Determination of the temperature dependence of water solubilities of polycyclic aromatic hydrocarbons by a generator column-on-line solid-phase extraction-liquid chromatographic method

An improved dynamic coupled column liquid chromatographic (DCCLC) technique for determining water solubility data of hydrophobic compounds is presented. The technique is based on pumping water through a thermostated generator column in order to generate emulsion-free, saturated aqueous solutions of the compound under study. Through a switching valve system the solute in the aqueous solution is extracted and concentrated by an on-line solid-phase extraction process and subsequently eluted and analyzed by high performance liquid chromatography (fluorescence detection coupled to photodiode array detection). The improvements carried out to the original DCCLC technique have given rise to savings in time for the experimental work and increased sensitivity during the detection and quantification stage. Applicability of the method for studying highly hydrophobic substances is demonstrated by determining water solubility of anthracene and pyrene in the temperature range of 8.9-49.9 and 8.5-32.2 degrees C, respectively. The measured water solubilities are in good agreement with the best available literature data. The method has also been applied to the determination of water solubility of m-terphenyl, 9, 10-dihydrophenanthrene and guaiazulene, in the temperature range of 4.8-49.9, 4.8-25.0, and 4.5-29.9 degrees C, respectively. The uncertainty in the Sw values determined in this work ranged from 0.7% to 4.6%. The experimental water solubility data, as a function of temperature, are fitted to the equation In Sw = A + B/T; where Sw and T are given in mole fraction and Kelvin, respectively.     

Land Resource Sustainability for Urban Development: Spatial Decision Support System Prototype

Land resource sustainability for urban development characterizes the problem of decision-making with multiplicity and uncertainty. A decision support system prototype aids in the assessment of incremental land development plan proposals put forth within the long-term community priority of a sustainable growth. Facilitating this assessment is the analytic hierarchy process (AHP), a multicriteria evaluation and decision support system. The decision support system incorporates multiple sustainability criteria, weighted strategically responsive to local public policy priorities and community–specific situations and values, while gauging and directing desirable future courses of development. Furthermore, the decision support system uses a GIS, which facilitates an assessment of urban form with multiple indicators of sustainability as spatial criteria thematically. The resultant land-use sustainability scores indicate, on the ratio-scale of AHP, whether or not a desirable urban form is likely in the long run, and if so, to what degree. The two alternative modes of synthesis in AHP—ideal and distributive—provide assessments of a land development plan incrementally (short-term) and city-wide pattern comprehensively (long-term), respectively. Thus, the spatial decision support system facilitates proactive and collective public policy determination of land resource for future sustainable urban development.     

Organochlorine pesticide and polychlorinated biphenyl residues in feathers of birds from different trophic levels of South-West Iran

Persistent organochlorine pesticides (OCPs), such as dichlorodiphenyltrichloroethane (DDT) and its metabolites, hexachlorobenzene (HCB), alpha, beta and gamma-hexachlorocyclohexane (HCH) isomers, together with polychlorinated biphenyl (PCB) congeners (IUPAC Nos. 28, 52, 101, 138, 153 and 180), were determined in tail feathers from 37 birds belonging to 18 species, all originating from the South-West of Iran (Khuzestan, coast of the Persian Gulf). This is the first report on organochlorine contaminants in feathers from museum collections and it is an indication of the concentrations of OCPs and PCBs in the past (1991-1996). Median concentrations of HCHs, DDTs, PCBs and HCB were 22, 14, 11 and 10 ng/g feather, respectively. Significant correlations (p<0.05) were calculated between OCPs (except HCB) and PCBs in the bird feathers. p,p'-DDE and gamma-HCH were the most abundant OCPs, while CB 180, CB 138 and CB 101 were the predominant PCB congeners in almost all species. Significant differences (p<0.05) in the mean concentrations of DDTs and PCBs were detected among species grouped according to their feeding habits. Levels of DDTs and PCBs were highest in the carnivorous species and lowest in the herbivorous species. Levels of OCPs and PCBs in feathers of bids in the 1990s were generally below the thresholds reported to affect reproduction.     

Application of Satellite Microwave Images in Estimating Snow Water Equivalent1

Abstract: Flood forecast and water resource management requires reliable estimates of snow pack properties [snow depth and snow water equivalent (SWE)]. This study focuses on application of satellite microwave images to estimate the spatial distribution of snow depth and SWE over the Great Lakes area. To estimate SWE, we have proposed the algorithm which uses microwave brightness temperatures (Tb) measured by the Special Sensor Microwave Imager (SSM/I) radiometer along with information on the Normalized Difference Vegetation Index (NDVI). The algorithm was developed and tested over 19 test sites characterized by different seasonal average snow depth and land cover type. Three spectral signatures derived from SSM/I data, namely T19V‐T37V (GTV), T19H‐T37H (GTH), and T22V‐T85V (SSI), were examined for correlation with the snow depth and SWE. To avoid melting snow conditions, we have used observations taken only during the period from December 1‐February 28. It was found that GTH, and GTV exhibit similar correlation with the snow depth/SWE and are most should be used over deep snowpack. In the same time, SSI is more sensitive to snow depth variations over a shallow snow pack. To account for the effect of dense forests on the scattering signal of snow we established the slope of the regression line between GTV and the snow depth as a function of NDVI. The accuracy of the new technique was evaluated through its comparison with ground‐based measurements and with results of SWE analysis prepared by the National Operational Hydrological Remote Sensing Center (NOHRSC) of the National Weather Service. The proposed algorithm was found to be superior to previously developed global microwave SWE retrieval techniques.     

A time series analysis of environmental and metrological factors impact on cutaneous leishmaniasis incidence in an endemic area of Dehloran,Iran

The aim of this study was to investigate the relationship between the environmental and metrological variables and cutaneous leishmaniasis (CL) transmission and its prediction in a region susceptible to this disease prevalence using a time series model. The accurate locations of 4437 CL diagnosed from 2011 to 2015 were obtained to be used in the time series model. Temperature, number of days with temperature over 30 °C, and number of earthquake were related to CL incidence using the Seasonal Auto-correlated Integrated Moving Average (SARIMA) model according to the Box-Jenkins method. In addition, the relationship between land use and surface soil type in 500- and 1000-m radius around the CL patients were investigated. The SARIMA models showed significant associations between environmental and meteorological variables and CL incidence adjusted for seasonality and auto-correlation. The result indicated that there are need more robust preventive programs in earthquake-prone areas with high temperature and inceptisol soil type than other areas. In addition, the region with these characteristics should be considered as high-risk areas for CL prevalence.     

Transformer oils as a potential source of environmental exposure to polychlorinated biphenyls (PCBs): an assessment in three central provinces of Iran

Environmental Science and Pollution Research - Polychlorinated biphenyl (PCB) contamination of oils from all transformers of the national electrical grid in Tehran, Qom, and Alborz, three central...     

Optimization of copper recovery from electronic waste using response surface methodology and Monte Carlo simulation under uncertainty

Journal of Material Cycles and Waste Management - Electronic waste (e-waste) production is currently the largest growing waste stream in the world. These wastes contain the precious metals such as...     

Higher efficiency and lower environmental impact of membrane separation for carbon dioxide capture in coal power plants

Environmental Chemistry Letters - Global warming may be slowed down by carbon capture and storage systems that allow to sequester carbon dioxide from large fixed point sources such as power plants...     

Application of soft computing to predict water quality in wetland

Prediction of water quality is a critical issue because of its significant impact on human and ecosystem health. This research aims to predict water quality index (WQI) for the free surface wetland using three soft computing techniques namely, adaptive neuro-fuzzy system (ANFIS), artificial neural networks (ANNs), and group method of data handling (GMDH). Seventeen wetland points for a period of 14 months were considered for monitoring water quality parameters including conductivity, suspended solid (SS), biochemical oxygen demand (BOD), ammoniacal nitrogen (AN), chemical oxygen demand (COD), dissolved oxygen (DO), temperature, pH, phosphate nitrite, and nitrate. The sensitivity analysis performed by ANFIS indicates that the significant parameters to predict WQI are pH, COD, AN, and SS. The results indicated that ANFIS with Nash-Sutcliffe Efficiency (NSE = 0.9634) and mean absolute error (MAE = 0.0219) has better performance to predict the WQI comparing with ANNs (NSE = 0.9617 and MAE = 0.0222) and GMDH (NSE = 0.9594 and MAE = 0.0245) models. However, ANNs provided a comparable prediction and the GMDH can be considered as a technique with an acceptable prediction for practical purposes. The findings of this study could be used as an effective reference for policy makers in the field of water resource management. Decreasing variables, reduction of running time, and high speed of these approaches are the most important reasons to employ them in any aquatic environment worldwide.

     

Improving the Antibacterial Activity of Curcumin Loaded Nanoparticles in Wastewater Treatment by Enhancing Permeability and Sustained Release

Journal of Polymers and the Environment - In this research, the antibacterial effect of curcumin entrapped in polymeric nanoparticles (mPEG-PCL/curcumin) on resistant bacteria were investigated....