Biological Approaches for Remediation of Metal-Contaminated Sites

Environmental pollution by several heavy metals and metalloids is a severe problem worldwide, as soils became increasingly contaminated, posing a threat to ecosystems and ultimately to human health. Contamination derives from large scale urbanization and industrialization, threatening land ecosystems, surface and groundwater, as well as food safety and human health. Remediation strategies for heavy metal-contaminated sites are necessary to protect from their toxic effects and conserve the environment for future generations. Numerous physicochemical techniques have been adopted including excavation and deposition in landfills, thermal treatment, leaching and electro-reclamation. These techniques are fast but inadequate, costly, cause adverse effects on soil physical, chemical and biological properties, and may lead to secondary pollution. In fact, many of these approaches only change the problem from one form or place to another, and do not completely destroy the pollutants. There was an urgent need to develop new technologies which are cost-effective and eco-friendly. In this context, biological remediation has tremendous potential. It uses plants and microorganisms to remove or contain toxic contaminants and is considered as the most effective method because it is a natural process, environmentally-friendly, has a low cost, and wide public acceptance. The present chapter aims to provide a comprehensive review of some of the promising processes mediated by plant and microbes to remediate metal-contaminated environments. Some biological processes used for the decontamination of organic compounds will also be included because of their relevance and potential common use for both purposes.

Comparison of Photovoltaic panel’s standard and simplified models

Shockley diode equation is basic for single diode model equation, which is overly used for characterizing the photovoltaic cell output and behavior. In the standard equation, it includes series resistance (Rs) and shunt resistance (Rsh) with different types of parameters. Maximum simulation and modeling work done previously, related to single diode photovoltaic cell used this equation. However, there is another form of the standard equation which has not included Series Resistance (Rs) and Shunt Resistance (Rsh) yet, as the Shunt Resistance is much bigger than the load resistance and the load resistance is much bigger than the Series Resistance. For this phenomena, very small power loss occurs within a photovoltaic cell. This research focuses on the comparison of two forms of basic Shockley diode equation. This analysis describes a deep understanding of the photovoltaic cell, as well as gives understanding about Series Resistance (Rs) and Shunt Resistance (Rsh) behavior in the Photovoltaic cell. For making estimation of a real time photovoltaic system, faster calculation is needed. The equation without Series Resistance and Shunt Resistance is appropriate for the real time environment. Error function for both Series resistance (Rs) and Shunt resistances (Rsh) have been analyzed which shows that the total system is not affected by this two parameters' behavior.

Sensitivity analysis of parameters of a photovoltaic cell under different condition

A photovoltaic cell is a component which converts light energy into electrical energy. Different environmental parameters and internal parameters have a great impact on the output of the photovoltaic cell. To identify its characteristics and estimate the output, the well known Shockley diode equation is used. This equation contains all the parameters, as one environmental and different internal. The properties of these parameters were studied and their sensitivity have been analyzed through the use of an error function; this error function allows the study of the behaviour of the parameters and their characteristics against the output of the photovoltaic cell through the analysis of its curves giving the sensitivity of the different parameters to the output of the photovoltaic cell. Using these results the impact of the parameters of the photovoltaic cell has been clearly identified. White noise is included both with the ideal values and the simulation and the ideal value is imposed to get the real time environment flavor. This work analyses both systems with and without white noise.