Intensive agriculture requires increasing application of fertilizers in order to sustain food production. Improper use of these substances in combination with increasing seawater intrusion results in long-term and nonpoint soil and groundwater contamination. In this work, a 3-D groundwater and solute transport numerical model was created to simulate the effect of excessive fertilizers application along the Bahr El Baqar drain system, in the eastern Nile Delta, Egypt. The geotechnical properties of the soils, hydrologic parameters, and unconfined compressive strength were determined at different sites and used as input parameters for the model. Model results showed that silty clay soils are able to contain the contaminations and preserve the groundwater quality. Nevertheless, sandy soils primarily located at the beginning of the Bahr El Baqar drain allow leakage of fertilizers to the groundwater. Thus, fertilizer application should be properly managed in the top sandy layers to protect the groundwater and soil, as increasing aquifer by excess irrigation water increased the groundwater contamination in confined layers due to the high value of cumulative salt for the current situation while the unconfined zone decreased groundwater and soil contamination. A mass transport 3-D multi-species (MT3D) model was set to identify the optimal measure to tackle soil and groundwater contamination along the Bahr El-Baqar drain system. A potential increase of the abstraction rates in the study area has a positive impact in reducing the transfer of fertilizer contamination to groundwater while it has a negative impact for soil contamination. The scenario analysis further indicated that the installation of a drainage network decreases the groundwater and soil contamination. Both solutions are potentially effective for protection against nonpoint contamination along the Bahr El Baqar drain system. However, a more sustainable management approach of fertilizer application is needed to adequately protect the receptors located further downstream in the Nile Delta.

No abstract

This study compare liquid limit values obtained by the Casagrande apparatus, Russian and British drop cone penetrometer. Liquid limit determined for clay samples collected from 10 boreholes at the Cairo-Suez district (north western part of Badr City), Egypt. It is the most important geotechnical parameter used of fine-grained soils. The liquid limits for 40 natural clay samples varying between 27.8 and 69.7%. It is found that the liquid limit determined by the Casagrande apparatus and Russian standard (cone penetrometer) give essentially the same results with a difference of < 5%. It is observed that the values of Casagrande apparatus were generally lower than those obtained by the cone penetrometer with Russian standard. In addition, Casagrande apparatus for some samples, which had liquid limits of more than 60%, give a higher result. The liquid limits determined by the Russian and British standards are not consistent with a difference of < 1%. Besides, the results which determined by the Casagrande apparatus and British standards (BS), provide equivalent values with a difference of < 3%.

The swelling potentiality is a vital property of fine-grained soils strictly related to the index properties and chemical composition. The integration of machine learning techniques and geotechnical parameters provided a new integrative approach for predicting the free swelling index (FSI) and the swelling pressure (SP). In this paper, an adaptive neuro-fuzzy inference system (ANFIS) using named Reptile Search Algorithm (RSA) is presented to predict the swelling potentiality for fine-grained soils in the foundation bed at El Sherouk city, Egypt. The developed predictive model, named RSA-ANFIS, used as input measured 108 natural fine-grained soil samples of index geotechnical parameters and chemical composition as input data and the measured data of the free swelling index and the swelling pressure as output data. To justify the performance of the developed model, a comparative study was carried out, and the results show that the developed RSA-ANFIS has a high performance over the competitive methods in terms of coefficient of determination, root mean square error (RMSE), and mean absolute error (MAE). This new integrative approach is considered at the highly developed stage to predict and improve the analysis of multi-parameter soil behavior and could be applied in other objective variable datasets.

No abstract