Guideline on Speciated Particulate Monitoring
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Research Plan 2 3 A
Arctic Pre-proposal 3.4-Galloway 1 Research Plan 2 3 A. Project Title: Characterizing lipid production hotspots, phenology, and trophic transfer at the 4 algae-herbivore interface in the Chukchi Sea 5 6 B. Category: 3. Oceanography and lower trophic level productivity: Influence of sea ice dynamics and 7 advection on the phenology, magnitude and location of primary and secondary production, match- 8 mismatch, benthic-pelagic coupling, and the influence of winter conditions. 9 10 C. Rationale and justification: 11 The spatial extent of arctic sea ice is declining and earlier seasonal sea ice melt may dramatically 12 affect the magnitude, and spatial and temporal scale of primary production (Kahru et al. 2011, Wassmann 13 2011). In order to predict the consequences of these changes to ecosystems, it is important that we 14 understand the mechanistic links between temporally dynamic ice conditions and the physical factors 15 which govern phytoplankton growth (Popova et al. 2010). The mechanisms that govern productivity of 16 the Chukchi Sea ecosystem are of considerable interest due to dramatically changing temporal and spatial 17 patterns of sea ice coverage and because this area is likely to be the subject of intense fossil fuel 18 exploration in coming decades (Dunton et al. 2014). Tracing the biochemical pathways of basal resources 19 (pelagic and attached micro- and macroalgae) in this system is critical if we are to better understand how 20 the Chukchi Sea ecosystem might be modified in the future by a changing climate and offshore oil and 21 gas exploration and production (McTigue and Dunton 2014). -
UC Riverside UC Riverside Electronic Theses and Dissertations
UC Riverside UC Riverside Electronic Theses and Dissertations Title Designing New Structures of Magnetic Materials: Cases of Metal Borides and Metal Chalcogenides Permalink https://escholarship.org/uc/item/9ns4w70p Author Scheifers, Jan Phillip Publication Date 2020 License https://creativecommons.org/licenses/by-nc-sa/4.0/ 4.0 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA RIVERSIDE Designing New Structures of Magnetic Materials: Cases of Metal Borides and Metal Chalcogenides A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Chemistry by Jan Phillip Scheifers March 2020 Dissertation Committee: Dr. Boniface B. T. Fokwa, Chairperson Dr. Ming Lee Tang Dr.Yadong Yin Copyright by Jan Phillip Scheifers 2020 The Dissertation of Jan Phillip Scheifers is approved: Committee Chairperson University of California, Riverside Für Papa iv Acknowledgments Some of the research presented in here has previously been published as: Jan P. Scheifers, Boniface P. T. Fokwa: Unprecedented Selective Substitution of Si by Cu in the New Ternary Silicide Ir4-xCuSi2. Manuscript submitted to Z. Kristallogr. Cryst. Mater. Ryland Forsythe, Jan P. Scheifers, Yuemei Zhang, Boniface Tsinde Polequin Fokwa: HT‐ NbOsB: Experimental and Theoretical Investigations of a New Boride Structure Type Containing Boron Chains and Isolated Boron Atoms. European Journal of Inorganic Chemistry 02/2018; 2018(28)., DOI:10.1002/ejic.201800235 Jan P. Scheifers, Michael Küpers, Rashid Touzani, Fabian C. Gladisch, Rainer Poettgen, Boniface P. T. Fokwa: 1D iron chains in the complex metal-rich boride Ti5-xFe1- yOs6+x+yB6 (x = 0.66(1), y = 0.27(1)) representing an unprecedented structure type based on unit cell twinning. -
Kalman and Particle Filtering
Abstract: The Kalman and Particle filters are algorithms that recursively update an estimate of the state and find the innovations driving a stochastic process given a sequence of observations. The Kalman filter accomplishes this goal by linear projections, while the Particle filter does so by a sequential Monte Carlo method. With the state estimates, we can forecast and smooth the stochastic process. With the innovations, we can estimate the parameters of the model. The article discusses how to set a dynamic model in a state-space form, derives the Kalman and Particle filters, and explains how to use them for estimation. Kalman and Particle Filtering The Kalman and Particle filters are algorithms that recursively update an estimate of the state and find the innovations driving a stochastic process given a sequence of observations. The Kalman filter accomplishes this goal by linear projections, while the Particle filter does so by a sequential Monte Carlo method. Since both filters start with a state-space representation of the stochastic processes of interest, section 1 presents the state-space form of a dynamic model. Then, section 2 intro- duces the Kalman filter and section 3 develops the Particle filter. For extended expositions of this material, see Doucet, de Freitas, and Gordon (2001), Durbin and Koopman (2001), and Ljungqvist and Sargent (2004). 1. The state-space representation of a dynamic model A large class of dynamic models can be represented by a state-space form: Xt+1 = ϕ (Xt,Wt+1; γ) (1) Yt = g (Xt,Vt; γ) . (2) This representation handles a stochastic process by finding three objects: a vector that l describes the position of the system (a state, Xt X R ) and two functions, one mapping ∈ ⊂ 1 the state today into the state tomorrow (the transition equation, (1)) and one mapping the state into observables, Yt (the measurement equation, (2)). -
Aethalometer Is Located, Data from the Aethalometer Will Also Be Compared to the Sunset Semi-Continuous Analyzer
Sunset Carbon Evaluation Project QA Project Plan, Version 1, September 27, 2011 Sunset Carbon Evaluation Project Quality Assurance Project Plan (QAPP) QA Level IV U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Air Quality Assessment Division Ambient Air Monitoring Group September 27, 2011 Page 1 of 41 Sunset Carbon Evaluation Project QA Project Plan, Version 1, September 27, 2011 Page 2 of 41 Sunset Carbon Evaluation Project QA Project Plan, Version 1, September 27, 2011 DISTRIBUTION LIST The following individuals have been provided with copies of this QAPP. Lewis Weinstock EPA Group Lead U.S. Environmental Protection Agency, Office of Air quality Planning and Standards, Research Triangle Park, NC Elizabeth Landis EPA Project Lead U.S. Environmental Protection Agency, Office of Air quality Planning and Standards, Research Triangle Park, NC Mike Papp QA EPA QA Officer U.S. Environmental Protection Agency, Office of Air quality Planning and Standards, Research Triangle Park, NC Joann Rice EPA Technical Advisor U.S. Environmental Protection Agency, Office of Air quality Planning and Standards, Research Triangle Park, NC Page 4 of 41 Sunset Carbon Evaluation Project QA Project Plan, Version 1, September 27, 2011 1.0 Introduction This is a level IV Quality Assurance Project Plan (QAPP) that covers an Environmental Data Operation (EDO) conducted by the EPA’s Office of Air Quality Planning and Standards (OAQPS) Ambient Air Monitoring Group (AAMG) and State and local monitoring agencies to collect field data at seven (7) sites across the United States. The selection of participating sites is in progress, and to date, monitoring agencies at 3 sites have agreed to participate. -
Single Particle Analysis of Particulate Pollutants in Yellowstone National Park During the Winter Snowmobile Season
SINGLE PARTICLE ANALYSIS OF PARTICULATE POLLUTANTS IN YELLOWSTONE NATIONAL PARK DURING THE WINTER SNOWMOBILE SEASON. Richard E. Peterson* and Bonnie J. Tyler** * Dept. of Chemistry and Biochemistry **Dept. of Chemical Engineering Montana State University, Bozeman, MT 59717, USA [email protected] Introduction: Particulate, or aerosol, pollution is a complex mixture of organic and inorganic compounds which includes a wide range of sizes and whose composition can vary widely depending on the time of year, geographical location, and both local and long range sources. Aerosol are important because of participation in atmospheric electricity, absorption and scattering of radiation (i.e. sunlight and thus affecting climate and visibility), their role as condensation nuclei for water vapor (and thus affecting precipitation chemistry and pH), and health effects. Particles greater than 2 micrometers in diameter (coarse) are generally formed by mechanical processes while smaller particles (fine) are formed by gas to particle conversion and accumulation/coagulation of these smallest aerosol. Because particles smaller than 2.5 micrometers (USEPA PM2.5) can become trapped deep in the lungs, it is of particular interest to identify toxic substances, such as heavy metals and polyaromatic hydrocarbons, that may be present in particles of this size range. Epidemiological studies have typically used particle size as the metric for identifying adverse health effects of particulate matter (PM), largely because data on PM size is available. Data on particle composition or other characteristics are less well known, if known at all in many cases. We are evaluating the potential for using Time of Flight Secondary Ion Mass Spectroscopy (TOF-SIMS) to study the composition of single particles from atmospheric aerosol. -
Chapter 8 and 9 – Energy Balances
CBE2124, Levicky Chapter 8 and 9 – Energy Balances Reference States . Recall that enthalpy and internal energy are always defined relative to a reference state (Chapter 7). When solving energy balance problems, it is therefore necessary to define a reference state for each chemical species in the energy balance (the reference state may be predefined if a tabulated set of data is used such as the steam tables). Example . Suppose water vapor at 300 oC and 5 bar is chosen as a reference state at which Hˆ is defined to be zero. Relative to this state, what is the specific enthalpy of liquid water at 75 oC and 1 bar? What is the specific internal energy of liquid water at 75 oC and 1 bar? (Use Table B. 7). Calculating changes in enthalpy and internal energy. Hˆ and Uˆ are state functions , meaning that their values only depend on the state of the system, and not on the path taken to arrive at that state. IMPORTANT : Given a state A (as characterized by a set of variables such as pressure, temperature, composition) and a state B, the change in enthalpy of the system as it passes from A to B can be calculated along any path that leads from A to B, whether or not the path is the one actually followed. Example . 18 g of liquid water freezes to 18 g of ice while the temperature is held constant at 0 oC and the pressure is held constant at 1 atm. The enthalpy change for the process is measured to be ∆ Hˆ = - 6.01 kJ. -
Air Quality Comic Book
Hi, I'm Dr. Knox Our Narrator, Dr. Knox, is on his way to Today we hope to view a pollution visit a site where air pollution is likely to source and visit with environmental occur and to visit with environmental specialists from the Oklahoma personnel in action. Department of Environmental Here we are in Quality. Oklahoma trying to understand air pollution. Its a complex problem with many factors. Come join us as we learn about air Dr. Knox stops at a pollution and how to possible pollution source. control it. A Pollution Critter Questions are often asked about air pollution. Sources of air pollution come in many forms. We see many sources Just then a truck starts. in our daily lives. Some are colorless or odorless. Cough . others are more apparent, cough . If you breathe these fumes inside a building, like your garage, they could be very harmful. Dr. Knox goes to a monitoring site and waits for the Join us as specialists to arrive. we explore how pollution sources are monitored and visit At this site and with some of the others air is people involved in collected and tested the monitoring for pollutants. Lets process. find out more. Looks like no one is home. Ust then a van pulls up and two environmental specialists step out. Hi, Im Monica Hi, Im Excuse me! Aaron I want to Hi, Im Dr. Knox. ask you about how air pollution is monitored. The specialists invite us in to Youve come to show us some pollution charts. the right place. We You see, we would be glad monitor several to discuss it. -
Applying Particle Filtering in Both Aggregated and Age-Structured Population Compartmental Models of Pre-Vaccination Measles
bioRxiv preprint doi: https://doi.org/10.1101/340661; this version posted June 6, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Applying particle filtering in both aggregated and age-structured population compartmental models of pre-vaccination measles Xiaoyan Li1*, Alexander Doroshenko2, Nathaniel D. Osgood1 1 Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada 2 Department of Medicine, Division of Preventive Medicine, University of Alberta, Edmonton, Alberta, Canada * [email protected] Abstract Measles is a highly transmissible disease and is one of the leading causes of death among young children under 5 globally. While the use of ongoing surveillance data and { recently { dynamic models offer insight on measles dynamics, both suffer notable shortcomings when applied to measles outbreak prediction. In this paper, we apply the Sequential Monte Carlo approach of particle filtering, incorporating reported measles incidence for Saskatchewan during the pre-vaccination era, using an adaptation of a previously contributed measles compartmental model. To secure further insight, we also perform particle filtering on an age structured adaptation of the model in which the population is divided into two interacting age groups { children and adults. The results indicate that, when used with a suitable dynamic model, particle filtering can offer high predictive capacity for measles dynamics and outbreak occurrence in a low vaccination context. We have investigated five particle filtering models in this project. Based on the most competitive model as evaluated by predictive accuracy, we have performed prediction and outbreak classification analysis. -
Are Black Carbon and Soot the Same? Title Page
Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Atmos. Chem. Phys. Discuss., 12, 24821–24846, 2012 Atmospheric www.atmos-chem-phys-discuss.net/12/24821/2012/ Chemistry ACPD doi:10.5194/acpd-12-24821-2012 and Physics © Author(s) 2012. CC Attribution 3.0 License. Discussions 12, 24821–24846, 2012 This discussion paper is/has been under review for the journal Atmospheric Chemistry Are black carbon and Physics (ACP). Please refer to the corresponding final paper in ACP if available. and soot the same? P. R. Buseck et al. Are black carbon and soot the same? Title Page P. R. Buseck1,2, K. Adachi1,2,3, A. Gelencser´ 4, E.´ Tompa4, and M. Posfai´ 4 Abstract Introduction 1School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85282, USA Conclusions References 2 Department of Chemistry and Biochemistry, Arizona State University, Tempe, AZ 85282, USA Tables Figures 3Atmospheric Environment and Applied Meteorology Research Department, Meteorological Research Institute, Tsukuba, Ibaraki, Japan 4Department of Earth and Environmental Sciences, University of Pannonia, Veszprem,´ J I Hungary J I Received: 1 September 2012 – Accepted: 3 September 2012 – Published: 21 September 2012 Back Close Correspondence to: P. R. Buseck ([email protected]) Full Screen / Esc Published by Copernicus Publications on behalf of the European Geosciences Union. Printer-friendly Version Interactive Discussion 24821 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Abstract ACPD The climate change and environmental literature, including that on aerosols, is replete with mention of black carbon (BC), but neither reliable samples nor standards exist. 12, 24821–24846, 2012 Thus, there is uncertainty about its exact nature. -
OGI School of Science & Engineering at OHSU
OGI School of Science & Engineering OGI School of Science & Engineering 01/02 Catalog 01/02 Catalog 01/02 20000 N.W. Walker Road • Beaverton, OR • 97006-8921 phone (503) 748-1027 toll free (800) 685-2423 fax (503) 748-1285 e-mail [email protected] URL www.ogi.edu www.ogi.edu/catalog OGI School of Science & Engineering OGI School of Science & Engineering 01/02 Catalog 01/02 Catalog 01/02 20000 N.W. Walker Road • Beaverton, OR • 97006-8921 phone (503) 748-1027 toll free (800) 685-2423 fax (503) 748-1285 e-mail [email protected] URL www.ogi.edu www.ogi.edu/catalog OGI School of Science & Engineering 01/02 Catalog www.ogi.edu/catalog TABLE OF CONTENTS | OGI WELCOME TO THE OGI SCHOOL OF SCIENCE & ENGINEERING HISTORICAL BACKGROUND AND RECENT MERGER ..............................................................................................................4 ACADEMIC CALENDAR ................................................................................................................................................4 LETTER FROM THE DEAN .............................................................................................................................................5 SCHOOL MISSION......................................................................................................................................................5 EQUAL OPPORTUNITY .................................................................................................................................................5 ABOUT THIS CATALOG ................................................................................................................................................5 -
Linking Mesopelagic Prey Abundance and Distribution to the Foraging
Deep–Sea Research Part II 140 (2017) 163–170 Contents lists available at ScienceDirect Deep–Sea Research II journal homepage: www.elsevier.com/locate/dsr2 Linking mesopelagic prey abundance and distribution to the foraging MARK behavior of a deep-diving predator, the northern elephant seal ⁎ Daisuke Saijoa,1, Yoko Mitanib,1, , Takuzo Abec,2, Hiroko Sasakid, Chandra Goetsche, Daniel P. Costae, Kazushi Miyashitab a Graduate School of Environmental Science, Hokkaido University, 20-5 Bentencho, Hakodate, Hokkaido 040-0051, Japan b Field Science Center for Northern Biosphere, Hokkaido University, 20-5 Bentencho, Hakodate, Hokkaido 040-0051, Japan c School of Fisheries Science, Hokkaido University, 3-1-1 Minato cho, Hakodate, Hokkaido 041-8611, Japan d Arctic Environment Research Center, National Institute of Polar Research, 10-3, Midori-cho, Tachikawa, Tokyo 190-8518, Japan e Department of Ecology & Evolutionary Biology, University of California, Santa Cruz, CA 95060, United States ARTICLE INFO ABSTRACT Keywords: The Transition Zone in the eastern North Pacific is important foraging habitat for many marine predators. Deep-scattering layer Further, the mesopelagic depths (200–1000 m) host an abundant prey resource known as the deep scattering Transition Zone layer that supports deep diving predators, such as northern elephant seals, beaked whales, and sperm whales. fi mesopelagic sh Female northern elephant seals (Mirounga angustirostris) undertake biannual foraging migrations to this myctophid region where they feed on mesopelagic fish and squid; however, in situ measurements of prey distribution and subsurface chlorophyll abundance, as well as the subsurface oceanographic features in the mesopelagic Transition Zone are limited. While concurrently tracking female elephant seals during their post-molt migration, we conducted a ship-based oceanographic and hydroacoustic survey and used mesopelagic mid-water trawls to sample the deep scattering layer. -
NON-HAZARDOUS CHEMICALS May Be Disposed of Via Sanitary Sewer Or Solid Waste
NON-HAZARDOUS CHEMICALS May Be Disposed Of Via Sanitary Sewer or Solid Waste (+)-A-TOCOPHEROL ACID SUCCINATE (+,-)-VERAPAMIL, HYDROCHLORIDE 1-AMINOANTHRAQUINONE 1-AMINO-1-CYCLOHEXANECARBOXYLIC ACID 1-BROMOOCTADECANE 1-CARBOXYNAPHTHALENE 1-DECENE 1-HYDROXYANTHRAQUINONE 1-METHYL-4-PHENYL-1,2,5,6-TETRAHYDROPYRIDINE HYDROCHLORIDE 1-NONENE 1-TETRADECENE 1-THIO-B-D-GLUCOSE 1-TRIDECENE 1-UNDECENE 2-ACETAMIDO-1-AZIDO-1,2-DIDEOXY-B-D-GLYCOPYRANOSE 2-ACETAMIDOACRYLIC ACID 2-AMINO-4-CHLOROBENZOTHIAZOLE 2-AMINO-2-(HYDROXY METHYL)-1,3-PROPONEDIOL 2-AMINOBENZOTHIAZOLE 2-AMINOIMIDAZOLE 2-AMINO-5-METHYLBENZENESULFONIC ACID 2-AMINOPURINE 2-ANILINOETHANOL 2-BUTENE-1,4-DIOL 2-CHLOROBENZYLALCOHOL 2-DEOXYCYTIDINE 5-MONOPHOSPHATE 2-DEOXY-D-GLUCOSE 2-DEOXY-D-RIBOSE 2'-DEOXYURIDINE 2'-DEOXYURIDINE 5'-MONOPHOSPHATE 2-HYDROETHYL ACETATE 2-HYDROXY-4-(METHYLTHIO)BUTYRIC ACID 2-METHYLFLUORENE 2-METHYL-2-THIOPSEUDOUREA SULFATE 2-MORPHOLINOETHANESULFONIC ACID 2-NAPHTHOIC ACID 2-OXYGLUTARIC ACID 2-PHENYLPROPIONIC ACID 2-PYRIDINEALDOXIME METHIODIDE 2-STEP CHEMISTRY STEP 1 PART D 2-STEP CHEMISTRY STEP 2 PART A 2-THIOLHISTIDINE 2-THIOPHENECARBOXYLIC ACID 2-THIOPHENECARBOXYLIC HYDRAZIDE 3-ACETYLINDOLE 3-AMINO-1,2,4-TRIAZINE 3-AMINO-L-TYROSINE DIHYDROCHLORIDE MONOHYDRATE 3-CARBETHOXY-2-PIPERIDONE 3-CHLOROCYCLOBUTANONE SOLUTION 3-CHLORO-2-NITROBENZOIC ACID 3-(DIETHYLAMINO)-7-[[P-(DIMETHYLAMINO)PHENYL]AZO]-5-PHENAZINIUM CHLORIDE 3-HYDROXYTROSINE 1 9/26/2005 NON-HAZARDOUS CHEMICALS May Be Disposed Of Via Sanitary Sewer or Solid Waste 3-HYDROXYTYRAMINE HYDROCHLORIDE 3-METHYL-1-PHENYL-2-PYRAZOLIN-5-ONE