A supplementary approach for estimating reaeration rate coefficients

Different commonly used predictive equations for the reaeration rate coefficient (K₂) have been evaluated using 231 data sets obtained from the literature and 576 data sets measured at different reaches of the River Kali in western Uttar Pradesh, India. The data sets include stream/channel velocity, bed slope, flow depth, cross‐sectional area and reaeration rate coefficient (K₂), obtained from the literature and generated during the field survey of River Kali, and were used to test the applicability of the predictive equations. The K₂ values computed from the predictive equations have been compared with the corresponding K₂ values measured in streams/channels. The performance of the predictive equations has been evaluated using different error estimation, namely standard error (SE), normal mean error (NME), mean multiplicative error (MME) and coefficient of determination (r²). The results show that the reaeration rate equation developed by Parkhurst and Pomeroy yielded the best agreement, with the values of SE, NME, MME and r² as 33·387, 4·62, 3·58 and 0·95, respectively, for literature data sets (case 1) and 37·567, 3·57, 2·6 and 0·95, respectively, for all the data sets (literature data sets and River Kali data sets) (case 2). Further, to minimize error estimates and improve correlation between measured and computed reaeration rate coefficients, supplementary predictive equations have been developed based on Froude number criteria and a least‐squares algorithm. The supplementary predictive equations have been verified using different error estimates and by comparing measured and computed reaeration rate coefficients for data sets not used in the development of the equations. Copyright © 2003 John Wiley & Sons, Ltd.     

Refinement of predictive reaeration equations for a typical Indian river

Dissolved oxygen mass balance has been computed for different reaches of River Kali in western Uttar Pradesh (India) to obtain the reaeration coefficient (K₂). A total of 270 field data sets have been collected during the period from March 1999 to February 2000. Eleven most popular predictive equations, used for reaeration prediction and utilizing mean stream velocity, bed slope, flow depth, friction velocity and Froude number, have been tested for their applicability in the River Kali using data generated during field survey. The K₂ values computed from these predictive equations have been compared with the K₂ values observed from dissolved oxygen balance measurements in the field. The performance of predictive equations have been evaluated using error estimation, namely standard error (SE), normal mean error (NME), mean multiplicative error (MME) and correlation statistics. The equations developed by Smoot and by Cadwallader and McDonnell showed comparatively better results. Moreover, a refined predictive equation has been developed using a least‐squares algorithm for the River Kali that minimizes error estimates and improves correlation between observed and computed reaeration coefficients. Copyright © 2001 John Wiley & Sons, Ltd.     

Determination of reaeration‐rate coefficient by modified tracer gas technique

The modified tracer gas technique is used to determine the reaeration coefficient in six different water bodies of the Itajaí River basin, three with rural land use and three in urban areas. Propane was used as the tracer gas and Rhodamine WT as the conservative tracer, providing information on dilution, mixing and dispersion. Liquefied petroleum gas was used instead of high purity propane, aimed at reducing the costs associated with the field trials. Reaeration‐rate coefficients observed in the field ranged from 25.8 to 367.7 d^?1. Two data sets could be observed where smaller streams had substantially larger coefficients of between 133.1 and 367.7 d^?1, while the larger streams had values ??ranging from 25.8 to 54.5 d^?1. Five empirical equations were evaluated by comparing the values ??obtained in the field. The equations proposed by Tsivolgou and Wallace and Tsivoglou and Neal showed greater adherence to the values ??determined in the tests. Reaeration‐rate coefficients obtained in the field were correlated with the hydrodynamic characteristics of the watercourses, thus establishing a mathematical function through which to obtain estimates for future evaluations. The R² value obtained using this equation was 0.959, indicating a high correlation between the calculated values ??and those estimated in the field. Copyright © 2012 John Wiley & Sons, Ltd.     

Monitoring and modeling dissolved oxygen dynamics through continuous longitudinal sampling: a case study in Wen‐Rui Tang River,Wenzhou, China

Synoptic water sampling at a fixed site monitoring station provides only limited ‘snap‐shots’ of the complex water quality dynamics within a surface water system. However, water quality often changes rapidly in both spatial and temporal dimensions, especially in highly polluted urban rivers. In this study, we designed and applied a continuous longitudinal sampling technique to monitor the fine‐scale spatial changes of water quality conditions, assess water pollutant sources, and determine the assimilative capacity for biochemical oxygen demand (BOD) in an urban segment of the hypoxic Wen‐Rui Tang River in eastern China. The continuous longitudinal sampling was capable of collecting dissolved oxygen (DO) data every 5 s yielding a ~11 m sampling interval with a precision of ±0.1 mg L^?1. The Streeter and Phelps BOD‐DO model was used to calculate: (1) the oxygen consumption coefficient (K₁) required for calibration of water quality models, (2) BOD assimilative capacity, and (3) BOD source and load identification. In the 2014 m river segment sampled, the oxygen consumption coefficient (K₁) was 0.428 d^?1 (20°C), the total BOD discharge was 916 kg d^?1, and the BOD assimilative capacity was 382 kg d^?1 when the minimum DO level was set to 2 mg L^?1. In addition, the longitudinal analysis identified eight major drainage outlets (BOD point sources), which were verified by field observations. This new approach provides a simple, cost‐effective method of evaluating BOD‐DO dynamics over large spatial areas with rapidly changing water quality conditions, such as urban environments. It represents a major breakthrough in the development and application of water quality sampling techniques to obtain spatially distributed DO and BOD in real time. Copyright © 2012 John Wiley & Sons, Ltd.     

冰封期湖泊溶解氧变化特征及代谢速率

冰封期湖泊与大气的气体交换受冰盖阻碍,影响湖泊溶解氧含量,进而影响湖泊水质.为探究冰封期湖泊溶解氧和新陈代谢速率变化特征及影响因素,本研究通过监测典型季节性冰封湖泊不同深度的溶解氧(DO)、水温和光合有效辐射(PAR),结合水质检测结果,分析冰封期湖泊DO变化及代谢速率影响因素,对湖泊日新陈代谢速率计算方法进行改进并估算冰封期湖泊新陈代谢速率.结果表明：2021年1—3月间,岱海DO浓度平均值为15.49 mg/L,并出现昼夜变化和分层现象.DO在冰封期变化趋势呈现出先逐渐升高,然后保持稳定,进入消融期(2021年3月2—11日)分层现象逐渐消失的规律.岱海冰封期净初级生产力和生态系统呼吸速率平均值分别为0.11和0.10 mg/(L·d),在水温未出现分层时净初级生产力呈现较高水平;之后水温沿水深方向出现分层,净初级生产力明显下降;当冰层融化后,PAR显著上升,净初级生产力又逐渐恢复至0.10 mg/(L·d).统计分析表明,岱海冰封期DO与水温、PAR、总氮等变化具有一定相关关系,且由于湖泊流域生态环境条件及冰封期物候特征不同,岱海与内蒙古其他湖泊相比,冰封期DO变化趋势存在一定差...     

Artificial neural network modelling of concentrations of nitrogen,phosphorus and dissolved oxygen in a non‐point source polluted river in Zhejiang Province,southeast China

A back‐propagation algorithm neural network (BPNN) was developed to synchronously simulate concentrations of total nitrogen (TN), total phosphorus (TP) and dissolved oxygen (DO) in response to agricultural non‐point source pollution (AGNPS) for any month and location in the Changle River, southeast China. Monthly river flow, water temperature, flow travel time, rainfall and upstream TN, TP and DO concentrations were selected as initial inputs of the BPNN through coupling correlation analysis and quadratic polynomial stepwise regression analysis for the outputs, i.e. downstream TN, TP and DO concentrations. The input variables and number of hidden nodes of the BPNN were then optimized using a combination of growing and pruning methods. The final structure of the BPNN was determined from simulated data based on experimental data for both the training and validation phases. The predicted values obtained using a BPNN consisting of the seven initial input variables (described above), one hidden layer with four nodes and three output variables matched well with observed values. The model indicated that decreasing upstream input concentrations during the dry season and control of NPS along the reach during average and flood seasons may be an effective way to improve Changle River water quality. If the necessary water quality and hydrology data are available, the methodology developed here can easily be applied to other case studies. The BPNN model is an easy‐to‐use modelling tool for managers to obtain rapid preliminary identification of spatiotemporal water quality variations in response to natural and artificial modifications of an agricultural drainage river. Copyright © 2009 John Wiley & Sons, Ltd.     

Modeling of nitrobenzene in the river with ice process in high-latitude regions

To deal with the accidental release of pollutants into frozen rivers, those rivers in high-latitude regions should be treated differently. Pollutants could be stored up in the ice and released when the ice melts in spring, perhaps resulting in secondary pollution in the river. A water quality model of nitrobenzene has been developed in this paper to assess the influence on river water quality due to the freezing process. The model is made up of three modules: thermodynamic module, hydrodynamic module and water quality module. The thermodynamic module considers the complex heat exchange processes between the water body and the atmosphere, the water body and the river bed, the water body and the ice cover, and so on. The growth of the ice cover is simulated in a simplified form whilst taking consideration of heat balance. The hydrodynamic model uses the Saint-Venant equations in non-constant flow and the influence of the ice cover is measured. The degradation, diffusion, absorption and desorption, and the influence of the freezing process are incorporated in the water quality module and the concept of Continuously Stirred Tanked Reactors (CSTRs) model is applied in the model construction. The model has been applied to supporting the management of accidental pollution in the Songhua River. The model was calibrated and validated with monitored data. Regional sensitivity analysis based on a task-based Hornberger-Spear-Young (HSY) algorithm was carried out to examine the model structure and the result showed that the model could describe the system very well. Then the conditioned model was applied to predicting the concentration of nitrobenzene in the water when the ice melted in spring. The predictive result showed that the release of pollutants in the ice could lead to an increase in the concentration of pollutants in the water, but the increase would be very small because there was only a small amount of pollutants stored in the ice. A secondary pollution could therefore be avoided. Also, with necessary modifications, the model established in this study could be applied to the water quality management in rivers that freeze in winter.     

有色可溶性有机物(CDOM)吸收作为湖库化学需氧量监测替代指标的探讨

化学需氧量是衡量水体中有机物量及污染程度的综合性指标,也是我国《地表水环境质量标准(GB 3838—2002)》的重要评价指标.然而,由于测定过程缓慢和使用了有毒有害试剂易于形成二次污染,现行标准的高锰酸钾和重铬酸钾化学需氧量测定方法无法做到环境友好,也不能反映当前快速和实时监测的技术需求.因此,迫切需要发展操作简便、快速高效、灵敏可靠、环境友好和环保绿色的化学需氧量替代检测方法.本文首先从文献计量学视角比较我国与世界上发达国家化学需氧量研究主题论文发文量,剖析了我国发展化学需氧量替代检测方法的迫切性.基于全国大范围65个湖库706个样本有色可溶性有机物吸收系数、化学需氧量和其他水质参数同步调查数据,构建广覆盖范围的有色可溶性有机物特征波长吸收系数和化学需氧量间高精度线性关系模型,确定了地表水环境质量评价的吸收系数阈值,模型可以广泛应用于不同类型(深水、浅水、大型、中型、小型)和不同营养状态(贫、中、富营养)湖库水体有机物浓度的定量表征,具有一定普适性.通过对比有色可溶性有机物吸收系数和传统的高锰酸钾、重铬酸钾法优势和不足,明确了有色可溶性有机物吸收系数替代化学需氧量用于湖库水体开展有...     

巢湖南淝河口黑水团区流速和溶解氧与Fe2+、S2-浓度的空间关联性

黑臭现象在我国东部地区浅水湖泊频繁发生,已经严重影响环湖地区的社会经济发展.厌氧环境和高浓度Fe~(2+)、S~(2-)是引起黑臭现象的必要条件.本文解析巢湖南淝河口区黑臭水团范围内Fe~(2+)、S~(2-)与DO、流速的分布特性;基于空间计量模型重点探讨了流速、DO和Fe~(2+)、S~(2-)分布的空间关联性.结果发现,水体流动在黑臭水团中主要有两个作用:使Fe~(2+)、S~(2-)和DO彼此产生空间相关性以及通过分散作用改变局部Fe~(2+)、S~(2-)浓度分布;同时流速通过增强复氧间接影响Fe~(2+)及S~(2-)总体浓度的作用较小.流速与Fe~(2+)、S~(2-)之间均满足线性空间滞后模型;而线性回归模型中流速对Fe~(2+)的影响低估了约7%,对S~(2-)的影响则低估了12%.