Volume 16 - 2014

In the present study, batch adsorption studies were performed for the removal of malachite green and acid blue 161 dyes from aqueous solutions by varying parameters such as contact time, waste marble dust amount, initial dye concentration and temperature. The equilibrium adsorption data were analyzed by Langmuir, Freundlich and Temkin adsorption isotherm models. The Langmuir and Freundlich adsorption models agree well with experimental data. The pseudo-second order, intraparticle intraparticle diffusion and Elovich kinetic models were applied to the experimental data in order to describe the removal mechanism of dye ions by waste marble dust. The pseudo-second order kinetic was the best fit kinetic model for the experimental data. Thermodynamics parameters such as ΔG, ΔH and ΔS were also calculated for the adsorption processes. The experimental data were used to construct an artificial neural network (ANN) model to predict removal of malachite green and acid blue 161 dyes by waste marble dust. A three-layer ANN, an input layer with four neurons, a hidden layer with 12 neurons, and an output layer with one neuron is constructed. Different training algorithms were tested on the model to obtain the proper weights and bias values for ANN model. The results show that waste marble dust is an efficient sorbent for malachite green dye and ANN network, which is easy to implement and is able to model the batch experimental system.

This paper examines the effect of humic acids (HA) and other solution constituents (Ca²⁺, Mg²⁺, Na⁺), frequently encountered in potable water sources, on the removal of a well-known herbicide, atrazine (ATR), by a laboratory-scale pilot Photocatalytic Membrane Reactor (PMR) system. Experimental results with different HA concentrations, in the presence (or not) of background cations, demonstrate the attainment of steady-state operation with constant degradation efficiencies and controlled membrane fouling phenomena. The excellent performance of the system is attributed to the efficient photocatalytic degradation of the dissolved organics, which is enhanced by steric interactions between the HA-ATR pseudo-complexes and the membrane. The successful laboratory tests hold promise for practical water treatment applications of the proposed PMR system.

The performed analysis could be used to quantify the impact of climatic change on evaporation, to determine the accuracy of predicted evaporation against that obtained from the meteorological instruments, and to investigate the alternative values of several meteorological variables in the case of limited data sets. 

This paper analyzes the potential contribution of electric vehicles in greenhouse gas (GHG) emissions reduction over the next decade following a simulation procedure.  Emissions were assessed through a stepwise methodological approach at the transportation link level in the Hamilton Census Metropolitan Area (CMA). Firstly, different EV market penetration scenarios were introduced and compared to the base case scenario. Following these, the spatial distribution patterns of EVs were predicted using vehicle registration data for the Hamilton CMA as well as socioeconomic data obtained from census records. Properly modified matrices were used as input into our traffic simulation model in order to assign traffic on the network and estimate volumes for each of the links. To this end MOBILE 6.2C[1] was customized so as to compute the emission factors. The hourly emissions of each link were mapped in a GIS environment. We conclude that different utilization patterns result to varying spatial distributions of traffic related emissions in the links and even a modest adoption of EV technology may lead to their significant reduction.