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.     

APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING1

The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall‐runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km² watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth‐order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea.     

高温堆肥法处理污泥的探讨

对于污水处理后所产生的污泥,较好的方法是采用无害化、资源化农用技术,利用污泥为原料,制成有机复合肥,不仅解决了污水污泥的处理问题,还可推动环保产业的发展,促进农业生产,具有明显的经济、社会和环境效益。     

基于神经网络的温度预测

室内温度与诸多影响因素之间的非线性、复杂性等关系 ,给建模、预测带来了难度 ,引入了人工神经网络 ;利用人工神经网络的非线性、并行计算和自学习特性进行建模 ,实现了对温度模拟     

SOIL SALINITY PREDICTION USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: This study explores the applicability of Artificial Neural Networks (ANNs) for predicting salt build‐up in the crop root zone. ANN models were developed with salinity data from field lysimeters subirrigated with brackish water. Different ANN architectures were explored by varying the number of processing elements (PEs) (from 1 to 30) for replicate data from a 0.4 m water table, 0.8 m water table, and both 0.4 and 0.8 m water table lysimeters. Different ANN models were developed by using individual replicate treatment values as well as the mean value for each treatment. For replicate data, the models with twenty, seven, and six PEs were found to be the best for the water tables at 0.4 m, 0.8 m and both water tables combined, respectively. The correlation coefficients between observed salinity and ANN predicted salinity of the test data with these models were 0.89, 0.91, and 0.89, respectively. The performance of the ANNs developed using mean salinity values of the replicates was found to be similar to those with replicate data. Not only was there agreement between observed and ANN predicted salinity values, the results clearly indicated the potential use of ANN models for predicting salt build‐up in soil profile at a specific site.     

WHAT DO WE KNOW ABOUT SALT WATER INTRUSION?1

Almost thirty years after the first known publication on salt water problems published in 1855 by Braithwaite, two investigators developed an approximate theory to find the boundaries of fresh water lenses in coastal aquifers. Their theory is now known as Ghyben-Herzberg Theory. Although their theory is based on oversimplified assumptions, it has stimulated others through various periods of time. A review of the main investigations in this field is summarized in this paper. The discussions are subdivided into three main eras: (1) The period through which the problems were identified and field observations were explained (from 1855 until the early forties); (2) the analytic approaches during the period from 1940 to the late sixties; and (3) the refined techniques during the past three years. A brief summary of the recharge methods is also given in addition to a more or less comprehensive list of references.     

长江流域的鱼类资源及其保护对策

长江水系的鱼类约有300种,其中鲤科鱼类占半数以上,主要的经济鱼类多数属于鲤科。对不同江段的鱼类资源作了简介,并提出了相应的保护措施。上游江段以维护生物多样性,保护特有种为主,需要建立鱼类自然保护区;中游应保证主要经济鱼类的自然繁殖条件,加强珍稀鱼类的人工繁殖放流工作,同时对湖泊幼鱼资源进行保护;下游应注意保持江水质量,严格遵守工业废水排放标准;河口江段应规定幼鲟保护期,建议每年6月15日至7月31日停止一切损害幼鲟资源的渔捞作业。     

长江河口越江工程在上海港建设中的重要作用

探讨了连接上海，崇明和江苏海门的长江河口桥隧工程在连接河口岛屿间交通，发挥崇明的区位优势，促进地区经济发展以及在上海港建设中的作用。详细论述了越江工程完成后上海新港的扩建，根据河口河水岸线的分布，提出了在横沙岛东滩修建人工岛的设想，最后对几种越江方案进行了分析比较，提出了作者的看法。     

人工神经网络方法在资源与环境预测方面的应用

用人工神经网络方法对不同水域、不同环境因子之间非线性和不确定性的复杂关系进行学习训练并预测检验。结果表明：人工神经网络方法在模拟和预测方面 优于传统的统计回归模型,在资源与环境方面的应用是可行的。具有较强的模拟预测能力。与传统的回归模型相比,人工神经网络方法不要求监测数据具有很强的规律性,就可用后的网络模型对其进行预报,燕且预测相对误差均比回归模型预测相对误差要小,具有一定的实用性。两个实例的应用