Phytoextraction of heavy metals from contaminated soil,water and atmosphere using ornamental plants: mechanisms and efficiency improvement strategies

Environmental Science and Pollution Research - Accumulation of heavy metals (HMs) in soil, water and air is one of the major environmental concerns worldwide, which mainly occurs due to...     

Degradation and detoxification of acid orange 52 by <Emphasis Type="Italic">Pseudomonas putida</Emphasis> mt-2: a laboratory study

Introduction  

Acid orange 52 (AO52), extensively used in textile industries, was decolorized by Pseudomonas putida mt-2. AO52 azoreduction products such as N,N′-dimethyl-p-phenylenediamine (DMPD) and 4-aminobenzenesulfonic acid (4-ABS), were identified in the static degradation mixture. These amines were identified only in media of static incubation, which is consistent with their biotransformation under shaken incubation (aerobic conditions).     

GIS‐Based Predictive Models of Hillslope Runoff Generation Processes1

Abstract: Successful nonpoint source pollution control using best management practice placement is a complex process that requires in‐depth knowledge of the locations of runoff source areas in a watershed. Currently, very few simulation tools are capable of identifying critical runoff source areas on hillslopes and those available are not directly applicable under all runoff conditions. In this paper, a comparison of two geographic information system (GIS)‐based approaches: a topographic index model and a likelihood indicator model is presented, in predicting likely locations of saturation excess and infiltration excess runoff source areas in a hillslope of the Savoy Experimental Watershed located in northwest Arkansas. Based on intensive data collected from a two‐year field study, the spatial distributions of hydrologic variables were processed using GIS software to develop the models. The likelihood indicator model was used to produce probability surfaces that indicated the likelihood of location of both saturation and infiltration excess runoff mechanisms on the hillslope. Overall accuracies of the likelihood indicator model predictions varied between 81 and 87% for the infiltration excess and saturation excess runoff locations respectively. On the basis of accuracy of prediction, the likelihood indicator models were found to be superior (accuracy 81‐87%) to the predications made by the topographic index model (accuracy 69.5%). By combining statistics with GIS, runoff source areas on a hillslope can be identified by incorporating easily determined hydrologic measurements (such as bulk density, porosity, slope, depth to bed rock, depth to water table) and could serve as a watershed management tool for identifying critical runoff source areas in locations where the topographic index or other similar methods do not provide reliable results.