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In-situ nitrate remediation using nano iron/nickel particles

Tehrani M. R. F., Vossoughi M., Shamsai A.

Environment Protection Engineering

2014

Vol. 40, nr 3

75--86

Originally, the application of nano zero valent iron/nickel (nZVI/Ni) particles for nitrate removal in porous media was studied. nZVI/Ni was prepared and employed in batch and continuous modes. Based on batch experiments, the reaction kinetics was consistent with the adsorption model by the order of 1–1.5. The variation of the kinetics order depends on pH and nickel content. So that highest reactivity was observed for nZVI with 10% of Ni at pH ≤ 3. Nitrate remediation in a continuous system was mostly influenced by seepage velocity, quantity and freshness of nZVI/Ni and particle size of porous media. In a batch mode, the maximum nitrate removal was 99% while in a continuous mode it did not exceed 85%.

Vulnerability assessment of southern coastal areas of Iran to sea level rise : evaluation of climate change impact

Goharnejad H., Shamsai A., Hosseini S. A.

Oceanologia

2013

No. 55 (3)

611--637

Recent investigations have demonstrated global sea level rise as being due to climate change impact. Probable changes in sea level rise need to be evaluated so that appropriate adaptive strategies can be implemented. This study evaluates the impact of climate change on sea level rise along the Iranian south coast. Climatic data simulated by a GCM (General Circulation Model) named CGCM3 under two-climate change scenarios A1b and A2 are used to investigate the impact of climate change. Among the different variables simulated by this model, those of maximum correlation with sea level changes in the study region and least redundancy among themselves are selected for predicting sea level rise by using stepwise regression. Two Discrete Wavelet artificial Neural Network (DWNN) models and a Discrete Wavelet Adaptive Neuro-Fuzzy Inference system (DWANFIS) are developed to explore the relationship between selected climatic variables and sea level changes. In these models, wavelets are used to disaggregate the time series of input and output data into different components. ANFIS/ANN are then used to relate the disaggregated components of predictors and predictand (sea level) to each other. The results show a significant rise in sea level in the study region under climate change impact, which should be incorporated into coastal area management.