排序方式： 共有6条查询结果，搜索用时 125 毫秒
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Laleh Seifi Ali Torabian Hossein Kazemian Gholamreza Nabi Bidhendi Ali Akbar Azimi Shapoor Nazmara Mohammad AliMohammadi 《洁净——土壤、空气、水》2011,39(10):939-948
In this paper, a novel adsorbent developed by means of granulating of natural zeolite nanoparticles (i.e., clinoptilolite) was evaluated for possible removal of the petroleum monoaromatics (i.e., benzene, toluene, ethylbenzene, and xylene, BTEX). To do this, the natural zeolite was ground to produce nanosized particulate, then modified by two cationic surfactants and granulated. The effect of various parameters including temperature, initial pH of the solution, total dissolved solids (TDS), and concentration of a competitive substance (i.e., methyl tert‐butyl ether, MTBE) were studied and optimized using a Taguchi statistical approach. The results ascertained that initial pH of the solution was the most effective parameter. However, the low pH (acidic) was favorable for BTEX adsorption onto the developed adsorbents. In this study, the experimental parameters were optimized and the best adsorption condition by determination of effective factors was chosen. Based on the S/N ratio, the optimized conditions for BTEX removal were temperature of 40°C, initial pH of 3, TDS of 0 mg/L, and MTBE concentration of 100 µg/L. At the optimized conditions, the uptake of each BTEX compounds reached to more than 1.5 mg/g of adsorbents. 相似文献
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Natural Resources Research - The aim of this paper is to improve estimation of shear-wave velocity in carbonate rocks. The region being studied is an oil field located in southwest Iran, where... 相似文献
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Bhaleka D. Persaud Krysha A. Dukacz Gopal C. Saha Amber Peterson Laleh Moradi Stephen O'Hearn Erin Clary Juliane Mai Michael Steeleworthy Jason J. Venkiteswaran Homa Kheyrollah Pour Brent B. Wolfe Sean K. Carey John W. Pomeroy Chris M. DeBeer James M. Waddington Philippe Van Cappellen Jimmy Lin 《水文研究》2021,35(11):e14385
Water science data are a valuable asset that both underpins the original research project and bolsters new research questions, particularly in view of the increasingly complex water issues facing Canada and the world. Whilst there is general support for making data more broadly accessible, and a number of water science journals and funding agencies have adopted policies that require researchers to share data in accordance with the findable, accessible, interoperable, reusable (FAIR) principles, there are still questions about effective management of data to protect their usefulness over time. Incorporating data management practices and standards at the outset of a water science research project will enable researchers to efficiently locate, analyse and use data throughout the project lifecycle, and will ensure the data maintain their value after the project has ended. Here, some common misconceptions about data management are highlighted, along with insights and practical advice to assist established and early career water science researchers as they integrate data management best practices and tools into their research. Freely available tools and training opportunities made available in Canada through Global Water Futures, The Gordon Foundation DataStream, the Digital Research Alliance of Canada Portage Network, Compute Canada, and university libraries, among others are compiled. These include webinars, training videos, and individual support for the water science community that together enable researchers to protect their data assets and meet the expectations of journals and funders. The perspectives shared here have been developed as part of the Global Water Futures programme's efforts to improve data management and promote the use of common data practices and standards in the context of water science in Canada. Ten best practices are proposed that may be broadly applicable to other disciplines in the natural sciences and can be adopted and adapted globally. 相似文献
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Katharine P. North Douglas M. Mackay Julian S. Kayne Daniel Petersen Ehsan Rasa Laleh Rastegarzadeh Reef B. Holland Kate M. Scow 《Ground Water Monitoring & Remediation》2012,32(3):52-62
The potential for in situ biodegradation of tert‐butyl alcohol (TBA) by creation of aerobic conditions in the subsurface with recirculating well pairs was investigated in two field studies conducted at Vandenberg Air Force Base. In the first experiment, a single recirculating well pair with bromide tracer and oxygen amendment successfully delivered oxygen to the subsurface for 42 d. TBA concentrations were reduced from approximately 500 μg/L to below the detection limit within the treatment zone and the treated water was detected in a monitoring transect several meters downgradient. In the second experiment, a site‐calibrated model was used to design a double recirculating well pair with oxygen amendment, which successfully delivered oxygen to the subsurface for 291 d and also decreased TBA concentrations to below the detection limit. Methylibium petroleiphilum strain PM1, a known TBA‐degrading bacterium, was detectable at the study site but addition of oxygen had little impact on the already low baseline population densities, suggesting that there was not enough carbon within the groundwater plume to support significant new growth in the PM1 population. Given favorable hydrogeologic and geochemical conditions, the use of recirculating well pairs to introduce dissolved oxygen into the subsurface is a viable method to stimulate in situ biodegradation of TBA or other aerobically degradable aquifer contaminants. 相似文献
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River flow is a complex dynamic system of hydraulic and sediment transport. Bed load transport have a dynamic nature in gravel bed rivers and because of the complexity of the phenomenon include uncertainties in predictions. In the present paper, two methods based on the Artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are developed by using 360 data points. Totally, 21 different combination of input parameters are used for predicting bed load transport in gravel bed rivers. In order to acquire reliable data subsets of training and testing, subset selection of maximum dissimilarity (SSMD) method, rather than classical trial and error method, is used in finding randomly manipulation of these subsets. Furthermore, uncertainty analysis of ANN and ANFIS models are determined using Monte Carlo simulation. Two uncertainty indices of d factor and 95% prediction uncertainty and uncertainty bounds in comparison with observed values show that these models have relatively large uncertainties in bed load predictions and using of them in practical problems requires considerable effort on training and developing processes. Results indicated that ANFIS and ANN are suitable models for predicting bed load transport; but there are many uncertainties in determination of bed load transport by ANFIS and ANN, especially for high sediment loads. Based on the predictions and confidence intervals, the superiority of ANFIS to those of ANN is proved. 相似文献
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