An electrochemical nanocomposite modified carbon paste electrode as a sensor for simultaneous determination of hydrazine and phenol in water and wastewater samples

In this study, we report preparation of a high sensitive electrochemical sensor for determination of hydrazine in the presence of phenol in water and wastewater samples. In the first step, we describe synthesis and characterization of ZnO/CNTs nanocomposite with different methods such as transmission electron microscopy (TEM) and X-ray diffraction (XRD). In the second step, application of the synthesis nanocomposite describes the preparation of carbon paste electrode modified with n-(4-hydroxyphenyl)-3,5-dinitrobenzamide as a high sensitive and selective voltammetric sensor for determination of hydrazine and phenol in water and wastewater samples. The mediated oxidation of hydrazine at the modified electrode was investigated by cyclic voltammetry, chronoamperometry, and electrochemical impedance spectroscopy (EIS). Also, the values of catalytic rate constant (k) and diffusion coefficient (D) for hydrazine were calculated. Square wave voltammetry (SWV) of hydrazine at the modified electrode exhibited two linear dynamic ranges with a detection limit (3σ) of 8.0 nmol L^?1. SWV was used for simultaneous determination of hydrazine and phenol at the modified electrode and quantitation of hydrazine and phenol in some real samples by the standard addition method.     

Source apportionment of PAHs and n-alkanes in respirable particles in Tehran,Iran by wind sector and vertical profile

The vertical concentration profiles and source contributions of polycyclic aromatic hydrocarbons (PAHs) and n-alkanes in respirable particle samples (PM₄) collected at 10, 100, 200 and 300-m altitude from the Milad Tower of Tehran, Iran during fall and winter were investigated. The average concentrations of total PAHs and total n-alkanes were 16.7 and 591 ng/m³, respectively. The positive matrix factorization (PMF) model was applied to the chemical composition and wind data to apportion the contributing sources. The five PAH source factors identified were: ‘diesel’ (56.3 % of total PAHs on average), ‘gasoline’ (15.5 %), ‘wood combustion, and incineration’ (13 %), ‘industry’ (9.2 %), and ‘road soil particle’ (6.0 %). The four n-alkane source factors identified were: ‘petrogenic’ (65 % of total n-alkanes on average), ‘mixture of petrogenic and biomass burning’ (15 %), ‘mixture of biogenic and fossil fuel’ (11.5 %), and ‘biogenic’ (8.5 %). Source contributions by wind sector were also estimated based on the wind sector factor loadings from PMF analysis. Directional dependence of sources was investigated using the conditional probability function (CPF) and directional relative strength (DRS) methods. The calm wind period was found to contribute to 4.4 % of total PAHs and 5.0 % of total n-alkanes on average. Highest average concentrations of PAHs and n-alkanes were found in the 10 and 100 m samples, reflecting the importance of contributions from local sources. Higher average concentrations in the 300 m samples compared to those in the 200 m samples may indicate contributions from long-range transport. The vertical profiles of source factors indicate the gasoline and road soil particle-associated PAHs, and the mixture from biogenic and fossil fuel source-associated n-alkanes were mostly from local emissions. The smaller average contribution of diesel-associated PAHs in the lower altitude samples also indicates that the restriction of diesel-fueled vehicle use in the central area of Tehran has been effective in reducing the PAHs concentration.     

Performance evaluation of a continuous flow photocatalytic reactor for wastewater treatment

A novel photocatalytic reactor for wastewater treatment was designed and constructed. The main part of the reactor was an aluminum tube in which 12 stainless steel circular baffles and four quartz tube were placed inside of the reactor like shell and tube heat exchangers. Four UV–C lamps were housed within the space of the quartz tubes. Surface of the baffles was coated with TiO₂. A simple method was employed for TiO₂ immobilization, while the characterization of the supported photocatalyst was based on the results obtained through performing some common analytical methods such as X-ray diffraction (XRD), scanning electron microscope (SEM), and BET. Phenol was selected as a model pollutant. A solution of a known initial concentration (20, 60, and 100 ppmv) was introduced to the reactor. The reactor also has a recycle flow to make turbulent flow inside of the reactor. The selected recycle flow rate was 7?×?10^?5 m³.s^?1, while the flow rate of feed was 2.53?×?10^?7, 7.56?×?10^?7, and 1.26?×?10^?6 m³.s^?1, respectively. To evaluate performance of the reactor, response surface methodology was employed. A four-factor three-level Box–Behnken design was developed to evaluate the reactor performance for degradation of phenol. Effects of phenol inlet concentration (20–100 ppmv), pH (3–9), liquid flow rate (2.53?×?10^?7?1.26?×?10^?6 m³.s^?1), and TiO₂ loading (8.8–17.6 g.m^?2) were analyzed with this method. The adjusted R ² value (0.9936) was in close agreement with that of corresponding R ² value (0.9961). The maximum predicted degradation of phenol was 75.50 % at the optimum processing conditions (initial phenol concentration of 20 ppmv, pH?～?6.41, and flow rate of 2.53?×?10^?7 m³.s^?1 and catalyst loading of 17.6 g.m^?2). Experimental degradation of phenol determined at the optimum conditions was 73.7 %. XRD patterns and SEM images at the optimum conditions revealed that crystal size is approximately 25 nm and TiO₂ nanoparticles with visible agglomerates distribute densely and uniformly over the surface of stainless steel substrate. BET specific surface area of immobilized TiO₂ was 47.2 and 45.8 m² g^?1 before and after the experiments, respectively. Reduction in TOC content, after steady state condition, showed that maximum phenol decomposition occurred at neutral condition (pH?～?6). Figure

The schematic view of the experimental set-up     

Recovery of heavy metals from waste printed circuit boards: statistical optimization of leaching and residue characterization

Environmental Science and Pollution Research - Despite attempts to enhance the recycling of waste printed circuit boards (WPCBs), the simultaneous recovery of major metals of WPCBs using an...     

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.     

ANN-based mapping of monthly reference crop evapotranspiration by using altitude, latitude and longitude data in Fars province, Iran

The main goal of this study was to evaluate the different feed-forward back-propagation artificial neural networks’ (ANNs) potential to estimate and interpolate the reference crop evapotranspiration (ET₀) in Fars province of Iran. ET₀ was calculated using the FAO-56 Penman–Monteith method over 24 synoptic stations. Then, altitude, latitude, longitude and the month’s number as inputs and the monthly ET₀ as output (target) were used to train the ANNs. In addition, the three-layered ANNs optimized with different training algorithms including gradient descent back-propagation (gd), gradient descent with adaptive learning rate back-propagation (gda), gradient descent with momentum and adaptive learning rate back-propagation (gdx) and scaled conjugate gradient back-propagation (scg). The results indicated that scg algorithm with architecture (4 2 1) had more satisfactory results with the RMSE and R correlation coefficient equal to 18.538 mm and 0.967 in validation phase, respectively. Based on the mentioned architecture of scg algorithm, and input data form different parts of Fars province and surrounding areas, monthly ET₀ maps were produced and annual one achieved by summation of monthly maps. The maps particularly annual one showed that highest values of ET₀ could be found in the southern and especially southeastern regions, while the lowest values of ET₀ could be seen in the northern parts. Contribution of geographic and topographic variables improved the accuracy and spatial details of the resulting maps. It is interesting to note that the fundamental capability of this model is the usage of just a few parameters for ET₀ mapping. Since ET₀ is a key parameter in water demand planning, therefore, the derived maps could be useful and applicable for many purposes mainly irrigation scheduling in Fars province, Iran.     

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.