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。     

响应面法和神经网络优化Acinetobacter sp. DNS32发酵基质

为了提高阿特拉津降解菌Acinetobacter sp.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、K2HPO4)进行优化研究。响应曲面法确定3种成分的含量为玉米粉39.494 g/L,豆饼粉25.638 g/L和K2HPO43.265 g/L时,预测发酵活菌最大生物量为7.079×108CFU/mL,实测量为7.194×108CFU/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39.650 g/L,豆饼粉为25.500 g/L,K2HPO4为2.624 g/L时,预测最大值为7.199×108CFU/mL,实测量为7.244×108CFU/mL;最终确定培养基配方:玉米粉为39.650 g/L,豆饼粉为25.500 g/L,K2HPO4为2.624 g/L,CaCO3为3.000 g/L,MgSO4.7H2O和NaCl均为0.200 g/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。     

芦苇和香蒲在人工湿地城市污泥处理系统中的应用

用有机玻璃材料做成模拟人工湿地,分别种植芦苇和香蒲考察2种模拟植物湿地对城市污泥干化和稳定化、污泥中营养元素和重金属去除效果,并和空白湿地对比。实验污泥取自当地污水厂二沉池,污水处理采用厌氧池加氧化沟工艺。模拟人工湿地进泥负荷为0.72 kg TS/(m2.d),进泥中TS平均含量为5.58 g/L,pH为6.80。实验期为1年,采用间歇式进泥,渗滤液月采集测样一次,湿地剩余污泥2月1次。实验结果受天气和气候影响较大,处理效果夏季好于冬季,出水水质和剩余污泥的干化及稳定效果芦苇湿地好于香蒲湿地。剩余污泥中各种重金属含量低于污泥农用标准中各重金属离子浓度要求。结果表明,人工湿地植物净化系统对城市污泥有较好的脱水和稳定化效果。     

Simultaneous determination of three herbicides by differential pulse voltammetry and chemometrics

A novel differential pulse voltammetry method (DPV) was researched and developed for the simultaneous determination of Pendimethalin, Dinoseb and sodium 5-nitroguaiacolate (5NG) with the aid of chemometrics. The voltammograms of these three compounds overlapped significantly, and to facilitate the simultaneous determination of the three analytes, chemometrics methods were applied. These included classical least squares (CLS), principal component regression (PCR), partial least squares (PLS) and radial basis function-artificial neural networks (RBF-ANN). A separately prepared verification data set was used to confirm the calibrations, which were built from the original and first derivative data matrices of the voltammograms. On the basis relative prediction errors and recoveries of the analytes, the RBF-ANN and the DPLS (D – first derivative spectra) models performed best and are particularly recommended for application. The DPLS calibration model was applied satisfactorily for the prediction of the three analytes from market vegetables and lake water samples.     

ATFITUDES TOWARDS ARTIFICIAL WETLANDS IN ONTARIO FOR STORMWATER CONTROL AND WATERFOWL HABITAT1

ABSTRACT: The objective was to survey individuals from different interest groups in Ontario to ascertain attitudes towards the use of artificial wetlands for stormwater management and to provide waterfowl habitat. In total, 166 questionnaires were distributed and 124 cempleted returns were received. The results of the survey show that the provision of wetlands as waterfowl habitats in the context of stormwater impoundments is severely limited and is not considered an important management option. The overwhelming rejection of this integrated approach seems to be due to a lack of understanding and its comparative newness. Some of the perceived problems and concerns could be addressed by improvements in the design of the artificial wetlands. The design and implementation of an artificial wetland prototype as part of an education program in Ontario is recommended. A few research opportunities also are outlined.     

Drainage Investment and Wetlands Loss: an Analysis of the National Resources Inventory Data

The United States Soil Conservation Service (SCS) conducts a survey for the purpose of establishing an agricultural land use database. This survey is called the National Resources Inventory (NRI) database. The complex NRI land classification system, in conjunction with the quantitative information gathered by the survey, has numerous applications. The current paper uses the wetland area data gathered by the NRI in 1982 and 1987 to examine empirically the factors that generate wetland loss in the United States. The cross-section regression models listed here use the quantity of wetlands, the stock of drainage capital, the realty value of farmland and drainage costs to explain most of the cross-state variation in wetland loss rates. Wetlands preservation efforts by federal agencies assume that pecuniary economic factors play a decisive role in wetland drainage. The empirical models tested in the present paper validate this assumption.     

WETLAND ECOSYSTEM STUDIES FROM A HYDROLOGIC PERSPECTIVE1

ABSTRACT: Selected studies from the literature were reviewed to determine the extent of knowledge about the relationship between hydrology and wetland ecosystem studies. Wetland studies of chemical input-output relationships have been the most dependent on hydrologic data of all wetland investigations; yet, very few of these studies have attempted to measure all components of a wetland's water balance. Usually, unmeasured components were calculated as the difference between measured inputs and outputs. Ground water frequently was overlooked. Chemical input-output investigations primarily were concerned with determining the amount of input retained in the wetlands. Few studies also included direct measurement of biogeochemical processes within wetlands of elements that were part of simultaneous input-output investigations. The importance of uncertainties in chemical budgets that are due to uncertainties in hydrologic budgets has been addressed in very few wetland investigations. Although many studies have emphasized the importance of hydrology to wetland ecosystem research, few studies have documented this, so that hydrology remains one of the least understood components of wetland ecosystems.     

Large-scale constructed wetlands for nutrient removal from stormwater runoff: An everglades restoration project

The South Florida Water Management District (SFWMD) constructed a wetland south of Lake Okeechobee to begin the process of removing nutrients (especially phosphorus) from agricultural stormwater runoff entering the Everglades. The project, called the Everglades Nutrient Removal (ENR) project, is a prototype for larger, similarly constructed wetlands that the SFWMD will build as part of the Everglades restoration program. This innovative project is believed to be one of the largest agricultural stormwater cleanup projects in the United States, if not in the world. This publication describes the ENR project's design, construction, and proposed operation, as well as the proposed research program to be implemented over the next few years.     

DESIGN BASIS FOR EVERGLADES STORMWATER TREATMENT AREAS1

ABSTRACT: The State of Florida (1994) has adopted a plan for addressing Everglades eutrophication problems by reducing anthropogenic phosphorus loads. The plan involves implementation of Best Management Practices in agricultural watersheds and construction of regional treatment marshes (Stormwater Treatment Areas or STA's). This paper describes the development, testing, and application of a mass-balance model for sizing STA's to achieve treatment objectives. The model is calibrated and tested against peat and water-column data collected in Water Conservation Area-2A (WCA-2A), where phosphorus dynamics and eutrophication impacts have been intensively studied. The 26-year-average rate of phosphorus accretion in peat is shown to be proportional to average water-column phosphorus concentration, with a proportionality constant of 10.2 m/yr (90 percent Confidence Interval = 8.9 to 11.6 m/yr). Spatial and temporal variations in marsh water-column data suggest that drought-induced recycling of phosphorus was important during periods of low stage in WCA-2A. Maintaining wet conditions will be important to promote phosphorus removal in STA's. Sensitivity analysis of STA performance is conducted over the range of uncertainty in model parameter estimates to assess the adequacy of the model as a basis for STA design.