Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande1

Abudu, S., J.P. King, Z. Sheng, 2011. Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande. Journal of the American Water Resources Association (JAWRA) 48(1): 10‐23. DOI: 10.1111/j.1752‐1688.2011.00587.x Abstract: This paper presents the application of autoregressive integrated moving average (ARIMA), transfer function‐noise (TFN), and artificial neural networks (ANNs) modeling approaches in forecasting monthly total dissolved solids (TDS) of water in the Rio Grande at El Paso, Texas. Predictability analysis was performed between the precipitation, temperature, streamflow rates at the site, releases from upstream reservoirs, and monthly TDS using cross‐correlation statistical tests. The chi‐square test results indicated that the average monthly temperature and precipitation did not show significant predictability on monthly TDS series. The performances of one‐ to three‐month‐ahead model forecasts for the testing period of 1984‐1994 showed that the TFN model that incorporated the streamflow rates at the site and Caballo Reservoir release improved monthly TDS forecasts slightly better than the ARIMA models. Except for one‐month‐ahead forecasts, the ANN models using the streamflow rates at the site as inputs resulted in no significant improvements over the TFN models at two‐month‐ahead and three‐month‐ahead forecasts. For three‐month‐ahead forecasts, the simple ARIMA showed similar performance compared to all other models. The results of this study suggested that simple deseasonalized ARIMA models could be used in one‐ to three‐month‐ahead TDS forecasting at the study site with a simple, explicit model structure and similar model performance as the TFN and ANN models for better water management in the Basin.     

Eichhornia crassipes capability to remove naphthalene from wastewater in the absence of bacteria

The aim of the current study was to investigate the potential of an aquatic plant, the water hyacinth (Eichhornia crassipes) devoid rhizospheric bacteria, to reduce naphthalene (a polyaromatic hydrocarbon) present in wastewater and wetlands.The capability of sterile water hyacinth plants to remove naphthalene from water and wastewater was studied in batch systems. Water hyacinths enhance the removal of pollutants through their consumption as nutrients and also through microbial activity of their rhizospheric bacteria.Experimental kinetics of naphthalene removal by water hyacinth coupled with natural rhizospheric bacteria was 100% after 9 d. Plants, decoupled of rhizospheric bacteria, reduced naphthalene concentration up to 45% during 7 d. Additionally, naphthalene uptake by water hyacinth revealed a biphasic behavior: a rapid first phase completed after 2.5 h, and a second, considerably slower rate, phase (2.5-225 h). In conclusion, water hyacinth devoid rhizospheric bacteria reduced significantly naphthalene concentration in water, revealing a considerable plant contribution in the biodegradation process of this pollutant.     

水生植物修复氮、磷污染水体研究进展

氮、磷是引起水体富营养化、导致水质恶化的重要因素,因此去除氮、磷一直是污水处理的重要任务.鉴于传统物理、化学方法存在的操作难、成本高、易产生二次污染等问题,人们越来越多地将目光转向利用水生植物去除氮、磷营养物质、净化水质上.介绍了近年来国内外应用水生植物修复氮、磷污染水体的方法、效果及其影响因素,探讨了水生植物净化污染水体的机制.针对目前研究中的不足,提出今后应在不同植物种类开发、植物组合优化以及植物的净化机制等方面加强研究.     

基于人工蜂群算法与BP神经网络的水质评价模型

针对BP网络水质评价模型的不足,引入人工蜂群(ABC)算法,将求解BP神经网络各层权值、阀值的过程转化为蜜蜂寻找最佳蜜源的过程,提出了一种新的结合人工蜂群算法的BP网络水质评价方法(ABC-BP)。并以2000—2006年渭河监测断面的10组实测数据作为测试样本对其水质进行了评价,实验结果表明该方法得到的水质评价结果准确,并具有很强的稳定性和鲁棒性。     

人工浮床对汾江河水质净化的研究

对佛山市汾江河内布置浮床的水域的7个断面进行采样监测,分析了布置浮床侧与未布置浮床侧各水质指标的差异,以考察人工浮床对汾江河水质的净化效果。同时比较了圆币草、水罂粟、梭鱼草、狐尾草、美人蕉和鸢尾6种浮床植物的生物量和氮磷吸收量。结果表明,布置人工浮床一侧的水质TN、TP、COD和NH4＋-N含量均显著低于未布置浮床一侧的水质,表明人工浮床对水体水质的有净化效果。总面积约4 900 m2的浮床植物经过3个月的生长,从水体中共吸收了192.5 kg的氮和76.1 kg的磷。6种浮床植物中,狐尾草的净增生物量最高,达到64.2 kg/m2;圆币草次之,为62.2 kg/m2。狐尾草和圆币草对氮磷的吸收能力在6种植物中处于较高水平,每平方米狐尾草和圆币草从水体中分别吸收了51.61g氮、19.79 g磷和46.90 g氮、22.93 g磷。综合比较得出,狐尾草和圆币草在生物量和氮磷吸收量上均保持在较高水平,是较好的浮床植物。本研究为人工浮床在南方类似河流中的应用及植物选择提供了参考依据。     

Prymnesium parvum: The Texas Experience1

Southard, Gregory M., Loraine T. Fries, and Aaron Barkoh, 2010. Prymnesium parvum: The Texas Experience. Journal of the American Water Resources Association (JAWRA) 46(1):14-23. DOI: 10.1111/j.1752-1688.2009.00387.x Abstract: Golden alga Prymnesium parvum was first identified in Texas during a fish kill investigation on the Pecos River in 1985. Since then golden alga kills occurred sporadically in a variety of waters in the western part of the state until 2001 when the alga became endemic in the Brazos, Canadian, Colorado, Red, and Rio Grande river systems, including the water supplies of two public fish hatcheries, the Possum Kingdom and Dundee state fish hatcheries. The increasing area adversely affected by the alga and frequent massive fish kills heightened public and political awareness and concerns regarding the ecological and economic impacts of P. parvum blooms. The Texas Parks and Wildlife Department (TPWD), the wildlife conservation agency of the state, responded to these concerns with a program to assess the ecological and economic impacts and to develop management options. To date 33 water bodies have been affected and losses are conservatively estimated at 34 million fish valued at US$13 million. Several sport fisheries, including smallmouth bass Micropterus dolomieu, striped bass Morone saxatilis, channel catfish Ictalurus punctatus, and blue catfish Ictalurus furcatus, have been severely affected. Additionally, 26 imperiled fish species occur in the affected water basins and some have been adversely affected. Economic losses associated with reduced fishing and other water-based recreational activities appear considerable. The combined economic losses to three counties (Palo Pinto, Stephens, and Young) surrounding Possum Kingdom reservoir for 2001 and 2003 were estimated at US$2.8 million and US$1.1 million, respectively. This paper describes how the TPWD responded to public and political concerns relative to the emergence of golden alga, its harmful effects to fisheries, and its historic and current statewide distribution.     

Ecologically Functional Floodplains: Connectivity,Flow Regime,and Scale1

Opperman, Jeffrey J., Ryan Luster, Bruce A. McKenney, Michael Roberts, and Amanda Wrona Meadows, 2010. Ecologically Functional Floodplains: Connectivity, Flow Regime, and Scale. Journal of the American Water Resources Association (JAWRA) 46(2):211-226. DOI: 10.1111/j.1752-1688.2010.00426.x Abstract: This paper proposes a conceptual model that captures key attributes of ecologically functional floodplains, encompassing three basic elements: (1) hydrologic connectivity between the river and the floodplain, (2) a variable hydrograph that reflects seasonal precipitation patterns and retains a range of both high and low flow events, and (3) sufficient spatial scale to encompass dynamic processes and for floodplain benefits to accrue to a meaningful level. Although floodplains support high levels of biodiversity and some of the most productive ecosystems on Earth, they are also among the most converted and threatened ecosystems and therefore have recently become the focus of conservation and restoration programs across the United States and globally. These efforts seek to conserve or restore complex, highly variable ecosystems and often must simultaneously address both land and water management. Thus, such efforts must overcome considerable scientific, technical, and socioeconomic challenges. In addition to proposing a scientific conceptual model, this paper also includes three case studies that illustrate methods for addressing these technical and socioeconomic challenges within projects that seek to promote ecologically functional floodplains through river-floodplain reconnection and/or restoration of key components of hydrological variability.     

水生植物应用于富营养化控制的研究趋向

阐述了水生植物在治理富营养化水体中的作用机理和研究现状,指出了水生植物治理富营养化水体存在的问题,展望了水生植物在治理富营养化水体方面的发展方向。     

响应面法和神经网络优化Acinetobactersp．DNS32发酵基质

为了提高阿特拉津降解菌Acinetobactersp．DNS32的产量,分别采用响应曲面法和基于人工神经网络的遗传算法对阿特拉津降解菌DNS32发酵培养基中3个重要基质成分（玉米粉、豆饼粉、K：HPO。）进行优化研究。响应曲面法确定3种成分的含量为玉米粉39．494g／L,豆饼粉25．638g／L和K。HPO。3．265g／L时,预测发酵活菌最大生物量为7．079×10^8CFU／mL,实测量为7．194×10^8CFU／mL;人工神经网络结合遗传算法优化确定3种主要成分含量为玉米粉为39．650g／L,豆饼粉为25．500g／L,K2HPO4为2．624g／L时,预测最大值为7．199×10^8CFU／mL,实测量为7．244×10。CFU／mL;最终确定培养基配方：玉米粉为39．650g／L,豆饼粉为25．500g／L,K2HPO4为2．624g／L,CaCO3为3．000g／L,MgSO4·7H2O和NaCl均为0．200g／L;优化后阿特拉津降解菌DNS32发酵生物量比优化前提高了36．6％。结果表明,在阿特拉津降解菌DNS32发酵培养基组分优化方面,响应面法和基于人工神经网络的遗传算法都是可行的,基于人工神经网络的遗传算法具有更好的拟合度和预测准确度。