Flow injection spectrophotometric determination of methamidophos using online hydrolysis

A new protocol for the online spectrophotometric determination of methamidophos has been developed. The method is based on online hydrolysis of methamidophos, and the resulting hydrolyzed product is reacted with sodium nitroproside to form a colored complex. The calibration curve is linear over the concentration range of 0.5–20 μgml^?–?1, with a molar absorptivity of 2.5×10⁴ L mol^?1 cm^?1. The method is fast and reproducible with a sample throughput of 90 samples/h. The method is successfully applied to formulations and real samples.     

Amygdalin in bitter and sweet seeds of apricots

Hydrogen cyanide (HCN) poisoning due to amygdalin (AMY) in apricot seeds is one of the public health issues in Turkey. The aim of this study was to investigate the AMY content of 13 different apricot seeds including bitter and sweet ones, and which are either sulfurized or roasted. The AMY content was determined by a high-performance liquid chromatography. Release of HCN was predicted and total amount of seeds which can cause poisoning was calculated. The mean AMY content of bitter seeds was 26 ± 14 mg g^?1 and that of sweet seeds was 0.16 ± 0.09 mg g^?1. The consumption of small amounts of bitter seeds may cause cyanide poisoning.     

A multi-criteria GIS-based model for wind farm site selection with the least impact on environmental pollution using the OWA-ANP method

Environmental Science and Pollution Research - Wind energy is considered one of the most efficient and cost-effective ways to generate electricity, since it has a low environmental impact. So, it...     

Direct capture of carbon dioxide from air via lime-based sorbents

Mitigation and Adaptation Strategies for Global Change - Direct air capture (DAC) is a developing technology for removing carbon dioxide (CO2) from the atmosphere or from low-CO2-containing...     

Adsorptive Removal of Lead from Water Using a Novel Cysteine-Bentonite/Poly(vinyl alcohol)/Alginate Nanocomposite

Journal of Polymers and the Environment - Adsorptive removal of toxic metals from water using materials with high removal capability and sufficient environmental degradability is of great...     

A hybrid model for robust design of sustainable closed-loop supply chain in lead-acid battery industry

Environmental Science and Pollution Research - Considering supply chain efficiency during the network design process significantly affect chain performance improvement. In this paper, the design...     

Assessment of the current municipal solid waste management system in Tehran,Iran: challenges and opportunities for sustainable development

Journal of Material Cycles and Waste Management - With over 9 million population, challenges of waste management system (WMS) of Tehran can offer some lessons to other middle-income cities. Despite...     

Acute and chronic toxicity of nickel to nitrifiers at different temperatures

This study investigated the acute nickel toxicity on nitrification of low ammonia synthetic wastewater at 10, 23, and 35°C. The nickel inhibition half-velocity constants(K_(I,Ni)) for ammonia oxidizing bacteria(AOB) and nitrite oxidizing bacteria(NOB) based on Ni/MLSS ratio at 10, 23, and 35°C were 5.4 and 5.6 mg Ni/g MLSS, 4.6 and 3.5 mg Ni/g MLSS, and 9.1 and 2.7 mg Ni/g MLSS, respectively. In addition, chronic toxicity of nickel to nitrification of low ammonia synthetic wastewater was investigated at 10°C in two sequencing batch reactors(SBRs). Long-term SBRs operation and short-term batch tests were comparable with respect to the extent of inhibition and corresponding Ni/MLSS ratio. The μ_(max), b, and K_o of AOB were 0.16 day~(-1), 0.098 day~(-1) and 2.08 mg O_2/L after long-term acclimatization to nickel of 1 mg/L at 10°C, high dissolved oxygen(DO)(7 mg/L) and long solids retention time(SRT) of 63–70 days. Acute nickel toxicity of nitrifying bacteria was completely reversible.     

Optimal temporal scale for the correlation of AOD and ground measurements of PM2.5 in a real-time air quality estimation system

Aerosol optical depth (AOD), an indirect estimate of particulate matter using satellite observations, has shown great promise in improving estimates of PM_2.5 (particulate matter with aerodynamic diameter less than or equal to 2.5 μm) surface. Currently, few studies have been conducted to explore the optimal way to apply AOD data to improve the model accuracy of PM_2.5 in a real-time air quality system. We believe that two major aspects may be worthy of consideration in that area: 1) an approach that integrates satellite measurements with ground measurements in the estimates of pollutants and 2) identification of an optimal temporal scale to calculate the correlation of AOD and ground measurements. This paper is focused on the second aspect, identifying the optimal temporal scale to correlate AOD with PM_2.5. Five following different temporal scales were chosen to evaluate their impact on the model performance: 1) within the last 3 days, 2) within the last 10 days, 3) within the last 30 days, 4) within the last 90 days, and 5) the time period with the highest correlation in a year. The model performance is evaluated for its accuracy, bias, and errors based on the following selected statistics: the Mean Bias, the Normalized Mean Bias, the Root Mean Square Error, Normalized Mean Error, and the Index of Agreement. This research shows that the model with the temporal scale: within the last 30 days, displays the best model performance in a southern multi-state area centered in Mississippi using 2004 and 2005 data sets.     

Synthesis of TiO2 nanoparticles in different thermal conditions and modeling its photocatalytic activity with artificial neural network

Titanium dioxide (TiO₂) nanoparticles were prepared by sol gel route. The preparation parameters were optimized in the removal of 4-nitrophenol (4-NP). All catalysts were analyzed by X-ray diffraction (XRD) and scanning electron microscopy (SEM). An artificial neural network model (ANN) was developed to predict the photocatalytic removal of 4-NP in the presence of TiO₂ nanoparticles prepared under desired conditions. The comparison between the predicted results by designed ANN model and the experimental data proved that modeling of the removal process of 4-NP using artificial neural network was a precise method to predict the extent of 4-NP removal under different conditions.