[1]郑家珂,甘 容,左其亭,等.基于 PNPI 与 SWAT 模型的非点源污染风险空间分布[J].郑州大学学报(工学版),2023,44(03):22-29.[doi:10.13705/j.issn.1671-6833.2023.03.014]
　ZHENG Jiake,GAN ong,ZUO Qiting,et al.Spatial Distribution of Non-point Source Pollution Ｒisk Based on PNPI and SWAT Model[J].Journal of Zhengzhou University (Engineering Science),2023,44(03):22-29.[doi:10.13705/j.issn.1671-6833.2023.03.014]
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基于 PNPI 与 SWAT 模型的非点源污染风险空间分布()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
44
期数:
2023年03期
页码:
22-29
栏目:
出版日期:
2023-04-30

文章信息/Info

Title:
Spatial Distribution of Non-point Source Pollution Ｒisk Based on PNPI and SWAT Model
作者:
郑家珂1; 2; 甘 容1; 2; 左其亭1; 3; 杨 峰4
1．郑州大学 水利科学与工程学院，河南 郑州 450001; 2．郑州大学 河南省地下水污染防治与修复重点实验室，河南 郑州 450001; 3．郑州大学 郑州市水资源与水环境重点实验室，河南 郑州 450001; 4．河南省出山店水库建设管理局， 河南 信阳 464043
Author(s):
ZHENG Jiake; GAN Ｒong; ZUO Qiting; YANG Feng
1.School of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 450001, China; 2.Henan Key Laboratory of Groundwater Pollution Prevention and Rehabilitation, Zhengzhou University,Zhengzhou 450001, China; 3.Zhengzhou Key Laboratory of Water Resources and Water Environment, Zhengzhou University, Zhengzhou 450001, China; 4.Construction Management Company for Chushandian Reservoir of Project of Henan Pro-vince, Xinyang 464043, China
关键词:
氮磷污染;  潜在非点源污染指数模型;  输出系数法;  SWAT 模型;  伊洛河流域
Keywords:
nitrogen and phosphorus pollution;  potential non-point source pollution index model;  output coefficient method;  SWAT model;  Yiluo River Basin
分类号:
X52
DOI:
10.13705/j.issn.1671-6833.2023.03.014
文献标志码:
A
摘要:
为了明确流域非点源氮( N) 、磷( P) 营养物质污染风险空间分布特征，识别流域非点源关键污染源区，以伊 洛河流域为例，采用输出系数法量化流域不同土地利用类型、居民生活和禽畜养殖所产生的 N、P 污染负荷，借助改 进后的潜在非点源污染指数( PNPI) 模型和 SWAT 模型，阐述流域非点源 N、P 污染风险空间分布特征，识别 N、P 污 染关键源区; 采用皮尔逊相关系数法计算两模型模拟结果的相关性，评价改进后的 PNPI 模型模拟结果的可靠性。 结果表明: 2020 年伊洛河流域 N、P 非点源污染风险空间分布较为相似，均呈现出伊河、洛河两支流上游污染风险 较低，中下游污染风险较高的空间分布特征，N 和 P 的极低、低、中、高、极高风险区面积分别占伊洛河总面积的 48. 85%、14. 61%、9. 68%、14. 64%、12. 22%和 55. 48%、8. 76%、11. 14%、13. 25%、11. 38%; 土地利用输出是流域产生 N、P 污染的主要源头，负荷量分别为 20 643. 62、3 033. 31 t /a，不同的土地利用类型中耕地输出的 N、P 负荷量最大， 分别为 13 000. 07、1 956. 44 t /a，草地输出产生的 N 负荷量最小，为 1 322. 99 t /a，居民用地产生的 P 负荷量最小，为 113. 61 t /a; 改进后的 PNPI 模型与 SWAT 模型模拟的 N、P 污染负荷间的皮尔逊相关系数均达到 0. 6，表明改进后 的 PNPI 模型适用于该研究区。
Abstract:
In order to clarify the spatial distribution characteristics of non-point source nitrogen (N) and phosphorus (P) nutrient pollution risk in the watershed, the key non-point source pollution source areas in the watershed were identified. This study took the Yiluo River Basin as an example. The output coefficient method was used to quantify the N and P pollution loads generated by different land use types, residents′ lives and livestock breeding in the watershed. With the improved potential non-point pollution indicator (PNPI) model and SWAT (soil and water assessment tool) model, the spatial distribution characteristics of N and P pollution risk were described, and the key source areas of N and P pollution were identified. Pearson correlation coefficient method was used to calculate the correlation between the simulation results of the two models, and the reliability of the simulation results of the improved PNPI model was evaluated. The results showed that in 2020, the spatial distribution of N and P non-point source pollution risks in the Yiluo River Basin was similar. It showed the spatial distribution characteristics of lower pollution risks in the upper reaches of the Yi River and Luo River tributaries, and higher pollution risks in the middle and lower reaches. N and P very low, low, medium, high and extremely high risk areas accounted for 48.85%, 14.61%, 9.68%, 14.64%, 12.22% and 55.48%, 8.76%, 11.14%, 13.25%, 11.38% of the total area of Yiluo River, respectively. The output of land use was the main source of N and P pollution in the basin, with the load of 20 643.62 t/a and 3 033.31 t/a, respectively. Among different land use types, the output of N and P pollution from arable land was the most, which were 13 000.07 t/a and 1 956.44 t/a, respectively; the output of grassland produced the least N pollution, with a pollution load of 1 322.99 t/a. The P pollution of residential land was the least, and the pollution load was 113.61 t/a. The Pearson correlation coefficients between the N and P pollution loads simulated by the improved PNPI model and the SWAT model reached 0.6, indicating that the improved PNPI model was suitable for the study area.

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更新日期/Last Update: 2023-05-08