Treatment of benzene and n-hexane mixtures in trickle-bed air biofilters

Trickle-bed air biofilters (TBABs) are suitable for treatment of hydrophilic volatile organic compounds, but they pose a challenge for hydrophobic compounds. Three laboratory-scale TBABs were used for the treatment of an airstream contaminated with different ratios of n-hexane and benzene mixtures. The ratios studied were 1:1, 2:1, and 1:3 n-hexane:benzene by volume. Each TBAB was operated at a pH of 4 and a temperature of 20 degrees C. The use of acidic-buffered nutrient solution was targeted for changing the microorganism consortium to fungi as the main biodegradation element. The experimental plan was designed to investigate the long-term performance of the TBABs with an emphasis on different mixture loading rates, removal efficiency with TBAB depth, volatile suspended solids, and carbon mass balance closure. n-Hexane loading rate was kept constant in the TBABs for comparison reasons and ranged from 4 to 22 g/(m3 x hr). Corresponding benzene loadings ranged from 4 to 43 g/(m3 x hr). Generally, benzene behavior in the TBAB was superior to that of n-hexane because of its higher solubility. n-Hexane showed improved performance in the 2:1 mixing ratio as compared with the other two ratios.     

Uranium biosorption by <Emphasis Type="Italic">Padina</Emphasis> sp. algae biomass: kinetics and thermodynamics

Introduction  

Kinetic, thermodynamic, and equilibrium isotherms of the biosorption of uranium ions onto Padina sp., a brown algae biomass, in a batch system have been studied.     

Comparison of different static methods for assessment of AMD generation potential in mining waste dumps in the Muteh Gold Mines,Iran

Acid mine drainage (AMD) gives rise to several problems in sulfide-bearing mineral deposits whether in an ore body or in the mining wastes and tailings. Hence, several methods and parameters have been proposed to evaluate the acid-producing and acid-neutralizing potential of a material. This research compares common static methods for evaluation of acid-production potential of mining wastes in the Muteh gold mines by using 62 samples taken from six waste dumps around Senjedeh and Chah-Khatoun mines. According to a detailed mineralogical study, the waste materials are composed of mica-schist and quartz veins with a high amount of pyrite and are supposed to be susceptible to acid production, and upon a rainfall, they release acid drainage. All parameters introduced in different methods were calculated and compared in this research in order to predict the acid-generating and neutralization potential, including APP, NNP, MPA, NPR, and NAGpH. Based on the analytical results and calculation of different parameters, all methods are in a general consensus that DWS-02 and DWS-03 waste dumps are acid-forming which is clearly attributed to high content of pyrite in samples. DWS-04 is considered as non-acid forming in all methods except method 8 which is uncertain about its acid-forming potential and method 7 which considers a low potential for it. DWC-01 is acid-forming based on all methods except 8, 9, 10, and 11 which are also uncertain about its potential. The methods used are not reached to a compromise on DWS-01 and DWC-02 waste dumps. It is supposed that method 7 gives the conservationist results in all cases. Method 8 is unable to decide on some cases. It is recommended to use and rely on results provided by methods 1, 2, 3, and 12 for taking decisions for further studies. Therefore, according to the static tests used, the aforementioned criteria in selected methods can be used with much confidence as a rule of thumb estimation.     

Empirical gas emission and oxidation measurement at cover soil of dumping site: example from Malaysia

Methane (CH₄) is one of the most relevant greenhouse gases and it has a global warming potential 25 times greater than that of carbon dioxide (CO₂), risking human health and the environment. Microbial CH₄ oxidation in landfill cover soils may constitute a means of controlling CH₄ emissions. The study was intended to quantify CH₄ and CO₂ emissions rates at the Sungai Sedu open dumping landfill during the dry season, characterize their spatial and temporal variations, and measure the CH₄ oxidation associated with the landfill cover soil using a homemade static flux chamber. Concentrations of the gases were analyzed by a Micro-GC CP-4900. Two methods, kriging values and inverse distance weighting (IDW), were found almost identical. The findings of the proposed method show that the ratio of CH₄ to CO₂ emissions was 25.4 %, indicating higher CO₂ emissions than CH₄ emissions. Also, the average CH₄ oxidation in the landfill cover soil was 52.5 %. The CH₄ and CO₂ emissions did not show fixed-pattern temporal variation based on daytime measurements. Statistically, a negative relationship was found between CH₄ emissions and oxidation (R ²?=?0.46). It can be concluded that the variation in the CH₄ oxidation was mainly attributed to the properties of the landfill cover soil.     

The effectiveness of digital soil mapping to predict soil properties over low-relief areas

This study investigates the ability of different digital soil mapping (DSM) approaches to predict some of physical and chemical topsoil properties in the Shahrekord plain of Chaharmahal-Va-Bakhtiari province, Iran. According to a semi-detailed soil survey, 120 soil samples were collected from 0 to 30 cm depth with approximate distance of 750 m. Particle size distribution, coarse fragments (CFs), electrical conductivity (EC), pH, organic carbon (OC), and calcium carbonate equivalent (CCE) were determined. Four machine learning techniques, namely, artificial neural networks (ANNs), boosted regression tree (BRT), generalized linear model (GLM), and multiple linear regression (MLR), were used to identify the relationship between soil properties and auxiliary information (terrain attributes, remote sensing indices, geology map, existing soil map, and geomorphology map). Root-mean-square error (RMSE) and mean error (ME) were considered to determine the performance of the models. Among the studied models, GLM showed the highest performance to predict pH, EC, clay, silt, sand, and CCE, whereas the best model is not necessarily able to make accurate estimation. According to RMSE%, DSM has a good efficiency to predict soil properties with low and moderate variabilities. Terrain attributes were the main predictors among different studied auxiliary information. The accuracy of the estimations with more observations is recommended to give a better understanding about the performance of DSM approach over low-relief areas.     

Chemical composition and antimicrobial activities of the essential oil from the seeds of Enterolobium contortisiliquum (leguminosae)

Seeds of Enterolobium contortisiliquum were subjected to steam distillation to obtain a light yellow essential oil in a yield of 3 ml/kg of seeds. The major components of the oil were identified using gas chromatography/mass spectrometry (GC-MS) and were furfural, limonene, linalool, estragole, carvone, and apiole with carvone representing more than 50% of the total composition. Antimicrobial activities of the essential oil were determined against four species of gram positive bacteria (Bacillus subtilis, Bacillus cereus, Staphylococcus aureus, Micrococcus luteus) and two gram negative bacteria (Klebsiella pneumoniae, Serratia Marcescencs). The essential oil inhibited the growth of all tested bacteria but was most effective against the gram positive bacteria. Chemicals that are responsible for the antibacterial effect of the essential oil were determined using the bio-autography thin layer chromatography (TLC) technique. The active compounds responsible for the activity were found to be carvone and estragole.     

The trend analysis of neurological disorders as major causes of death and disability according to human development, 1990-2019

Environmental Science and Pollution Research - This study aims to assess the trends in the incidence rate of neurological disorders in developed and developing countries worldwide during...     

Hybrid-based Bayesian algorithm and hydrologic indices for flash flood vulnerability assessment in coastal regions: machine learning,risk prediction,and environmental impact

Environmental Science and Pollution Research - Natural hazards and severe weather events are a matter of serious threat to humans, economic activities, and the environment. Flash floods are one of...