Academic literature on the topic 'Environmental quantification'

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Journal articles on the topic "Environmental quantification"

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Bottausci, Sara, Elena-Diana Ungureanu-Comanita, Maria Gavrilescu, and Alessandra Bonoli. "ENVIRONMENTAL IMPACTS QUANTIFICATION OF PVC PRODUCTION." Environmental Engineering and Management Journal 20, no. 10 (2021): 1693–702. http://dx.doi.org/10.30638/eemj.2021.158.

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Robu, Brindusa Mihaela, and Gabriel Dan Suditu. "DIGITIZATION OF THE ENVIRONMENTAL IMPACT QUANTIFICATION PROCESS." Environmental Engineering and Management Journal 11, no. 4 (2012): 841–48. http://dx.doi.org/10.30638/eemj.2012.107.

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NARA, Matsunori. "Quantification of Concerns for Environmental Risk Evaluation." Proceedings of the Symposium on Global Environment 11 (2003): 183–88. http://dx.doi.org/10.2208/proge.11.183.

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Horneck, G. "Quantification of biologically effective environmental UV irradiance." Advances in Space Research 26, no. 12 (January 2000): 1983–94. http://dx.doi.org/10.1016/s0273-1177(00)00172-1.

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Allaire, Douglas, George Noel, Karen Willcox, and Rebecca Cointin. "Uncertainty quantification of an Aviation Environmental Toolsuite." Reliability Engineering & System Safety 126 (June 2014): 14–24. http://dx.doi.org/10.1016/j.ress.2014.01.002.

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Ye, Ming, Philip D. Meyer, Yu-Feng Lin, and Shlomo P. Neuman. "Quantification of model uncertainty in environmental modeling." Stochastic Environmental Research and Risk Assessment 24, no. 6 (April 28, 2010): 807–8. http://dx.doi.org/10.1007/s00477-010-0377-0.

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Fowler, Brian, Dale Hoover, and M. Coreen Hamilton. "The quantification of toxaphene in environmental samples." Chemosphere 27, no. 10 (November 1993): 1891–905. http://dx.doi.org/10.1016/0045-6535(93)90385-i.

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ZENITANI, Kenji, and Hidefumi IMURA. "Quantification of the environmental load associated with construction." ENVIRONMENTAL SYSTEMS RESEARCH 22 (1994): 147–53. http://dx.doi.org/10.2208/proer1988.22.147.

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Cartailler, Thomas, Anais Guaus, Alexandre Janon, Hervé Monod, Clémentine Prieur, and Nathalie Saint-Geours. "Sensitivity analysis and uncertainty quantification for environmental models." ESAIM: Proceedings 44 (January 2014): 300–321. http://dx.doi.org/10.1051/proc/201444019.

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Ehling, U. H. "Quantification of the Genetic Risk of Environmental Mutagens." Risk Analysis 8, no. 1 (March 1988): 45–57. http://dx.doi.org/10.1111/j.1539-6924.1988.tb01153.x.

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Dissertations / Theses on the topic "Environmental quantification"

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Garraghan, Peter Michael. "Holistic cloud computing environmental quantification and behavioural analysis." Thesis, University of Leeds, 2014. http://etheses.whiterose.ac.uk/7192/.

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Cloud computing has been characterized to be large-scale multi-tenant systems that are able to dynamically scale-up and scale-down computational resources to consumers with diverse Quality-of-Service requirements. In recent years, a number of dependability and resource management approaches have been proposed for Cloud computing datacenters. However, there is still a lack of real-world Cloud datasets that analyse and extensively model Cloud computing characteristics and quantify their effect on system dimensions such as resource utilization, user behavioural patterns and failure characteristics. This results in two research problems: First, without the holistic analysis of real-world systems Cloud characteristics, their dimensions cannot be quantified resulting in inaccurate research assumptions of Cloud system behaviour. Second, simulated parameters used in state-of-the-art Cloud mechanisms currently rely on theoretical values which do not accurately represent real Cloud systems, as important parameters such as failure times and energy-waste have not been quantified using empirical data. This presents a large gap in terms of practicality and effectiveness between developing and evaluating mechanisms within simulated and real Cloud systems. This thesis presents a comprehensive method and empirical analysis of large-scale production Cloud computing environments in order to quantify system characteristics in terms of consumer submission and resource request patterns, workload behaviour, server utilization and failures. Furthermore, this work identifies areas of operational inefficiency within the system, as well as quantifies the amount of energy waste created due to failures. We discover that 4-10% of all server computation is wasted due to Termination Events, and that failures contribute to approximately 11% of the total datacenter energy waste. These analyses of empirical data enables researchers and Cloud providers an enhanced understanding of real Cloud behaviour and supports system assumptions and provides parameters that can be used to develop and validate the effectiveness of future energy-efficient and dependability mechanisms.
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Mondaca, Fernandez Iram. "Spectroscopy Techniques for quantification of Microorganisms in Environmental Samples." Tucson, Arizona : University of Arizona, 2006. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1416%5F1%5Fm.pdf&type=application/pdf.

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Speak, Andrew Francis. "Quantification of the environmental impacts of urban green roofs." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/quantification-of-the-environmental-impacts-of-urban-green-roofs(6dc863d5-53bd-462b-b37f-37faa9ae3db0).html.

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Urban populations worldwide are expanding rapidly and consequently a large number of people are becoming exposed to hazards inherent in cites. Phenomena such as the urban heat island can exacerbate the effects of heatwaves, and land surface sealing can lead to flash flooding. Cities are also the sites of enhanced air and water pollution from non-point sources such as concentrated motor vehicle use. Climate change predictions for the UK include increased winter precipitation and an increase in frequency of summer heatwaves. This will put further pressure on urban residents and infrastructure. Roof greening can be used within climate change adaptation schemes because green roofs have a range of environmental benefits which can help urban infrastructure become more sustainable. This thesis empirically quantifies several of these benefits, and the processes influencing them, by monitoring real green roofs in Manchester. A number of novel discoveries were made. Green roofs act as passive filters of airborne particulate matter. 0.21 tonnes of PM10 (2.3% of the inputs) could be removed from Manchester city centre in a maximum extensive green roof scenario. Species and site differences in particle capture were exhibited and related to morphology and proximity to sources respectively. An intensive green roof was able to lower the monthly median overlying air temperature at 300 mm by up to 1.06 oC. A combination of drought and mismanagement caused damage to the vegetation on one of the green roofs, with a subsequent reduction in the cooling effect. Daytime air temperatures were higher than over an adjacent bare roof for a larger proportion of the day than over the undamaged roof, and lower cooling was observed at night. A site-specific methodology was devised to monitor the rainwater runoff from an intensive green roof and an adjacent bare roof. Average runoff retention of 65.7% was observed on the green roof, compared to 33.6% on the bare roof. Season and rainfall amount had significant impacts on retention, however, many other explanatory variables such as Antecedent Dry Weather Period (ADWP) and peak rainfall intensity had no demonstrable, significant impact. Intensive roof construction on 10% of the rooftops in Manchester city centre would increase annual rainfall retention by 2.3%. The runoff was characterised with regards to heavy metals and nutrients. Nutrient levels were found to be not a significant problem for water quality, however, Environmental Quality Standards (EQS) values for protection of freshwater were exceeded for concentrations of Cu, Pb and Zn. High metal concentrations within the sediments may be acting as sources of pollution, particularly in the case of Pb. The age of the green roof means that past atmospheric deposition of Pb could be contributing to the runoff quality. The multi-benefit aspect of green roofs is discussed in the light of the results of this thesis and recommendations made for policy makers and the green roof construction industry.
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Mondaca, Fernandez Iram. "Spectroscopy Techniques for quantification of Microorganisms in Environmental Samples." Diss., The University of Arizona, 2005. http://hdl.handle.net/10150/194103.

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Microbiological monitoring of water is of primary importance for preservation of human health, particularly in an arid zone like the U.S. southwest. In this work, infrared spectroscopic methods were developed to identify and quantify microorganisms present in water and water-related environmental samples. Focus of the work was primarily on evaluating the impact of various sterilization methods on microorganism physiology as gauged by the non-invasive approach of infrared spectroscopy. This work demonstrates that FTIR techniques can be used to identify changes in the physiology of microorganisms and that for heat treatment, a correlation between spectral changes and the viability of microorganisms can be made.
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Mohammadi, Ghazi Reza. "Inference and uncertainty quantification for unsupervised structural monitoring problems." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/115791.

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Thesis: Ph. D. in Structures and Materials, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 261-272).
Health monitoring is an essential functionality for smart and sustainable infrastructures that helps improving their safety and life span. A major element of such functionality is statistical inference and decision making which aims to process the dynamic response of structures in order to localize the defects in those systems as well as quantifying the uncertainties associated with such predictions. Accomplishing this task requires dealing with special constraints, in addition to the general challenges of inference problems, which are imposed by the uniqueness and size of civil infrastructures. These constraints are mainly associated with the small size and high dimensionality of the relevant data sets, low spatial resolution of measurements, and lack of prior information about the response of structures at all possible damaged states. Additionally, the measured responses at various locations on a structure are statistically dependent due to their connectivity via the structural elements. Ignoring such dependencies may result in inaccurate predictions, usually by blurring the damage localization resolution. In this thesis work, a comprehensive investigation has been carried out on developing appropriate signal processing, inference, and uncertainty quantification techniques with applications to data driven structural health monitoring (SHM). For signal processing, we have developed a feature extraction scheme that uses nonlinear non-stationary signal decomposition techniques to capture the effect of damages on the dynamic response of structures. We have also developed a general purpose signal processing method by combining the sparsity based regularization with the singularity expansion method. This method can provide a sparse representation of signals in complex-frequency plane and hence, more robust system identification schemes. For uncertainty quantification and decision making, we have developed three different learning algorithms which are capable of characterizing the statistical dependencies of the relevant random variables in novelty detection inference problems under various constraints related to the quality, size, and dimensionality of data sets. In doing so, we have mainly used the statistical graphical models and Markov random fields, optimization methods, kernel two sample tests, and kernel dependence analysis. The developed methods may be applied to a wide range of problems such as SHM, medical diagnostic, network security, and event detection. We have experimentally evaluated these techniques by applying them to SHM application problems for damage localization in various laboratory prototypes as well as a full scale structure.
by Reza Mohammadi Ghazi.
Ph. D. in Structures and Materials
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Fugate, David C. "Quantification of Tidal Creek Network Patterns using Fractal Methods." W&M ScholarWorks, 1996. https://scholarworks.wm.edu/etd/1539617716.

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Nkongolo, Nsalambi Vakanda. "Quantification of greenhouse gas fluxes from soil in agricultural fields." Thesis, Nelson Mandela Metropolitan University, 2010. http://hdl.handle.net/10948/1474.

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Field studies were conducted at Lincoln University of Missouri (USA) and Hokkaido University (Japan) to: (i) study the relationships between greenhouse gases emissions and soil properties, (ii) assess the influence of agricultural practices on greenhouse gas fluxes and soil properties and (iii) improve the quantification of greenhouse gases from soil in agricultural fields using geospatial technologies. Results showed that besides soil temperature (T), soil thermal properties such as thermal conductivity (K), resistivity (R) and diffusivity (D) and soil pore spaces indices such as the pore tortuosity factor and the relative gas diffusion coefficient (Ds/Do) are controlling factors for greenhouse gases emissions. Soil thermal properties correlated with greenhouse gases emissions when soil temperature could not. The study has found that predicted Ds/Do and correlate with greenhouse gas fluxes even when the air-filled porosity and the total porosity from which they are predicted did not. We have also showed that Ds/Do and can be predicted quickly from routine measurements of soil water and air and existing diffusivity models found in the literature. Agricultural practices do seriously impact greenhouse gases emissions as showed by the effect of mechanized tillage operations on soil physical properties and greenhouse gas fluxes in a corn and soybean fields. In fact, our results showed that tractor compaction increased soil resistance to penetration, water, bulk density and pore tortuosity while reducing air-filled porosity, total pore space and the soil gas diffusion coefficient. Changes in soil properties resulted in increased CO2, NO and N2O emissions. Finally, our results also confirmed that greenhouse gas fluxes vary tremendously in space and time. As estimates of greenhouse gas emissions are influenced by the data processing approach, differences between the different calculation approaches leads to uncertainty. Thus, techniques for developing better estimates are needed. We have showed that Geographic Information Systems (GIS), Global Positioning System (GPS), computer mapping and geo-statistics are technologies that can be used to better understand systems containing large amounts of spatial and temporal variability. Our GIS-based approach for quantifying CO2, CH4 and N2O fluxes from soil in agricultural fields showed that estimating (extrapolating) total greenhouse gas fluxes using the “standard” approach – multiplying the average flux value by the total field area – results in biased predictions of field total greenhouse gases emissions. In contrast, the GIS-based approach we developed produces an interpolated map portraying the spatial distribution of gas fluxes across the field from point measurements and later process the interpolated map produced to determine flux zones. Furthermore, processing, classification and modeling enables the computation of field total fluxes as the sum of fluxes in different zones, therefore taking into account the spatial variability of greenhouse gas fluxes.
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Galloway, P. W. "Performance quantification of tidal turbines subjected to dynamic loading." Thesis, University of Southampton, 2013. https://eprints.soton.ac.uk/361524/.

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The behaviour of Tidal Stream Turbines (TST) in the dynamic flow field caused by waves and rotor misalignment to the incoming flow (yaw) is currently poorly understood. The dynamic loading applied to the turbine could drive the structural design of the power capture and support subsystems, device size and its proximity to the water surface and sea bed. In addition, the strongly bi directional nature of the flow encountered at many tidal energy sites may lead to devices omitting yaw drives; accepting the additional dynamic loading associated with rotor misalignment and reduced power production in return for a reduction in capital cost. For such a design strategy it is imperative to quantify potential unsteady rotor loads so that the TST device design accommodates the inflow conditions and avoids an unacceptable increase in maintenance action or, more seriously, suffers sudden structural failure. The experiments presented as part of this work were conducted using a 1:20th scale 3-bladed horizontal axis TST at a large towing tank facility. The turbine had the capability to measure rotor thrust and torque, blade root strain, azimuthal position and speed. The maximum outof- plane bending moment was found to be as much as 9.5 times the in-plane bending moment, within the range of experiments conducted. A maximum loading range of 175% of the median out-of-plane bending moment and 100% of the median in-plane bending moment was observed for a turbine test case with zero yaw, scaled wave height of 2m and intrinsic wave period of 12.8s. A Blade Element Momentum (BEM) numerical model has been developed and modified to account for wave motion and yawed flow effects. This model includes a new dynamic inflow correction which is shown to be in close agreement with the measured experimental loads. The gravitational component was significant to the experimental in-plane blade bending moment and was included in the BEM model. Steady yaw loading on an individual blade was found to be negligible in comparison to wave loading (for the range of experiments conducted), but becomes important for the turbine rotor as a whole, reducing power capture and rotor thrust.
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He, Yang. "Quantification of Carbon Nanotubes in the Environmental Matrices by Using a Microwave Induced Heating Method." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin154392139689757.

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de, Luis Jorge. "A Process for the Quantification of Aircraft Noise and Emissions Interdependencies." Diss., Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24618.

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The main purpose of this dissertation is to develop a process to improve actual policy-making procedures in terms of aviation environmental effects. This research work expands current practices with physics based publicly available models. The process herein proposed provides information regarding the interdependencies between the environmental effects of aircraft. These interdependencies are also tied to the actual physical parameters of the aircraft and the engine, making it more intuitive for decision-makers to understand the impacts to the vehicle due to different policy scenarios. These scenarios involve the use of fleet analysis tools in which the existing aircraft are used to predict the environmental effects of imposing new stringency levels. The aircraft used are reduced to a series of coefficients that represent their performance, in terms of flight characteristics, fuel burn, noise, and emissions. These coefficients are then utilized to model flight operations and calculate what the environmental impacts of those aircraft are. If a particular aircraft does not meet the stringency to be analyzed, a technology response is applied to it, in order to meet that stringency. Depending on the level of reduction needed, this technology response can have an effect on the fuel burn characteristic of the aircraft. The proposed alternative is to create a fleet of replacement aircraft to the current fleet that does not meet stringency. These replacement aircraft represent the achievable physical limits for state of the art systems. In addition, the replacement aircraft show the linkage between environmental effects and fundamental aircraft and engine characteristics, something that has been neglected in previous policy making procedures. Another aspect that has been ignored is the creation of the coefficients used for the fleet analyses. In current literature, a defined process for the creation of those coefficients does not exist, but this research work develops a process to do so and demonstrates that the characteristics of the aircraft can be propagated to the coefficients and to the fleet analysis tools.
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Books on the topic "Environmental quantification"

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Feng-bin, Sun, ed. Environmental stress screening: Its quantification, optimization and management. Upper Saddle River, N.J: Prentice Hall, 1995.

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Kececioglu, Dimitri. Burn-in testing: Its quantification and optimization. Upper Saddle River, N.J: Prentice Hall PTR, 1997.

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Corporation, Concord Scientific. Quantification of hydrocarbon emissions from the Canadian gasoline marketing distribution system. Ottawa: Petroleum Association for Conservation of the Canadian Environment, 1986.

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Hall, John Christopher. Detection and quantification of herbicide residues in the environment using immunochemical techniques. [Toronto, Ont.]: Environment Ontario, 1990.

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Hall, John Christopher. Detection and quantification of herbicide residues in the environment using immunochemical techniques. [Toronto, Ont.]: Environment Ontario, 1990.

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Specified gas emitters regulation: Quantification protocol for landfill gas capture and combustion. [Edmonton]: Alberta Environment, 2007.

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Ching-Hung, Hsu, and Stedeford Todd, eds. Cancer risk assessment: Chemical carcinogenesis, hazard evaluation, and risk quantification. Hoboken, N.J: Wiley, 2010.

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Specified gas emitters regulation: Quantification protocol for reducing slaughter age of cattle. [Edmonton]: Alberta Environment, 2007.

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Accardi-Dey, AmyMarie. Black carbon in marine sediments: Quantification and implications for the sorption of polycyclic aromatic hydrocarbons. Cambridge, Mass: Massachusetts Institute of Technology, 2003.

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Specified gas emitters regulation: Quantification protocol for nitrous oxide abatement from nitric acid production. [Edmonton]: Alberta Environment, 2009.

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Book chapters on the topic "Environmental quantification"

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Iannone, A. Pablo. "Risk Assessment Beyond Quantification." In Practical Environmental Ethics, 115–26. Title: Practical environmental ethics / A. Pablo Iannone. Description: New Brunswick : Transaction Publishers, 2016. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315127200-4.

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Gradinaru, Giani. "Environment Benefits Quantification Through Statistical Indicators." In Environmental Indicators, 225–36. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9499-2_13.

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Inghels, Dirk. "Quantification of the Environmental Impact." In Introduction to Modeling Sustainable Development in Business Processes, 89–107. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58422-1_5.

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Kumagai, Hiroyuki. "Volcano Seismic Signals, Source Quantification of." In Extreme Environmental Events, 1179–206. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-7695-6_59.

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Friedrich, Rainer, and Peter Bickel. "Quantification of Total and Average Externalities (Aggregation)." In Environmental External Costs of Transport, 161–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04329-5_12.

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Bemow, Stephen, Bruce Biewald, and Donald Marron. "Environmental Externalities Measurement: Quantification, Valuation and Monetization." In External Environmental Costs of Electric Power, 81–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76712-8_7.

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Dey, Sudip, Tanmoy Mukhopadhyay, and Sondipon Adhikari. "Effect of Environmental Uncertainties on the Free Vibration Analysis of Composite Laminates." In Uncertainty Quantification in Laminated Composites, 145–64. Boca Raton, FL : CRC Press, Taylor & Francis Group, [2018] | “A science publishers book.”: CRC Press, 2018. http://dx.doi.org/10.1201/9781315155593-7.

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Grimm, Daniel, Björn Schödwell, Koray Erek, and Ruediger Zarnekow. "Conceptualizing the Quantification of the Carbon Footprint of IT-Services." In Environmental Science and Engineering, 77–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36011-4_7.

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Isaac-Olivé, Keila, Eunice Olivé-Alvarez, Amado E. Navarro-Frómeta, Ninfa Ramírez-Durán, Enrique Morales-Avila, Liliana Aranda-Lara, Horacio Sandoval-Trujillo, and Pablo Moreno-Pérez. "Quantification of Non-steroidal Anti-inflammatory Drug in Water." In The Handbook of Environmental Chemistry, 83–103. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/698_2020_543.

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Herrmann, L., R. Jahn, and K. Stahr. "Identification and Quantification of Dust Additions in Perisaharan Soils." In Environmental Science and Technology Library, 173–82. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-017-3354-0_16.

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Conference papers on the topic "Environmental quantification"

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Schultz, N. U., D. M. Wood, V. Adderly, and D. Bennett. "RDI/I Quantification Research Results." In World Water and Environmental Resources Congress 2001. Reston, VA: American Society of Civil Engineers, 2001. http://dx.doi.org/10.1061/40569(2001)28.

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Anderson, Brooke, Steve Blattnig, and Martha Clowdsley. "Numerical Uncertainty Quantification for Radiation Analysis Tools." In International Conference On Environmental Systems. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2007. http://dx.doi.org/10.4271/2007-01-3110.

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Preis, Ami, Lina Perelman, and Avi Ostfeld. "Uncertainty Quantification of Contamination Source Identification." In World Environmental and Water Resources Congress 2008. Reston, VA: American Society of Civil Engineers, 2008. http://dx.doi.org/10.1061/40976(316)505.

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Mattley, Yvette D., and Luis H. Garcia-Rubio. "Multiwavelength spectroscopy for the detection, identification, and quantification cells." In Environmental and Industrial Sensing, edited by Yud-Ren Chen and Shu-I. Tu. SPIE, 2001. http://dx.doi.org/10.1117/12.418743.

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Chamberland, M., P. Lagueux, P. Tremblay, S. Savary, M. A. Gagnon, M. Kastek, T. Piątkowski, and R. Dulski. "Standoff gas detection, identification and quantification with a thermal hyperspectral imager." In ENVIRONMENTAL IMPACT 2014. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/eid140571.

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Nowak Da Costa, Joanna, Elzbieta Bielecka, and Beata Calka. "Uncertainty Quantification of the Global Rural-Urban Mapping Project over Polish Census Data." In Environmental Engineering. VGTU Technika, 2017. http://dx.doi.org/10.3846/enviro.2017.221.

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The aim of this study is to describe uncertainty of the Global Rural-Urban Mapping Project (GRUMP) data based on Polish population reference grid created by the Central Statistical Office of Poland, using INSPIRE grid coding system. The adopted population data uncertainty analysis methodology combined three different approaches, i.e. simple change detection algorithm to obtain discrepancies at the grid cell level, statistical analytical approach to investigate these discrepancies’ frequency distribution, and GIS approach to analyse spatial pattern of distinguished population difference classes. The results showed significant differences in population count at the grid cell level. The maximum magnitude of GRUMP vs. Polish Reference Grid overestimation equals 4087 people per 1 sq. km, while the underestimation equals 20,086 people per 1 sq. km. Very few grid cell shows no difference in population count, i.e. 1.5% of total grid cell count. GRUMP data overestimates Polish total population by 0.15%, while it underestimates the average population density by 50%. The highest population underestimations were identified in the centers of the cities, while suburban areas were characterised by the large and regular population overestimations within GRUMP dataset. These GRUMP dataset imperfections can be attributed to country-specific administrative divisions and to the varying effectiveness of the urban centers delimitation mapping using the night sky light intensity, including blooming effects as well as not frequently illuminated small settlements.
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Hill, D. J. "Pervasive Sensing for Real-Time Rainfall Quantification." In World Environmental and Water Resources Congress 2013. Reston, VA: American Society of Civil Engineers, 2013. http://dx.doi.org/10.1061/9780784412947.137.

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Peterson, B. V., B. R. Linnell, K. B. Brooks, and T. P. Griffin. "Electronic Nose for Toxic Vapor Detection, Identification, and Quantification." In International Conference On Environmental Systems. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2005. http://dx.doi.org/10.4271/2005-01-2879.

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Wavering, Thomas A., Jennifer L. Elster, Shufang Luo, Mishell K. Evans, Charles Pennington, Roger Van Tassell, and Mark E. Jones. "Fiber optic affinity ligand sensor for quantification of petroleum and bioremediation." In Environmental and Industrial Sensing, edited by Tuan Vo-Dinh and Stephanus Buettgenbach. SPIE, 2001. http://dx.doi.org/10.1117/12.417446.

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Roma, Gerard, Waldo Nogueira, and Perfecto Herrera. "Recurrence quantification analysis features for environmental sound recognition." In 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE, 2013. http://dx.doi.org/10.1109/waspaa.2013.6701890.

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Reports on the topic "Environmental quantification"

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Lee, A. D., J. M. Callaway, C. S. Glantz, M. C. Baechler, and L. O. Foley. Information and issues related to the quantification of environmental externalities for new powerplants. Office of Scientific and Technical Information (OSTI), October 1990. http://dx.doi.org/10.2172/6455883.

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Ye, Ming. Computational Bayesian Framework for Quantification and Reduction of Predictive Uncertainty in Subsurface Environmental Modeling. Office of Scientific and Technical Information (OSTI), January 2019. http://dx.doi.org/10.2172/1491235.

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Dumas, Melissa, Binita Kc, and Colin I. Cunliff. Extreme Weather and Climate Vulnerabilities of the Electric Grid: A Summary of Environmental Sensitivity Quantification Methods. Office of Scientific and Technical Information (OSTI), August 2019. http://dx.doi.org/10.2172/1558514.

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Schipani, Salvatore P., Richard S. Bruno, Michael A. Lattin, Bobby M. King, and Debra J. Patton. Quantification of Cognitive Process Degradation While Mobile, Attributable to the Environmental Stressors Endurance, Vibration, and Noise. Fort Belvoir, VA: Defense Technical Information Center, April 1998. http://dx.doi.org/10.21236/ada346416.

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Tomasko, Maria S. Evaluating open-path FTIR spectrometer data using different quantification methods, libraries, and background spectra obtained under varying environmental conditions. Office of Scientific and Technical Information (OSTI), January 1995. http://dx.doi.org/10.2172/587725.

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Corriveau, Elizabeth, Ashley Mossell, Holly VerMeulen, Samuel Beal, and Jay Clausen. The effectiveness of laser-induced breakdown spectroscopy (LIBS) as a quantitative tool for environmental characterization. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40263.

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Laser-induced breakdown spectroscopy (LIBS) is a rapid, low-cost analytical method with potential applications for quantitative analysis of soils for heavy metal contaminants found in military ranges. The Department of Defense (DoD), Army, and Department of Homeland Security (DHS) have mission requirements to acquire the ability to detect and identify chemicals of concern in the field. The quantitative potential of a commercial off-the-shelf (COTS) hand-held LIBS device and a classic laboratory bench-top LIBS system was examined by measuring heavy metals (antimony, tungsten, iron, lead, and zinc) in soils from six military ranges. To ensure the accuracy of the quantified results, we also examined the soil samples using other hand-held and bench-top analytical methods, to include Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and X-Ray Fluorescence (XRF). The effects of soil heterogeneity on quantitative analysis were reviewed with hand-held and bench-top systems and compared multivariate and univariate calibration algorithms for heavy metal quantification. In addition, the influence of cold temperatures on signal intensity and resulting concentration were examined to further assess the viability of this technology in cold environments. Overall, the results indicate that additional work should be performed to enhance the ability of LIBS as a reliable quantitative analytical tool.
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Furman, Alex, Jan Hopmans, Shmuel Assouline, Jirka Simunek, and Jim Richards. Soil Environmental Effects on Root Growth and Uptake Dynamics for Irrigated Systems. United States Department of Agriculture, February 2011. http://dx.doi.org/10.32747/2011.7592118.bard.

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Root water uptake is perhaps the most important unknown in the mass balance of hydrological and agricultural systems. The understanding and the ability to predict root uptake and the way it is influence by environmental conditions has great potential in increasing water and fertilizer use efficiency and allowing better control of water and contaminant leach towards groundwater. This BARD supported research is composed of several components, including a) intensive laboratory work for the quantification of root uptake and the way it is controlled by environmental conditions; b) development of tools for laboratory and field use that can help in sensing very low water fluxes and water content, which is a necessity for studying root uptake; c) development of capabilities to model compensated root uptake; and d) development of a database that will allow calibration of such a model. In addition some auxiliary research was performed as reported later. Some of the components, and especially the modeling and the HPP development, were completed in the framework of the project and even published in the international literature. The completed components provide a modeling environment that allows testing root compensated uptake modeling, a tool that is extremely important for true mechanistic understanding of root uptake and irrigation design that is based on mechanistic and not partially based myth. The new button HPP provides extended level of utilization of this important tool. As discussed below, other components did not get to maturity stage during the period of the project, but comprehensive datasets were collected and will be analyzed in the near future. A comprehensive dataset of high temporal and spatial resolution water contents for two different setups was recorded and should allow us understanding f the uptake at these fine resolutions. Additional important information about root growth dynamics and its dependence in environmental conditions was achieved in both Israel and the US. Overall, this BARD supported project provided insight on many important phenomena related to root uptake and to high resolution monitoring in the vadose zone. Although perhaps not to the level that we initially hoped for, we achieved better understanding of the related processes, better modeling capabilities, and better datasets that will allow continuation of this effort in the near future.
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McKay, S., Nate Richards, and Todd Swannack. Ecological model development : evaluation of system quality. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45380.

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Ecological models are used throughout the US Army Corps of Engineers (USACE) to inform decisions related to ecosystem restoration, water operations, environmental impact assessment, environmental mitigation, and other topics. Ecological models are typically developed in phases of conceptualization, quantification, evaluation, application, and communication. Evaluation is a process for assessing the technical quality, reliability, and ecological basis of a model and includes techniques such as calibration, verification, validation, and review. In this technical note (TN), we describe an approach for evaluating system quality, which generally includes the computational integrity, numerical accuracy, and programming of a model or modeling system. Methods are presented for avoiding computational errors during development, detecting errors through model testing, and updating models based on review and use. A formal structure is proposed for model test plans and subsequently demonstrated for a hypothetical habitat suitability model. Overall, this TN provides ecological modeling practitioners with a rapid guide for evaluating system quality.
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Haeckel, Matthias, and Peter Linke. RV SONNE Fahrtbericht/Cruise Report SO268 - Assessing the Impacts of Nodule Mining on the Deep-sea Environment: NoduleMonitoring, Manzanillo (Mexico) – Vancouver (Canada), 17.02. – 27.05.2019. GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, November 2021. http://dx.doi.org/10.3289/geomar_rep_ns_59_20.

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Cruise SO268 is fully integrated into the second phase of the European collaborative JPI-Oceans project MiningImpact and is designed to assess the environmental impacts of deep-sea mining of polymetallic nodules in the Clarion-Clipperton Fracture Zone (CCZ). In particular, the cruise aimed at conducting an independent scientific monitoring of the first industrial test of a pre-protoype nodule collector by the Belgian company DEME-GSR. The work includes collecting the required baseline data in the designated trial and reference sites in the Belgian and German contract areas, a quantification of the spatial and temporal spread of the produced sediment plume during the trials as well as a first assessment of the generated environmental impacts. However, during SO268 Leg 1 DEME-GSR informed us that the collector trials would not take place as scheduled due to unresolvable technical problems. Thus, we adjusted our work plan accordingly by implementing our backup plan. This involved conducting a small-scale sediment plume experiment with a small chain dredge to quantify the spatial and temporal dispersal of the suspended sediment particles, their concentration in the plume as well as the spatial footprint and thickness of the deposited sediment blanket on the seabed.
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Rycroft, Taylor, Kerry Hamilton, Charles Haas, and Igor Linkov. A quantitative risk assessment method for synthetic biology products in the environment. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41331.

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The need to prevent possible adverse environmental health impacts resulting from synthetic biology (SynBio) products is widely acknowledged in both the SynBio risk literature and the global regulatory community. However, discussions of potential risks of SynBio products have been largely speculative, and the attempts to characterize the risks of SynBio products have been non-uniform and entirely qualitative. As the discipline continues to accelerate, a standardized risk assessment framework will become critical for ensuring that the environmental risks of these products are characterized in a consistent, reliable, and objective manner that incorporates all SynBio-unique risk factors. Current established risk assessment frameworks fall short of the features required of this standard framework. To address this, we propose the Quantitative Risk Assessment Method for Synthetic Biology Products (QRASynBio) – an incremental build on established risk assessment methodologies that supplements traditional paradigms with the SynBio risk factors that are currently absent and necessitates quantitative analysis for more transparent and objective risk characterizations. The proposed framework facilitates defensible quantification of the environmental risks of SynBio products in both foreseeable and hypothetical use scenarios. Additionally, we show how the proposed method can promote increased experimental investigation into the likelihood of hazard and exposure parameters and highlight the parameters where uncertainty should be reduced, leading to more targeted risk research and more precise characterizations of risk.
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