International Journal Of Environmental Research
Volume:6 Issue: 3, Summer 2012

A newly developed natural rubber deproteinization process produces deproteinized natural rubber (DPNR) wastewater as an intermediate product containing a high concentration of sodium dodecyl sulfate (SDS) and rubber. In this study, a novel process to recover the residual rubber and energy as methane from DPNR wastewater was developed. As a pretreatment, SDS and residual rubber in DPNR wastewater were coagulated and recovered by addition of CaCl2 at Ca2+/SDS and Ca2+/rubber mass ratios of 0.070 and 0.055, respectively. The remaining organic matter in the pre-treated DPNR wastewater was converted to methane by using a mesophilic up-flow anaerobic sludge bed (UASB) reactor. The UASB reactor with the diluted pre-treated DPNR wastewater showed a total chemical oxygen demand (COD) removal efficiency of 92 ± 2% at a maximum loading rate of 6.8 ± 1.8 kgCOD·m-3·d-1 at a hydraulic retention time (HRT) of 12 h. Under the condition of effluent recirculation with raw pre-treated DPNR wastewater, the UASB reactor showed a total COD removal efficiency of 84 ± 8% at the maximum loading rate of 6.4 ± 1.7 kgCOD·m-3·d-1 at HRT of 39 h. The results suggest that the newly developed resource recovery process for DPNR wastewater could be a promising treatment system.

This paper seeks to make an empirical analysis of the degree to which the research effort and functional integration into the design of environmental activity account for a company’s environmental actionbased competitive advantage. To do so, we have used a sample of 110 factories. It has been proven that both factors (the research effort and functional integration into the design of environmental activity) have a positive influence on the factories’ environmental action-based competitive advantage. These outcomes are the main in this paper and they have major implications for companies’ research policy. It proves that there is a new advantage to the managerial effort dedicated to this area: this effort has a positive influence on the relation between a company’s environmental management and its environmental action-based competitive advantage.

This study is aimed to identify sources of particulate matter with mean aerodynamic diameter below 10 μm (PM10) present in the atmosphere of the Metropolitan Area of Mexico City (MAMC), using samples obtained from January 1st to June 30th, 2009, analyzed with X-ray spectrometric techniques. MiniVol samplers were used to collect samples on polycarbonate filters in three sites (North, Center, and South) of the MAMC. The filters were exposed along 24 h every two days, starting at 8:00 AM, and then analyzed with particle induced X-ray emission (PIXE), a microPIXE (μPIXE) system, and X-ray fluorescence (XRF). Statistical multivariate tests with positive matrix factorization (PMF) were conducted to identify possible contributing factors. The model HYSPLIT was used to determine back-trajectories and the MODIS database for fire spot localization. The multivariate methods identified five factors in the Center and South, and four in the North, including Soil, Sulfate, Fuel/Industry, and Biomass burning, with certain differences in the factors and contributions. Application of HYSPLIT back-trajectories associated these factors to three main Soil sources and points of secondary aerosols production, as well as locations where Biomass burning aerosols were originated. The combination of X-ray spectrometric methods, receptor modeling, back-trajectory determination, and fire site localization, allowed the identification of possible sources of PM10 in the MAMC, namely, the dry Texcoco lake, the Toluca Valley, and the North dry plains for Soil aerosols, the influence of local industrial areas for Sulfate (secondary) aerosols, and the appearance of fires for Biomass burning.

Eight selected heavy metals (Cu, Zn, Pb, Cd, Cr, Mn, Fe and Al) in surface and sub-surface sediments in the northern coast of Sfax (Tunisia) were studied in order to assess the sediment quality and to highlight the anthropogenic contributions to heavy metal distributions in the two study sediment levels. Multiple chemometric approaches based on Geographic Information System (GIS), Enrichment factors (EFs), geoaccumulation index (Igeo) and Principal Component Analysis (ACP) were applied. Enrichment factors (EFs) and principal component analysis (PCA) revealed two distinct groups of metals. The first group attributed to Mn, Fe and Al that were derived from natural sources, and the second group contained Cu, Zn, Pb, Cd and Cr mainly originated from man-made sources. The contribution rates of these later in terms of the sediment’s enrichment exceed 98%. The geoaccumulation index (Igeo) values explained that only Zn, Pb, Cd and Cr can be considered as moderate to extreme pollutants both in surface and sub-surface sediments.

Recently, the production of biosurfactants in bioreactors and their use in various pharmaceutical, chemical and food industries have been developed. Optimum production is directly related to the physicochemical condition of culture medium (such as pH and temperature) and engineering parameters of bioreactors (such as aeration rate, volume of operation and the amount of energy input). Understanding the gas transfer in shaken bioreactors equipped with a sterile closure is advantageous to avoid oxygen limitation or carbon dioxide inhibition of a microbial culture. In this study, the effect of aeration rates (due to using different design closures) on the amount of biosurfactin production by Bacillus subtilis ATCC 6633 in a ventilation flask as a miniaturized bioreactor was investigated. The highest biosurfactin concentration (0.0485 g/L/h) was obtained in the optimum conditions in which the amount of filling volume and shaking frequency were 15 ml and 300 rpm, respectively. The specific aeration rate (qin) and maximum oxygen transfer rate (OTRmax), were calculated 1.88 vvm and 0.01 mol/L/h, respectively. The results showed the significant biosurfactin productivity increase under non-oxygen limiting condition.

This article outlines the Government Facilitator Roles in Ecopreneurship functions in Environmental NGOs. Target Group was environmental NGOs in Iran (Key actors, committed to preserving ecological and environmental integrity). Accelerating innovation way for improving the environmental conservation by Environmental NGOs is the conceptual mean of Ecopreneurship in this research. Questionnaire as a research instrument was to measure the constructs. Content validity by panel study confirmed. To determine reliability alpha coefficient was calculated. Value ranged from 0.74 to. 96 showed that research instrument has acceptable capability to collect data and satisfy accepted condition. Dominant statistical method was SEM and for data analysis PLS-Graph was employed. According to path analysis results, four dimensions of Government facilitator have a significant role in Ecopreneurship in Iranian environmental NGOs. End of article some of implications were illustrated for improving Ecopreneurship functions by Government facilitator roles.

One of the difficulties in using absolute altitudes is the separation between the mean open sea level and geoid. Theoretically, geoid is the base level in absolute altitudes, but practically, the mean open sea level is used as a base level for absolute altitudes. The difference between these two levels is called as the sea surface topography. In this research, it is dealt the mean sea level modeling by using the observations of three altimeter satellites (i.e. Topex/Poseidon, Jason-1 and GFO) in Persian Gulf and then it is dealt with the evaluation of existing models of the sea surface topography based on the altimeter satellites data and the global geopotential geoid models (i.e. European Improved Gravity model of the Earth by New techniques, Gravity field and steady-state Ocean Circulation Explorer, Earth Gravitational Model 2008. The results of this research indicate that the sea surface topographical model resulting from the EIGEN06C geoid is the most precise model with changes range between -2.482 m and -1.511 m and mean -0.23 m.

Given the critical role played by urban green spaces and the emergence of remote sensing as a valuable natural resource management tool, this study sought to identify trends in green spaces within the context of South Africa’s transition period (1990 - 2000). Using the city of Port Elizabeth as a case study, three sets of Landsat - 5 Thematic Mapper images (1990, 1995 and 2000) were geo-processed, classified into vegetation density categories and verified using respective aerial photographs. There was a steady decline in areas covered by Very sparse vegetation, Sparse vegetation and Dense vegetation classes. However, areas covered by Very dense vegetation showed a steady increase during the study period. Using remote sensing applications, this study provides an insight into trends in green spaces in the city of Port Elizabeth during the transition period. This study further shows the importance of remote sensing as a mapping tool that can be used to provide information for physical, social and ecological planning to achieve urban socio-ecological sustainability in rapidly changing urban environments.

Using a geographic information system and statistics, we evaluated spatial distributions of nitrate and chloride concentrations in groundwater in an area of north-central Texas with agricultural activity, in addition to oil and natural gas exploration and production. Data were compiled from 40 water wells sampled in 2007. Nitrate concentrations in three wells exceeded the maximum contaminant level (44 mg/L) for drinking water. The highest nitrate concentration was 149 mg/L, and concentrations were generally higher in shallower wells. Chloride concentrations exceeded the 250 mg/L secondary drinking water standard in two wells, with no significant association between chloride concentration and well depth. Results of this study suggest localized human impacts, especially for nitrate, and identify areas warranting future monitoring.

Wastewater treatment and re-use of industrial process water are critical issues for the development of human activities and environment conservation. The present study was undertaken to evaluate the degradation of organic pollutants taking phenol as the model compound over aluminium pillared montmorillonite and the rare earth exchanged analogues, underlining the use of environ friendly clay catalysts for the effective removal of water pollutants. From the study it was noted that for reaction variables like temperature, peroxide load, catalyst load, phenol concentration etc, optimal values exist that must be taken into account for obtaining best results. The catalysts used were aluminium pillared montmorillonite prepared by partial hydrolysis method exchanged with La, Ce and Th metal salts at room temperature. Characterisation of the prepared systems were done using X-ray diffraction and surface area and pore volume measurements.The pillared montmorillonite showed considerable increase in basal spacing and BET surface area compared to parent montmorillonite.

The objective of this paper is to develop an artificial neural network (ANN) model which can be used to predict temperature rise due to climate change in regional scale. In the present work data recorded over years 1985-2008 have been used at training and testing steps for ANN model. The multilayer perceptron (MLP) network architecture is used for this purpose. Three applied optimization methods are backpropagation (BP) (in both input selection and weight optimization), genetic algorithm (GA) (in both input selection and weight optimization) and combined GA-particle swarm optimization (PSO) (input selection by GA and weight optimization by PSO). In this framework, natural and anthropogenic parameters which affect the incoming solar radiation are considered in order to predict the climate change induced temperature rise in regional scale. Inputs of ANN model are mean temperature, dew point temperature, relative humidity, wind speed, solar radiation, cloudiness, rainfall, station-level pressure (QFE) and greenhouse gases. For predicting monthly mean temperature, input data include one month, six months, 12 months and 24 months before recorded data. In this work, nine stations namely Tehran, Mashhad, Ramsar, Orumiyeh, Sanandaj, Yazd, Ahwaz, Bandar Abbas and Chabahar in nine different climatic region of Iran are chosen to determine the temperature rise over Iran. Results show that the averaged minimum square errors (MSE) are 0.0196, 0.0224 and 0.0228 for ANN-BP, ANN-GA and ANN-GA-PSO methods, respectively. The ANN model associated with BP optimization method predict annual mean temperature rise as 0.44, 0.49, 0.20, 0.12, 0.17, 0.46, 0.41, 0.06 and 0.01°C after 10 years for mentioned stations, respectively. These values show the average temperature rise of 0.26 °C after 10 years (the base year is 2008) for Iran.

Medical environments such as hospital waiting rooms can affect a client’s anxiety level as well as psychological and physiological responses to his or her situation. The aim of this research was to evaluate the use of environmental design, specifically the design which incorporates elements of nature, in clinics and hospitals to decrease anxiety, blood pressure and pulse rates of waiting clients. Representations of nature and the natural environment are known to recover a dynamic union between an environment and its user, therefore the effects of environmental design on subjects’ responses are measured and analyzed. In order to examine these hypotheses a sample of 145 people were chosen as subjects for the experiment. They were divided into control and experiment groups, both of which included males and females. The designed environment was applied for the experiment group which included elements of nature, green plants, sounds of waterfall and birds. Both control and experiment groups were pre tested and then post tested. The findings showed that being in the designed hospital’s waiting room was clearly effective at decreasing a client’s level of anxiety (p< 0.001), blood pressure (p< 0.001) and pulse rate (0.001). We propose that using an environmental design for medical treatment centers can reduce levels of anxiety in clients and can effectively foster a sense of wellbeing.

A field study was conducted around Pb slag contaminated sites in Ibadan, Nigeria to assess the intake of trace metals by cows reared around the contaminated sites as indication of heavy metals contamination. Levels of Pb, Cd, Cu and Zn in blood, milk and faeces were determined in 20 cows exposed to the sites and 20 reference cows from uncontaminated areas. Chemical analysis of pasture grasses and leachate from the contaminated sites showed high levels of Pb. Range and mean levels of Pb, Cd, Cu and Zn in the forage grasses are: 209-899 (425 ± 79.0), ND-1.87 (0.94 ± 0.23), 4.01-8.78 (6.26 ± 0.62) and 17.4-202 (79.2 ± 23.5) mg/kg respectively. The mean values of 8.81±0.06, 0.041±0.003, 0.20±0.01 and 1.00±0.004 mg/L were obtained l for leachate Pb, Cd, Cu and Zn. Difference in blood and milk Pb was highly significant (p<0.01) between the two groups of animals. Mean, median and range blood Pb concentration of 349±82.0, 312, Keywords: Lead slag, Heavy metals, Lactating cows, Forage grasses, Leachate