基于流域空间属性的水环境响应关系研究
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摘要
国外水环境管理发展历程揭示，实行基于流域的水环境管理是我国的发展趋势和必然要求。在管理的技术支撑方面，我国目前面临着从流域环境数据收集提取、到流域环境状况模分析模拟等急需解决的问题。本论文主要在这两方面开展研究工作。
本论文探索了适合我国国情的流域空间属性数据提取方法。选择单流向法，使用ArcHydro工具生成松花江流域河网结构及相关编码系统；应用ArcGIS工具实现了流域属性的切割以及批量化数据提取；使用美国CN值平衡方程，选择松花江流域水文站点监测数据，拟合退水系数、融雪系数、各土地利用类型CN值等重要水文相关参数，进而利用这些水文参数估计子流域年均径流深，模拟各子流域年均流量；选用NANI插件实现了非点源排放数据由基于区县划分向基于子流域划分的转化。
本论文利用多元统计方法进行流域环境问题识别。选用描述统计量、相关分析、因子分析、时间序列分析等统计方法，利用流域内25个国控点位、13个指标、2005-2009年逐月数据表征和分析了松花江流域水环境状况。分析结果表明：松花江流域近年水质总体低于国家III类水标准，其中高锰酸盐指数、生化需氧量、总氮、总磷、石油类等指标平均水平低于III级标准，氨氮均值低于国家IV类水标准，化学需氧量均值达劣五类。以化学需氧量为代表的有机物指标以及总氮是松花江流域水环境污染的代表性指标。指标间相关关系表明，溶解氧与耗氧有机物、总氮等主要污染物呈显著负相关，而总氮与耗氧有机物的相关性高于总磷与耗氧有机物的相关性。因子分析结果呈现出三个因子，第一个因子方差贡献率52.18%，为主要污染物表征因子，与氮及耗氧有机物指标高度正相关，与溶解氧高度负相关；第二个因子方差贡献率19.97%，累积贡献率72.14%，为重金属污染物表征因子，与汞、铅、锌高度正相关；第三个因子方差贡献率11.64%，累积贡献率83.79%，为背景因子，反应pH状况。利用每个站点主要水质指标的水质类别百分率进行聚类分析，结合流域内主要超标污染物总氮的污染源普查数据，说明总氮对流域水环境状态有指示作用，以及水质浓度与污染源分布可能存在响应关系。针对松花江流域氮与耗氧有机物为主的水环境状况，进行氨氮与COD的季节性时间序列分析，结果显示，COD于2007年出现拐点，近期其浓度已呈下降趋势；NH3-N尚未见明显好转。
本论文使用水环境模型进行流域主要污染物的深入分析。选择SPARROW模型对总氮进行模拟。根据我国国情选择模型输入变量，采用Levenberg-Marquardt算法进行非线性优化。在明确模型结构算法、借鉴国外应用经验的基础上，经运行调试，得到了可接受的模型结果。模拟精度R2为0.79，松花江流域对总氮有显著贡献的污染源为点源、农业种植源、畜禽养殖源以及水产养殖源，对总氮传输具有重要影响的环境因子为温度、坡度、降水以及河网密度，对总氮传输具有重要影响的环境过程为河段中的一级衰减反应。
模拟结果表明，松花江流域河段中，三类水的超标率为63%，五类水的超标率为47%。超标河段为松花江哈尔滨段，牡丹江，伊通河，饮马河，呼兰河，拉林河，汤旺河等。总氮总负荷较高的地区为松花江哈尔滨及其下游、牡丹江流域、呼兰河、拉林河、汤旺河等；其中产率较高的子流域为嫩江东部支流、呼兰河及其支流、拉林河等河段。流域对总氮来源进行解析，松花江流域来自农业种植源的污染物占51.5%；来自点源的污染物占28.8%；来自水产养殖源的污染物占14.0%；来自畜禽养殖源的污染物占5.7%。总氮在子流域河段间的衰减表现出干流衰减比例小，支流衰减大的规律。选择三个代表性子流域，设置三个消减策略，预评估消减策略的效果。在各污染源不同消减时，主要污染源结构可能出现“拐点”，应及时进行主要削减方向调整，保证消减效率。
Based on the developing experience of water resources management in theforeign countries, it reveals that the implementation of basin-based water resourcesmanagement is the development trend and inevitable requirement of China, while weare facing many problems, such as the extraction and collection of watershedenvironmental data and the analysis of watershed environmental situation, etc. Thispaper is focusing on the first two problems.
This paper explores the suitable method for China to extract the watershedspatial attribute data in the following steps. First, choose a single flow method whichuses the ArcHydro tools to generate the Songhuajiang River basin river networkstructure and coding system. Second, apply ArcGIS tools to cut the watershedproperty based on the basin river network and extract data in a batch way. Third,according to the CN value balance equation, select the hydrological monitoring dataof Songhuajiang River basin to fit the recession coefficient, snowmelt factor, CNvalues for different land types and other important hydrological parameters, then usethese hydrological parameters to estimate the average annual runoff depth and theaverage annual flow of sub-basins. And at last achieve the division conversing fromcounty-based to basin-based of the non-point source emission data using NANIplug-ins.
Many statistical methods, such as description the statistics, correlation analysis,factor analysis, and time series analysis are selected in this paper to analyze theSonghua River Basin Water Environment conditions based on a large data set,including13parameters monitoring at25sites from2005to2009. The results showthat water quality of Songhuajiang River is generally lower than the national Class IIIwater standards, in which the average level of permanganate index, BOD, totalnitrogen, total phosphorus, oil and other indicators are lower than Class III standard,the level of ammonia nitrogen is lower than Class IV standard, while the averagelevel of chemical oxygen demand below Class V, the worst of all. The organic chemical oxygen demand and total nitrogen are the two representative pamameters ofwater pollution in Songhuajiang River Basin. The relationships between theparameters show that the dissolved oxygen, oxygen consumption of organic matterand total nitrogen exist a significant negative correlation, while the correlationbetween the total nitrogen and oxygen consumption of organic matter is higher thanwhich between the total phosphorus and oxygen consumption of organic matter. FAidentified three factors capturing52.18%,19.97%and11.64%of the total variance,respectively. Factor1had strong positive loadings on the total nitrogen and oxygenconsumption of organic matter and strong negative loading on the dissolved oxygen,this factor may be interpreted as the dominant pollutants characterization factor.Factor2had strong positive loadings on mercury, lead and zinc, mainly representedthe heavy metal pollution. Factor3is correlated with pH and explained asbackground factor. The dominant pollutions of Songhuajiang River are total nitrogenand oxygen consumption of organic matter, lead to seasonal time series analysis ofammonia nitrogen and COD and the result indicated that the COD with a inflectionpoint in2007occurred downward trend in recent years while the NH3-N has notimproved markedly.
SPARROW model is used in this paper to simulate the total nitrogen pollution.The model input variables, which are under the consideration of our state situation,are using the Levenberg-Marquardt algorithm for nonlinear optimization. With theunderstanding of model structure and the experience of foreign applications, themodel results are acceptable for the study area. Values of R2for the final TN modelsare0.79, and contaminant sources are included point sources, applied fertilizers foragricultural land, livestock wastes and aquiculture wastes. Coefficients estimated inthe model for temperature, land slope, precipitation and stream density arestatistically significant while the first in-stream decay is the most important processfor the total nitrogen transport.
The model results showed that there were63%reaches of Songhuajiang Riverbelow Class III as the ratio below Class V was47%. The worst stream reaches mainlylocated in Harbin included Mudanjiang River, Yitong River, Yinma River, HulanRiver, Lalin River, Tangwang River, etc. and the sub-basins with high yield were Nenjiang River eastern tributaries, Hulan River as well as its tributaries and LalinRiver. The application of SPARROW model on Songhuajiang River gave the sourceidentification of this watershed. The share of non-point agricultural source is thehighest,51.5%, the point source contributed approximately28.8%, aquiculture wastesoccupied14.0%and the share of livestock wastes was only5.7%, while the decaypercent in main stream is lower than which in tributaries among sub-basins.
本论文探索了适合我国国情的流域空间属性数据提取方法。选择单流向法，使用ArcHydro工具生成松花江流域河网结构及相关编码系统；应用ArcGIS工具实现了流域属性的切割以及批量化数据提取；使用美国CN值平衡方程，选择松花江流域水文站点监测数据，拟合退水系数、融雪系数、各土地利用类型CN值等重要水文相关参数，进而利用这些水文参数估计子流域年均径流深，模拟各子流域年均流量；选用NANI插件实现了非点源排放数据由基于区县划分向基于子流域划分的转化。
本论文利用多元统计方法进行流域环境问题识别。选用描述统计量、相关分析、因子分析、时间序列分析等统计方法，利用流域内25个国控点位、13个指标、2005-2009年逐月数据表征和分析了松花江流域水环境状况。分析结果表明：松花江流域近年水质总体低于国家III类水标准，其中高锰酸盐指数、生化需氧量、总氮、总磷、石油类等指标平均水平低于III级标准，氨氮均值低于国家IV类水标准，化学需氧量均值达劣五类。以化学需氧量为代表的有机物指标以及总氮是松花江流域水环境污染的代表性指标。指标间相关关系表明，溶解氧与耗氧有机物、总氮等主要污染物呈显著负相关，而总氮与耗氧有机物的相关性高于总磷与耗氧有机物的相关性。因子分析结果呈现出三个因子，第一个因子方差贡献率52.18%，为主要污染物表征因子，与氮及耗氧有机物指标高度正相关，与溶解氧高度负相关；第二个因子方差贡献率19.97%，累积贡献率72.14%，为重金属污染物表征因子，与汞、铅、锌高度正相关；第三个因子方差贡献率11.64%，累积贡献率83.79%，为背景因子，反应pH状况。利用每个站点主要水质指标的水质类别百分率进行聚类分析，结合流域内主要超标污染物总氮的污染源普查数据，说明总氮对流域水环境状态有指示作用，以及水质浓度与污染源分布可能存在响应关系。针对松花江流域氮与耗氧有机物为主的水环境状况，进行氨氮与COD的季节性时间序列分析，结果显示，COD于2007年出现拐点，近期其浓度已呈下降趋势；NH3-N尚未见明显好转。
本论文使用水环境模型进行流域主要污染物的深入分析。选择SPARROW模型对总氮进行模拟。根据我国国情选择模型输入变量，采用Levenberg-Marquardt算法进行非线性优化。在明确模型结构算法、借鉴国外应用经验的基础上，经运行调试，得到了可接受的模型结果。模拟精度R2为0.79，松花江流域对总氮有显著贡献的污染源为点源、农业种植源、畜禽养殖源以及水产养殖源，对总氮传输具有重要影响的环境因子为温度、坡度、降水以及河网密度，对总氮传输具有重要影响的环境过程为河段中的一级衰减反应。
模拟结果表明，松花江流域河段中，三类水的超标率为63%，五类水的超标率为47%。超标河段为松花江哈尔滨段，牡丹江，伊通河，饮马河，呼兰河，拉林河，汤旺河等。总氮总负荷较高的地区为松花江哈尔滨及其下游、牡丹江流域、呼兰河、拉林河、汤旺河等；其中产率较高的子流域为嫩江东部支流、呼兰河及其支流、拉林河等河段。流域对总氮来源进行解析，松花江流域来自农业种植源的污染物占51.5%；来自点源的污染物占28.8%；来自水产养殖源的污染物占14.0%；来自畜禽养殖源的污染物占5.7%。总氮在子流域河段间的衰减表现出干流衰减比例小，支流衰减大的规律。选择三个代表性子流域，设置三个消减策略，预评估消减策略的效果。在各污染源不同消减时，主要污染源结构可能出现“拐点”，应及时进行主要削减方向调整，保证消减效率。
Based on the developing experience of water resources management in theforeign countries, it reveals that the implementation of basin-based water resourcesmanagement is the development trend and inevitable requirement of China, while weare facing many problems, such as the extraction and collection of watershedenvironmental data and the analysis of watershed environmental situation, etc. Thispaper is focusing on the first two problems.
This paper explores the suitable method for China to extract the watershedspatial attribute data in the following steps. First, choose a single flow method whichuses the ArcHydro tools to generate the Songhuajiang River basin river networkstructure and coding system. Second, apply ArcGIS tools to cut the watershedproperty based on the basin river network and extract data in a batch way. Third,according to the CN value balance equation, select the hydrological monitoring dataof Songhuajiang River basin to fit the recession coefficient, snowmelt factor, CNvalues for different land types and other important hydrological parameters, then usethese hydrological parameters to estimate the average annual runoff depth and theaverage annual flow of sub-basins. And at last achieve the division conversing fromcounty-based to basin-based of the non-point source emission data using NANIplug-ins.
Many statistical methods, such as description the statistics, correlation analysis,factor analysis, and time series analysis are selected in this paper to analyze theSonghua River Basin Water Environment conditions based on a large data set,including13parameters monitoring at25sites from2005to2009. The results showthat water quality of Songhuajiang River is generally lower than the national Class IIIwater standards, in which the average level of permanganate index, BOD, totalnitrogen, total phosphorus, oil and other indicators are lower than Class III standard,the level of ammonia nitrogen is lower than Class IV standard, while the averagelevel of chemical oxygen demand below Class V, the worst of all. The organic chemical oxygen demand and total nitrogen are the two representative pamameters ofwater pollution in Songhuajiang River Basin. The relationships between theparameters show that the dissolved oxygen, oxygen consumption of organic matterand total nitrogen exist a significant negative correlation, while the correlationbetween the total nitrogen and oxygen consumption of organic matter is higher thanwhich between the total phosphorus and oxygen consumption of organic matter. FAidentified three factors capturing52.18%,19.97%and11.64%of the total variance,respectively. Factor1had strong positive loadings on the total nitrogen and oxygenconsumption of organic matter and strong negative loading on the dissolved oxygen,this factor may be interpreted as the dominant pollutants characterization factor.Factor2had strong positive loadings on mercury, lead and zinc, mainly representedthe heavy metal pollution. Factor3is correlated with pH and explained asbackground factor. The dominant pollutions of Songhuajiang River are total nitrogenand oxygen consumption of organic matter, lead to seasonal time series analysis ofammonia nitrogen and COD and the result indicated that the COD with a inflectionpoint in2007occurred downward trend in recent years while the NH3-N has notimproved markedly.
SPARROW model is used in this paper to simulate the total nitrogen pollution.The model input variables, which are under the consideration of our state situation,are using the Levenberg-Marquardt algorithm for nonlinear optimization. With theunderstanding of model structure and the experience of foreign applications, themodel results are acceptable for the study area. Values of R2for the final TN modelsare0.79, and contaminant sources are included point sources, applied fertilizers foragricultural land, livestock wastes and aquiculture wastes. Coefficients estimated inthe model for temperature, land slope, precipitation and stream density arestatistically significant while the first in-stream decay is the most important processfor the total nitrogen transport.
The model results showed that there were63%reaches of Songhuajiang Riverbelow Class III as the ratio below Class V was47%. The worst stream reaches mainlylocated in Harbin included Mudanjiang River, Yitong River, Yinma River, HulanRiver, Lalin River, Tangwang River, etc. and the sub-basins with high yield were Nenjiang River eastern tributaries, Hulan River as well as its tributaries and LalinRiver. The application of SPARROW model on Songhuajiang River gave the sourceidentification of this watershed. The share of non-point agricultural source is thehighest,51.5%, the point source contributed approximately28.8%, aquiculture wastesoccupied14.0%and the share of livestock wastes was only5.7%, while the decaypercent in main stream is lower than which in tributaries among sub-basins.
引文
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