Small-scale spatial variability in the distribution of ectomycorrhizal fungi affects plant performance and fungal diversity

Small-scale spatial variability in NO2 concentrations is analysed with the help of pollution maps. Maps of NO2 estimated by the Airviro dispersion model and land use regression (LUR) model are fused with measured NO2 concentrations from low-cost sensors (LCS), reference sensors and diffusion tubes. In this study, geostatistical universal kriging was employed for fusing (integrating) model estimations with measured NO2 concentrations. The results showed that the data fusion approach was capable of estimating realistic NO2 concentration maps that inherited spatial patterns of the pollutant from the model estimations and adjusted the modelled values using the measured concentrations. Maps produced by the fusion of NO2-LCS with NO2-LUR produced better results, with r-value 0.96 and RMSE 9.09. Data fusion adds value to both measured and estimated concentrations: the measured data are improved by predicting spatiotemporal gaps, whereas the modelled data are improved by constraining them with observed data. Hotspots of NO2 were shown in the city centre, eastern parts of the city towards the motorway (M1) and on some major roads. Air quality standards were exceeded at several locations in Sheffield, where annual mean NO2 levels were higher than 40 µg/m3. Road traffic was considered to be the dominant emission source of NO2 in Sheffield.

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Small-scale spatial variability of particle concentrations and traffic levels in Montreal: a pilot study

The Science of The Total Environment ◽

10.1016/j.scitotenv.2004.07.013 ◽

2005 ◽

Vol 338 (3) ◽

pp. 243-251 ◽

Cited By ~ 30

Author(s):

Audrey Smargiassi ◽

Mary Baldwin ◽

Charles Pilger ◽

Rose Dugandzic ◽

Michael Brauer

Keyword(s):

Pilot Study ◽

Spatial Variability ◽

Small Scale

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Small-scale temporal and spatial variability in the erosion threshold and properties of cohesive intertidal sediments

Continental Shelf Research ◽

10.1016/j.csr.2005.11.007 ◽

2006 ◽

Vol 26 (3) ◽

pp. 351-362 ◽

Cited By ~ 59

Author(s):

T.J. Tolhurst ◽

E.C. Defew ◽

J.F.C. de Brouwer ◽

K. Wolfstein ◽

L.J. Stal ◽

...

Keyword(s):

Spatial Variability ◽

Small Scale ◽

Intertidal Sediments ◽

Erosion Threshold ◽

Temporal And Spatial Variability ◽

Temporal And Spatial

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Microwave snow emission modeling uncertainties in boreal and subarctic environments

The Cryosphere Discussions ◽

10.5194/tcd-9-5719-2015 ◽

2015 ◽

Vol 9 (5) ◽

pp. 5719-5773

Author(s):

A. Roy ◽

A. Royer ◽

O. St-Jean-Rondeau ◽

B. Montpetit ◽

G. Picard ◽

...

Keyword(s):

Grain Size ◽

Spatial Variability ◽

Boreal Forest ◽

Frozen Soil ◽

Microwave Emission ◽

Small Scale ◽

Radiometric Measurements ◽

Snow Properties ◽

Modeling Uncertainties

Abstract. This study aims to better understand and quantify the uncertainties in microwave snow emission models using the Dense Media Radiative Theory-Multilayer model (DMRT-ML) with in situ measurements of snow properties. We use surface-based radiometric measurements at 10.67, 19 and 37 GHz in boreal forest and subarctic environments and a new in situ dataset of measurements of snow properties (profiles of density, snow grain size and temperature, soil characterization and ice lens detection) acquired in the James Bay and Umijuaq regions of Northern Québec, Canada. A snow excavation experiment – where snow was removed from the ground to measure the microwave emission of bare frozen ground – shows that small-scale spatial variability in the emission of frozen soil is small. Hence, variability in the emission of frozen soil has a small effect on snow-covered brightness temperature (TB). Grain size and density measurement errors can explain the errors at 37 GHz, while the sensitivity of TB at 19 GHz to snow increases during the winter because of the snow grain growth that leads to scattering. Furthermore, the inclusion of observed ice lenses in DMRT-ML leads to significant improvements in the simulations at horizontal polarization (H-pol) for the three frequencies (up to 20 K of root mean square error). However, the representation of the spatial variability of TB remains poor at 10.67 and 19 GHz at H-pol given the spatial variability of ice lens characteristics and the difficulty in simulating snowpack stratigraphy related to the snow crust. The results also show that for ground-based radiometric measurements, forest emission reflected by the surface leads to TB underestimation of up to 40 K if neglected. We perform a comprehensive analysis of the components that contribute to the snow-covered microwave signal, which will help to develop DMRT-ML and to improve the required field measurements. The analysis shows that a better consideration of ice lenses and snow crusts is essential to improve TB simulations in boreal forest and subarctic environments.

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Variability in organic matter lost by combustion in a boreal bog during the 2001 Chisholm fire

Canadian Journal of Forest Research ◽

10.1139/x03-162 ◽

2003 ◽

Vol 33 (12) ◽

pp. 2509-2513 ◽

Cited By ~ 84

Author(s):

Brian W Benscoter ◽

R Kelman Wieder

Keyword(s):

Organic Matter ◽

Species Composition ◽

Spatial Variability ◽

Spatial Variation ◽

Fire Severity ◽

Small Scale ◽

Western Canada ◽

Direct Combustion ◽

Site Variation

Fire directly releases carbon (C) to the atmosphere through combustion of biomass. An estimated 1470 ± 59 km2 of peatland burns annually in boreal, western Canada, releasing 4.7 ± 0.6 Tg C to the atmosphere via direct combustion. We quantified within-site variation in organic matter lost via combustion in a bog peatland in association with the 116 000-ha Chisholm, Alberta, fire in 2001. We hypothesized that for peatlands with considerable small-scale microtopography (bogs and treed fens), hummocks will burn less than hollows. We found that hollows exhibit more combustion than hummocks, releasing nearly twice as much C to the atmosphere. Our results suggest that spatial variability in species composition and site hydrology within a landform and across a landscape could contribute to considerable spatial variation in the amounts of C released via combustion during peatland fire, although the magnitude of this variation may be dependent on fire severity.

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Small-Scale Spatial Variability of Soil Chemical and Biochemical Properties in a Rewetted Degraded Peatland

Frontiers in Environmental Science ◽

10.3389/fenvs.2019.00116 ◽

2019 ◽

Vol 7 ◽

Author(s):

Wakene Negassa ◽

Christel Baum ◽

Andre Schlichting ◽

Jürgen Müller ◽

Peter Leinweber

Keyword(s):

Spatial Variability ◽

Biochemical Properties ◽

Small Scale

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Introducing ASME PTC 48

Volume 2: Simple and Combined Cycles; Advanced Energy Systems and Renewables (Wind, Solar and Geothermal); Energy Water Nexus; Thermal Hydraulics and CFD; Nuclear Plant Design, Licensing and Construction; Performance Testing and Performance Test Codes; Student Paper Competition ◽

10.1115/power2014-32067 ◽

2014 ◽

Author(s):

Olivier Le Galudec ◽

James Oszewski ◽

John Preston ◽

David Thimsen

Keyword(s):

Co2 Capture ◽

Carbon Capture ◽

Power Plants ◽

Performance Test ◽

Heat Rate ◽

Air Separation ◽

Plant Performance ◽

Small Scale ◽

Separation Unit ◽

Combined Cycles

In the field of Power Generation, Operators — Plant Owners, Utilities, IPPs … — have had to face severe constraints linked not only with price of electricity and cost of fuel, but also with more and more demanding environmental constraints. It appears that the next atmospheric emission coming under scrutiny is CO2. Some small scale laboratory size experiments and pilot scale tests demonstrating the ability to capture CO2 before it reaches the atmosphere have already been conducted, and some industrial scale demonstrators are already at the permitting stage and will soon reach construction. In order to anticipate the needs of Performance Tests within this coming market, ASME decided to form a new committee in order to prepare and deliver ASME Performance Test Code – PTC 48 “Overall Plant Performance with Carbon Capture” test code. This new code may be seen as an evolution of ASME PTC 46 “Performance Test Code on Overall Plant Performance” 1996 (currently under revision), which goes beyond the sole verification of components to provide guidelines for testing a full Plant. Capturing CO2 from fuel–fired power plants will have a significant impact on net capacity and net heat rate of the plant. Such plants will, in addition to the Power Block and Steam Generator, also include systems not commonly included in non-CO2 capture power plants. The addition of an ASU (Air Separation Unit, for oxy-combustion with CO2 capture) and/or CPU (CO2 Purification Unit, for oxy-combustion or post-combustion CO2 capture) has made necessary the preparation of a dedicated test code based upon same guiding principle than PTC 46, i.e. treating the plant globally as a “Black Box”. This approach allows correction of output and efficiency at the plant interfaces, but at the exclusion of internal parameters. It is anticipated that the code can inform development of regulations that define the rules and obligations of Operators. Currently, the proposed PTC 48 aims at fossil fuel fired Steam-electric power plants using either post-combustion CO2 capture or oxy-combustion with CO2 capture technologies. Combined cycles and Integrated Gasification Combined Cycles — IGCCs — are not addressed.

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