers.google.cn/earth-engine/datasets.
[14]	DIDAN K. MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006[DB/OL]. 2015, distributed by NASA EOSDIS Land Processes DAAC. [2023-1-4]. https://lpdaac.usgs.gov/.
[15]	FISCHER G, F NACHTERGAELE, S PRIELER, et al. Global Agro-ecological Zones Assessment for Agriculture (GAEZ 2008)[DB/OL]. IIASA, Laxenburg, Austria and FAO, Rome, Italy. [2023-1-4]. https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/.
[16]	NASA JPL. NASADEM Slope and Curvature Global 1 arc second V001[DB/OL]. 2020, distributed by NASA EOSDIS Land Processes DAAC. [2023-1-4]. https://search.earthdata.nasa.gov/search.
[17]	地表径流分布数据[DB/OL]. [2023-1-7]. 地理遥感生态网科学数据注册与出版系统(www.gisrs.cn).
[18]	西安市统计局, 国家统计局西安调查队. 西安市统计年鉴[M]. 北京: 中国统计出版社, 2021.
[19]	吴义根, 冯开文, 李谷成. 我国农业面源污染的时空分异与动态演进[J]. 中国农业大学学报, 2017, 22(7): 186-199.
[20]	高莹, 孙喜军, 吕爽,等. 陕西省化肥施用时空分异及面源污染环境风险评价[J]. 西北农林科技大学学报(自然科学版), 2021, 49(2): 76-83,96.
[21]	索龙, 赵晓进, 张俊丽, 等. 基于统计数据的陕西省农业面源污染现状分析[J]. 中国农学通报, 2021, 37(8): 137-144.
[22]	WANG X L, FENG A P, WANG Q, et al. Spatial variability of the nutrient balance and related NPSP risk analysis for agro-ecosystems in China in 2010[J]. Agriculture, Ecosystems & Environment, 2014, 193:42-52.
[23]	JIA K, LIANG S, LIU S, et al. Global land surface fractional vegetation cover estimation using general regression neural networks from MODIS surface reflectance[J]. IEEE Transactions on Geoscience & Remote Sensing, 2015, 53(9):4787-4796.
[24]	WANG X L, WANG Q, WU C Q, et al. A method coupled with remote sensing data to evaluate non-point source pollution in the Xin'anjiang catchment of China[J]. Science of the Total Environment, 2012, 430:132-143.
[25]	王略, 屈创赵国栋. 基于中国土壤流失方程模型的区域土壤侵蚀定量评价[J]. 水土保持通报, 2018, 38(1):122-125 ,130.
[26]	黄满湘, 周成虎, 章申,等. 农田暴雨径流侵蚀泥沙流失及其对氮磷的富集[J]. 水土保持学报, 2002, 16(4):13-16 ,33.
[27]	薛金凤, 夏军, 梁涛,等. 颗粒态氮磷负荷模型研究[J]. 水科学进展, 2005, 16(3):334-337.
[28]	刘宇林, 赵广举, 穆兴民,等. 近55年渭河流域降雨侵蚀力变化及对输沙量的影响[J]. 中国水土保持科学, 2019, 17(3):15-22.