Trapping Ionic Mercury Using Different Adsorbents

Of recent, adsorption process has gained a lot of attention as a cheap and effective means of removing trace metals from wastewater prior to discharge into water bodies. Being flexible in design and operation, the process has enabled an optimal recovery of trace metals such that the treated effluents meet the desired standards for waste disposal. Mercury is a toxicant released into the environment from natural and anthropogenic sources. It is notorious for having an unusual tendency to bio‐accumulate and persist in the food chain. Presence of mercury in food, especially those of aquatic sources has drastic implications on human health. Therefore, efforts have been made to develop and optimize low‐cost activated carbon (AC) as adsorbents for scavenging mercury from aqueous effluents. Herein, how mercury accumulates across the food chain, its health implications, and the recent advancement in the use of low‐cost ACs as adsorbent for trapping mercury from wastewater are highlighted. Relationship between the mercury removal efficiency and the surface morphology of the adsorbents as well as the influence of prevailing experimental condition on the sorption process were addressed. Challenges and future prospects of the use of low‐cost adsorbents in addressing mercury pollution in the environment are discussed.     

A Critical Re-Examination of the Factors Influencing Determination of Organic Matter in Soils

Geotechnical and Geological Engineering - Organic matter is an undesirable constituent of the soil. Soils containing organic matter are associated with significant secondary compression and...     

Prediction of Groundwater Level in Ardebil Plain Using Support Vector Regression and M5 Tree Model

The Ardebil plain, which is located in northwest Iran, has been faced with a recent and severe decline in groundwater level caused by a decrease of precipitation, successive long‐term droughts, and overexploitation of groundwater for irrigating the farmlands. Predictions of groundwater levels can help planners to deal with persistent water deficiencies. In this study, the support vector regression (SVR) and M5 decision tree models were used to predict the groundwater level in Ardebil plain. The monthly groundwater level data from 24 piezometers for a 17‐year period (1997 to 2013) were used for training and test of models. The model inputs included the groundwater levels of previous months, the volume of entering precipitation into every cell, and the discharge of wells. The model output was the groundwater level in the current month. In order to evaluate the performance of models, the correlation coefficient (R) and the root‐mean‐square error criteria were used. The results indicated that both SVR and M5 decision tree models performed well for the prediction of groundwater level in the Ardebil plain. However, the results obtained from the M5 decision tree model are more straightforward, more easily applied, and simpler to interpret than those from the SVR. The highest accuracy was obtained using the SVR model to predict the groundwater level from the Ghareh Hasanloo and Khalifeloo piezometers with R = 0.996 and R = 0.983, respectively.     

A warm-season comparison of WRF coupled to the CLM4.0, Noah-MP,and Bucket hydrology land surface schemes over the central USA

In climate modeling studies, there is a need to choose a suitable land surface model (LSM) while adhering to available resources. In this study, the viability of three LSM options (Community Land Model version 4.0 [CLM4.0], Noah-MP, and the five-layer thermal diffusion [Bucket] scheme) in the Weather Research and Forecasting model version 3.6 (WRF3.6) was examined for the warm season in a domain centered on the central USA. Model output was compared to Parameter-elevation Relationships on Independent Slopes Model (PRISM) data, a gridded observational dataset including mean monthly temperature and total monthly precipitation. Model output temperature, precipitation, latent heat (LH) flux, sensible heat (SH) flux, and soil water content (SWC) were compared to observations from sites in the Central and Southern Great Plains region. An overall warm bias was found in CLM4.0 and Noah-MP, with a cool bias of larger magnitude in the Bucket model. These three LSMs produced similar patterns of wet and dry biases. Model output of SWC and LH/SH fluxes were compared to observations, and did not show a consistent bias. Both sophisticated LSMs appear to be viable options for simulating the effects of land use change in the central USA.     

Cross-entropy optimizer: a new tool to study precession in astrophysical jets

Evidence of jet precession in many galactic and extragalactic sources has been reported in the literature. Much of this evidence is based on studies of the kinematics of the jet knots, which depends on the correct identification of the components to determine their respective proper motions and position angles on the plane of the sky. Identification problems related to fitting procedures, as well as observations poorly sampled in time, may influence the follow-up of the components in time, which consequently might contribute to a misinterpretation of the data. In order to deal with these limitations, we introduce a very powerful statistical tool to analyse jet precession: the cross-entropy method for continuous multi-extremal optimization. Only based on the raw data of the jet components (right ascension and declination offsets from the core), the cross-entropy method searches for the precession model parameters that better represent the data. In this work we present a large number of tests to validate this technique, using synthetic precessing jets built from a given set of precession parameters. With the aim of recovering these parameters, we applied the cross-entropy method to our precession model, varying exhaustively the quantities associated with the method. Our results have shown that even in the most challenging tests, the cross-entropy method was able to find the correct parameters within a 1 per cent level. Even for a non-precessing jet, our optimization method could point out successfully the lack of precession.