Arsenic accumulation in edible vegetables and health risk reduction by groundwater treatment using an adsorption process

A Review of the Dynamic Mathematical Modeling of Heavy Metal Removal with the Biosorption Process

Biosorption has great potential in removing toxic effluents from wastewater, especially heavy metal ions such as cobalt, lead, copper, mercury, cadmium, nickel and other ions. Mathematically modeling of biosorption process is essential for the economical and robust design of equipment employing the bioadsorption process. However, biosorption is a complex physicochemical process involving various transport and equilibrium processes, such as absorption, adsorption, ion exchange and surface and interfacial phenomena. The biosorption process becomes even more complex in cases of multicomponent systems and needs an extensive parametric analysis to develop a mathematical model in order to quantify metal ion recovery and the performance of the process. The biosorption process involves various process parameters, such as concentration, contact time, pH, charge, porosity, pore size, available sites, velocity and coefficients, related to activity, diffusion and dispersion. In this review paper, we describe the fundamental physical and chemical processes involved in the biosorption of heavy metals on various types of commonly employed biosorbents. The most common steady state and dynamic mathematical models to describe biosorption in batch and fixed-bed columns are summarized. Mathematical modeling of dynamic process models results in highly coupled partial differential equations. Approximate methods to study the sensitivity analysis of important parameters are suggested.

Increased urinary arsenic concentration in newly diagnosed type 2 diabetes mellitus: a gender-independent, smoking-dependent exposure biomarker in older adults in Tehran

Arsenic is ranked in the top ten environmental toxicants but its impact on type 2 diabetes mellitus (T2DM) and its association with other human health effects is contradictory. We aimed in this study to compare the urinary arsenic concentration (u As) in older age adults (> 40 years) and their T2DM subgroup in an age and gender-matched case control study to find the association of u As with, diet, oxidative stress, smoking, anthropometric factors, and lifestyle in our study participants. Face-to-face interviews based on structured questionnaires were conducted on 200 female and male volunteers (100 cases and 100 control). Considering the exclusion criteria, u As concentration and serum biomarkers of oxidative stress (malondialdehyde, superoxide dismutase, catalase) of 30 newly diagnosed T2DM and 30 control were determined by ICP-mass analysis and ELISA reader respectively. Despite the similarities in sociodemographic, diet, and lifestyle factors in males and females and their T2DM subgroups, a 4 times difference in u As levels between T2DM (93.7 ng/L (32)) and their healthy counterparts (23.7 ng/L (2.3)) without meaningful associations with gender, age, BMI, diet, and lifestyle was observed. Mean u As concentration in total population of smokers was significantly higher than non-smokers ((119 ng/L vs. 22.5 ng/L (p = 0.03)) and oxidative stress markers were not significantly higher in T2DM smokers than non-smokers. Chronic arsenic exposure through smoking could be contributed to the incidence of T2DM in older age adults. Oxidative stress markers were not significantly increased in smoker subgroup compared with non-smokers but except smoking pattern, other variables did not affect u As concentration. Precautionary measure to reduce the exposure of people with this element is recommended to prevent the arsenic-induced T2DM in human populations.