-Drought treatments were applied to seven sets of dwarf bean plants in pots. Each was subjected to water stress during one of the seven phenological phases, ranging from bud stage to grain filling. Treatments

In order to better manage the limited water resources in arid regions, accurate determination of plant water requirements is necessary. For that, the evaluation of reference evapotranspiration (ET0)—a basic component of the hydrological cycle—is essential. In this context, the Penman Monteith equation, known for its accuracy, requires a high number of climatic parameters that are not always fully available from most meteorological stations. Our study examines the effectiveness of the use of artificial neural networks (ANN) for the evaluation of ET0 using incomplete meteorological parameters. These neural networks use daily climatic data (temperature, relative humidity, wind speed and the insolation duration) as inputs, and ET0 values estimated by the Penman-Monteith formula as outputs. The results show that the proper choice of neural network architecture allows not only error minimization but also maximizes the relationship between the dependent variable and the independent variables. In fact, with a network of two hidden layers and eight neurons per layer, we obtained, during the test phase, values of 1, 1 and 0.01 for the determination coefficient, the criterion of Nash and the mean square error, respectively. Comparing results between multiple linear regression and the neural method revealed the good modeling quality and high performance of the latter, due to the possibility of improving performance criteria. In this work, we considered correlations between input variables that improve the accuracy of the model and do not pose problems of multi-collinearity. Furthermore, we succeeded in avoiding overfitting and could generalize the model for other similar areas.

This study was carried out to assess the degree of contamination by trace metals in sediments of the main rivers of the eastern Mitidja plain. In order to achieve this objective, the enrichment factor, the geoaccumulation index and the potential ecological risk were calculated. Possible sources of trace metals in sediments were performed using a multivariate statistical analysis. The concentrations of the studied metals decreased in the order of Fe ≫ Zn > Cr > Pb > Ni > Cu> Cd. The results showed that Cd had a higher enrichment factor than the other metals. The geoaccumulation index results of trace metals revealed that the studied sediment were strongly contaminated with Cd, moderately contaminated with Zn and Pb and not contaminated with Cr, Cu, Fe, and Ni. Among the studied trace metals, only the Cd may present an ecological potential risk to the ecosystem. Furthermore, the results indicated a geochemical association of the elements Cr, Fe and Ni, suggesting natural source of these metals. In addition, Cd, Cu, Pb, and Zn appear to have originated from different sources. The results of this study are important for the development of any future strategies to protect the aquatic environment. © 2017 American Institute of Chemical Engineers Environ Prog, 36: 1650–1657, 2017

In the last few decades, the Mitidja plain has undergone economic development which may have altered the concentrations of some trace metals in its soils. Therefore, this study was aimed at investigating the concentrations and sources of Cd, Cr, Cu, Fe, Ni, Pb and Zn in 180 composite topsoil samples using a combination of environmental quality indicators and multivariate statistical approaches with a geographic information system (GIS). Based on spatial distribution maps, various hot-spots have been identified. Enrichment factors (EFs) indicated that Cd, Cu, Pb and Zn were from anthropogenic sources, while Ni and Cr were controlled mainly by natural lithogenic source. Multivariate statistical analyses were in agreement, except for Cu which was classified as coming from both anthropogenic and lithogenic sources.

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