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At least 163 records · Page 9

All the light we cannot see: Climate manipulations leave short and long‐term imprints in spectral reflectance of trees

Abstract Anthropogenic climate change, particularly changes in temperature and precipitation, affects plants in multiple ways. Because plants respond dynamically to stress and acclimate to changes in growing conditions, diagnosing quantitative plant‐environment relationships is a major challenge. One approach to this problem is to quantify leaf responses using spectral reflectance, which provides rapid, inexpensive, and nondestructive measurements that capture a wealth of information about genotype as well as phenotypic responses to the environment. However, it is unclear how warming and drought affect spectra. To address this gap, we used an open‐air field experiment that manipulates temperature and rainfall in 36 plots at two sites in the boreal‐temperate ecotone of northern Minnesota, USA. We collected leaf spectral reflectance (400–2400 nm) at the peak of the growing season for three consecutive years on juveniles (two to six years old) of five tree species planted within the experiment. We hypothesized that these mid‐season measurements of spectral reflectance capture a snapshot of the leaf phenotype encompassing a suite of physiological, structural, and biochemical responses to both long‐ and short‐time scale environmental conditions. We show that the imprint of environmental conditions experienced by plants hours to weeks before spectral measurements is linked to regions in the spectrum associated with stress, namely the water absorption regions of the near‐infrared and short‐wave infrared. In contrast, the environmental conditions plants experience during leaf development leave lasting imprints on the spectral profiles of leaves, attributable to leaf structure and chemistry (e.g., pigment content and associated ratios). Our analyses show that after accounting for baseline species spectral differences, spectral responses to the environment do not differ among the species. This suggests that building a general framework for understanding forest responses to climate change through spectral metrics may be possible, likely having broader implications if the common responses among species detected here represent a widespread phenomenon. Consequently, these results demonstrate that examining the entire spectrum of leaf reflectance for environmental imprints in contrast to single features (e.g., indices and traits) improves inferences about plant‐environment relationships, which is particularly important in times of unprecedented climate change.

Stefanski, Artur [Department of Forest Resources U↗

Modeling the Effect of the Heliospheric Magnetic Field on Cosmic Ray Muon Shadows

Shadows cast in the cosmic ray (CR) muon sky by the Sun were located using muon data from the MINOS far detector in Northern Minnesota. The shadows were observed independently across three time periods; near solar minimum, near solar maximum, and over the entire 13 year span of the data. A distribution of muon positions for each shadow was then sampled to simulate CR motions near the Sun using the Parker spiral model of the Heliospheric Magnetic Field (HMF) and a dipole model of the Geomagnetic Field (GMF). The resulting particle distributions were then compared to their position with respect to the Sun. Results show that the Parker spiral model is most consistent with the solar minimum shadow and least consistent with the solar maximum shadow, as expected. The simple Parker spiral is more consistent with the data for a harder CR spectrum than is actually present, indicating the need for a more detailed HMF model. Plausible modifications to the Parker spiral model which would affect the overall shift of the Sun's CR shadow are discussed.

Adamson, P. [Fermilab]↗

A Novel Sediment Passage Module Design in Support of Standard Modular Hydropower

This document is the Final Technical Report (FTR) for University of Minnesota (UMN) project on the development of a novel sediment (e.g., sand) bypass technology for new stream reach development (NSD). The project duration was slightly over four years and organized over two budget periods (BPs), i.e., BP1 and BP2, with a Go/No-Go review at the end of BP1. The initial technology had a Technology Readiness Level (TRL) of two (2) and, over the life of the project, advanced substantially to TRL 3 or 4.

13 HYDRO ENERGY↗

The stable isotopes of natural waters at the Marcell Experimental Forest

Here, we present a new data set from the Marcell Experimental Forest (MEF) that compiles water isotope measurements from multiple research catchments, some of which have been studied since the 1960s. The MEF is located in northern Minnesota, USA, and is home to heavily studied and monitored forests, streams, bogs, and fens. Peat-forming systems (bogs and fens) are an important component of the MEF landscape and have a profound impact on the water cycle in these catchments. Within the last decade, analysis of stable isotopes of water (expressed as δD and δ 18 O) has been implemented to characterize the different components of the water budget, and to allow researchers to look at catchment and peatland-specific hydrologic effects in the watershed. This δD and δ 18 O data set of natural waters from MEF catchments is primarily composed of measurements from three peatlands (S1, S2, S6) during an 11-year period. More recently collection and analysis were expanded to also include samples from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) project in the S1 bog, peatlands S3, S4, S5, as well as nearby lakes. We establish a local meteoric water line by analyzing the isotopic composition of precipitation, which fills a void in regional meteoric water lines for Minnesota. Furthermore, we establish baseline isotopic composition for bog outlet streams, bog porewater, aquifer groundwater, overland flow, subsurface stormflow, and snowpack, as well as runoff from the SPRUCE experimental chambers. These data are publicly available and will be expanded upon in the future.

54 ENVIRONMENTAL SCIENCES↗

Measuring and modeling soil moisture and runoff at solar farms using a disconnected impervious surface approach

Abstract Ground‐mounted photovoltaic sites are often treated as impervious surfaces in stormwater permits. This ignores the pervious soils beneath and between solar arrays and leads to an overestimation of runoff. Our objective was to improve solar farm stormwater hydrology models by explicitly considering the disconnected impervious nature of solar design and site characteristics. Experimental sites established on utility scale solar farms in Colorado, Georgia, Minnesota, New York, and Oregon had perennial vegetative plantings with mean precipitation ranging from 40.6 to 124.5 cm, and soil texture ranging from loamy sand to clay. Soil moisture measurements were collected beneath arrays, under drip edges, and in the vegetated area between arrays at each site. Hydrus‐3D models for soil moisture and stormwater hydrology were developed that accounted for precipitation falling on solar panels, drip edge redistribution of rainfall, infiltration, and runoff in the pervious areas between solar arrays and beneath panels. Drip edge runoff averaged 3‐ to 10‐times incident precipitation at the New York and Minnesota sites, respectively. Root mean square error values between measured sub‐hourly soil moisture and predicted moisture for large measured single storm events averaged 0.029 across all five sites. Predicted runoff depths were strongly affected by precipitation depth, soil texture, soil profile depth, and soil bulk density. Runoff depths across the five experimental sites averaged 13%, 25%, and 45% of the 2‐, 10‐, and 100‐year design storm depths, clearly showing that these solar farms do not behave like impervious surfaces, but rather as disconnected impervious surfaces with substantial infiltration of runoff in the vegetated areas between and beneath solar arrays.

Agriculture↗

Nitrogen and phosphorus cycling in an ombrotrophic peatland: a benchmark for assessing change

Aims Slow decomposition and isolation from groundwater mean that ombrotrophic peatlands store a large amount of soil carbon (C) but have low availability of nitrogen (N) and phosphorus (P). To better understand the role these limiting nutrients play in determining the C balance of peatland ecosystems, we compile comprehensive N and P budgets for a forested bog in northern Minnesota, USA. Methods N and P within plants, soils, and water are quantified based on field measurements. The resulting empirical dataset are then compared to modern-day, site-level simulations from the peatland land surface version of the Energy Exascale Earth System Model (ELM-SPRUCE).

Salmon, Verity G.↗

Biomass Production of Prairie Cordgrass (Spartina pectinata Link.) Using Urea and Kura Clover (Trifolium ambiguum Bieb.) as a Source of Nitrogen

Abstract Optimizing nitrogen (N) management is an important factor for sustainable perennial biomass systems. However, N application is costly, both financially and environmentally. Our objectives were to determine: (1) N rate and plant spacing effects on yield and yield components of prairie cordgrass swards and (2) fertilizer N replacement value (FNRV) of kura clover in prairie cordgrass-kura clover binary mixtures. Plots were established in Illinois, Minnesota, South Dakota, and Wisconsin, USA, in 2010. Kura clover was transplanted on 30-cm centers in all treatments in which it was a component; prairie cordgrass seedlings were transplanted within the kura clover on 60- and 90-cm centers. Monoculture prairie cordgrass stands were established at the same population densities of mixed stands and fertilized with 0, 75, 150, or 225 kg N ha -1 . Biomass was harvested in the autumn from 2011 to 2013. N (urea), year, plant spacing, and year × plant spacing affected prairie cordgrass production at all locations. Prairie cordgrass yield increased with N application, but the response varied by location. N application tended to increase prairie cordgrass tiller density and consistently increased tiller mass. Prairie cordgrass yield with 0 N was equal to or less than the yield of prairie cordgrass/kura clover mixtures at all locations in 2011 and 2012; however, kura clover provided a FNRV of 25–82 kg N ha -1 to prairie cordgrass in 2013. Kura clover has potential to provide N to prairie cordgrass in binary mixtures of these two species and on land that may not be easily farmed due to wetness.

Energy & Fuels↗

The nth-plant scenario for blended feedstock conversion and preprocessing nationwide: biorefineries and depots

The sustainability of the biofuel industry depends on the development of a mature conversion technology on a national level that can take advantage of the economies of scale: the nth-plant. Defining the future location and supply logistics of conversion plants is imperative to ultimately transform the nation’s renewable biomass resources into cost-competitive, high-performance feedstock for production of biofuels and bioproducts. Since the US has put restrictions on production levels of conventional biofuels from edible resources, the nation needs to plan for the widespread accessibility and development of the cellulosic biofuel scenario. Conventional feedstock supply systems will be unable to handle cellulosic biomass nationwide, making it essential to expand the industry with an advanced feedstock supply system incorporating a distributed network of preprocessing depots and conversion plants, or biorefineries. Current studies are mostly limited to designing supply systems for specific regions of the country. We developed a national database with potential locations for depots and biorefineries to meet the nation’s target demand of cellulosic biofuel. Blended feedstock with switchgrass and corn stover (harvested by either a two- or three-pass method) are considered in a Mixed Integer Linear Programming model to deliver on-spec biomass that considers both, a desired quantity and quality at the biorefinery. A total delivered feedstock cost that is less than $79.07/dt (2016$) is evaluated for years 2022, 2030, and 2040. In 2022, 124 depots and 59 biorefineries could be supplied with 42.8 million dt of corn stover and switchgrass. In 2030 and 2040, the total accessible biomass could increase to 215% and 393% respectively when compared to 2022. However, an $8/dry tons reduction in targeted delivery cost could reduce total accessible biomass by 67%. Kansas, Nebraska, South Dakota and Texas were identified as potential states with a strong biofuel economy given that they had six or more biorefineries located in all scenarios. In some scenarios, Colorado, Alabama, Georgia, Minnesota, Mississippi and South Carolina would greatly benefit from a depot network as these could only deliver to a biorefinery in a nearby state. To elaborate the impact of a nationwide consideration, the findings were compared with existing literature for different US regions. We also present results for biorefinery capacities that are double, triple, and quadruple in size.

09 BIOMASS FUELS↗

Scale and Regionality of Nonelectric Markets for U.S. Nuclear Light Water Reactors

This study assesses existing and potential industries that could conceivably be directly coupled to existing nuclear reactors. The goal is to identify the scale, location, and accessibility of the candidate industrial-product markets, as well as process feedstocks that are available near the plants to establish new industries. For example, CO 2 as a feedstock can be combined with H 2 to produce formic acid (FA), transportation fuels, and lubricants. These new plants can be entirely supported with the heat and electricity provided by a nearby NPP. The potential demand for nonelectric industrial products was assessed by documenting current and possible growth of nonelectricity product markets considered. This assessment used DOE- and industry-supported tools, data, and projections to capture regional industrial market opportunities. Electricity-capacity markets that reward large and reliable generators, such as NPPs, were considered because the electricity market will likely continue to be an important revenue source to NPPs. The key is to balance the needs of energy customers so as to optimize revenue for the affiliated energy customers or partners. In most cases, flexible plant energy delivery and power generation for the grid will require either energy storage or a stock of intermediate products to sustain the industrial customers when the NPP dispatches electricity to the grid. A diverse mix of temperate regions with operating NPPs around the U.S.—representing a variety of operating markets, local generation mix, and seasonal climates—were chosen for this market study. Both current and future market opportunities for candidate industrial-product markets surrounding these NPPs were studied. Figure 2 illustrates the regions chosen for this study. The success of developing nonelectric industrial-product markets as alternative revenue-generating sources for LWRs depends, not only on demand from growing existing markets, such as petroleum refining and NH 3 production, but also on the development of new markets such as light-duty (LD) and heavy-duty (HD) hydrogen FCEVs, synfuels, chemical production, biofuels, metal refining, injection of hydrogen into NG pipelines for gas power-generating units, FA, polymers, and close-coupled industrial heat applications, all of which can significantly increase demand relative to current levels while decarbonizing energy sectors. This study also presents a sample analysis of the economics of hydrogen production in an area of Minnesota, considering the capital and operating costs of a hydrogen plant as well as the local market demand for hydrogen. It includes some assumptions on electricity-grid pricing, showing how hydrogen could be integrated with an NPP and be competitive with the incumbent hydrogen-production process, steam methane reforming (SMR). The objectives of this study include: Provide U.S. NPP operators a robust sampling of the market demand location, scale, and accessibility (including storage and transportation) of the wide variety of industrial-product choices that can be produced using nuclear thermal energy and electricity proximate to a subset of U.S. NPPs to inform the industry of the potential opportunity; show examples and trade-off analyses of how U.S. LWR operators can access these markets, including storage and transportation of industrial products to their intended markets; and present a general analysis example for one industrial product (hydrogen) in one region (Minnesota area), including production, storage, and transportation, to show how nuclear-hybrid integrated energy systems (IESs) could access local markets and improve the profitability of an NPP.

03 NATURAL GAS↗

Collaborative Research: Understanding Nanoparticle-Plasma Interactions in Dusty Non-Thermal Plasmas by Nanoparticle Probes and Online Aerosol Characterization (Final Technical Report)

This report describes results from the University of Minnesota’s contribution to studies performed under the award DE-SC0018202 (principal Investigator: Christopher J. Hogan Jr.. This award is a collaborative research grant with support equally provided to Case Western Reserve University and to the University of Illinois (with principal investigator R. Mohan Sankaran). To date ten peer-reviewed published papers have resulted from the studies performed with University of Minnesota personnel contributing; this report describes the results of these studies as well as several unpublished studies, grouped into seven separate study types. The overarching goal of the carried out studies was to understand nanoparticle behavior and dynamics in non-thermal plasma synthesis reactors, specifically adapting aerosol measurement technology to provide online measurements of particle size, charge, and structure in plasma reactor systems.

42 ENGINEERING↗

PV Stormwater Management Research and Testing (PV-SMaRT) (Final Technical Report)

The objective of the Photovoltaic Stormwater Management Research and Testing (PV-SMaRT) project was to develop and disseminate research-based, solar-specific resources for estimating stormwater runoff at ground-mounted PV facilities and detail stormwater management and water quality best practices. The intended use of stormwater management and water quality best practices and stormwater runoff estimation resources is to reduce balance of system soft costs associated with stormwater infrastructure requirements and improve water quality through research-tested best practices. Currently, stormwater regulations and guidelines vary by jurisdiction and can lead to regulatory uncertainty and increased compliance costs for managing stormwater runoff at solar sites. To address these concerns, the NREL team and its partners (University of Minnesota, Great Plains Institute, and Fresh Energy) established and engaged an advisory Water Quality Task Force (WQTF); conducted field research on stormwater infiltration and runoff at five ground-mounted PV sites; validated a 3-D hydrologic model to predict water runoff and generate stormwater runoff coefficients for a range of site conditions and PV designs; developed a stormwater management and water quality best practices document; and engaged with local jurisdictions and other stakeholders to disseminate best practices, stormwater runoff coefficients, and other tools. Key outputs of this project were a PV-SMaRT Runoff Calculator developed by University of Minnesota, a webinar detailing project outcomes and how to use the PV-SMaRT runoff calculator, and a document on Best Practices for regulators and developers regarding stormwater runoff at PV sites.

14 SOLAR ENERGY↗

Improving the Freight Productivity of a Heavy-Duty, Battery Electric Truck by Intelligent Energy Management

This project aimed to enhance the range and reduce the operating costs of battery electric Class 8 trucks traveling over 250 miles daily. This was achieved through the development and implementation of an intelligent-Energy Management System (i-EMS) that leverages vehicle and operations data, physics-aware machine learning algorithms, and vehicle-to-cloud (V2C) connectivity. The project hypothesized that advanced machine learning algorithms and real-time data analytics could significantly improve the energy efficiency and range of these trucks. Key objectives included developing a physics-aware machine learning algorithm, implementing an i-EMS with V2C connectivity and physics-aware spatial data analytics (PSDA), and validating the system’s effectiveness with fleet partners HEB Companies and Murphy Logistics. Extensive data collection from vehicle operations, including vehicle characteristics, road conditions, and payload, was conducted. A machine learning algorithm was developed to predict energy consumption and enable proactive decision-making. The i-EMS was implemented on two Volvo VNR BEVs, with operators receiving charging and routing recommendations. Charging stations were installed at depot locations in Texas and Minnesota, with an additional on-route charger in Minnesota. Significant findings included a 14% range improvement for Murphy Logistics on a highway-driving eco-route and a 22% range improvement for HEB Companies on a city-driving eco-route. The i-EMS utilized rule-based methods and physics-based algorithms to predict and reduce energy consumption, with real-time monitoring and analysis through V2C connectivity enabling proactive decision-making. The project demonstrated the feasibility and economic viability of battery electric Class 8 trucks for long-haul operations, showcasing the potential of physics-aware machine learning in optimizing energy management. The successful implementation of the i-EMS in real-world scenarios validates its practical application and effectiveness, paving the way for the widespread adoption of battery electric vehicles in the freight transportation industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

FAIR Framework for Physics-Inspired AI in High Energy Physics (Final Technical Report)

The main deliverable of this proposal was to publish data from high energy physics experiments in a FAIR format so that non-specialists could develop machine learning technologies using our data. The Minnesota team of Profs. Cushman, Furmanski and Rusack, from the high energy experiments CDMS, Micro-Boone and CMS, respectively, and Prof J. Sun from Computer Science worked to organize the data, to provide code to access the data, and where relevant provide documentation describing the data. The FAIR4HEP collaboration was formed with groups from UC San Diego, MIT, and the University of Illinois, with the principal investigator was Dr. Huerta. Collectively we collaborated on the publication of datasets from the LHC experiments. Members of the Minnesota group contributed to the common papers published by the collaboration

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Final Report for ARPA-E LOCOMOTIVES Advanced Locomotive Technology and Rail Infrastructure Optimization System (ALTRIOS) Project

The Advanced Locomotive Technology and Rail Infrastructure Optimization System (ALTRIOS) is a unique, fully integrated, open-source software tool used to evaluate strategies for cost-effectively deploying advanced locomotive technologies and associated infrastructure. ALTRIOS simulates freight-demand-driven train scheduling, mainline meet-pass planning, locomotive dynamics, train dynamics, energy conversion efficiencies, and energy storage dynamics of line-haul train operations. Because new locomotives represent a significant long-term capital investment and new technologies must be thoroughly demonstrated before deployment, this tool provides guidance on the risk/reward trade-offs and operation integration of different technology rollout strategies. An open, integrated simulation tool is valuable for identifying future research needs and making decisions on technology development, routes, and train selection. This final report details the ALTRIOS software architecture, major modules and components, and data validation process. It demonstrates the software's utility through a 30-year rollout case study targeting high penetration of advanced powertrain technologies by 2050 for two BNSF Railway routes: loaded taconite ore trains from Hibbing, Minnesota, to Superior, Wisconsin, and mixed-freight trains from Superior to Minneapolis, Minnesota.

33 ADVANCED PROPULSION SYSTEMS↗

Multiphysics and Multiscale Modeling of Coupled Transport of Chloride Ions in Concrete

Chloride ions (Cl−)-induced corrosion is one of the main degradation mechanisms in reinforced concrete (RC) structures. In most situations, the degradation initiates with the transport of Cl− from the surface of the concrete towards the reinforcing steel. The accumulation of Cl− at the steel-concrete interface could initiate reinforcement corrosion once a threshold Cl− concentration is achieved. An accurate numerical model of the Cl− transport in concrete is required to predict the corrosion initiation in RC structures. However, existing numerical models lack a representation of the heterogenous concrete microstructure resulting from the varying environmental conditions and the indirect effect of time dependent temperature and relative humidity (RH) on the water adsorption and Cl− binding isotherms. In this study, a numerical model is developed to study the coupled transport of Cl− with heat, RH and oxygen (O2) into the concrete. The modeling of the concrete microstructure is performed using the Virtual Cement and Concrete Testing Laboratory (VCCTL) code developed by the U.S. National Institute of Standards and Technology (NIST). The concept of equivalent maturation time is utilized to eliminate the limitation of simulating concrete microstructure using VCCTL in specific environmental conditions such as adiabatic. Thus, a time-dependent concrete microstructure, which depends on the hydration reactions coupled with the temperature and RH of the environment, is achieved to study the Cl− transport. Additionally, Cl− binding isotherms, which are a function of the pH of the concrete pore solution, Cl− concentration, and weight fraction of mono-sulfate aluminate (AFm) and calcium-silicate-hydrate (C-S-H), obtained from an experimental study by the same authors are utilized to account for the Cl− binding of cement hydration products. The temperature dependent RH diffusion was considered to account for the transport of Cl− with moisture transport. The temperature and RH diffusion in the concrete domain, composite theory, and Cl− binding and water adsorption isotherms are used in combination, to estimate the ensuing Cl− diffusion field within the concrete. The coupled transport process of heat, RH, Cl−, and O2 is implemented in the Multiphysics Object-Oriented Simulation Environment (MOOSE) developed by the U.S. Idaho National Laboratory (INL). The model was verified and validated using data from multiple experimental studies with different concrete mixture proportions, curing durations, and environmental conditions. Additionally, a sensitivity analysis was performed to identify that the water-to-cement (w/c) ratio, the exposure duration, the boundary conditions: temperature, RH, surface Cl− concentration, Cl− diffusion coefficient in the capillary water, and the critical RH are the important parameters that govern the Cl− transport in RC structures. In a case study, the capabilities of the developed numerical model are demonstrated by studying the complex 2D diffusion of Cl− in a RC beam located in two different climatic regions: warm and humid weather in Galveston, Texas, and cold and dry weather in North Minnesota, Minnesota, subjected to time varying temperature, RH, and surface Cl− concentrations.

composite theory↗

Identifying Interaction Location in SuperCDMS Detectors

The Super Cryogenic Dark Matter Search (SuperCDMS) experiment uses silicon and germanium particle detectors operated at temperatures of ∼ 30 mK to search for Weakly Interacting Massive Particles (WIMPs), which are candidate dark matter particles that interact weakly with nuclei in the detectors. In operating these detectors, it is required not only to measure the energy of the interaction between the WIMP and the nuclei, but also to reconstruct where the interaction occurred, as the location can be used to separate background interactions from signal and to correct for variations with the location of the energy response. In this project, we, as a team from the University of Minnesota, aim to address the problem of accurately reconstructing the locations of interactions in the SuperCDMS detectors using machine learning methods. The dataset we provided here includes interactions at thirteen different locations from test data taken at the University of Minnesota. For each interaction, a set of parameters was extracted from the signals from each of the five sensors. These parameters represent information known to be sensitive to interaction location, including the relative timing between pulses in different channels, and features like the pulse shape. The relative amplitudes of the pulses are also relevant but due to instabilities in amplification during the test, this data is not included. The parameters included for each interaction are described in our project document. For more details, feel free to check our Github page: https://fair-umn.github.io/FAIR-UMN-CDMS/

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Vigor loss in conifers due to dwarf mistletoe

Practical remote sensing techniques were developed for detecting and evaluating vigor loss in forest conifers due to dwarf mistletoe. Eastern dwarf mistletoe (Arceuthobium pusillum) infection of black spruce (Picea mariana) was investigated. A tower-tramway system, 100 feet high, was erected over an infected stand in northeast Minnesota in June and multiband/multidate photography was initiated in July and is continuing. Four 70mm film-filter combinations were used in a multicamera unit: Plus-X/Wratten 58, Plus-X/Wratten 25A, Aero Infrared/Wratten 89B, and Ektachrome Infrared/Wratten 12. The stand of mistletoe-infected black spruce under the tramway was photographed three times per day (0900, 1200 and 1500 local sun time) at approximately 10 day intervals. An extensive test site, several square miles in area, was selected in north-central Minnesota for the purpose of testing photographic specifications developed on the tramway test site. One aerial photographic flight at a variety of altitudes was accomplished over the extensive test site in August. Data analyses are not available at this time.

Meyer, M. P.↗

Tectonic setting and evolution of late Archaean greenstone belts of Superior Province, Canada

Late Archean (3.0-2.5 Ga) greenstone belts are a major component of the Superior Province of the Canadian Shield where alternating, metavolcanic - rich and metasedimentary - rich subprovinces form a prominent central striped region bordered in part by high-grade gneiss subprovinces, the Pikiwitonei and Minto in the north, and the Minnesota River Valley in the south. The high-grade gneiss subprovinces are characterized by granulite facies gneiss of plutonic and supracrustal origin, and by abundant plutonic rocks. Minnesota River Valley has rocks older than 3.5 Ga; absolute ages of Pikiwitonei and Minto rocks are unknown but Minto does have north-south structural trends distinctive from the dominant east-west structures of Superior Province. A discussion follows.

Card, K. D.↗