Herbicides in the upper Poxim River, Sergipe, and the risk of contamination of water resources

Increased agricultural activity in watershed areas has been causing concern over contamination by herbicides in agricultural areas. The problem becomes more important when contamination can affect water for human consumption, as happens with water from the Poxim river, which supplies the city of Aracaju, capital of the State of Sergipe. The aim of this study was to evaluate the risk of contamination by herbicides to both surface and groundwater in the upper sub-basin of the Poxim River, and to detect the presence of the active ingredients Diuron and Ametrine up-river from the sugar-cane plantations. Risk analysis was carried out using criteria from the Environmental Protection Agency (EPA), the GUS index, and the GOSS method. It was observed that several active ingredients are at risk of leaching, demonstrating the importance of monitoring the river to control both the quality of water and the frequency and volume of herbicides used in the region. Based on the results, monitoring was carried out bi-monthly from July 2009 to July 2010 at two sampling points. Water samples were analyzed in the laboratory, where the presence of Diuron and Ametrine was noted. Water quality in the Sub-basin of the Rio Poxim is being influenced by the use of herbicides in the region. There was an increase in herbicide concentration in the surface water during the rainy season, possibly caused by soil runoff.

Development of methodology for determination of pesticides residue in water by SPE/HPLC/DAD

To boost crop yield, sugarcane growers are using increasing amounts of pesticides to combat insects and weeds. But residues of these compounds can pollute water resources, such as lakes, rivers and aquifers. The present paper reports the results of a study of water samples from the Feijao River, which is the source of drinking water for the city of Sao Carlos, Sao Paulo, Brazil. The samples were evaluated for the presence of four leading pesticides - ametryn, atrazine, diuron and fipronil - used on sugarcane, the dominant culture in the region. The samples were obtained from three points along the river: the headwaters, along the middle course of the river and just before the municipal water intake station. The pesticides were extracted from the water samples by solid-phase extraction (SPE) and then analyzed by liquid chromatography with diode array detection (LC-DAD). The analytical method was validated by traditional methods, obtaining recovery values between 90 and 95%, with precision deviations inferior to 2.56%, correlation coefficients above 0.99 and detection and quantification limits varying from 0.02 to 0.05 mg L-1 and 0.07 to 0.17 mg L-1, respectively. No presence of residues of the pesticides was detected in the samples, considering the detection limits of the method employed.

Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, Sa approximate to o Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of 3.3 L/s and 2.8 L/s for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models, and produced a MAE of 3.1 L/s and 3.0 L/s for training and test set respectively, which represented about 12% of average consumption. DOI: 10.1061/(ASCE)WR.1943-5452.0000177. (C) 2012 American Society of Civil Engineers.