Forecasting Daily Chlorophyll a Concentration during the Spring Phytoplankton Bloom Period in Xiangxi Bay of the Three-Gorges Reservoir by Means of a Recurrent Artificial Neural Network

A recurrent artificial neural network was used for 0-and 7-days-ahead forecasting of daily spring phytoplankton bloom dynamics in Xiangxi Bay of Three-Gorges Reservoir with meteorological, hydrological, and limnological parameters as input variables. Daily data from the depth of 0.5 m was used to train the model, and data from the depth of 2.0 m was used to validate the calibrated model. The trained model achieved reasonable accuracy in predicting the daily dynamics of chlorophyll a both in 0-and 7-days-ahead forecasting. In 0-day-ahead forecasting, the R-2 values of observed and predicted data were 0.85 for training and 0.89 for validating. In 7-days-ahead forecasting, the R-2 values of training and validating were 0.68 and 0.66, respectively. Sensitivity analysis indicated that most ecological relationships between chlorophyll a and input environmental variables in 0-and 7-days-ahead models were reasonable. In the 0-day model, Secchi depth, water temperature, and dissolved silicate were the most important factors influencing the daily dynamics of chlorophyll a. And in 7-days-ahead predicting model, chlorophyll a was sensitive to most environmental variables except water level, DO, and NH3N.

Sorption, degradation and mobility of microcystins in Chinese agriculture soils: Risk assessment for groundwater protection

In the present paper, sorption, persistence, and leaching behavior of three microcystin variants in Chinese agriculture soils were examined. Based on this study, the values of capacity factor and slope for three MCs variants in three soils ranged from 0.69 to 6.00, and 1.01 to 1.54, respectively. The adsorption of MCs in the soils decreased in the following order: RR > Dha(7) LR > LR. Furthermore, for each MC variant in the three soils, the adsorption rate in the soils decreased in the following order: soil A > soil C > soil B. The calculated half-time ranged between 7.9 and 17.8 days for MC-RR, 6.0-17.1 days for MC-LR, and 7.1-10.2 days for MC-Dha(7) LR. Results from leaching experiments demonstrated that recoveries of toxins in leachates ranged from 0-16.7% for RR, 73.2-88.9% for LR, and 8.9-73.1% for Dha 7 LR. The GUS value ranged from 1.48 to 2.06 for RR, 1.82-2.88 for LR, and 1.76-2.09 for Dha(7) LR. Results demonstrated the use of cyanobacterial collections as plant fertilizer is likely to be unsafe in soils. (c) 2006 Elsevier Ltd. All rights reserved.

Contamination and distribution of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/F) in agriculture fields in Ya-Er Lake area, China

The contamination and distribution of polychlorinated dibeinizo-p-dioxins and dibenzofurans (PCDD/Fs) from two agricultural fields of a heavily polluted lake area in China (Ya-Er Lake) are presented. The vertical distribution pattern of total PCDD/Fs in soil cores reveals that the maximum concentration was in the layer of 20-30 cm. The concentrations in the top layer of soil at the two sites were similar (17.48 ng/kg at Site 1 and 18.10 ng/kg at Site 2), but the maximum concentration of Site 1 (120.8 ng/kg) was two times higher than that of Site 2 (64.39 ng/kg). The maximum concentration of PCDD/Fs in mud cores in rice fields (0-50 cm) at Sites 1 and 2 was in the layer of 0-10 cm. The maximum PCDD/F concentration in the top layer in mud at Site 1 (203.1 ng/kg) was higher than that at Site 2: (143.3 ng/kg). Significant correlations were found between the mind PCDD/Fs and the organic carbon content (R = 0.9743, P< 0,05 at Site 1; R = 0.9821, P< 0.05 at Site 2), the two variables being highly correlated (R = 0.9049, P< 0.05, at Site 1; R = 0.9916, P< 0.05 at Site 2). All correlation coefficients were significant at the 95% level. Concentrations were highly correlated with organic carbon, indicating that sorption to organic carbon was the dominant mechanism. Using principal component analysis, the homologue profiles of soil, mud, and plants (rice and radish) were compared. The PCDD/F patterns in plants were found not to be correlated to those in soil and mud. This suggests that atmospheric deposition may be the main source of PCDD/Fs in rice grain. However, mixed exposure involving uptake mechanisms and atmospheric deposition is considered main the source of PCDD/F pollution in radishes. (C) 2002 Elsevier Science (USA).

A non-linear perturbation model considering catchment wetness and its application in river flow forecasting

A non-linear perturbation model for river flow forecasting is developed, based on consideration of catchment wetness using an antecedent precipitation index (API). Catchment seasonality, of the form accounted for in the linear perturbation model (the LPM), and non-linear behaviour both in the runoff generation mechanism and in the flow routing processes are represented by a constrained nan-linear model, the NLPM-API. A total of ten catchments, across a range of climatic conditions and catchment area magnitudes, located in China and in other countries, were selected for testing daily rainfall-runoff forecasting with this model. It was found that the NLPM-API model was significantly more efficient than the original linear perturbation model (the LPM). However, restric tion of explicit nan-linearity to the runoff generation process, in the simpler LPM-API form of the model, did not produce a significantly lower value of the efficiency in flood forecasting, in terms of the model efficiency index R-2. (C) 1997 Elsevier Science B.V.