A new magnetite-silica core/shell nanocomposite ($Fe_3O4@nSiO_2@mSiO_2$) was synthesized and functionalized with trimethylchlorosilane (TMCS). The prepared nanocomposite was used for the removal of diesel oil from aqueous media. The characterization of magnetite-silica nanocomposite was studied by X-ray diffraction (XRD), Fourier transform infrared (FTIR), transmission electron microscopy (TEM), surface area measurement, and vibrating sample magnetization (VSM). Results have shown that the desired structure was obtained and surface modification was successfully carried out. FTIR analysis has confirmed the presence of TMCS on the surface of magnetite silica nanocomposites. The low- angle XRD pattern of nanocomposites indicated the mesoscopic structure of silica shell. Furthermore, TEM results have shown the core/shell structure with porous silica shell. Adsorption kinetic studies indicated that the nanocomposite was able to remove 80% of the oil contaminant during 2 h and fit well with the pseudo-second order model. Equilibrium studies at room temperature showed that the experimental data fitted well with Freundlich isotherm. The magnetic property of nanocomposite facilitated the separation of solid phase from aqueous solution.

Air Quality Index (AQI) is a pointer to broadcast short term air quality. This paper presents one day ahead AQI forecasting on seasonal basis for three major cities in Maharashtra State, India by using Artificial Neural Networks (ANN) and Genetic Programming (GP). The meteorological observations & previous AQI from 2005-2008 are used to predict next day's AQI. It was observed that GP captures the phenomenon better than ANN and could also follow the peak values better than ANN. The overall performance of GP seems better as compared to ANN. Stochastic nature of the input parameters and the possibility of auto-correlation might have introduced time lag and subsequent errors in predictions. Spectral Analysis (SA) was used for characterization of the error introduced. Correlational dependency (serial dependency) was calculated for all 24 models prepared on seasonal basis. Particular lags (k) in all the models were removed by differencing the series, that is converting each i'th element of the series into its difference from the (i-k)"th element. New time series is generated for all seasonal models in synchronization with the original time line & evaluated using ANN and GP. The statistical analysis and comparison of GP and ANN models has been done. We have proposed a promising approach of use of GP coupled with SA for real time prediction of seasonal multicity AQI.

The groundwater is a very important part of the environment and must be protected for the benefit of the present and future generation. The contamination of soil and groundwater by chemicals has become an increasing concern in the recent past. These chemicals enter the groundwater system by a wide variety of mechanisms, including accidental spills, land disposal of domestic and industrial wastes and application of agricultural fertilizers. Once introduced into an aquifer, these contaminants will be transported by flowing groundwater and may degrade water quality at nearby wells and streams. For improving the management and protection of groundwater resources, it is important to first understand the various processes that control the transport of contaminants in groundwater. Predictions of the fate of groundwater contaminants can be made to assess the effect of these chemicals on local water resources and to evaluate the effectiveness of remedial actions. In this study, an attempt has been made to investigate the behaviour of solute transport through porous media using laboratory experiments. Sodium chloride was used as a conservative chemical in the experiment. During the experiment, pulse boundary condition and continuous boundary conditions were used. Experimental results have been presented for conservative solute transport in the sand. The pattern of the break through curve remains almost same in all the cases of varying flow rate and initial concentration of conservative chemical.

Water pollution means that the physical, chemical and biological properties of water are changing. In this study, adsorption was chosen as the treatment method because it is an eco-friendly and low cost approach. Magnetite is a magnetic material that can synthesize chemical precipitation. Magnetite was used for the removal of copper in artificial water samples. For this purpose, metal removal from water dependent on the pH, initial concentration of metal, amount of adsorbent and effect of sorption time were investigated. Magnetite was characterized using XRD, SEM and particle size distribution. The copper ions were determined by atomic absorption spectrometry. The adsorption of copper on the magnetite was studied in a batch process, with different aqueous solutions of Cu (II) at concentrations ranging from 10 to $50mg\;l^{-1}$. Optimum conditions for using magnetite were found to be concentration of $10mg\;L^{-1}$, pH: 4.5, contact time: 40 min. Optimum adsorbent was found to be 0.3 gr. Furthermore, adsorption isotherm data were analyzed using the Langmuir and Freundlich equations. The adsorption data fitted well with the Freundlich ($r^2=0.9701$) and Langmuir isotherm ($r^2=0.9711$) equations. Kinetic and equilibrium aspects of the adsorption process were studied. The time-dependent Cu (II) adsorption data were described well by a pseudo-second-order kinetic model.

In this study, we evaluated the adsorption/desorption of uranium (U) in pure soil environment using continuous column reactor. We additionally investigated the adsorption/desorption mechanism of U on vivianite surface in molecular scale using quantum calculation. We observed that below $0.1{\mu}M$ of U was detected after 20 d from U injection ($1{\mu}M$) in adsorption test. However, all of absorbed U was detached from vivianite surface in 24 h by injection of CARB solution ($1.44{\times}10^{-2}M\;NaHCO_3$ and $2.8{\times}10^{-3}M\;Na_2CO_3$). Based on exchange energy calculation, we found that $UO_2(CO_3)_2{^{2-}}$ and $UO_2(CO_3)_3{^{4-}}$ species have higher repulsive energy than $UO_2(OH)_2$ species. The results obtained from this study could be applied to predict the behavior of uranium in contaminated and remediation sites.