松花江典型有机污染物动态数值模型构建与模拟
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摘要
地表水承载着各种有机污染物，尤其是我国的天然河流——它无时无刻不在见证着当地的工业发展速度和由此带来的工业废水、非点源污染、突发事件所排放的污水。这些有毒有机化合物进入河流后将对水生态系统产生直接或间接的影响，危害着人类健康安全，因此明晰有机污染物在水体中的归趋，对于有效改善水质状况及风险评估将具有重要意义。
基于实验室分析有机污染物在环境中迁移转化规律常受到种种局限，譬如时间成本、人力成本、设备条件等等，而数学模型在这方面则扮演了重要角色。本文构建了两种数学模型对松花江中典型有机污染物的迁移转化规律进行了模拟和预测。
本文研发了动态多介质模型，它耦合了水力学子模型和逸度Ⅳ子模型，用于模拟有机污染物的长期环境行为。首先采用一维运动波方程计算天然河流动态流量、水深和流速；其次逸度Ⅳ方程模拟污染物在多介质环境中的分布，其内部的所有参数具有时空变化性。
动态多介质模型模拟了2007年松花江8种多环芳烃在多介质环境中的归趋变化，采用柯西不等式系数法(TIC)对松花江中8种多环芳烃的水相模拟浓度进行了验证，结果显示所有模拟结果与相应监测浓度匹配较好，TIC值均小于0.5。以污染物萘为代表，计算了它在每个环境介质的质量通量，结果显示环境系统的总输入和总输出分别为140.61×103Kg和140.26×103Kg，相对误差为0.25%，证明了模型质量守恒。从模型参数的灵敏度分析结果得知，点源源强和河水流量对污染物水中浓度影响最大，灵敏度系数分别为0.97和0.90。
动态多介质模型模拟了2007年松花江中8种酚类污染物在多介质环境的行为变化。基于2007年不同水文季节的监测浓度，通过纳什效率系数(NSE)、百分比偏差系数(PBIAS)和均方根-测量标准偏差比(RSR)三种统计学测试方法评估模型的预测能力。整体而言，模拟浓度与监测浓度吻合度较好，大多数数据点同时满足NSR<0.5，RSR<=0.7和PBIAS∈[-70%，+70%]条件。虽然存在若干奇异点，但配对T检验结果说明当置信水平为0.05时，P>0.05接受零假设即模型所有模拟值与监测值无显著差异。以2，4-二氯苯酚为代表对污染物从河流系统到其它环境介质的分配路径和质量通量变化进行了评估，模型模拟期间水体平流的质量通量最大，净河流输出通量约为4500.1kg。采用蒙特卡洛方法考查模型输入数据对输出结果的影响程度。模型预测了2012年枯水期河流中酚类污染水平，与2007年同期进行比较，发现松花江的酚类污染得到了显著削弱。研究结果显示，动态多介质模型完全可以应用于松花江点源污染，为当地决策者制定水污染控制方案提供可靠的数据支持。
针对突发水污染事件，本文研发了一维污染物迁移转化模型，它耦合了一维运动波方程和平流-扩散-反应方程。模型内部包括挥发、光解和生物氧化的动力学过程以及在悬浮颗粒物沉降和再悬浮作用下的水层和沉积层之间的扩散质量交换过程。模型应用在松花江硝基苯突发水污染事件中，模拟了污染物在水层和沉积层中的浓度变化。针对水层浓度采用NSE、PBIAS、RSR和TIC四种统计学方法评价模型表现力，结果显示模型的整体模拟效果令人满意。通过灵敏度分析得知，河流流速是影响水层硝基苯浓度最灵敏的输入参数，这意味着它对污染物的水中浓度分布和变化起到至关重要的作用。研究结果显示，污染物迁移转化模型完全有能力为松花江及与其水文特征相似的其它天然河流的水污染控制和预警提供可靠信息。
通过建立数学模型可为水环境生态保护提供科学指导，从而预防、减少和修复水污染所造成的生态破坏，避免发生巨大的经济损失，切实维护人类健康和生态系统平衡，使百姓生活与国民经济的发展更加和谐、永续。
Surface waters, especially natural rivers always witness the rapid developmentand advancement of local industries and act as receiving waters for various kinds oforganic contaminants from municipal and industrial wastewaters, organic chemicalsin use, non-point source pollutions. Organic compounds with toxicity dischargedinto river probably bring negative impact on aquatic ecosystem by direct andindirect effects on organisms, and put human’s life to much inconvenience. It is,therefore, of significance for us to clearly understand the transport and fate oforganic pollutants in aquatic environment in order to implement effectively waterquality management and risk assessment.
The demand for numerical models to simulate and predict the transport and fateof organic pollutants in the environment is a reflection of certain restrictions ofexperimental research, in some cases by time limitations, laborious fieldobservations, and undeveloped laboratory determination equipment. Numericalmodeling plays an important role in predicting the behavior of pollutants in theenvironment. For this, two kinds of models are developed in this paper.
Dynamic multimedia model has been developed in this study that coupleshydrodynamics submodel and fugacity Ⅳ submodel, to simulate the long-termenvironmental fate of organic pollutants. In the first, the one-dimensional networkkinematic wave equation is used to calculate varying water flow, depth and velocity.In the second, Fugacity Ⅳ equations in which all parameters are shown inspatio-temporal variability are implemented to predict contaminant distributions inmultimedia environments.
The dynamic multimedia model has been applied to simulate multimediaenvironmental fate of eight PAHs in the Songhua River in2007. Theil’s inequalitycoefficient test (TIC) is used to implement model validation for modeledconcentrations of eight PAHs in water compartment. From results, all TIC values areless than0.5, indicating simulated concentrations are in acceptable agreement withmonitoring data. Taking Nap as representative, calculation of mass balance of Naphas effectively demonstrates that the model program is running properly, of whichthe result shows total input and output mass fluxes in the whole environment systemare140.61×103Kg and40.26×103Kg respectively with the0.25%of relative error.From sensitivity analysis, contaminant concentration in water column is highly sensitive to emission intensity and riverine flow parameters whose sensitivitycoefficients are0.97and0.90, respectively.
The resulting model is also applied to describe the multimedia environmentalbehavior of eight phenol compounds in the Songhua River in2007. Based on fieldobservations during different hydrological seasons of the year2007, the predictivepower of this model is evaluated by three different kinds of quantitative statisticalmethods, namely Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS) and ratio ofthe root-mean-square to the standard deviation of measured data (RSR). Thegoodness-of-fit of model prediction for phenolic contaminants is, in general,agreeable. The conditions of NSR<0.5，RSR<=0.7and PBIAS∈[-70%，+70%] arefulfilled together for most of data points. The paired t-test result (P>0.05) indicatesthat there is no significant difference between simulated and observed data at thechosen significance level of0.05, although some outliers are present. For one focalcompound-2,4-DCP, the pathways and mass fluxes from the river system to thesurrounding environment are also evaluated. During simulation, mass flux ofriverine advection is the most remarkable and net output mass flux is around4500.1kg. Uncertainty analysis is performed by Monte Carlo stimulation to judgethe influence of variability of input parameters on modeled results. Modelsimulation indicates that phenolic pollution in the river during the low-flow periodof2012is remarkably reduced when compared to the same period of2007. Theresearch results indicate that this model can be applied in point-source pollutioncases in the river and has the ability to provide decision makers with valuablereference data for consideration of water pollution controls.
For water pollution emergency, contaminant transport and fate model has beendeveloped in this study, which couples kinematic wave flow option withadvection-dispersion-reaction equation. The model includes kinetic processes suchas volatilization, photolysis and biodegradation, and diffusive mass exchangebetween water column and sediment layer as a function of particles settling andresuspension. It has been applied to simulate Nitrobenzene pollution emergency inthe Songhua River and concentration variances of Nitrobenzene in overlying watercolumn and underneath sediment bottom are obtained. For aqueous concentration,four kinds of quantitative statistical tests, namely NSE, PBIAS, RSR and TIC, areadopted to evaluate model performance. The results generally show that the modeledand detected concentrations exhibit good consistency. Flow velocity in the river ismost sensitive parameter to Nitrobenzene concentration in water column based onsensitivity analysis. It indicates flow velocity has important impact on bothdistribution and variance of contaminant concentration. The model performs satisfactory for prediction of organic pollutant fate in the river, with the ability tosupply necessary information for pollution event control and early warning, whichcould be applied to similar long natural rivers.
We expect to establish numerical model to give a scientific guide for aquaticecosystem protection so that prevention, reduction and recovery of ecologicaldamage by water pollution can be realized with the aim of free-economic loss,maintenance of human health and ecosystem balance, harmony and sustainability ofpeople’s life and economic development.
基于实验室分析有机污染物在环境中迁移转化规律常受到种种局限，譬如时间成本、人力成本、设备条件等等，而数学模型在这方面则扮演了重要角色。本文构建了两种数学模型对松花江中典型有机污染物的迁移转化规律进行了模拟和预测。
本文研发了动态多介质模型，它耦合了水力学子模型和逸度Ⅳ子模型，用于模拟有机污染物的长期环境行为。首先采用一维运动波方程计算天然河流动态流量、水深和流速；其次逸度Ⅳ方程模拟污染物在多介质环境中的分布，其内部的所有参数具有时空变化性。
动态多介质模型模拟了2007年松花江8种多环芳烃在多介质环境中的归趋变化，采用柯西不等式系数法(TIC)对松花江中8种多环芳烃的水相模拟浓度进行了验证，结果显示所有模拟结果与相应监测浓度匹配较好，TIC值均小于0.5。以污染物萘为代表，计算了它在每个环境介质的质量通量，结果显示环境系统的总输入和总输出分别为140.61×103Kg和140.26×103Kg，相对误差为0.25%，证明了模型质量守恒。从模型参数的灵敏度分析结果得知，点源源强和河水流量对污染物水中浓度影响最大，灵敏度系数分别为0.97和0.90。
动态多介质模型模拟了2007年松花江中8种酚类污染物在多介质环境的行为变化。基于2007年不同水文季节的监测浓度，通过纳什效率系数(NSE)、百分比偏差系数(PBIAS)和均方根-测量标准偏差比(RSR)三种统计学测试方法评估模型的预测能力。整体而言，模拟浓度与监测浓度吻合度较好，大多数数据点同时满足NSR<0.5，RSR<=0.7和PBIAS∈[-70%，+70%]条件。虽然存在若干奇异点，但配对T检验结果说明当置信水平为0.05时，P>0.05接受零假设即模型所有模拟值与监测值无显著差异。以2，4-二氯苯酚为代表对污染物从河流系统到其它环境介质的分配路径和质量通量变化进行了评估，模型模拟期间水体平流的质量通量最大，净河流输出通量约为4500.1kg。采用蒙特卡洛方法考查模型输入数据对输出结果的影响程度。模型预测了2012年枯水期河流中酚类污染水平，与2007年同期进行比较，发现松花江的酚类污染得到了显著削弱。研究结果显示，动态多介质模型完全可以应用于松花江点源污染，为当地决策者制定水污染控制方案提供可靠的数据支持。
针对突发水污染事件，本文研发了一维污染物迁移转化模型，它耦合了一维运动波方程和平流-扩散-反应方程。模型内部包括挥发、光解和生物氧化的动力学过程以及在悬浮颗粒物沉降和再悬浮作用下的水层和沉积层之间的扩散质量交换过程。模型应用在松花江硝基苯突发水污染事件中，模拟了污染物在水层和沉积层中的浓度变化。针对水层浓度采用NSE、PBIAS、RSR和TIC四种统计学方法评价模型表现力，结果显示模型的整体模拟效果令人满意。通过灵敏度分析得知，河流流速是影响水层硝基苯浓度最灵敏的输入参数，这意味着它对污染物的水中浓度分布和变化起到至关重要的作用。研究结果显示，污染物迁移转化模型完全有能力为松花江及与其水文特征相似的其它天然河流的水污染控制和预警提供可靠信息。
通过建立数学模型可为水环境生态保护提供科学指导，从而预防、减少和修复水污染所造成的生态破坏，避免发生巨大的经济损失，切实维护人类健康和生态系统平衡，使百姓生活与国民经济的发展更加和谐、永续。
Surface waters, especially natural rivers always witness the rapid developmentand advancement of local industries and act as receiving waters for various kinds oforganic contaminants from municipal and industrial wastewaters, organic chemicalsin use, non-point source pollutions. Organic compounds with toxicity dischargedinto river probably bring negative impact on aquatic ecosystem by direct andindirect effects on organisms, and put human’s life to much inconvenience. It is,therefore, of significance for us to clearly understand the transport and fate oforganic pollutants in aquatic environment in order to implement effectively waterquality management and risk assessment.
The demand for numerical models to simulate and predict the transport and fateof organic pollutants in the environment is a reflection of certain restrictions ofexperimental research, in some cases by time limitations, laborious fieldobservations, and undeveloped laboratory determination equipment. Numericalmodeling plays an important role in predicting the behavior of pollutants in theenvironment. For this, two kinds of models are developed in this paper.
Dynamic multimedia model has been developed in this study that coupleshydrodynamics submodel and fugacity Ⅳ submodel, to simulate the long-termenvironmental fate of organic pollutants. In the first, the one-dimensional networkkinematic wave equation is used to calculate varying water flow, depth and velocity.In the second, Fugacity Ⅳ equations in which all parameters are shown inspatio-temporal variability are implemented to predict contaminant distributions inmultimedia environments.
The dynamic multimedia model has been applied to simulate multimediaenvironmental fate of eight PAHs in the Songhua River in2007. Theil’s inequalitycoefficient test (TIC) is used to implement model validation for modeledconcentrations of eight PAHs in water compartment. From results, all TIC values areless than0.5, indicating simulated concentrations are in acceptable agreement withmonitoring data. Taking Nap as representative, calculation of mass balance of Naphas effectively demonstrates that the model program is running properly, of whichthe result shows total input and output mass fluxes in the whole environment systemare140.61×103Kg and40.26×103Kg respectively with the0.25%of relative error.From sensitivity analysis, contaminant concentration in water column is highly sensitive to emission intensity and riverine flow parameters whose sensitivitycoefficients are0.97and0.90, respectively.
The resulting model is also applied to describe the multimedia environmentalbehavior of eight phenol compounds in the Songhua River in2007. Based on fieldobservations during different hydrological seasons of the year2007, the predictivepower of this model is evaluated by three different kinds of quantitative statisticalmethods, namely Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS) and ratio ofthe root-mean-square to the standard deviation of measured data (RSR). Thegoodness-of-fit of model prediction for phenolic contaminants is, in general,agreeable. The conditions of NSR<0.5，RSR<=0.7and PBIAS∈[-70%，+70%] arefulfilled together for most of data points. The paired t-test result (P>0.05) indicatesthat there is no significant difference between simulated and observed data at thechosen significance level of0.05, although some outliers are present. For one focalcompound-2,4-DCP, the pathways and mass fluxes from the river system to thesurrounding environment are also evaluated. During simulation, mass flux ofriverine advection is the most remarkable and net output mass flux is around4500.1kg. Uncertainty analysis is performed by Monte Carlo stimulation to judgethe influence of variability of input parameters on modeled results. Modelsimulation indicates that phenolic pollution in the river during the low-flow periodof2012is remarkably reduced when compared to the same period of2007. Theresearch results indicate that this model can be applied in point-source pollutioncases in the river and has the ability to provide decision makers with valuablereference data for consideration of water pollution controls.
For water pollution emergency, contaminant transport and fate model has beendeveloped in this study, which couples kinematic wave flow option withadvection-dispersion-reaction equation. The model includes kinetic processes suchas volatilization, photolysis and biodegradation, and diffusive mass exchangebetween water column and sediment layer as a function of particles settling andresuspension. It has been applied to simulate Nitrobenzene pollution emergency inthe Songhua River and concentration variances of Nitrobenzene in overlying watercolumn and underneath sediment bottom are obtained. For aqueous concentration,four kinds of quantitative statistical tests, namely NSE, PBIAS, RSR and TIC, areadopted to evaluate model performance. The results generally show that the modeledand detected concentrations exhibit good consistency. Flow velocity in the river ismost sensitive parameter to Nitrobenzene concentration in water column based onsensitivity analysis. It indicates flow velocity has important impact on bothdistribution and variance of contaminant concentration. The model performs satisfactory for prediction of organic pollutant fate in the river, with the ability tosupply necessary information for pollution event control and early warning, whichcould be applied to similar long natural rivers.
We expect to establish numerical model to give a scientific guide for aquaticecosystem protection so that prevention, reduction and recovery of ecologicaldamage by water pollution can be realized with the aim of free-economic loss,maintenance of human health and ecosystem balance, harmony and sustainability ofpeople’s life and economic development.
引文
[1] Malve O, Salo S, Verta M, et al. Modeling the transport of PCDD/F compoundsin a contaminated river and the possible influence of restoration dredging oncalculated fluxes [J]. Environ Sci Technol,2003,37(15):3413-3421.
[2] Singh K P, Malik A, Mohan D, et al. Multivariate statistical techniques for theevaluation of spatial and temporal variations in water quality of Gomti River (India)-a case study [J]. Water Research,2004,38(18):3980-3992.
[3] Zhang Z L, Huang J, Yu G, et al. Occurrence of PAHs, PCBs andorganochlorine pesticides in the Tonghui River of Beijing, China [J]. EnvironmentalPollution,2004,130(2):249-261.
[4] Fleeger J W, Carman K R, Nisbet R M. Indirect effects of contaminants inaquatic ecosystems [J]. Science of the Total Environment,2003,317(1-3):207-233.
[5] Bartell S M, Lefebvre G, Kaminski G, et al. An ecosystem model for assessingecological risks in Quebec rivers, lakes, and reservoirs [J]. Ecol Model,1999,124(1):43-67.
[6] Marinov D, Dueri S, Puillat I, et al. Integrated modelling of PolycyclicAromatic Hydrocarbons in the marine environment: Coupling of hydrodynamic,fate and transport, bioaccumulation and planktonic food-web models [J]. MarinePollution Bulletin,2009,58(10):1554-1561.
[7]李平.松花江水环境问题剖析与污染防治对策研究[J].环境科学与管理,2005,(03):5-8.
[8]申进朝,多克辛,王伟等.土壤中多环芳烃测定的精密度控制指标[J].环境化学,2012,(02):124-125.
[9]张枝焕,陶澍,沈伟然等.天津地区主要河流沉积物中多环芳烃化合物的组成与分布特征[J].环境科学学报,2005,(11):79-88.
[10]王超,杨忠芳,夏学齐等.中国不同地区典型河流中多环芳烃分布特征研究[J].现代地质,2012,(02):400-406.
[11]赵彩平.淮河中下游水生态系统中PAHs的生物地球化学研究[D].合肥:安徽大学,2010:27-31.
[12]张岩林,胡健,刘宝剑等.辽河流域多环芳烃(PAHs)的分布特征及来源解析[J].地球与环境,2012,(02):188-194.
[13]陆继龙,蔡波,郝立波等.第二松花江中下游河段底泥中多环芳烃的初步研究[J].岩矿测试,2007,(04):325-327.
[14]蔡波.第二松花江水体环境地球化学特征及潜在生态风险[D].长春:吉林大学,2010:26-30.
[15]马健,翟永越,王东辉.多环芳烃在松花江水环境中的富集及对生态环境的影响[J].环境科学与管理,2006,(01):91-92.
[16]王东辉.松花江水体中多环芳烃类污染物的污染研究[J].环境科学与管理,2006,(09):69-70+73.
[17]Feng Y J, Sun Q F, Gao P, et al. Polycyclic Aromatic Hydrocarbons in SurfaceWater from the Songhua River of China [J]. Fresenius Environmental Bulletin,2009,18(12):2388-2395.
[18]Gao Y N, Li W G, Zhang D Y, et al. Bio-enhanced activated carbon filter withimmobilized microorganisms for removing organic pollutants in the Songhua River[J]. Water Sci Technol,2010,62(12):2819-2828.
[19]Buikema A L, Mcginniss M J, Cairns J. Phenolics in Aquatic Ecosystems-Selected Review of Recent Literature [J]. Mar Environ Res,1979,2(2):87-181.
[20]Chen H L, Yao J, Wang F, et al. Toxicity of three phenolic compounds and theirmixtures on the gram-positive bacteria Bacillus subtilis in the aquatic environment[J]. Science of the Total Environment,2010,408(5):1043-1049.
[21]Bolz U, Hagenmaier H, Korner W. Phenolic xenoestrogens in surface water,sediments, and sewage sludge from Baden-Wurttemberg, south-west Germany [J].Environmental Pollution,2001,115(2):291-301.
[22]Davi M L, Gnudi F. Phenolic compounds in surface water [J]. Water Research,1999,33(14):3213-3219.
[23]Michalowicz J, Bukowska B, Duda W. The differences in phenolic content inrivers exposed and non-exposed to anthropogenic contamination [J]. Chemosphere,2008,71(4):735-741.
[24]Ko E J, Kim K W, Kang S Y, et al. Monitoring of environmental phenolicendocrine disrupting compounds in treatment effluents and river waters, Korea [J].Talanta,2007,73(4):674-683.
[25]孙英.北京地区地表水环境激素污染现状与环境风险性评价[D].北京:中国农业大学,2004:49-52.
[26]李正炎,傅明珠,王馨平等.冬季胶州湾及其周边河流中酚类环境激素的分布特征[J].中国海洋大学学报(自然科学版),2006,(03):451-455.
[27]高鹏.松花江水体中酚类污染物时空分布及迁移转化过程研究[D].哈尔滨:哈尔滨工业大学,2011:17-37.
[28]Zhu L Y, Ma B, Zhang L, et al. The study of distribution and fate ofnitrobenzene in a water/sediment microcosm [J]. Chemosphere,2007,69(10):1579-1585.
[29]Zhang W J, Lin X Y, Su X S. Transport and fate modeling of nitrobenzene ingroundwater after the Songhua River pollution accident [J]. Journal ofEnvironmental Management,2010,91(11):2378-2384.
[30]Bingli L, Shengbiao H, Min Q, et al. Prediction of the environmental fate andaquatic ecological impact of nitrobenzene in the Songhua River using the modifiedAQUATOX model [J]. J Environ Sci-China,2008,20(7):769-777.
[31]周宇.基于生态服务价值的松花江流域水资源可持续利用研究[D].哈尔滨:东北林业大学,2010:32-33.
[32]Mackay D. Finding fugacity feasible [J]. Environ Sci Technol,1979,13(10):1218-1223.
[33]Paraiba L C, Bru R, Carrasco J M. Level IV fugacity model depending ontemperature by a periodic control system [J]. Ecol Model,2002,147(3):221-232.
[34]Warren C S, Mackay D, Bahadur N P, et al. A suite of multi-segment fugacitymodels describing the fate of organic contaminants in aquatic systems: applicationto the Rihand Reservoir, India [J]. Water Research,2002,36(17):4341-4355.
[35]Warren C, Mackay D, Whelan M, et al. Mass balance modelling ofcontaminants in river basins: A flexible matrix approach [J]. Chemosphere,2005,61(10):1458-1467.
[36]Mackay D, Paterson S. Fugacity revisited. The fugacity approach toenvironmental transport [J]. Environ Sci Technol,1982,16(12):654-660.
[37]Mackay D, Paterson S. CALCULATING FUGACITY [J]. Environ Sci Technol,1981,15(9):1006-1014.
[38]Mackay D. Multimedia Environmental Models: The Fugacity Approach [M].Chelsea MI: Lewis Publishers,1991:69-183.
[39]Mackay D, Paterson S, Joy M. A quantitative water, air, sediment interaction(QWASI) fugacity model for describing the fate of chemicals in rivers [J].Chemosphere,1983,12(9-10):1193-1208.
[40]Mackay D, Paterson S, Cheung B, et al. Evaluating the environmental behaviorof chemicals with a level III fugacity model [J]. Chemosphere,1985,14(3):335-374.
[41]Paterson S, Mackay D. A model illustrating the environmental fate, exposureand human uptake of persistent organic chemicals [J]. Ecol Model,1989,47(1-2):85-114.
[42]Mackay D, Paterson S, Shiu W. Generic models for evaluating the regional fateof chemicals [J]. Chemosphere,1992,24(6):695-717.
[43]Wania F, Mackay D. Modelling the global distribution of toxaphene: adiscussion of feasibility and desirability [J]. Chemosphere,1993,27(10):2079-2094.
[44]Wania F, Mackay D. A global distribution model for persistent organicchemicals [J]. Science of the Total Environment,1995,160:211-232.
[45]Mackay D, Di Guardo A, Paterson S, et al. Evaluating the environmental fate ofa variety of types of chemicals using the EQC model [J]. Environmental Toxicologyand Chemistry,1996,15(9):1627-1637.
[46]Wania F, Mackay D. The evolution of mass balance models of persistent organicpollutant fate in the environment [J]. Environmental Pollution,1999,100(1-3):223-240.
[47]MacLeod M, Woodfine D G, Mackay D, et al. BETR North America: aregionally segmented multimedia contaminant fate model for North America [J].Environ Sci Pollut R,2001,8(3):156-163.
[48]Sweetman A J, Cousins I T, Seth R, et al. A dynamic level IV multimediaenvironmental model: Application to the fate of polychlorinated biphenyls in theUnited Kingdom over a60‐year period [J]. Environmental Toxicology andChemistry,2002,21(5):930-940.
[49]Warren C, Mackay D, Whelan M, et al. Mass balance modelling ofcontaminants in river basins: Application of the flexible matrix approach [J].Chemosphere,2007,68(7):1232-1244.
[50]Gama S, Mackay D, Arnot J A. Selecting and designing chemicals: applicationof a mass balance model of chemical fate, exposure and effects in the environment[J]. Green Chemistry,2012,14(4):1094-1102.
[51]Campfens J, Mackay D. Fugacity-based model of PCB bioaccumulation incomplex aquatic food webs [J]. Environ Sci Technol,1997,31(2):577-583.
[52]Gobas F A P C, Muir D C G, Mackay D. Dynamics of dietary bioaccumulationand faecal elimination of hydrophobic organic chemicals in fish [J]. Chemosphere,1988,17(5):943-962.
[53]Hickie B E, Mackay D, de Koning J. Lifetime pharmacokinetic model forhydrophobic contaminants in marine mammals [J]. Environmental Toxicology andChemistry,1999,18(11):2622-2633.
[54]Paterson S, Mackay D, McFarlane C. A model of organic chemical uptake byplants from soil and the atmosphere [J]. Environ Sci Technol,1994,28(13):2259-2266.
[55]Diamond M L, Mackay D, Cornett R J, et al. A model of the exchange ofinorganic chemicals between water and sediments [J]. Environ Sci Technol,1990,24(5):713-722.
[56]Woodfine D G, Seth R, Mackay D, et al. Simulating the response of metalcontaminated lakes to reductions in atmospheric loading using a modified QWASImodel [J]. Chemosphere,2000,41(9):1377-1388.
[57]Diamond M L, Poulton D J, Mackay D, et al. Development of a mass balancemodel of the fate of17chemicals in the Bay of Quinte [J]. J Great Lakes Res,1994,20(4):643-666.
[58]Lun R, Lee K, De Marco L, et al. A model of the fate of polycyclic aromatichydrocarbons in the Saguenay Fjord, Canada [J]. Environmental Toxicology andChemistry,1998,17(2):333-341.
[59]Mackay D. Modeling the Long-Term Behavior of an Organic Contaminant in aLarge Lake-Application to Pcbs in Lake-Ontario [J]. J Great Lakes Res,1989,15(2):283-297.
[60]Mackay D, Hickie B. Mass balance model of source apportionment, transportand fate of PAHs in Lac Saint Louis, Quebec [J]. Chemosphere,2000,41(5):681-692.
[61]Seth R, Webster E, Mackay D. Continued development of a mass balance modelof chemical fate in a sewage treatment plant [J]. Water Research,2008,42(3):595-604.
[62]曹红英,梁涛,陶澍.北京地区有机氯农药的跨界面迁移与归趋[J].应用基础与工程科学学报,2004,(03):250-259.
[63]Cao H Y, Tao S, Xu F L, et al. Multimedia fate model forhexachlorocyclohexane in Tianjin, China [J]. Environ Sci Technol,2004,38(7):2126-2132.
[64]刘振宇,杨凤林,全燮等.河流环境持久性有机污染物归宿的动态逸度模型[J].环境科学,2006,(01):121-125.
[65]刘国金,王志辉,马召坤等.多介质逸度模型研究鄱阳湖流域p,p-DDT [J].江西科学,2007,(02):141-146.
[66]谭大,张剑波,丁剑等.金坛地区五氯酚在环境介质中分布迁移和转化的计算模拟[J].北京大学学报(自然科学版)网络版(预印本),2007,(04):69-74.
[67]Liu Z Y, Chen S O, Quan X, et al. Evaluating the fate of three HCHs in thetypically agricultural environment of Liaoning Province, China [J]. Chemosphere,2009,76(6):792-798.
[68]Giri B S, Karimi I A, Ray M B. Modeling and Monte Carlo simulation of TCDDtransport in a river [J]. Water Research,2001,35(5):1263-1279.
[69]McConnell L L, Harman-Fetcho J A, Hagy J D. Measured concentrations ofherbicides and model predictions of atrazine fate in the Patuxent River estuary [J]. JEnviron Qual,2004,33(2):594-604.
[70]Deksissa T, De Pauw D, Vanrolleghem P A. Dynamic in-stream fate modelingof xenobiotic organic compounds: A case study of linear alkylbenzene sulfonates inthe Lambro River, Italy [J]. Environmental Toxicology and Chemistry,2004,23(9):2267-2278.
[71]Carafa R, Marinov D, Dueri S, et al. A3D hydrodynamic fate and transportmodel for herbicides in Sacca di Goro coastal lagoon (Northern Adriatic)[J].Marine Pollution Bulletin,2006,52(10):1231-1248.
[72]Chung S W, Gu R R. Prediction of the Fate and Transport Processes of Atrazinein a Reservoir [J]. Environ Manage,2009,44(1):46-61.
[73]Wang R, Cao H, Li W, et al. Spatial and seasonal variations of polycyclicaromatic hydrocarbons in Haihe Plain, China [J]. Environmental Pollution,2011,159(5):1413-1418.
[74]Kilic S G, Aral M M. A fugacity based continuous and dynamic fate andtransport model for river networks and its application to Altamaha River [J].Science of the Total Environment,2009,407(12):3855-3866.
[75]李博.多介质迁移转化逸度模型的建立及软件实现[D].哈尔滨:哈尔滨工业大学,2007:13-27.
[76]Mackay D. Multimedia Environmental Models: The Fugacity Approach [M].Boca Raton, FL: Lewis Publishers,2001:5-183.
[77]Mackay D, Fraser A. Bioaccumulation of persistent organic chemicals:mechanisms and models [J]. Environmental Pollution,2000,110(3):375-391.
[78]Ambrose R B, Wool T A. WASP7Stream Transport Model Theory and User'sGuide [M]. Athens, Georgia: United States Environmental ProtectionAgency-Region4,2009:4-8.
[79]Kang J H, Kayhanian M, Stenstrom M K. Implications of a kinematic wavemodel for first flush treatment design [J]. Water Research,2006,40(20):3820-3830.
[80]Kothyari U C, Tiwari A K, Singh R. Estimation of temporal variation ofsediment yield from small catchments through the kinematic method [J]. Journal ofHydrology,1997,203(1-4):39-57.
[81]Ambrose R B, Wool T A, EPA/600/R-09/100[R]. Atlanta, GA: United StatesEnvironmental Protection Agency,2009:4-8.
[82]陈俊合,江涛,陈建耀.环境水文学[M].北京:科学出版社,2007:125-126.
[83]Liss P, Slater P. Flux of gases across the air-sea interface [J]. Nature,1974,247:181-184.
[84]Mackay D, Leinonen P J. Rate of evaporation of low-solubility contaminantsfrom water bodies to atmosphere [J]. Environ Sci Technol,1975,9(13):1178-1180.
[85]CMA. China Meteorological Administration [M/OL].2007[http://www.cma.gov.cn/.
[86]MEP. Ministry of Environmental Protection of the People's Republic of China
[M/OL].2007[http://www.zhb.gov.cn/.
[87]Connolly J P, Zahakos H A, Benaman J, et al. A model of PCB fate in the UpperHudson River [J]. Environ Sci Technol,2000,34(19):4076-4087.
[88]高鹏.松花江水体中酚类污染物时空分布及迁移转化过程研究[D].哈尔滨:哈尔滨工业大学,2011:28-37.
[89]US E P A. Analysis of Polycyclic Aromatic Hydrocarbons-Method8100[M].OH: US EPA,1986:1-10.
[90]魏复盛,国家环境保护总局,水和废水监测分析方法编委会.水和废水监测分析方法[M].北京:中国环境科学出版社,2002:533-538,611-620.
[91]Chickos J S, Acree W E. Enthalpies of vaporization of organic andorganometallic compounds,1880-2002[J]. J Phys Chem Ref Data,2003,32(2):519-878.
[92]Delle Site A. Factors affecting sorption of organic compounds in naturalsorbent/water systems and sorption coefficients for selected pollutants. A review [J].J Phys Chem Ref Data,2001,30(1):187-439.
[93]Mackay D. Handbook of physical-chemical properties and environmental fatefor organic chemicals [M]. CRC Press/Taylor&Francis,2006:620-919,2781-3022,3297-3303.
[94]U.S.EPA. Exposure Assessment Tools and Models, Estimation ProgramsInterface (EPI) Suite [M]. Washington, DC: US Environmental Protection Agency,Exposure Assessment Branch,2009.
[95]Roper W L. Toxicological profile for Nitrophenols:2-Nitrophenol and4-Nitrophenol [M]. Agency for Toxic Substances and Disease Registry, USA,1992:5-74.
[96]Brooks K M. Literature review, computer model and assessment of the potentialenvironmental risks associated with pentachlorophenol treated wood products usedin aquatic environments [M]. Vancouver, WA: Western Wood Preservers Institute,1998:2-34.
[97]Act C E P. Priority substances list assessment report: phenol [J]. PhenolProtection Act,2000:1–73.
[98]U.S.EPA. Estimation Program Interface (EPI) suite Version4.10[M]. Office ofPollution Prevention&Toxics (OPPT) and Syracuse Research Corporation (SRC),Washington, DC, USA,2003.
[99]Sarraute S, Delepine H, Gomes M F C, et al. Aqueous solubility, Henry's lawconstants and air/water partition coefficients of n-octane and two halogenatedoctanes [J]. Chemosphere,2004,57(10):1543-1551.
[100] Susarla S, Yonezawa Y, Masunaga S. Reductive transformations ofhalogenated aromatics in anaerobic estuarine sediment: Kinetics, products andpathways [J]. Water Research,1998,32(3):639-648.
[101] Ma B, Chen H H, Xu M M, et al. Quantitative structure-activityrelationship (QSAR) models for polycyclic aromatic hydrocarbons (PAHs)dissipation in rhizosphere based on molecular structure and effect size [J].Environmental Pollution,2010,158(8):2773-2777.
[102] Belanger S E, Farris J L, Cherry D S. Reduction in Organic Effluent StaticAcute Toxicity to Fathead Minnows by Various Aeration Techniques [J].Environmental Pollution,1988,50(3):189-210.
[103] Gupta S, Verma S R, Saxena P K. Toxicity of Phenolic-Compounds inRelation to the Size of a Fresh-Water Fish, Notopterus-Notopterus (Pallas)[J].Ecotox Environ Safe,1982,6(5):433-438.
[104] Wang W C. The Use of Plant Seeds in Toxicity Tests ofPhenolic-Compounds [J]. Environ Int,1985,11(1):49-55.
[105] Feng Y J, Wang C, Liu J F, et al. Electrochemical degradation of17-alpha-ethinylestradiol (EE(2)) and estrogenic activity changes [J]. J EnvironMonitor,2010,12(2):404-408.
[106] Min F Y, Yang M, Wang Z C. Knowledge-based method for the validationof complex simulation models [J]. Simul Model Pract Th,2010,18(5):500-515.
[107] Bliemel F. Theil's forecast accuracy coefficient: A clarification [J]. Journalof Marketing Research,1973,10(4):444-446.
[108] Murray-smith D. Methods for the external validation of contiuous systemsimulation models: a review [J]. Mathematical and Computer Modelling ofDynamical Systems,1998,4(1):5-31.
[109] Topp C F E, Doyle C J. Modelling the comparative productivity andprofitability of grass and legume systems of silage production in northern Europe[J]. Grass Forage Sci,2004,59(3):274-292.
[110] Deksissa T. Dynamic integrated modelling of basic water quality and fateand effect of organic contaminants in rivers [D]. Belgium: Ghent University,2004:147-148.
[111] U.S.EPA. National Recommended Water Quality Criteria:2009[M].Washington, DC: United States Environmental Protection Agency,2009:1-8.
[112] Firestone M, Fenner-Crisp P, Barry T, et al. Guiding principles for MonteCarlo analysis [M]. Washington, DC: Risk Assessment Forum, US EnvironmentalProtection Agency,1997:6-8.
[113] Luo Y Z, Zhang X Y, Liu X M, et al. Dynamic modeling oforganophosphate pesticide load in surface water in the northern San Joaquin Valleywatershed of California [J]. Environmental Pollution,2008,156(3):1171-1181.
[114] Krause P, Boyle D, B se F. Comparison of different efficiency criteria forhydrological model assessment [J]. Advances in Geosciences,2005,5:89-97.
[115] Moriasi D N, Arnold J G, Van Liew M W, et al. Model evaluationguidelines for systematic quantification of accuracy in watershed simulations [J]. TAsabe,2007,50(3):885-900.
[116] Wachal D J, Harmel R D, Banks K E, et al. Evaluation of Wepp for Runoffand Sediment Yield Prediction on Natural Gas Well Sites [J]. T Asabe,2008,51(6):1977-1986.
[117] Mandeville A, O'connell P, Sutcliffe J, et al. River flow forecasting throughconceptual models part III-The Ray catchment at Grendon Underwood [J]. Journalof Hydrology,1970,11(2):109-128.
[118] Nash J E, Sutcliffe J. River flow forecasting through conceptual modelspart I—A discussion of principles [J]. Journal of Hydrology,1970,10(3):282-290.
[119] O'Connell P, Nash J, Farrell J. River flow forecasting through conceptualmodels part II-The Brosna catchment at Ferbane [J]. Journal of Hydrology,1970,10(4):317-329.
[120] Koprivnjak J F, Poissant L. Evaluation and application of a fugacity modelto explain the partitioning of contaminants in the St. Lawrence River valley [J].Water Air Soil Poll,1997,97(3-4):379-395.
[121] Ao J T, Chen J W, Tian F L, et al. Application of a level IV fugacity modelto simulate the long-term fate of hexachlorocyclohexane isomers in the lower reachof Yellow River basin, China [J]. Chemosphere,2009,74(3):370-376.
[122] Dong J, Wang S, Shang K. Simulation of the long-term transfer and fate ofDDT in Lanzhou, China [J]. Chemosphere,2010,81(4):529-535.
[123] Lang C, Tao S, Wang X, et al. Seasonal variation of polycyclic aromatichydrocarbons (PAHs) in Pearl River Delta region, China [J]. Atmos Environ,2007,41(37):8370-8379.
[124] Van Liew M W, Arnold J, Bosch D. Problems and potential ofautocalibrating a hydrologic model [J]. T Asae,2005,48(3):1025-1040.
[125] Gupta H V, Sorooshian S, Yapo P O. Status of Automatic Calibration forHydrologic Models: Comparison with Multilevel Expert Calibration [J]. J HydrolEng,1999,4(2):135-143.
[126] Parajuli P B, Nelson N O, Frees L D, et al. Comparison of AnnAGNPS andSWAT model simulation results in USDA‐CEAP agricultural watersheds in south‐central Kansas [J]. Hydrol Process,2009,23(5):748-763.
[127] Rosner B. Fundamentals of biostatistics [M]. Boston, MA: Duxbury Pr,2010:269-326.
[128] Wilde F, Radtke D, Gibs J, et al. National field manual for the collection ofwater-quality data: US Geological Survey Techniques of Water-ResourcesInvestigations, book9, chap [J]. A-6, variously paged,1998:
[129] Shelton L R. USGS NAWQA Protocol for Collecting and ProcessingStream Water Samples [M]. Sacramenot, California: United States GeologicalSurvey,1994:7-57.
[130] US E P A. National Recommended Water Quality Criteria:2009[M].Washington, DC: United States Environmental Protection Agency,2009:1-8.
[131] EEC Drinking Water Guideline80/779/EECNOL229/11-29[M]. BrusselsBelgium: Environment European Commission,1980.
[132]75/440/EEC of16June1975concerning the quality required of surfacewater intended for the abstraction of drinking water in the Member States [M].Brussels Belgium: Environment European Commission,1975.
[133] Motulsky H. Analyzing data with GraphPad prism [M]. San Diego CA:GraphPad Software Inc.,1999:39-59.
[134] Liu C, Bennett D H, Kastenberg W E, et al. A multimedia, multiple pathwayexposure assessment of atrazine: fate, transport and uncertainty analysis [J]. ReliabEng Syst Safe,1999,63(2):169-184.
[135] MacLeod M, Fraser A J, Mackay D. Evaluating and expressing thepropagation of uncertainty in chemical fate and bioaccumulation models [J].Environmental Toxicology and Chemistry,2002,21(4):700-709.
[136] Meyer T, Wania F, Breivik K. Illustrating sensitivity and uncertainty inenvironmental fate models using partitioning maps [J]. Environ Sci Technol,2005,39(9):3186-3196.
[137] Wool T, Ambrose R, Martin J, et al. Water quality analysis simulationprogram (WASP) version6.0[M]. Draft: User’s Manual. Atlanta, GA: United StatesEnvironmental Protection Agency-Region4,2001.
[138] Di Toro D M, Fitzpatrick J J, Thomann R V. Documentation for waterquality analysis simulation program (WASP) and model verification program (MVP)
[M]. Duluth, Minnesota: United States Environmental Protection Agency,1983:35-45.
[139] Liu W C, Liu S Y, Hsu M H, et al. Water quality modeling to determineminimum instream flow for fish survival in tidal rivers [J]. Journal ofEnvironmental Management,2005,76(4):293-308.
[140] Massoudieh A, Bombardelli F A, Ginn T R. A biogeochemical model ofcontaminant fate and transport in river waters and sediments [J]. Journal ofContaminant Hydrology,2010,112(1-4):103-117.
[141] Burns L A. Exposure analysis modeling system (EXAMS): User manualand system documentation [M]. Research Triangle Park, NC: National exposureresearch laboratory office of research and development U.S. EnvironmentalProtection Agency,2000:42-53.
[142] Paris D F, Steen W C, Baughman G L, et al.2nd-Order Model to PredictMicrobial-Degradation of Organic-Compounds in Natural-Waters [J]. Appl EnvironMicrob,1981,41(3):603-609.
[143] Ataie-Ashtiani B, Hosseini S A. Error analysis of finite difference methodsfor two-dimensional advection-dispersion-reaction equation [J]. Advances in WaterResources,2005,28(8):793-806.
[144] Azamathulla H M, Wu F C. Support vector machine approach forlongitudinal dispersion coefficients in natural streams [J]. Applied Soft Computing,2011,11(2):2902-2905.
[145] Bowie G L, Laboratory E R, Tech T. Rates, constants, and kineticsformulations in surface water quality modeling [M]. Environmental ResearchLaboratory, Office of Research and Development, US Environmental ProtectionAgency,1985:45-55.
[146] Wang A M, Hu C, Qu J H, et al. Phototransformation of nitrobenzene in theSonghua River: Kinetics and photoproduct analysis [J]. J Environ Sci-China,2008,20(7):787-795.
[147] Pozdnyakov I P, Glebov E M, Plyusnin V F, et al. Mechanism ofFe(OH)(2+)(aq) photolysis in aqueous solution [J]. Pure Appl Chem,2000,72(11):2187-2197.
[2] Singh K P, Malik A, Mohan D, et al. Multivariate statistical techniques for theevaluation of spatial and temporal variations in water quality of Gomti River (India)-a case study [J]. Water Research,2004,38(18):3980-3992.
[3] Zhang Z L, Huang J, Yu G, et al. Occurrence of PAHs, PCBs andorganochlorine pesticides in the Tonghui River of Beijing, China [J]. EnvironmentalPollution,2004,130(2):249-261.
[4] Fleeger J W, Carman K R, Nisbet R M. Indirect effects of contaminants inaquatic ecosystems [J]. Science of the Total Environment,2003,317(1-3):207-233.
[5] Bartell S M, Lefebvre G, Kaminski G, et al. An ecosystem model for assessingecological risks in Quebec rivers, lakes, and reservoirs [J]. Ecol Model,1999,124(1):43-67.
[6] Marinov D, Dueri S, Puillat I, et al. Integrated modelling of PolycyclicAromatic Hydrocarbons in the marine environment: Coupling of hydrodynamic,fate and transport, bioaccumulation and planktonic food-web models [J]. MarinePollution Bulletin,2009,58(10):1554-1561.
[7]李平.松花江水环境问题剖析与污染防治对策研究[J].环境科学与管理,2005,(03):5-8.
[8]申进朝,多克辛,王伟等.土壤中多环芳烃测定的精密度控制指标[J].环境化学,2012,(02):124-125.
[9]张枝焕,陶澍,沈伟然等.天津地区主要河流沉积物中多环芳烃化合物的组成与分布特征[J].环境科学学报,2005,(11):79-88.
[10]王超,杨忠芳,夏学齐等.中国不同地区典型河流中多环芳烃分布特征研究[J].现代地质,2012,(02):400-406.
[11]赵彩平.淮河中下游水生态系统中PAHs的生物地球化学研究[D].合肥:安徽大学,2010:27-31.
[12]张岩林,胡健,刘宝剑等.辽河流域多环芳烃(PAHs)的分布特征及来源解析[J].地球与环境,2012,(02):188-194.
[13]陆继龙,蔡波,郝立波等.第二松花江中下游河段底泥中多环芳烃的初步研究[J].岩矿测试,2007,(04):325-327.
[14]蔡波.第二松花江水体环境地球化学特征及潜在生态风险[D].长春:吉林大学,2010:26-30.
[15]马健,翟永越,王东辉.多环芳烃在松花江水环境中的富集及对生态环境的影响[J].环境科学与管理,2006,(01):91-92.
[16]王东辉.松花江水体中多环芳烃类污染物的污染研究[J].环境科学与管理,2006,(09):69-70+73.
[17]Feng Y J, Sun Q F, Gao P, et al. Polycyclic Aromatic Hydrocarbons in SurfaceWater from the Songhua River of China [J]. Fresenius Environmental Bulletin,2009,18(12):2388-2395.
[18]Gao Y N, Li W G, Zhang D Y, et al. Bio-enhanced activated carbon filter withimmobilized microorganisms for removing organic pollutants in the Songhua River[J]. Water Sci Technol,2010,62(12):2819-2828.
[19]Buikema A L, Mcginniss M J, Cairns J. Phenolics in Aquatic Ecosystems-Selected Review of Recent Literature [J]. Mar Environ Res,1979,2(2):87-181.
[20]Chen H L, Yao J, Wang F, et al. Toxicity of three phenolic compounds and theirmixtures on the gram-positive bacteria Bacillus subtilis in the aquatic environment[J]. Science of the Total Environment,2010,408(5):1043-1049.
[21]Bolz U, Hagenmaier H, Korner W. Phenolic xenoestrogens in surface water,sediments, and sewage sludge from Baden-Wurttemberg, south-west Germany [J].Environmental Pollution,2001,115(2):291-301.
[22]Davi M L, Gnudi F. Phenolic compounds in surface water [J]. Water Research,1999,33(14):3213-3219.
[23]Michalowicz J, Bukowska B, Duda W. The differences in phenolic content inrivers exposed and non-exposed to anthropogenic contamination [J]. Chemosphere,2008,71(4):735-741.
[24]Ko E J, Kim K W, Kang S Y, et al. Monitoring of environmental phenolicendocrine disrupting compounds in treatment effluents and river waters, Korea [J].Talanta,2007,73(4):674-683.
[25]孙英.北京地区地表水环境激素污染现状与环境风险性评价[D].北京:中国农业大学,2004:49-52.
[26]李正炎,傅明珠,王馨平等.冬季胶州湾及其周边河流中酚类环境激素的分布特征[J].中国海洋大学学报(自然科学版),2006,(03):451-455.
[27]高鹏.松花江水体中酚类污染物时空分布及迁移转化过程研究[D].哈尔滨:哈尔滨工业大学,2011:17-37.
[28]Zhu L Y, Ma B, Zhang L, et al. The study of distribution and fate ofnitrobenzene in a water/sediment microcosm [J]. Chemosphere,2007,69(10):1579-1585.
[29]Zhang W J, Lin X Y, Su X S. Transport and fate modeling of nitrobenzene ingroundwater after the Songhua River pollution accident [J]. Journal ofEnvironmental Management,2010,91(11):2378-2384.
[30]Bingli L, Shengbiao H, Min Q, et al. Prediction of the environmental fate andaquatic ecological impact of nitrobenzene in the Songhua River using the modifiedAQUATOX model [J]. J Environ Sci-China,2008,20(7):769-777.
[31]周宇.基于生态服务价值的松花江流域水资源可持续利用研究[D].哈尔滨:东北林业大学,2010:32-33.
[32]Mackay D. Finding fugacity feasible [J]. Environ Sci Technol,1979,13(10):1218-1223.
[33]Paraiba L C, Bru R, Carrasco J M. Level IV fugacity model depending ontemperature by a periodic control system [J]. Ecol Model,2002,147(3):221-232.
[34]Warren C S, Mackay D, Bahadur N P, et al. A suite of multi-segment fugacitymodels describing the fate of organic contaminants in aquatic systems: applicationto the Rihand Reservoir, India [J]. Water Research,2002,36(17):4341-4355.
[35]Warren C, Mackay D, Whelan M, et al. Mass balance modelling ofcontaminants in river basins: A flexible matrix approach [J]. Chemosphere,2005,61(10):1458-1467.
[36]Mackay D, Paterson S. Fugacity revisited. The fugacity approach toenvironmental transport [J]. Environ Sci Technol,1982,16(12):654-660.
[37]Mackay D, Paterson S. CALCULATING FUGACITY [J]. Environ Sci Technol,1981,15(9):1006-1014.
[38]Mackay D. Multimedia Environmental Models: The Fugacity Approach [M].Chelsea MI: Lewis Publishers,1991:69-183.
[39]Mackay D, Paterson S, Joy M. A quantitative water, air, sediment interaction(QWASI) fugacity model for describing the fate of chemicals in rivers [J].Chemosphere,1983,12(9-10):1193-1208.
[40]Mackay D, Paterson S, Cheung B, et al. Evaluating the environmental behaviorof chemicals with a level III fugacity model [J]. Chemosphere,1985,14(3):335-374.
[41]Paterson S, Mackay D. A model illustrating the environmental fate, exposureand human uptake of persistent organic chemicals [J]. Ecol Model,1989,47(1-2):85-114.
[42]Mackay D, Paterson S, Shiu W. Generic models for evaluating the regional fateof chemicals [J]. Chemosphere,1992,24(6):695-717.
[43]Wania F, Mackay D. Modelling the global distribution of toxaphene: adiscussion of feasibility and desirability [J]. Chemosphere,1993,27(10):2079-2094.
[44]Wania F, Mackay D. A global distribution model for persistent organicchemicals [J]. Science of the Total Environment,1995,160:211-232.
[45]Mackay D, Di Guardo A, Paterson S, et al. Evaluating the environmental fate ofa variety of types of chemicals using the EQC model [J]. Environmental Toxicologyand Chemistry,1996,15(9):1627-1637.
[46]Wania F, Mackay D. The evolution of mass balance models of persistent organicpollutant fate in the environment [J]. Environmental Pollution,1999,100(1-3):223-240.
[47]MacLeod M, Woodfine D G, Mackay D, et al. BETR North America: aregionally segmented multimedia contaminant fate model for North America [J].Environ Sci Pollut R,2001,8(3):156-163.
[48]Sweetman A J, Cousins I T, Seth R, et al. A dynamic level IV multimediaenvironmental model: Application to the fate of polychlorinated biphenyls in theUnited Kingdom over a60‐year period [J]. Environmental Toxicology andChemistry,2002,21(5):930-940.
[49]Warren C, Mackay D, Whelan M, et al. Mass balance modelling ofcontaminants in river basins: Application of the flexible matrix approach [J].Chemosphere,2007,68(7):1232-1244.
[50]Gama S, Mackay D, Arnot J A. Selecting and designing chemicals: applicationof a mass balance model of chemical fate, exposure and effects in the environment[J]. Green Chemistry,2012,14(4):1094-1102.
[51]Campfens J, Mackay D. Fugacity-based model of PCB bioaccumulation incomplex aquatic food webs [J]. Environ Sci Technol,1997,31(2):577-583.
[52]Gobas F A P C, Muir D C G, Mackay D. Dynamics of dietary bioaccumulationand faecal elimination of hydrophobic organic chemicals in fish [J]. Chemosphere,1988,17(5):943-962.
[53]Hickie B E, Mackay D, de Koning J. Lifetime pharmacokinetic model forhydrophobic contaminants in marine mammals [J]. Environmental Toxicology andChemistry,1999,18(11):2622-2633.
[54]Paterson S, Mackay D, McFarlane C. A model of organic chemical uptake byplants from soil and the atmosphere [J]. Environ Sci Technol,1994,28(13):2259-2266.
[55]Diamond M L, Mackay D, Cornett R J, et al. A model of the exchange ofinorganic chemicals between water and sediments [J]. Environ Sci Technol,1990,24(5):713-722.
[56]Woodfine D G, Seth R, Mackay D, et al. Simulating the response of metalcontaminated lakes to reductions in atmospheric loading using a modified QWASImodel [J]. Chemosphere,2000,41(9):1377-1388.
[57]Diamond M L, Poulton D J, Mackay D, et al. Development of a mass balancemodel of the fate of17chemicals in the Bay of Quinte [J]. J Great Lakes Res,1994,20(4):643-666.
[58]Lun R, Lee K, De Marco L, et al. A model of the fate of polycyclic aromatichydrocarbons in the Saguenay Fjord, Canada [J]. Environmental Toxicology andChemistry,1998,17(2):333-341.
[59]Mackay D. Modeling the Long-Term Behavior of an Organic Contaminant in aLarge Lake-Application to Pcbs in Lake-Ontario [J]. J Great Lakes Res,1989,15(2):283-297.
[60]Mackay D, Hickie B. Mass balance model of source apportionment, transportand fate of PAHs in Lac Saint Louis, Quebec [J]. Chemosphere,2000,41(5):681-692.
[61]Seth R, Webster E, Mackay D. Continued development of a mass balance modelof chemical fate in a sewage treatment plant [J]. Water Research,2008,42(3):595-604.
[62]曹红英,梁涛,陶澍.北京地区有机氯农药的跨界面迁移与归趋[J].应用基础与工程科学学报,2004,(03):250-259.
[63]Cao H Y, Tao S, Xu F L, et al. Multimedia fate model forhexachlorocyclohexane in Tianjin, China [J]. Environ Sci Technol,2004,38(7):2126-2132.
[64]刘振宇,杨凤林,全燮等.河流环境持久性有机污染物归宿的动态逸度模型[J].环境科学,2006,(01):121-125.
[65]刘国金,王志辉,马召坤等.多介质逸度模型研究鄱阳湖流域p,p-DDT [J].江西科学,2007,(02):141-146.
[66]谭大,张剑波,丁剑等.金坛地区五氯酚在环境介质中分布迁移和转化的计算模拟[J].北京大学学报(自然科学版)网络版(预印本),2007,(04):69-74.
[67]Liu Z Y, Chen S O, Quan X, et al. Evaluating the fate of three HCHs in thetypically agricultural environment of Liaoning Province, China [J]. Chemosphere,2009,76(6):792-798.
[68]Giri B S, Karimi I A, Ray M B. Modeling and Monte Carlo simulation of TCDDtransport in a river [J]. Water Research,2001,35(5):1263-1279.
[69]McConnell L L, Harman-Fetcho J A, Hagy J D. Measured concentrations ofherbicides and model predictions of atrazine fate in the Patuxent River estuary [J]. JEnviron Qual,2004,33(2):594-604.
[70]Deksissa T, De Pauw D, Vanrolleghem P A. Dynamic in-stream fate modelingof xenobiotic organic compounds: A case study of linear alkylbenzene sulfonates inthe Lambro River, Italy [J]. Environmental Toxicology and Chemistry,2004,23(9):2267-2278.
[71]Carafa R, Marinov D, Dueri S, et al. A3D hydrodynamic fate and transportmodel for herbicides in Sacca di Goro coastal lagoon (Northern Adriatic)[J].Marine Pollution Bulletin,2006,52(10):1231-1248.
[72]Chung S W, Gu R R. Prediction of the Fate and Transport Processes of Atrazinein a Reservoir [J]. Environ Manage,2009,44(1):46-61.
[73]Wang R, Cao H, Li W, et al. Spatial and seasonal variations of polycyclicaromatic hydrocarbons in Haihe Plain, China [J]. Environmental Pollution,2011,159(5):1413-1418.
[74]Kilic S G, Aral M M. A fugacity based continuous and dynamic fate andtransport model for river networks and its application to Altamaha River [J].Science of the Total Environment,2009,407(12):3855-3866.
[75]李博.多介质迁移转化逸度模型的建立及软件实现[D].哈尔滨:哈尔滨工业大学,2007:13-27.
[76]Mackay D. Multimedia Environmental Models: The Fugacity Approach [M].Boca Raton, FL: Lewis Publishers,2001:5-183.
[77]Mackay D, Fraser A. Bioaccumulation of persistent organic chemicals:mechanisms and models [J]. Environmental Pollution,2000,110(3):375-391.
[78]Ambrose R B, Wool T A. WASP7Stream Transport Model Theory and User'sGuide [M]. Athens, Georgia: United States Environmental ProtectionAgency-Region4,2009:4-8.
[79]Kang J H, Kayhanian M, Stenstrom M K. Implications of a kinematic wavemodel for first flush treatment design [J]. Water Research,2006,40(20):3820-3830.
[80]Kothyari U C, Tiwari A K, Singh R. Estimation of temporal variation ofsediment yield from small catchments through the kinematic method [J]. Journal ofHydrology,1997,203(1-4):39-57.
[81]Ambrose R B, Wool T A, EPA/600/R-09/100[R]. Atlanta, GA: United StatesEnvironmental Protection Agency,2009:4-8.
[82]陈俊合,江涛,陈建耀.环境水文学[M].北京:科学出版社,2007:125-126.
[83]Liss P, Slater P. Flux of gases across the air-sea interface [J]. Nature,1974,247:181-184.
[84]Mackay D, Leinonen P J. Rate of evaporation of low-solubility contaminantsfrom water bodies to atmosphere [J]. Environ Sci Technol,1975,9(13):1178-1180.
[85]CMA. China Meteorological Administration [M/OL].2007[http://www.cma.gov.cn/.
[86]MEP. Ministry of Environmental Protection of the People's Republic of China
[M/OL].2007[http://www.zhb.gov.cn/.
[87]Connolly J P, Zahakos H A, Benaman J, et al. A model of PCB fate in the UpperHudson River [J]. Environ Sci Technol,2000,34(19):4076-4087.
[88]高鹏.松花江水体中酚类污染物时空分布及迁移转化过程研究[D].哈尔滨:哈尔滨工业大学,2011:28-37.
[89]US E P A. Analysis of Polycyclic Aromatic Hydrocarbons-Method8100[M].OH: US EPA,1986:1-10.
[90]魏复盛,国家环境保护总局,水和废水监测分析方法编委会.水和废水监测分析方法[M].北京:中国环境科学出版社,2002:533-538,611-620.
[91]Chickos J S, Acree W E. Enthalpies of vaporization of organic andorganometallic compounds,1880-2002[J]. J Phys Chem Ref Data,2003,32(2):519-878.
[92]Delle Site A. Factors affecting sorption of organic compounds in naturalsorbent/water systems and sorption coefficients for selected pollutants. A review [J].J Phys Chem Ref Data,2001,30(1):187-439.
[93]Mackay D. Handbook of physical-chemical properties and environmental fatefor organic chemicals [M]. CRC Press/Taylor&Francis,2006:620-919,2781-3022,3297-3303.
[94]U.S.EPA. Exposure Assessment Tools and Models, Estimation ProgramsInterface (EPI) Suite [M]. Washington, DC: US Environmental Protection Agency,Exposure Assessment Branch,2009.
[95]Roper W L. Toxicological profile for Nitrophenols:2-Nitrophenol and4-Nitrophenol [M]. Agency for Toxic Substances and Disease Registry, USA,1992:5-74.
[96]Brooks K M. Literature review, computer model and assessment of the potentialenvironmental risks associated with pentachlorophenol treated wood products usedin aquatic environments [M]. Vancouver, WA: Western Wood Preservers Institute,1998:2-34.
[97]Act C E P. Priority substances list assessment report: phenol [J]. PhenolProtection Act,2000:1–73.
[98]U.S.EPA. Estimation Program Interface (EPI) suite Version4.10[M]. Office ofPollution Prevention&Toxics (OPPT) and Syracuse Research Corporation (SRC),Washington, DC, USA,2003.
[99]Sarraute S, Delepine H, Gomes M F C, et al. Aqueous solubility, Henry's lawconstants and air/water partition coefficients of n-octane and two halogenatedoctanes [J]. Chemosphere,2004,57(10):1543-1551.
[100] Susarla S, Yonezawa Y, Masunaga S. Reductive transformations ofhalogenated aromatics in anaerobic estuarine sediment: Kinetics, products andpathways [J]. Water Research,1998,32(3):639-648.
[101] Ma B, Chen H H, Xu M M, et al. Quantitative structure-activityrelationship (QSAR) models for polycyclic aromatic hydrocarbons (PAHs)dissipation in rhizosphere based on molecular structure and effect size [J].Environmental Pollution,2010,158(8):2773-2777.
[102] Belanger S E, Farris J L, Cherry D S. Reduction in Organic Effluent StaticAcute Toxicity to Fathead Minnows by Various Aeration Techniques [J].Environmental Pollution,1988,50(3):189-210.
[103] Gupta S, Verma S R, Saxena P K. Toxicity of Phenolic-Compounds inRelation to the Size of a Fresh-Water Fish, Notopterus-Notopterus (Pallas)[J].Ecotox Environ Safe,1982,6(5):433-438.
[104] Wang W C. The Use of Plant Seeds in Toxicity Tests ofPhenolic-Compounds [J]. Environ Int,1985,11(1):49-55.
[105] Feng Y J, Wang C, Liu J F, et al. Electrochemical degradation of17-alpha-ethinylestradiol (EE(2)) and estrogenic activity changes [J]. J EnvironMonitor,2010,12(2):404-408.
[106] Min F Y, Yang M, Wang Z C. Knowledge-based method for the validationof complex simulation models [J]. Simul Model Pract Th,2010,18(5):500-515.
[107] Bliemel F. Theil's forecast accuracy coefficient: A clarification [J]. Journalof Marketing Research,1973,10(4):444-446.
[108] Murray-smith D. Methods for the external validation of contiuous systemsimulation models: a review [J]. Mathematical and Computer Modelling ofDynamical Systems,1998,4(1):5-31.
[109] Topp C F E, Doyle C J. Modelling the comparative productivity andprofitability of grass and legume systems of silage production in northern Europe[J]. Grass Forage Sci,2004,59(3):274-292.
[110] Deksissa T. Dynamic integrated modelling of basic water quality and fateand effect of organic contaminants in rivers [D]. Belgium: Ghent University,2004:147-148.
[111] U.S.EPA. National Recommended Water Quality Criteria:2009[M].Washington, DC: United States Environmental Protection Agency,2009:1-8.
[112] Firestone M, Fenner-Crisp P, Barry T, et al. Guiding principles for MonteCarlo analysis [M]. Washington, DC: Risk Assessment Forum, US EnvironmentalProtection Agency,1997:6-8.
[113] Luo Y Z, Zhang X Y, Liu X M, et al. Dynamic modeling oforganophosphate pesticide load in surface water in the northern San Joaquin Valleywatershed of California [J]. Environmental Pollution,2008,156(3):1171-1181.
[114] Krause P, Boyle D, B se F. Comparison of different efficiency criteria forhydrological model assessment [J]. Advances in Geosciences,2005,5:89-97.
[115] Moriasi D N, Arnold J G, Van Liew M W, et al. Model evaluationguidelines for systematic quantification of accuracy in watershed simulations [J]. TAsabe,2007,50(3):885-900.
[116] Wachal D J, Harmel R D, Banks K E, et al. Evaluation of Wepp for Runoffand Sediment Yield Prediction on Natural Gas Well Sites [J]. T Asabe,2008,51(6):1977-1986.
[117] Mandeville A, O'connell P, Sutcliffe J, et al. River flow forecasting throughconceptual models part III-The Ray catchment at Grendon Underwood [J]. Journalof Hydrology,1970,11(2):109-128.
[118] Nash J E, Sutcliffe J. River flow forecasting through conceptual modelspart I—A discussion of principles [J]. Journal of Hydrology,1970,10(3):282-290.
[119] O'Connell P, Nash J, Farrell J. River flow forecasting through conceptualmodels part II-The Brosna catchment at Ferbane [J]. Journal of Hydrology,1970,10(4):317-329.
[120] Koprivnjak J F, Poissant L. Evaluation and application of a fugacity modelto explain the partitioning of contaminants in the St. Lawrence River valley [J].Water Air Soil Poll,1997,97(3-4):379-395.
[121] Ao J T, Chen J W, Tian F L, et al. Application of a level IV fugacity modelto simulate the long-term fate of hexachlorocyclohexane isomers in the lower reachof Yellow River basin, China [J]. Chemosphere,2009,74(3):370-376.
[122] Dong J, Wang S, Shang K. Simulation of the long-term transfer and fate ofDDT in Lanzhou, China [J]. Chemosphere,2010,81(4):529-535.
[123] Lang C, Tao S, Wang X, et al. Seasonal variation of polycyclic aromatichydrocarbons (PAHs) in Pearl River Delta region, China [J]. Atmos Environ,2007,41(37):8370-8379.
[124] Van Liew M W, Arnold J, Bosch D. Problems and potential ofautocalibrating a hydrologic model [J]. T Asae,2005,48(3):1025-1040.
[125] Gupta H V, Sorooshian S, Yapo P O. Status of Automatic Calibration forHydrologic Models: Comparison with Multilevel Expert Calibration [J]. J HydrolEng,1999,4(2):135-143.
[126] Parajuli P B, Nelson N O, Frees L D, et al. Comparison of AnnAGNPS andSWAT model simulation results in USDA‐CEAP agricultural watersheds in south‐central Kansas [J]. Hydrol Process,2009,23(5):748-763.
[127] Rosner B. Fundamentals of biostatistics [M]. Boston, MA: Duxbury Pr,2010:269-326.
[128] Wilde F, Radtke D, Gibs J, et al. National field manual for the collection ofwater-quality data: US Geological Survey Techniques of Water-ResourcesInvestigations, book9, chap [J]. A-6, variously paged,1998:
[129] Shelton L R. USGS NAWQA Protocol for Collecting and ProcessingStream Water Samples [M]. Sacramenot, California: United States GeologicalSurvey,1994:7-57.
[130] US E P A. National Recommended Water Quality Criteria:2009[M].Washington, DC: United States Environmental Protection Agency,2009:1-8.
[131] EEC Drinking Water Guideline80/779/EECNOL229/11-29[M]. BrusselsBelgium: Environment European Commission,1980.
[132]75/440/EEC of16June1975concerning the quality required of surfacewater intended for the abstraction of drinking water in the Member States [M].Brussels Belgium: Environment European Commission,1975.
[133] Motulsky H. Analyzing data with GraphPad prism [M]. San Diego CA:GraphPad Software Inc.,1999:39-59.
[134] Liu C, Bennett D H, Kastenberg W E, et al. A multimedia, multiple pathwayexposure assessment of atrazine: fate, transport and uncertainty analysis [J]. ReliabEng Syst Safe,1999,63(2):169-184.
[135] MacLeod M, Fraser A J, Mackay D. Evaluating and expressing thepropagation of uncertainty in chemical fate and bioaccumulation models [J].Environmental Toxicology and Chemistry,2002,21(4):700-709.
[136] Meyer T, Wania F, Breivik K. Illustrating sensitivity and uncertainty inenvironmental fate models using partitioning maps [J]. Environ Sci Technol,2005,39(9):3186-3196.
[137] Wool T, Ambrose R, Martin J, et al. Water quality analysis simulationprogram (WASP) version6.0[M]. Draft: User’s Manual. Atlanta, GA: United StatesEnvironmental Protection Agency-Region4,2001.
[138] Di Toro D M, Fitzpatrick J J, Thomann R V. Documentation for waterquality analysis simulation program (WASP) and model verification program (MVP)
[M]. Duluth, Minnesota: United States Environmental Protection Agency,1983:35-45.
[139] Liu W C, Liu S Y, Hsu M H, et al. Water quality modeling to determineminimum instream flow for fish survival in tidal rivers [J]. Journal ofEnvironmental Management,2005,76(4):293-308.
[140] Massoudieh A, Bombardelli F A, Ginn T R. A biogeochemical model ofcontaminant fate and transport in river waters and sediments [J]. Journal ofContaminant Hydrology,2010,112(1-4):103-117.
[141] Burns L A. Exposure analysis modeling system (EXAMS): User manualand system documentation [M]. Research Triangle Park, NC: National exposureresearch laboratory office of research and development U.S. EnvironmentalProtection Agency,2000:42-53.
[142] Paris D F, Steen W C, Baughman G L, et al.2nd-Order Model to PredictMicrobial-Degradation of Organic-Compounds in Natural-Waters [J]. Appl EnvironMicrob,1981,41(3):603-609.
[143] Ataie-Ashtiani B, Hosseini S A. Error analysis of finite difference methodsfor two-dimensional advection-dispersion-reaction equation [J]. Advances in WaterResources,2005,28(8):793-806.
[144] Azamathulla H M, Wu F C. Support vector machine approach forlongitudinal dispersion coefficients in natural streams [J]. Applied Soft Computing,2011,11(2):2902-2905.
[145] Bowie G L, Laboratory E R, Tech T. Rates, constants, and kineticsformulations in surface water quality modeling [M]. Environmental ResearchLaboratory, Office of Research and Development, US Environmental ProtectionAgency,1985:45-55.
[146] Wang A M, Hu C, Qu J H, et al. Phototransformation of nitrobenzene in theSonghua River: Kinetics and photoproduct analysis [J]. J Environ Sci-China,2008,20(7):787-795.
[147] Pozdnyakov I P, Glebov E M, Plyusnin V F, et al. Mechanism ofFe(OH)(2+)(aq) photolysis in aqueous solution [J]. Pure Appl Chem,2000,72(11):2187-2197.