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...     

Who cares about climate change reporting in developing countries? The market response to,and corporate accountability for,climate change in Bangladesh

Undoubtedly, climate change is one of the greatest problems facing today’s world. Despite this, traditional research has ignored the market response to, and accountability for, climate change reporting in developing countries. Hence, this study critically examines climate change reporting practices in the most affected countries in the world, with specific reference to Bangladesh. In the study, 32 semi-structured interviews and 71 annual reports are evaluated. Using legitimacy theory, the study contributes to building an understanding of companies’ attitude toward stakeholder accountability regarding climate change. The study finds that Bangladeshi companies are reporting climate change information on an average of 2.23 %. More specifically, the study demonstrates that large companies are reporting on more climate change issues than others because of their legitimized positions in the market. Again, a lack of regulation and a culture of low social accountability among the companies contribute to a very low level of disclosure on climate change. Surprisingly, multinationals are not providing satisfactory disclosure. The study has policy implications in developing countries for both local policy makers (the government) and international policy makers (the Intergovernmental Panel on Climate Change, the European Union, the World Bank, the UN Environment Programme, the International Energy Agency and the World Economic Forum) as to how to engage local companies so that they become more socially accountable to climate change reporting.     

Health risk assessment due to heavy metal exposure from commonly consumed fish and vegetables

Contamination of heavy metals in fish and vegetables is regarded as a major crisis globally, with a large share in many developing countries. In Bogra District of Bangladesh, concentrations of six heavy metals, i.e., chromium (Cr), nickel (Ni), copper (Cu), arsenic (As), cadmium (Cd) and lead (Pb), were evaluated in the most consumed vegetables and fish species. The sampling was conducted during February–March 2012 and August–September 2013. The levels of metals varied between different fish and vegetable species. Elevated concentrations of As, Cd and Pb were observed in vegetable species (Solanum tuberosum, Allium cepa and Daucus carota), and fish species (Anabas testudineus and Heteropneustes fossilis) were higher than the FAO/WHO permissible limits, indicating these three metals might pose risk from the consumption of these vegetable and fish species. The higher concentration of heavy metals in these vegetable species might be due to the higher uptake from soil and sediment ingestion behavior in fish species. Multivariate principal component analysis (PCA) showed significant anthropogenic contributions of Cr, Ni, Cu and Pb in samples as the PCA axis scores were correlated with scores of anthropogenic activities. Target hazard quotients showed that the intakes of Cu, As and Pb through vegetables and fish were higher than the recommended health standards, indicated non-carcinogenic risk. Therefore, intakes of these elements via fish and vegetables for Bangladeshi people are a matter of concern.     

Modeling of methane oxidation in landfill cover soil using an artificial neural network

Knowing the fraction of methane (CH₄) oxidized in landfill cover soils is an important step in estimating the total CH₄ emissions from any landfill. Predicting CH₄ oxidation in landfill cover soils is a difficult task because it is controlled by a number of biological and environmental factors. This study proposes an artificial neural network (ANN) approach using feedforward backpropagation to predict CH₄ oxidation in landfill cover soil in relation to air temperature, soil moisture content, oxygen (O₂) concentration at a depth of 10 cm in cover soil, and CH₄ concentration at the bottom of cover soil. The optimum ANN model giving the lowest mean square error (MSE) was configured from three layers, with 12 and 9 neurons at the first and the second hidden layers, respectively, log-sigmoid (logsig) transfer function at the hidden and output layers, and the Levenberg-Marquardt training algorithm. This study revealed that the ANN oxidation model can predict CH₄ oxidation with a MSE of 0.0082, a coefficient of determination (R ²) between the measured and predicted outputs of up to 0.937, and a model efficiency (E) of 0.8978. To conclude, further developments of the proposed ANN model are required to generalize and apply the model to other landfills with different cover soil properties.

Implications:

To date, no attempts have been made to predict the percent of CH₄ oxidation within landfill cover soils using an ANN. This paper presents modeling of CH₄ oxidation in landfill cover soil using ANN based on field measurements data under tropical climate conditions in Malaysia. The proposed ANN oxidation model can be used to predict the percentage of CH₄ oxidation from other landfills with similar climate conditions, cover soil texture, and other properties. The predicted value of CH₄ oxidation can be used in conjunction with the Intergovernmental Panel on Climate Change (IPCC) First Order Decay (FOD) model by landfill operators to accurately estimate total CH₄ emission and how much it contributes to global warming.     

River water quality assessment using environmentric techniques: case study of Jakara River Basin

Jakara River Basin has been extensively studied to assess the overall water quality and to identify the major variables responsible for water quality variations in the basin. A total of 27 sampling points were selected in the riverine network of the Upper Jakara River Basin. Water samples were collected in triplicate and analyzed for physicochemical variables. Pearson product-moment correlation analysis was conducted to evaluate the relationship of water quality parameters and revealed a significant relationship between salinity, conductivity with dissolved solids (DS) and 5-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), and nitrogen in form of ammonia (NH₄). Partial correlation analysis (r _p) results showed that there is a strong relationship between salinity and turbidity (r _p?=?0.930, p?=?0.001) and BOD₅ and COD (r _p?=?0.839, p?=?0.001) controlling for the linear effects of conductivity and NH₄, respectively. Principal component analysis and or factor analysis was used to investigate the origin of each water quality parameter in the Jakara Basin and identified three major factors explaining 68.11 % of the total variance in water quality. The major variations are related to anthropogenic activities (irrigation agricultural, construction activities, clearing of land, and domestic waste disposal) and natural processes (erosion of river bank and runoff). Discriminant analysis (DA) was applied on the dataset to maximize the similarities between group relative to within-group variance of the parameters. DA provided better results with great discriminatory ability using eight variables (DO, BOD₅, COD, SS, NH₄, conductivity, salinity, and DS) as the most statistically significantly responsible for surface water quality variation in the area. The present study, however, makes several noteworthy contributions to the existing knowledge on the spatial variations of surface water quality and is believed to serve as a baseline data for further studies. Future research should therefore concentrate on the investigation of temporal variations of water quality in the basin.     

Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations

Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to optimize the ANN models to achieve the most accurate hourly prediction for a case study (city of Tehran), and to examine a methodology to analyze uncertainties based on ANN and Monte Carlo simulations (MCS). In the current study, the ANNs were constructed to predict criteria pollutants of nitrogen oxides (NO_x), nitrogen dioxide (NO₂), nitrogen monoxide (NO), ozone (O₃), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10 μm (PM₁₀) in Tehran based on the data collected at a monitoring station in the densely populated central area of the city. The best combination of input variables was comprehensively investigated taking into account the predictability of meteorological input variables and the study of model performance, correlation coefficients, and spectral analysis. Among numerous meteorological variables, wind speed, air temperature, relative humidity and wind direction were chosen as input variables for the ANN models. The complex nature of pollutant source conditions was reflected through the use of hour of the day and month of the year as input variables and the development of different models for each day of the week. After that, ANN models were constructed and validated, and a methodology of computing prediction intervals (PI) and probability of exceeding air quality thresholds was developed by combining ANNs and MCSs based on Latin Hypercube Sampling (LHS). The results showed that proper ANN models can be used as reliable metamodels for the prediction of hourly air pollutants in urban environments. High correlations were obtained with R ² of more than 0.82 between modeled and observed hourly pollutant levels for CO, NO_x, NO₂, NO, and PM₁₀. However, predicted O₃ levels were less accurate. The combined use of ANNs and MCSs seems very promising in analyzing air pollution prediction uncertainties. Replacing deterministic predictions with probabilistic PIs can enhance the reliability of ANN models and provide a means of quantifying prediction uncertainties.     

Abundance of denitrification genes under different peizometer depths in four Irish agricultural groundwater sites

This study examined the relationship between the abundance of bacterial denitrifiers in groundwater at four sites, differing with respect to overlaying land management and peizometer depth. Groundwater was sourced from 36 multilevel piezometers, which were installed to target different groundwater zones: (1) subsoil, (2) subsoil to bedrock interface, and (3) bedrock. The gene copy concentrations (GCCs), as gene copies per liter, for bacterial 16S rRNA genes and the denitrifying functional genes, nirK, nirS, and nosZ, were determined using quantitative polymerase chain reaction assays. The results were related to gaseous nitrogen emissions and to the physicochemical properties of the four sites. Overall, nirK and nirS abundance appeared to show no significant correlation to N₂O production (P?=?0.9989; P?=?0.3188); and no significant correlation was observed between nosZ and excess N₂ concentrations (P?=?0.0793). In the majority of piezometers investigated, the variation of nirK and nirS gene copy concentrations was considered significant (P?<?0.0001). Dissolved organic carbon (DOC) decreased with aquifer depth and ranged from 1.0–4.0 mg l^?1, 0.9–2.4 mg l^?1, and 0.8–2.4 mg l^?1 within piezometers located in the subsoil, subsoil/bedrock interface, and bedrock depths, respectively. The availability of increasing DOC and the depth of the water table were positively correlated with increasing nir and nosZ GCCs (P?=?0.0012). A significant temporal correlation was noted between nirS and piezometer depth (P?<?0.001). Interestingly, the nirK, nirS, and nosZ GCCs varied between piezometer depths within specific sites, while GCCs remained relatively constant from site to site, thus indicating no direct impact of agricultural land management strategies investigated on denitrifier abundance.     

The Fate of Hydrogen Peroxide as an Oxygen Source for Bioremediation Activities within Saturated Aquifer Systems

ABSTRACT

In situ bioremediation is an innovative technique for the remediation of contaminated aquifers that involves the use of microorganisms to remediate soils and groundwaters polluted by hazardous substances. During its application, this process may require the addition of nutrients and/or electron acceptors to stimulate appropriate biological activity. Hydrogen peroxide has been commonly used as an oxygen source because of the limited concentrations of oxygen that can be transferred into the groundwater using above-ground aeration followed by reinjection of the oxygenated groundwater into the aquifer or subsurface air sparging of the aquifer. Because of several potential interactions of H₂O₂ with various aquifer material constituents, its decomposition may be too rapid, making effective introduction of the H₂O₂ into targeted treatment zones extremely difficult and costly. Therefore, a bench-scale study was conducted to determine the fate of H₂O₂ within subsurface aquifer environments. The purpose of this investigation was to identify those aquifer constituents, both biotic and abiotic, that are most active in controlling the fate of H₂O₂. The decomposition rates of H₂O₂ were determined using both equilibrated water samples and soil slurries. Results showed H₂O₂ decomposition to be effected by several commonly found inorganic soil components; however, biologically mediated catalytic reactions were determined to be the most substantial.     

Effects of N-acetyl-L-cysteine on fish hepatoma cells treated with mercury chloride and ionizing radiation

Organisms are exposed to natural radiations from cosmic or terrestrial origins. Furthermore the combined action of radiation with various chemicals is an inevitable feature of modern life. Radiation is known to cause cell death, mainly due to its ability to produce reactive oxygen species in cells. N-acetyl-l-cysteine (NAC) is a well-known sulfhydryl-containing antioxidant whose role in radioprotection has been reported. Synergistic effects of radiation and mercury chloride on human cells was previously reported by the authors. Based on the previous report, this study was designed to assess the synergistic effects of radiation and mercury chloride on fish hepatoma cells, as well as to investigate the protective effects of NAC on the cells. The cytotoxicity of radiation was enhanced in the presence of mercury chloride. NAC in lower concentrations prevented cells from death after irradiation with lower doses (<300 Gy) while it did not prevent cells from radiation-induced death after irradiation with higher doses (300, 500 Gy). The intracellular glutathione (GSH) levels significantly decreased after irradiation while the combined treatment of NAC and radiation alleviated the decrease in the GSH levels. The investigations give a clue for the action mechanism of synergistic or protective effects of NAC on the cells. Due to their high resistance to ionizing radiation, the PLHC-1 cells can be effectively used as a screening tool for assessing the combined effects of radiation with toxic chemicals.     

Eco-Friendly, Vascular Shape and Interpenetrating Poly (Acrylic Acid) Grafted Pectin Hydrogels; Biosorption and Desorption Investigations

The synthesis and characterization of poly (acrylic acid) grafted pectin hydrogel followed by biosorption and desorption characteristics of cadmium, as a model heavy metal, have been studied. The grafted eco-friendly pectin based interpenetrating hydrogel was prepared in the presence of gluteraldehyde crosslinker under N₂ atmosphere and characterized using ¹H-NMR, FTIR, TGA and SEM techniques. Gluteraldehyde was found to form one-arm and two-arm crosslinks in the copolymer. Upon grafting, two-dimensional sheet structures bounded to tubular and vascular cylindrical rods were observed. The biosorption and desorption data, determined experimentally, were fitted to pseudo-second order reaction kinetics. At higher ionic strength values, the maximum metal uptake value (q _max) was lowered and pseudo-second order rate constant (k ₂) was increased. Whereas, at higher pH values the maximum metal uptake value (q _max) was increased and Pseudo-second order rate constant (k ₂) was decreased. 0.1?M HCl solution was a suitable eluent to regenerate the hydrogel surface and recover the adsorbed cadmium metal ions. Pectin based copolymer could be used as an efficient candidature biosorbent for the recovery of cadmium metal ions from aqueous solutions.