Watershed ‘chemical cocktails’: forming novel elemental combinations in Anthropocene fresh waters

Este artículo contiene 25 páginas, 9 figuras.In the Anthropocene, watershed chemical transport is increasingly dominated by novel combinations of elements, which are hydrologically linked together as ‘chemical cocktails.’ Chemical cocktails are novel because human activities greatly enhance elemental concentrations and their probability for biogeochemical interactions and shared transport along hydrologic flowpaths. A new chemical cocktail approach advances our ability to: trace contaminant mixtures in watersheds, develop chemical proxies with high-resolution sensor data, and manage multiple water quality problems. We explore the following questions: (1) Can we classify elemental transport in watersheds as chemical cocktails using a new approach? (2) What is the role of climate and land use in enhancing the formation and transport of chemical cocktails in watersheds? To address these questions, we first analyze trends in concentrations of carbon, nutrients, metals, and salts in fresh waters over 100 years. Next, we explore how climate and land use enhance the probability of formation of chemical cocktails of carbon, nutrients, metals, and salts. Ultimately, we classify transport of chemical cocktails based on solubility, mobility, reactivity, and dominant phases: (1) sieved chemical cocktails (e.g., particulate forms of nutrients, metals and organic matter); (2) filtered chemical cocktails (e.g., dissolved organic matter and associated metal complexes); (3) chromatographic chemical cocktails (e.g., ions eluted from soil exchange sites); and (4) reactive chemical cocktails (e.g., limiting nutrients and redox sensitive elements). Typically, contaminants are regulated and managed one element at a time, even though combinations of elements interact to influence many water quality problems such as toxicity to life, eutrophication, infrastructure corrosion, and water treatment. A chemical cocktail approach significantly expands evaluations of water quality signatures and impacts beyond single elements to mixtures. High-frequency sensor data (pH, specific conductance, turbidity, etc.) can serve as proxies for chemical cocktails and improve real-time analyses of water quality violations, identify regulatory needs, and track water quality recovery following storms and extreme climate events. Ultimately, a watershed chemical cocktail approach is necessary for effectively co-managing groups of contaminants and provides a more holistic approach for studying, monitoring, and managing water quality in the Anthropocene.This work was funded by USDA (award # 2016-67019-25280) and NSF-EPSCoR (#1641157) for supporting collaborations at the AGU Chapman Conference on Extreme Climate Events. Significant funding for data collection/analyses in this paper was provided by NSF EAR1521224, NSF CBET1058502, NSF Coastal SEES1426844, NSF DEB-0423476 and DEB-1027188, NSF RI EPSCoR NEWRnet Grant No. IIA-1330406, EPA ORD, Chesapeake Bay Trust, and Multi-state Regional Hatch Project S-1063.Peer reviewe

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This document describes Offline Software and Computing for the Deep Underground Neutrino Experiment (DUNE) experiment, in particular, the conceptual design of the offline computing needed to accomplish its physics goals. Our emphasis in this document is the development of the computing infrastructure needed to acquire, catalog, reconstruct, simulate and analyze the data from the DUNE experiment and its prototypes. In this effort, we concentrate on developing the tools and systems thatfacilitate the development and deployment of advanced algorithms. Rather than prescribing particular algorithms, our goal is to provide resources that are flexible and accessible enough to support creative software solutions as HEP computing evolves and to provide computing that achieves the physics goals of the DUNE experiment

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DUNE Offline Computing Conceptual Design Report

International audienceThis document describes Offline Software and Computing for the Deep Underground Neutrino Experiment (DUNE) experiment, in particular, the conceptual design of the offline computing needed to accomplish its physics goals. Our emphasis in this document is the development of the computing infrastructure needed to acquire, catalog, reconstruct, simulate and analyze the data from the DUNE experiment and its prototypes. In this effort, we concentrate on developing the tools and systems thatfacilitate the development and deployment of advanced algorithms. Rather than prescribing particular algorithms, our goal is to provide resources that are flexible and accessible enough to support creative software solutions as HEP computing evolves and to provide computing that achieves the physics goals of the DUNE experiment

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

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This document describes Offline Software and Computing for the Deep Underground Neutrino Experiment (DUNE) experiment, in particular, the conceptual design of the offline computing needed to accomplish its physics goals. Our emphasis in this document is the development of the computing infrastructure needed to acquire, catalog, reconstruct, simulate and analyze the data from the DUNE experiment and its prototypes. In this effort, we concentrate on developing the tools and systems thatfacilitate the development and deployment of advanced algorithms. Rather than prescribing particular algorithms, our goal is to provide resources that are flexible and accessible enough to support creative software solutions as HEP computing evolves and to provide computing that achieves the physics goals of the DUNE experiment

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

DUNE Offline Computing Conceptual Design Report

This document describes Offline Software and Computing for the Deep Underground Neutrino Experiment (DUNE) experiment, in particular, the conceptual design of the offline computing needed to accomplish its physics goals. Our emphasis in this document is the development of the computing infrastructure needed to acquire, catalog, reconstruct, simulate and analyze the data from the DUNE experiment and its prototypes. In this effort, we concentrate on developing the tools and systems thatfacilitate the development and deployment of advanced algorithms. Rather than prescribing particular algorithms, our goal is to provide resources that are flexible and accessible enough to support creative software solutions as HEP computing evolves and to provide computing that achieves the physics goals of the DUNE experiment