基于水质模型的区域污染控制研究
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摘要
近年来,随着社会经济的不断发展和人口数量不断增加,污染物排放也随之增加,造成的环境问题越来越明显。目前,我国实行的目标总量控制已经不能很好地适应区域环境管理的要求,因此实施以环境容量为基础的污染物总量控制是控制水环境污染、改善区域环境质量的重要措施。区域环境污染现状分析评价、区域环境容量的确定以及污染物排放总量的优化分配是实施容量总量控制的几个关键问题。本研究分别对以上几个关键问题进行了研究,根据研究区域水污染控制的现状,识别了区域主要污染源；探讨了不同水质评价方法对区域水环境质量评价的适用性；利用GIS和水质模型等工具建立了适合研究区域的水质模型；通过水质模拟分析对区域环境容量进行了计算,同时引入经济学参数—基尼系数对区域总量进行了分配；对不同年份的污染物总量进行了预测分析,提出了各类污染控制措施,还定量化分析了这些措施的污染控制效果。这些工作可以为做好区域污染控制工作提供借鉴和指导。主要研究方法与成果如下：
本研究共调查了研究区域重点排污企业100家,分别对这些企业的污水排放量、污染物质(COD和NH3-N)排放量、生产量等进行调查。调查的企业主要包括纺织印染企业、造纸及纸制品企业、食品加工企业、污水处理厂等。同时,还对研究区域的生活污染、畜禽养殖污染、农业面源污染等污染源的入河量进行了计算,并对研究区域的主要污染源进行了识别。研究发现研究区域COD和NH3-N的总排放量分别为15388.9 t/a和2361.89 t/a,其中来自生活污染和工业污染的COD分别为10495.11t/a和3772.86t/a,NH3-N分别为1893.132t/a和385.89t/a,由此可以看出研究区域主要污染源为生活污染。研究区域的工业污染主要来自纺织印染、造纸及纸制品和食品加工三个行业。这三个行业的废水量、COD和NH3-N排放量占区域总排放量的比例分别为83%、87%和92%。按照污染排放由大到小排序依次为：纺织印染业,造纸及纸制品,食品加工业。
本研究分析了各类评价方法在研究区域的适用性,分别选择单因子评价法、模糊综合评价法、灰色关联评价法、主成分分析法和神经网络法对区域26个水质功能区的水质现状进行了评价,评价指标包括：溶解氧、高锰酸盐指数、五日生化需氧量、氨氮、铜、锌、挥发酚、石油类。分析各评价方法的原理和评价过程发现,单因子评价主要对单个污染指标进行评价,并以最差评价因子所属的级别来评判水质级别。单因子评价法计算最简便,可以直接反映超标的污染因子,但是其评价结果不能全面的反映整体水质,往往出现评价结果过于严格的情况。灰色关联评价法和模糊综合评价法均可以避免出现水质级别不连续的缺点,同时还可以对同级别的水质进行分析判别。但是,这两种方法均存在分辨率不高,易受权重影响的问题。BP神经网络法实现了一个从输入到输出的映射功能,判定水质级别更加明显,但是也不能对主要污染因子进行识别。研究发现,主成分分析法可以对水质污染情况进行综合分析,将多个评价因子转化为少数具有代表性的主成分因子,同时可以识别区域主要污染因子的类别和对污染因子进行排序,满足本次研究的目的,因此将主成分分析法的评价结果作为后续研究的基础。评价结果显示各监测断面水质污染情况均表现为：枯水期>平水期>丰水期。综合各水期水质状况来看,中大河、鄞州河网水质最差,甬江下游和奉化江水质整体较好。姚江、鄞江上游水质最好。污染较严重的水域主要集中在鄞州河网、中大河和鄞江下游,这些污染较严重的河段可以作为未来重点治理区域。
本研究利用ArcGIS对研究区域水域进行了概化,并利用开放的水质模型软件WASP7.4,开发建立适合研究区域的水质模型,利用该模型进行了水质模拟,确定甬江、奉化江、鄞江和姚江的9个河段作为模拟河段,主要模拟的污染物为COD和NH3-N。同时,利用实测数据对水质模型进行了率定,还分析了COD衰减系数、流量、水温、大气复氧系数和硝化速率系数这些主要参数的敏感性。研究发现影响COD的主要参数为：COD衰减系数、流量和水温,按照敏感度由高到低依次为：COD衰减系数、流量、水温、大气复氧系数和硝化速率系数。影响NH3-N的主要参数为：流量和硝化速率系数,其次为水温,COD衰减系数对NH3-N的影响几乎可以忽略不计。分析水质模拟结果发现,模拟水质数据与实测水质数据的变化趋势基本一致,各河段COD模拟数值与实测数值的平均误差低于12%,NH3-N模拟数值与实测数值的平均误差低于15%,表明建立的水质模型可以用来模拟研究区域的水质变化。通过水质模拟分析发现：枯水期和平水期COD和NH3-N峰值出现在S-4段,然后逐渐降低。丰水期丰水期COD峰值出现在S-5段,NH3-N峰值出现在S-4段。S-8至S-9河段在各水期表现一致,均为上升趋势。各河段水质污染程度均表现为：丰水期<平水期<枯水期,水质超标主要集中在11月至1月,6月至9月水质较好。
本研究利用水质模型和ArcGIS建立了研究区域污染动态管理系统,该系统的主要功能包括：数据查询、地图制作、区域水质现状统计分析、数据管理、高程分析、水质分析、水文分析等,并且可以实现水质评价结果和水质模拟结果的可视化输出。通过编辑将水质模拟结果对各河段进行赋值,可以实现水质模拟结果的可视化输出。同时利用数据库更新功能,可以对数据库进行数据添加、删除、修改等,便于实现区域污染动态化管理。
利用率定的水质模型分别计算了研究区域的环境容量和区域各河段的环境容量。计算结果显示整个区域的COD和NH3-N的环境容量分别为12900.72 t/a和2361.89 t/a。研究发现S-1至S-5河段COD现状排放量超过了环境容量,S-6至S-9河段COD现状排放量小于环境容量,其中S-4河段和S-5河段污染最严重,分别需要消减2090.02t/a和1177.49 t/a才能够满足水质达标的需求。同样,S-1至S-5河段NH3-N现状排污量也超过了环境容量,S-6至S-9河段NH3-N现状排放量小于环境容量,其中超标最为严重的河段为S-4河段,现状排放量超过环境容量的3倍,消减量达307.73 t/a。另外,本研究还将经济学参数-基尼系数引入总量分配,选择现状排放量、人口、GDP、水资源量、和土地面积作为控制指标,最终分配方案的COD基尼系数分别为0.187、0.162、0.061、0.598,NH3-N的基尼系数分别为0.222、0.208、0.095、0.634,由此可知分配方案对人口、经济发展、水资源量而言是较为公平的,该方案可以实施。
最后,利用模型分析了区域各行业的节水潜力,定量化分析了节水减污、工业预处理、产业调整、限期治理、集中处理和非点源污染控制等污染控制措施的效果,研究发现：研究区域主要用水企业节水量可达6614448 m3/a,占总取水量的43.8%,节水潜力巨大。利用节水减污和行政手段分别可实现COD减排587.8t/a和810.8 t/a,NH3-N减排74.5 t/a和49.2 t/a。实施所有减排措施后2012年区域COD排放总量可以达到10292.28t/a,NH3-N排放总量可以达到1555.03t/a,除S-5河段外,其余河段均可以达标；2015年区域COD排放总量可以达到12217.19t/a,NH3-N排放总量可以达到1910.67t/a,S-5和S-6河段未达标,其余河段均可以实现达标。
In recent years, social and economic development and continuously increasing population result in more and more environmental issues. In China, total amount control on the basis of environmental management objective could not meet the requirements of regional environmental management. It is a good measure to control water pollution and improve regional environmental quality through implementing total amount control on the basis of environmental capacity. There are some key steps of implementing total amount control on the basis of environmental capacity,which include to analyze regional environmental baseline condition, to calculate the regional environmental capacity, and to conduct optimal allocation of total pollutant amount. In this paper, these key issues have been studied respectively. The research are composed of:identifying the main regional pollution source based on the understanding of the current condition of water pollution in this area, discussing the applicability of different water quality evaluation method, establishing the applicable regional water quality model for river water quality simulation of the target area by using GIS and the water quality model, identifying regional environmental capacity through water quality modeling analysis, conducting total amount allocation by introducing the Gini coefficient. Meanwhile, the forecast has been done for the variety pollution load of different years, quantitative analysis on the effects of various pollution control measures has been made, which provided the reference and guidance for the regional pollution control. The main research method and results are as follows:
The investigation of 100 key enterprises has been conducted to understand the polluted water quantity, total amount of COD and NH3-N respectively. The enterprises mainly come from textile industry, paper making industry, food processing industry, waste water treatment plant, etc. The calculation has been done respectively on the quantity of the different type of pollution sources such as industrial pollution, domestic pollution, livestock pollution, agricultural nonpoint source pollution, etc, which is to identify the main pollution source of study area. Through the analysis, it has been found that the total amount of COD and NH3-N were 15,388.9 t/a and 2361.89 t/a respectively.Among which, the COD and NH3-N were 3,772.86t/a and 385.89t/a come from industrial wastewater, and they were 10,495.11t/a and 1,893.132t/a come from domestic wastewater in the study area, which has been found that the main pollution comes from domestic pollution. The main industrial pollution generated by textile industry, paper making industry and food processing industry. Sort by pollution:textile industry, paper making industry and food processing industry. The proportion of wastewater quantity, quntities of COD and NH3-N of these three industries occupied total amount of study area were 83%,87% and 92% respectively.
By selecting single factor evaluation, fuzzy comprehensive evaluation method, grey evaluation, principal component analysis, and neural network of the regional water quality status of water quality in 26 water functional areas were evaluated. The main indicators were:dissolved oxygen, permanganate index, five-days biochemical oxygen demand, ammonia nitrogen, copper, zinc, volatile phenol, petroleum. The results showed that single-factor evaluation classify the water quality based on the evaluation of wors factor. The method can response to excessive pollution factor directly, but not fully reflect the water quality. Fuzzy comprehensive evaluation method and grey evaluation can avoid the shortcomings that the water quality level is not continuous, and can analyze the water quality in the same level. However, it can be found that the results of these two method high affect by weight coefficents and with low resolution. BP neural network implements a mapping from input to output, to determine the water level more clearly. However this method can not identify the major pollution factor. Principal component analysis can transformed large number of multiple factors into a small number of representative evaluation factors, and identify and sort the major pollution factors.The results showed:the results of five overall consistent evaluation methods, only the individual sections of a discrepancy in the evaluation results more in line with the actual situation. The evaluation results showed that water pollution monitoring sections are as follows:the dry season> normal water> wet period. From the comprehensive analysis of water quality in different period, the worst were Zhongda River and Yinzhou River. Yongjiang River and Fenghua River is comparably good from the overall situation. Yao Jiang, Jiang Yin upstream water quality is the best. More polluting river water concentrated in downstream of Yinzhou River, Zhongda River and Yin River, which these rivers can be treated as the key governance areas in the future.
By using ArcGIS, the generalization has been done for targeting study area and the regional water quality model has been established by using WASP. The nine river sections of Yongjiang, Fenghua River, Yin Jiang and Yao River have been set as the simulations sections. COD and NH3-N have been selected as the main simulation pollutants. Meanwhile, The water quality model was calibrated by using the mornitoring data, and the sensitivity of key parameters were analyzed. The main parameters affecting COD were COD degration coefficient, flow and water temperature, the order of sensitivity from high to low were:COD degration coefficient, flow, water temperature, atmospheric reaeration coefficient and nitrification rate coefficient. The main parameters affecting NH3-N were flow and nitrification rate coefficient, followed by water temperature. The impact of COD degration coefficient on NH3-N is almost negligible. It was found that the variation trend of simulation data is consistent with the variation trend of monitoring data. The average error of simulation data and monitoring data of COD were less than 12%, and the error of of simulation data and monitoring data of NH3-N were less than 15% in average, which indicated that the established water quality model could be used to simulate water quality of the study area. It has been found that the concentration of COD and NH3-N reached the peak in the S-4 River Section in the dry season and normal season, and then gradually decreased. The concentration of COD and NH3-N reached the peak in S-5 River Section and S-4 River Section respectively in the wet season.. S-8 and S-9 reach the same performance in different period, and both of them were in the trend of increasing. The degree of water pollution of all river sections are as follows:The wet season, normal season, dry season. The water quality can not reach the requirements of water objective mainly was in November to next January, and the water quality during June to September quality was better.
The regional pollution dynamic management system has been established by using water quality model and ArcGIS. The main functions of this system include:data queries, statistical analysis of environmental condition, data management, elevation analysis, water analysis, hydrological analysis,map making, etc, as well as the visual output of water quality simulation. Water quality simulation results by editing the assignment of the river, the water quality simulation results can be achieved visual output. While taking advantage of the fuction of data update, adding, deleting and modifying data in the database can be made, which to facilitate dynamic management of regional pollution.
The calculation has been done by using the calibrated water quality model to get the environmental capacity of whole region and each section, and the environmental capacity of COD and NH3-N of the entire region were 12,900.72 t/a and 2,361.89 t/a respectively. It has been found that COD discharge in S-1 to S-5 River Sections were over the environmental capacity, COD discharge in S-6 to S-9 were less than the environmental capacity, in which S-4 and S-5 reach the most polluted river section, respectively, need to reduce 2,090.02t/a, and 1,177.49 t/a to meet the water objectives. And NH3-N discharge in S-1 to S-5 River Sections were over the environmental capacity, NH3-N discharge in S-6 to S-9 were less than the environmental capacity, in which S-4 was the most polluted river section, need to reduce 307.73 t/a to meet the water objectives. Meanwhile, an economic parameter Gini coefficient was introducted to total load allocation. Currenct pollution discharge, population, GDP, water resources and land area were selected as control indexes. The Gini coefficients with COD of final load allocation were 0.187,0.162,0.061 and 0.598 respectively. The Gini coefficients with NH3-N were 0.222,0.208,0.095 and 0.634. That can be concluded that the total load allocation is equitable based on the population, economic development, water resources. The allocation plan can be implemented well.
Finally, water conservation potential were analyzed by using water balance model and effectiveness water saving and pollution remediation, industrial pretreatment, time limited industrial restructuring and non-point source pollution control were analyzed quantitatively. The water conservation potential of main water using enterprises could reach 6,614,448 m3/a which occupy 43.8% of the total quantity of water using. By using water conservation, administrative methods to achieve 587.8t/a and 810.8 t/a of COD reduction,74.5 t/a and 49.2 t/a of NH3-N reduction. Implementation of all mitigation measures can achieve 10,292.28t/a of COD reduction and 1555.03t/a of NH3-N reduction. Except S-5 river section, the other river section can reach requirement of water objectives in 2012; COD and NH3-N will dischage 12,217.19t/a and 1,910.67t/a respecively in 2015, S-5 and S-6 were not compliance with the requirement of water objectives, the rest river sections could meet requirement of water objectives.
本研究共调查了研究区域重点排污企业100家,分别对这些企业的污水排放量、污染物质(COD和NH3-N)排放量、生产量等进行调查。调查的企业主要包括纺织印染企业、造纸及纸制品企业、食品加工企业、污水处理厂等。同时,还对研究区域的生活污染、畜禽养殖污染、农业面源污染等污染源的入河量进行了计算,并对研究区域的主要污染源进行了识别。研究发现研究区域COD和NH3-N的总排放量分别为15388.9 t/a和2361.89 t/a,其中来自生活污染和工业污染的COD分别为10495.11t/a和3772.86t/a,NH3-N分别为1893.132t/a和385.89t/a,由此可以看出研究区域主要污染源为生活污染。研究区域的工业污染主要来自纺织印染、造纸及纸制品和食品加工三个行业。这三个行业的废水量、COD和NH3-N排放量占区域总排放量的比例分别为83%、87%和92%。按照污染排放由大到小排序依次为：纺织印染业,造纸及纸制品,食品加工业。
本研究分析了各类评价方法在研究区域的适用性,分别选择单因子评价法、模糊综合评价法、灰色关联评价法、主成分分析法和神经网络法对区域26个水质功能区的水质现状进行了评价,评价指标包括：溶解氧、高锰酸盐指数、五日生化需氧量、氨氮、铜、锌、挥发酚、石油类。分析各评价方法的原理和评价过程发现,单因子评价主要对单个污染指标进行评价,并以最差评价因子所属的级别来评判水质级别。单因子评价法计算最简便,可以直接反映超标的污染因子,但是其评价结果不能全面的反映整体水质,往往出现评价结果过于严格的情况。灰色关联评价法和模糊综合评价法均可以避免出现水质级别不连续的缺点,同时还可以对同级别的水质进行分析判别。但是,这两种方法均存在分辨率不高,易受权重影响的问题。BP神经网络法实现了一个从输入到输出的映射功能,判定水质级别更加明显,但是也不能对主要污染因子进行识别。研究发现,主成分分析法可以对水质污染情况进行综合分析,将多个评价因子转化为少数具有代表性的主成分因子,同时可以识别区域主要污染因子的类别和对污染因子进行排序,满足本次研究的目的,因此将主成分分析法的评价结果作为后续研究的基础。评价结果显示各监测断面水质污染情况均表现为：枯水期>平水期>丰水期。综合各水期水质状况来看,中大河、鄞州河网水质最差,甬江下游和奉化江水质整体较好。姚江、鄞江上游水质最好。污染较严重的水域主要集中在鄞州河网、中大河和鄞江下游,这些污染较严重的河段可以作为未来重点治理区域。
本研究利用ArcGIS对研究区域水域进行了概化,并利用开放的水质模型软件WASP7.4,开发建立适合研究区域的水质模型,利用该模型进行了水质模拟,确定甬江、奉化江、鄞江和姚江的9个河段作为模拟河段,主要模拟的污染物为COD和NH3-N。同时,利用实测数据对水质模型进行了率定,还分析了COD衰减系数、流量、水温、大气复氧系数和硝化速率系数这些主要参数的敏感性。研究发现影响COD的主要参数为：COD衰减系数、流量和水温,按照敏感度由高到低依次为：COD衰减系数、流量、水温、大气复氧系数和硝化速率系数。影响NH3-N的主要参数为：流量和硝化速率系数,其次为水温,COD衰减系数对NH3-N的影响几乎可以忽略不计。分析水质模拟结果发现,模拟水质数据与实测水质数据的变化趋势基本一致,各河段COD模拟数值与实测数值的平均误差低于12%,NH3-N模拟数值与实测数值的平均误差低于15%,表明建立的水质模型可以用来模拟研究区域的水质变化。通过水质模拟分析发现：枯水期和平水期COD和NH3-N峰值出现在S-4段,然后逐渐降低。丰水期丰水期COD峰值出现在S-5段,NH3-N峰值出现在S-4段。S-8至S-9河段在各水期表现一致,均为上升趋势。各河段水质污染程度均表现为：丰水期<平水期<枯水期,水质超标主要集中在11月至1月,6月至9月水质较好。
本研究利用水质模型和ArcGIS建立了研究区域污染动态管理系统,该系统的主要功能包括：数据查询、地图制作、区域水质现状统计分析、数据管理、高程分析、水质分析、水文分析等,并且可以实现水质评价结果和水质模拟结果的可视化输出。通过编辑将水质模拟结果对各河段进行赋值,可以实现水质模拟结果的可视化输出。同时利用数据库更新功能,可以对数据库进行数据添加、删除、修改等,便于实现区域污染动态化管理。
利用率定的水质模型分别计算了研究区域的环境容量和区域各河段的环境容量。计算结果显示整个区域的COD和NH3-N的环境容量分别为12900.72 t/a和2361.89 t/a。研究发现S-1至S-5河段COD现状排放量超过了环境容量,S-6至S-9河段COD现状排放量小于环境容量,其中S-4河段和S-5河段污染最严重,分别需要消减2090.02t/a和1177.49 t/a才能够满足水质达标的需求。同样,S-1至S-5河段NH3-N现状排污量也超过了环境容量,S-6至S-9河段NH3-N现状排放量小于环境容量,其中超标最为严重的河段为S-4河段,现状排放量超过环境容量的3倍,消减量达307.73 t/a。另外,本研究还将经济学参数-基尼系数引入总量分配,选择现状排放量、人口、GDP、水资源量、和土地面积作为控制指标,最终分配方案的COD基尼系数分别为0.187、0.162、0.061、0.598,NH3-N的基尼系数分别为0.222、0.208、0.095、0.634,由此可知分配方案对人口、经济发展、水资源量而言是较为公平的,该方案可以实施。
最后,利用模型分析了区域各行业的节水潜力,定量化分析了节水减污、工业预处理、产业调整、限期治理、集中处理和非点源污染控制等污染控制措施的效果,研究发现：研究区域主要用水企业节水量可达6614448 m3/a,占总取水量的43.8%,节水潜力巨大。利用节水减污和行政手段分别可实现COD减排587.8t/a和810.8 t/a,NH3-N减排74.5 t/a和49.2 t/a。实施所有减排措施后2012年区域COD排放总量可以达到10292.28t/a,NH3-N排放总量可以达到1555.03t/a,除S-5河段外,其余河段均可以达标；2015年区域COD排放总量可以达到12217.19t/a,NH3-N排放总量可以达到1910.67t/a,S-5和S-6河段未达标,其余河段均可以实现达标。
In recent years, social and economic development and continuously increasing population result in more and more environmental issues. In China, total amount control on the basis of environmental management objective could not meet the requirements of regional environmental management. It is a good measure to control water pollution and improve regional environmental quality through implementing total amount control on the basis of environmental capacity. There are some key steps of implementing total amount control on the basis of environmental capacity,which include to analyze regional environmental baseline condition, to calculate the regional environmental capacity, and to conduct optimal allocation of total pollutant amount. In this paper, these key issues have been studied respectively. The research are composed of:identifying the main regional pollution source based on the understanding of the current condition of water pollution in this area, discussing the applicability of different water quality evaluation method, establishing the applicable regional water quality model for river water quality simulation of the target area by using GIS and the water quality model, identifying regional environmental capacity through water quality modeling analysis, conducting total amount allocation by introducing the Gini coefficient. Meanwhile, the forecast has been done for the variety pollution load of different years, quantitative analysis on the effects of various pollution control measures has been made, which provided the reference and guidance for the regional pollution control. The main research method and results are as follows:
The investigation of 100 key enterprises has been conducted to understand the polluted water quantity, total amount of COD and NH3-N respectively. The enterprises mainly come from textile industry, paper making industry, food processing industry, waste water treatment plant, etc. The calculation has been done respectively on the quantity of the different type of pollution sources such as industrial pollution, domestic pollution, livestock pollution, agricultural nonpoint source pollution, etc, which is to identify the main pollution source of study area. Through the analysis, it has been found that the total amount of COD and NH3-N were 15,388.9 t/a and 2361.89 t/a respectively.Among which, the COD and NH3-N were 3,772.86t/a and 385.89t/a come from industrial wastewater, and they were 10,495.11t/a and 1,893.132t/a come from domestic wastewater in the study area, which has been found that the main pollution comes from domestic pollution. The main industrial pollution generated by textile industry, paper making industry and food processing industry. Sort by pollution:textile industry, paper making industry and food processing industry. The proportion of wastewater quantity, quntities of COD and NH3-N of these three industries occupied total amount of study area were 83%,87% and 92% respectively.
By selecting single factor evaluation, fuzzy comprehensive evaluation method, grey evaluation, principal component analysis, and neural network of the regional water quality status of water quality in 26 water functional areas were evaluated. The main indicators were:dissolved oxygen, permanganate index, five-days biochemical oxygen demand, ammonia nitrogen, copper, zinc, volatile phenol, petroleum. The results showed that single-factor evaluation classify the water quality based on the evaluation of wors factor. The method can response to excessive pollution factor directly, but not fully reflect the water quality. Fuzzy comprehensive evaluation method and grey evaluation can avoid the shortcomings that the water quality level is not continuous, and can analyze the water quality in the same level. However, it can be found that the results of these two method high affect by weight coefficents and with low resolution. BP neural network implements a mapping from input to output, to determine the water level more clearly. However this method can not identify the major pollution factor. Principal component analysis can transformed large number of multiple factors into a small number of representative evaluation factors, and identify and sort the major pollution factors.The results showed:the results of five overall consistent evaluation methods, only the individual sections of a discrepancy in the evaluation results more in line with the actual situation. The evaluation results showed that water pollution monitoring sections are as follows:the dry season> normal water> wet period. From the comprehensive analysis of water quality in different period, the worst were Zhongda River and Yinzhou River. Yongjiang River and Fenghua River is comparably good from the overall situation. Yao Jiang, Jiang Yin upstream water quality is the best. More polluting river water concentrated in downstream of Yinzhou River, Zhongda River and Yin River, which these rivers can be treated as the key governance areas in the future.
By using ArcGIS, the generalization has been done for targeting study area and the regional water quality model has been established by using WASP. The nine river sections of Yongjiang, Fenghua River, Yin Jiang and Yao River have been set as the simulations sections. COD and NH3-N have been selected as the main simulation pollutants. Meanwhile, The water quality model was calibrated by using the mornitoring data, and the sensitivity of key parameters were analyzed. The main parameters affecting COD were COD degration coefficient, flow and water temperature, the order of sensitivity from high to low were:COD degration coefficient, flow, water temperature, atmospheric reaeration coefficient and nitrification rate coefficient. The main parameters affecting NH3-N were flow and nitrification rate coefficient, followed by water temperature. The impact of COD degration coefficient on NH3-N is almost negligible. It was found that the variation trend of simulation data is consistent with the variation trend of monitoring data. The average error of simulation data and monitoring data of COD were less than 12%, and the error of of simulation data and monitoring data of NH3-N were less than 15% in average, which indicated that the established water quality model could be used to simulate water quality of the study area. It has been found that the concentration of COD and NH3-N reached the peak in the S-4 River Section in the dry season and normal season, and then gradually decreased. The concentration of COD and NH3-N reached the peak in S-5 River Section and S-4 River Section respectively in the wet season.. S-8 and S-9 reach the same performance in different period, and both of them were in the trend of increasing. The degree of water pollution of all river sections are as follows:The wet season, normal season, dry season. The water quality can not reach the requirements of water objective mainly was in November to next January, and the water quality during June to September quality was better.
The regional pollution dynamic management system has been established by using water quality model and ArcGIS. The main functions of this system include:data queries, statistical analysis of environmental condition, data management, elevation analysis, water analysis, hydrological analysis,map making, etc, as well as the visual output of water quality simulation. Water quality simulation results by editing the assignment of the river, the water quality simulation results can be achieved visual output. While taking advantage of the fuction of data update, adding, deleting and modifying data in the database can be made, which to facilitate dynamic management of regional pollution.
The calculation has been done by using the calibrated water quality model to get the environmental capacity of whole region and each section, and the environmental capacity of COD and NH3-N of the entire region were 12,900.72 t/a and 2,361.89 t/a respectively. It has been found that COD discharge in S-1 to S-5 River Sections were over the environmental capacity, COD discharge in S-6 to S-9 were less than the environmental capacity, in which S-4 and S-5 reach the most polluted river section, respectively, need to reduce 2,090.02t/a, and 1,177.49 t/a to meet the water objectives. And NH3-N discharge in S-1 to S-5 River Sections were over the environmental capacity, NH3-N discharge in S-6 to S-9 were less than the environmental capacity, in which S-4 was the most polluted river section, need to reduce 307.73 t/a to meet the water objectives. Meanwhile, an economic parameter Gini coefficient was introducted to total load allocation. Currenct pollution discharge, population, GDP, water resources and land area were selected as control indexes. The Gini coefficients with COD of final load allocation were 0.187,0.162,0.061 and 0.598 respectively. The Gini coefficients with NH3-N were 0.222,0.208,0.095 and 0.634. That can be concluded that the total load allocation is equitable based on the population, economic development, water resources. The allocation plan can be implemented well.
Finally, water conservation potential were analyzed by using water balance model and effectiveness water saving and pollution remediation, industrial pretreatment, time limited industrial restructuring and non-point source pollution control were analyzed quantitatively. The water conservation potential of main water using enterprises could reach 6,614,448 m3/a which occupy 43.8% of the total quantity of water using. By using water conservation, administrative methods to achieve 587.8t/a and 810.8 t/a of COD reduction,74.5 t/a and 49.2 t/a of NH3-N reduction. Implementation of all mitigation measures can achieve 10,292.28t/a of COD reduction and 1555.03t/a of NH3-N reduction. Except S-5 river section, the other river section can reach requirement of water objectives in 2012; COD and NH3-N will dischage 12,217.19t/a and 1,910.67t/a respecively in 2015, S-5 and S-6 were not compliance with the requirement of water objectives, the rest river sections could meet requirement of water objectives.
引文
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