近岸海域水环境容量的研究
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摘要
随着沿海地区经济的快速发展和人口的激增,使得近岸海域的污染日趋严重。为了有效地控制近岸海域的污染,就必须掌握其特征和规律,并在此基础上合理确定近海水环境容量。以往的机理性研究没有很好的利用环境监测数据,从经济上和实用性上不适用于环境管理实际。本文以天津市近岸海域为研究对象,完全依据环境监测数据,综合运用多学科理论与方法,成功实现了近海水质的预测、模拟和评价,并提出了计算水环境容量的新方法,为近海污染总量控制和环境管理提供了决策依据。
首先,根据灰色系统理论,对研究海域内的监测站位进行关联分析,计算站位之间的B型关联度,建立关联度矩阵和关联度排序矩阵; 依据上述信息,采用k-means方法对站位进行了分区,并提出了站位敏感度的概念,将研究海域分成了三个管理区域,得出了近岸海域的优化分区管理的思想;
其次,根据陆源入海水体的信息,成功地采用神经网络法预测监测站位的COD值,并采用Bayesian自动正则化技术解决了模型的过拟合问题; 首次采用Fourier级数对监测数据序列进行拟合,并应用强非线性加权最小二乘法成功确定级数中的系数用于预测; 而且,创新性的将上述预测结果采用由各自log-logistic概率密度确定的权重进行组合预测,误差大幅度降低。
再次,以批处理SOM算法和MATLAB环境下的SOM工具箱为背景和工具,编制了近海水质评价软件; 通过SOM的训练,将高维的监测数据转化成二维图形显示; 结合环境科学理论,将监测数据分为5种污染类型,并实现了图形化的监测数据的追踪和归类,大大简化和改进了监测数据的分析工作。
最后,本文首次采用地统计学原理分析了近岸海域污染物浓度的空间结构特征,在试验变异函数曲线的基础上,采用三种理论模型对其进行了拟合; 通过普通克里格插值成功实现了对于整个研究海域的污染物浓度估值; 结合GIS软件的地图代数运算的功能,确定研究海域的最不利区域,并依据质量守恒原理,最终确定了其在最不利条件下的水环境容量。
With the rapid economic development and the population booming in coastal zone, the coastal water quality was deteriorated. In order to control the coastal water pollution effectively, its characters and regulations must be mastered, based on which the coastal water environmental capacity could be calculated. The studies before which mainly concerned the mechanisms had taken little use of the environmental monitoring data, so they were inapplicable to management in the thinking of economics and practicability. In this dissertation, through the integrated using of many theories and methods in different fields, the prediction, simulation and assessment of the water quality in Tianjin coastal marine were studied totally based on the environmental monitoring data. Furthermore, a new calculation method for coastal water environmental capacity was proposed, which could supply the decision support for coastal pollution total amount control and environmental management.
Firstly, the association analysis among monitoring points was taken based on grey system theory. In this section, the B type association degrees between points were calculated and then the association degree matrix and its sort order matrix were set up. According to the information in the above two matrix, the monitoring points were clustered by k-means method. What’s more, the concept of point sensitivity was purposed. Then, the studied area was divided into three management subareas and the idea of coastal optimal management was presented.
Secondly, according to the information about the inlets, the COD values of each monitoring point were predicted by NN method successfully, during which the over-fitting problem was solved by Bayesian automatic regularization technology. And, the monitoring data serial of each point was also fitted by Fourier serial whose parameters were decided by robust weighted nonlinear least squares algorism, after which the Fourier serial was also used for prediction. Finally, the integrated prediction was taken based on the above two prediction results, whose weights were
calculated by its log-logistic probability density. This method was demonstrated to be effective to reduce the prediction error. Thirdly, based on the batch version of SOM algorism and SOM toolbox in MATLAB environment, the coastal water quality assessment software was compiled. Through the training of SOM, the monitoring data with high dimension could be abstracted and displayed in a two-dimension figure. Then, based on some research findings in environmental science, monitoring data was clustered into 5 pollution kinds. Furthermore, the data trajectory tracking and automatic classification were also realized in figure mode. So the water quality assessment could be simplified and improved. At last, for the first time, the spatial variation of contaminant concentration in the coastal marine was analyzed using geostatistical theory. Three theoretical models were used to fit the experimental variogram of each period, based on which the ordinary Kriging interpolation method was employed to estimate the COD concentration in the whole studied area. After that, the worst area was located by raster GIS maps algebraic functions. Finally, the water environmental capacity under the worst condition was calculated according to the conservation of mass.
首先,根据灰色系统理论,对研究海域内的监测站位进行关联分析,计算站位之间的B型关联度,建立关联度矩阵和关联度排序矩阵; 依据上述信息,采用k-means方法对站位进行了分区,并提出了站位敏感度的概念,将研究海域分成了三个管理区域,得出了近岸海域的优化分区管理的思想;
其次,根据陆源入海水体的信息,成功地采用神经网络法预测监测站位的COD值,并采用Bayesian自动正则化技术解决了模型的过拟合问题; 首次采用Fourier级数对监测数据序列进行拟合,并应用强非线性加权最小二乘法成功确定级数中的系数用于预测; 而且,创新性的将上述预测结果采用由各自log-logistic概率密度确定的权重进行组合预测,误差大幅度降低。
再次,以批处理SOM算法和MATLAB环境下的SOM工具箱为背景和工具,编制了近海水质评价软件; 通过SOM的训练,将高维的监测数据转化成二维图形显示; 结合环境科学理论,将监测数据分为5种污染类型,并实现了图形化的监测数据的追踪和归类,大大简化和改进了监测数据的分析工作。
最后,本文首次采用地统计学原理分析了近岸海域污染物浓度的空间结构特征,在试验变异函数曲线的基础上,采用三种理论模型对其进行了拟合; 通过普通克里格插值成功实现了对于整个研究海域的污染物浓度估值; 结合GIS软件的地图代数运算的功能,确定研究海域的最不利区域,并依据质量守恒原理,最终确定了其在最不利条件下的水环境容量。
With the rapid economic development and the population booming in coastal zone, the coastal water quality was deteriorated. In order to control the coastal water pollution effectively, its characters and regulations must be mastered, based on which the coastal water environmental capacity could be calculated. The studies before which mainly concerned the mechanisms had taken little use of the environmental monitoring data, so they were inapplicable to management in the thinking of economics and practicability. In this dissertation, through the integrated using of many theories and methods in different fields, the prediction, simulation and assessment of the water quality in Tianjin coastal marine were studied totally based on the environmental monitoring data. Furthermore, a new calculation method for coastal water environmental capacity was proposed, which could supply the decision support for coastal pollution total amount control and environmental management.
Firstly, the association analysis among monitoring points was taken based on grey system theory. In this section, the B type association degrees between points were calculated and then the association degree matrix and its sort order matrix were set up. According to the information in the above two matrix, the monitoring points were clustered by k-means method. What’s more, the concept of point sensitivity was purposed. Then, the studied area was divided into three management subareas and the idea of coastal optimal management was presented.
Secondly, according to the information about the inlets, the COD values of each monitoring point were predicted by NN method successfully, during which the over-fitting problem was solved by Bayesian automatic regularization technology. And, the monitoring data serial of each point was also fitted by Fourier serial whose parameters were decided by robust weighted nonlinear least squares algorism, after which the Fourier serial was also used for prediction. Finally, the integrated prediction was taken based on the above two prediction results, whose weights were
calculated by its log-logistic probability density. This method was demonstrated to be effective to reduce the prediction error. Thirdly, based on the batch version of SOM algorism and SOM toolbox in MATLAB environment, the coastal water quality assessment software was compiled. Through the training of SOM, the monitoring data with high dimension could be abstracted and displayed in a two-dimension figure. Then, based on some research findings in environmental science, monitoring data was clustered into 5 pollution kinds. Furthermore, the data trajectory tracking and automatic classification were also realized in figure mode. So the water quality assessment could be simplified and improved. At last, for the first time, the spatial variation of contaminant concentration in the coastal marine was analyzed using geostatistical theory. Three theoretical models were used to fit the experimental variogram of each period, based on which the ordinary Kriging interpolation method was employed to estimate the COD concentration in the whole studied area. After that, the worst area was located by raster GIS maps algebraic functions. Finally, the water environmental capacity under the worst condition was calculated according to the conservation of mass.
引文
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