A comparison of the RCM and ISC3 dispersion models against the Alaska data set

2006 ◽  
Vol 25 (3) ◽  
pp. 255-257
Author(s):  
Chen Gangcai ◽  
Yang Duoxing ◽  
Wang Zhongqiong
Keyword(s):  
Dispersion Models ◽  
Data Set

Inter-comparison of the AUSTAL2000 and CALPUFF dispersion models against the Kincaid data set

2010 ◽  
Vol 40 (1/2/3) ◽  
pp. 267 ◽  
Author(s):  
Ka Hing Yau ◽  
Robert W. Macdonald <suffix>(deceased)</ ◽  
Jesse L. The
Keyword(s):  
Dispersion Models ◽  
Data Set

scholarly journals Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study

2019 ◽  
Vol 19 (4) ◽  
pp. 2561-2576 ◽  
Author(s):  
Anna Karion ◽  
Thomas Lauvaux ◽  
Israel Lopez Coto ◽  
Colm Sweeney ◽  
Kimberly Mueller ◽  
...  
Keyword(s):  
Dispersion Model ◽  
Methane Emissions ◽  
Gas Production ◽  
Estimation Methods ◽  
Trace Gas ◽  
Barnett Shale ◽  
Dispersion Models ◽  
Data Set ◽  
Gas Dispersion ◽  
Tracer Dispersion

Abstract. Greenhouse gas emissions mitigation requires understanding the dominant processes controlling fluxes of these trace gases at increasingly finer spatial and temporal scales. Trace gas fluxes can be estimated using a variety of approaches that translate observed atmospheric species mole fractions into fluxes or emission rates, often identifying the spatial and temporal characteristics of the emission sources as well. Meteorological models are commonly combined with tracer dispersion models to estimate fluxes using an inverse approach that optimizes emissions to best fit the trace gas mole fraction observations. One way to evaluate the accuracy of atmospheric flux estimation methods is to compare results from independent methods, including approaches in which different meteorological and tracer dispersion models are used. In this work, we use a rich data set of atmospheric methane observations collected during an intensive airborne campaign to compare different methane emissions estimates from the Barnett Shale oil and natural gas production basin in Texas, USA. We estimate emissions based on a variety of different meteorological and dispersion models. Previous estimates of methane emissions from this region relied on a simple model (a mass balance analysis) as well as on ground-based measurements and statistical data analysis (an inventory). We find that in addition to meteorological model choice, the choice of tracer dispersion model also has a significant impact on the predicted downwind methane concentrations given the same emissions field. The dispersion models tested often underpredicted the observed methane enhancements with significant variability (up to a factor of 3) between different models and between different days. We examine possible causes for this result and find that the models differ in their simulation of vertical dispersion, indicating that additional work is needed to evaluate and improve vertical mixing in the tracer dispersion models commonly used in regional trace gas flux inversions.

scholarly journals Does the Zero Carry Essential Information for Artificial Neural Network learning to simulate the contaminant transport in Urban Areas?

2021 ◽  
Vol 2090 (1) ◽  
pp. 012027
Author(s):  
M. Berendt-Marchel ◽  
A. Wawrzynczak
Keyword(s):  
Neural Network ◽  
Urban Areas ◽  
Training Data ◽  
Industrial Complex ◽  
Dispersion Models ◽  
Contamination Source ◽  
Data Set ◽  
Essential Information ◽  
The Neural Network ◽  
Artificial Neural

Abstract The release of hazardous materials in urbanized areas is a considerable threat to human health and the environment. Therefore, it is vital to detect the contamination source quickly to limit the damage. In systems localizing the contamination source based on the measured concentrations, the dispersion models are used to compare the simulated and registered point concentrations. These models are run tens of thousands of times to find their parameters, giving the model output’s best fit to the registration. Artificial Neural Networks (ANN) can replace in localization systems the dispersion models, but first, they need to be trained on a large, diverse set of data. However, providing an ANN with a fully informative training data set leads to some computational challenges. For example, a single simulation of airborne toxin dispersion in an urban area might contain over 90% of zero concentration in the positions of the sensors. This leads to the situation when the ANN target includes a few percent positive values and many zeros. As a result, the neural network focuses on the more significant part of the set - zeros, leading to the non-adaptation of the neural network to the studied problem. Furthermore, considering the zero value of concentration in the training data set, we have to face many questions: how to include zero, scale a given interval to hide the zero in the set, and include zero values at all; or limit their number? This paper will try to answer the above questions and investigate to what extend zero carries essential information for the ANN in the contamination dispersion simulation in urban areas. For this purpose, as a testing domain, the center of London is used as in the DAPPLE experiment. Training data is generated by the Quick Urban & Industrial Complex (QUIC) Dispersion Modeling System.

scholarly journals A field study to estimate the impact of noise barriers on mitigation of near road air pollution

2021 ◽  
Author(s):  
Ranga Rajan Thiruvenkatachari ◽  
Yifan Ding ◽  
David Pankratz ◽  
Akula Venkatram
Keyword(s):  
Air Pollution ◽  
Field Study ◽  
Dispersion Model ◽  
Meteorological Data ◽  
Dispersion Models ◽  
Noise Barriers ◽  
Data Set ◽  
Wind Speeds ◽  
Sonic Anemometers ◽  
The Impact

AbstractAir pollution associated with vehicle emissions from roadways has been linked to a variety of adverse health effects. Wind tunnel and tracer studies show that noise barriers mitigate the impact of this pollution up to distances of 30 times the barrier height. Data from these studies have been used to formulate dispersion models that account for this mitigating effect. Before these models can be incorporated into Federal and State regulations, it is necessary to demonstrate their applicability under real-world conditions. This paper describes a comprehensive field study conducted in Riverside, CA, in 2019 to collect the data required to evaluate the performance of these models. Eight vehicles fitted with SF6 tracer release systems were driven in a loop on a 2-km stretch of Interstate 215 that had a 5-m tall noise barrier on the downwind side. The tracer, SF6, was sampled at over 40 locations at distances ranging from 5 to 200 m from the barrier. Meteorological data were measured with several 3-D sonic anemometers located upwind and downwind of the highway. The data set, corresponding to 10 h collected over 4 days, consists of information on emissions, tracer concentrations, and micrometeorological variables that can be used to evaluate barrier effects in dispersion models. An analysis of the data using a dispersion model indicates that current models are likely to overestimate concentrations, or underestimate the mitigation from barriers, at low wind speeds. We suggest an approach to correct this problem.

Assessment of the Impact of Spatial Data on the Results of Air Pollution Dispersion Modeling

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Robert Oleniacz ◽  
Mateusz Rzeszutek
Keyword(s):  
Land Cover ◽  
Spatial Data ◽  
Spatial Databases ◽  
Dispersion Model ◽  
Ambient Air ◽  
Air Pollutant ◽  
Dispersion Models ◽  
Data Set ◽  
Pollution Dispersion ◽  
The Impact

AbstractAdvanced dispersion models, taking into account information on the relief and land cover, as well as temporal and spatial variability of meteorological conditions, are beginning to play an increasingly important role in the assessment of the impact on the air quality. There are numerous spatial databases which can be used in this type of a calculation process, however, there is no answer to the question of how the use of appropriate data set of terrain characteristics affects the results of the distribution of air pollutant concentrations at the surface of the ground. This paper presents two different sets of spatial data of the relief and land cover. Then, their impact on the results of modeling the propagation of pollutants in the ambient air was characterized, using the meteorological processor CALMET and the dispersion model CALPUFF. The obtained results of concentrations in the adopted calculation area were compared on the basis of statistical indicators used to assess pollution dispersion models contained in the statistical package BOOT Statistical Model Evaluation Software Package Version 2.0. The obtained results of calculations of the maximum 1-hour concentrations, the maximum 24-hour mean concentrations and annual mean concentrations for the prepared computational grids with a resolution of 1×1 km were analyzed.

scholarly journals Inter-comparison of Atmospheric Trace Gas Dispersion Models: Barnett Shale Case Study

2018 ◽  
Author(s):  
Anna Karion ◽  
Thomas Lauvaux ◽  
Israel Lopez Coto ◽  
Colm Sweeney ◽  
Kimberly Mueller ◽  
...  
Keyword(s):  
Dispersion Model ◽  
Methane Emissions ◽  
Gas Production ◽  
Estimation Methods ◽  
Trace Gas ◽  
Barnett Shale ◽  
Dispersion Models ◽  
Data Set ◽  
Gas Dispersion ◽  
Tracer Dispersion

Abstract. Greenhouse gas emissions mitigation requires understanding dominant processes controlling fluxes of these trace gases at increasingly finer spatial and temporal scales. Trace gas fluxes can be estimated using a variety of approaches that translate observed atmospheric species mole fractions into fluxes or emission rates, often identifying the spatial and temporal characteristics of the emissions sources as well. Meteorological models are commonly combined with tracer dispersion models to estimate fluxes using an inverse approach that optimizes emissions to best fit the trace gas mole fraction observations. One way to evaluate the accuracy of atmospheric flux estimation methods is to compare results from independent methods, including approaches in which different meteorological and tracer dispersion models are used. In this work, we use a rich data set of atmospheric methane observations collected during an intensive airborne campaign to compare different methane emissions estimates from the Barnett Shale oil and natural gas production basin in Texas, U.S.A. We estimate emissions based on a variety of different meteorological and dispersion models. Previous estimates of methane emissions from this region relied on a simple model (a mass balance analysis) as well as on ground-based measurements and statistical data analysis (an inventory). We find that in addition to meteorological model choice, the choice of tracer dispersion model also has a significant impact on the predicted downwind methane concentrations given the same emissions field. The dispersion models tested often under-predicted the observed methane enhancements with significant variability between different models and between different days. We examine possible causes for this result and find that the models differ in their simulation of vertical dispersion, indicating that additional work is needed to evaluate and improve vertical mixing in the tracer dispersion models commonly used in regional trace gas flux inversions.

Rotational Characteristics of the Green Solar Corona: 1947-1991

1994 ◽  
Vol 144 ◽  
pp. 139-141 ◽  
Author(s):  
J. Rybák ◽  
V. Rušin ◽  
M. Rybanský
Keyword(s):  
Solar Corona ◽  
World Wide ◽  
Rotation Period ◽  
Original Data ◽  
Coronal Emission ◽  
Time Intervals ◽  
Data Set ◽  
The World ◽  
Coronal Emission Line ◽  
Homogeneous Data

AbstractFe XIV 530.3 nm coronal emission line observations have been used for the estimation of the green solar corona rotation. A homogeneous data set, created from measurements of the world-wide coronagraphic network, has been examined with a help of correlation analysis to reveal the averaged synodic rotation period as a function of latitude and time over the epoch from 1947 to 1991.The values of the synodic rotation period obtained for this epoch for the whole range of latitudes and a latitude band ±30° are 27.52±0.12 days and 26.95±0.21 days, resp. A differential rotation of green solar corona, with local period maxima around ±60° and minimum of the rotation period at the equator, was confirmed. No clear cyclic variation of the rotation has been found for examinated epoch but some monotonic trends for some time intervals are presented.A detailed investigation of the original data and their correlation functions has shown that an existence of sufficiently reliable tracers is not evident for the whole set of examinated data. This should be taken into account in future more precise estimations of the green corona rotation period.

Difference Fourier Analysis of Glucose Embedded and Frozen Hydrated Purple Membrane

1982 ◽  
Vol 40 ◽  
pp. 74-75
Author(s):  
Jules S. Jaffe ◽  
Robert M. Glaeser
Keyword(s):  
Purple Membrane ◽  
Data Set ◽  
High Resolution Data ◽  
X Ray ◽  
X Ray Crystallography ◽  
Fourier Techniques ◽  
Versus Protein ◽  
The Difference ◽  
Difference Fourier ◽  
Ideal Method

Although difference Fourier techniques are standard in X-ray crystallography it has only been very recently that electron crystallographers have been able to take advantage of this method. We have combined a high resolution data set for frozen glucose embedded Purple Membrane (PM) with a data set collected from PM prepared in the frozen hydrated state in order to visualize any differences in structure due to the different methods of preparation. The increased contrast between protein-ice versus protein-glucose may prove to be an advantage of the frozen hydrated technique for visualizing those parts of bacteriorhodopsin that are embedded in glucose. In addition, surface groups of the protein may be disordered in glucose and ordered in the frozen state. The sensitivity of the difference Fourier technique to small changes in structure provides an ideal method for testing this hypothesis.

Algorithms for automated montage synthesis of images from laser-scanning confocal microscopes

1995 ◽  
Vol 53 ◽  
pp. 650-651
Author(s):  
D. E. Becker
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Laser Scanning ◽  
Cell Counting ◽  
Wide Area ◽  
Data Set ◽  
Computational Synthesis ◽  
Automated Cell Counting ◽  
Partial View ◽  
The Individual

An efficient, robust, and widely-applicable technique is presented for computational synthesis of high-resolution, wide-area images of a specimen from a series of overlapping partial views. This technique can also be used to combine the results of various forms of image analysis, such as segmentation, automated cell counting, deblurring, and neuron tracing, to generate representations that are equivalent to processing the large wide-area image, rather than the individual partial views. This can be a first step towards quantitation of the higher-level tissue architecture. The computational approach overcomes mechanical limitations, such as hysterisis and backlash, of microscope stages. It also automates a procedure that is currently done manually. One application is the high-resolution visualization and/or quantitation of large batches of specimens that are much wider than the field of view of the microscope.The automated montage synthesis begins by computing a concise set of landmark points for each partial view. The type of landmarks used can vary greatly depending on the images of interest. In many cases, image analysis performed on each data set can provide useful landmarks. Even when no such “natural” landmarks are available, image processing can often provide useful landmarks.

Merging of electron-diffraction intensities acquired with a slow-scan CCD camera of crotoxin complex crystals to 3.5Å resolution

1994 ◽  
Vol 52 ◽  
pp. 104-105
Author(s):  
Jaap Brink ◽  
Wah Chiu
Keyword(s):  
Electron Diffraction ◽  
Ccd Camera ◽  
Crystal Thickness ◽  
Local Contrast ◽  
Camera Model ◽  
Data Set ◽  
3 Dimensional ◽  
Diffraction Mode ◽  
Diffraction Patterns ◽  
Complex Crystals

Crotoxin complex is the principal neurotoxin of the South American rattlesnake, Crotalus durissus terrificus and has a molecular weight of 24 kDa. The protein is a heterodimer with subunit A assigneda chaperone function. Subunit B carries the lethal activity, which is exerted on both sides ofthe neuro-muscular junction, and which is thought to involve binding to the acetylcholine receptor. Insight in crotoxin complex’ mode of action can be gained from a 3 Å resolution structure obtained by electron crystallography. This abstract communicates our progress in merging the electron diffraction amplitudes into a 3-dimensional (3D) intensity data set close to completion. Since the thickness of crotoxin complex crystals varies from one crystal to the other, we chose to collect tilt series of electron diffraction patterns after determining their thickness. Furthermore, by making use of the symmetry present in these tilt data, intensities collected only from similar crystals will be merged.Suitable crystals of glucose-embedded crotoxin complex were searched for in the defocussed diffraction mode with the goniometer tilted to 55° of higher in a JEOL4000 electron cryo-microscopc operated at 400 kV with the crystals kept at -120°C in a Gatan 626 cryo-holder. The crystal thickness was measured using the local contrast of the crystal relative to the supporting film from search-mode images acquired using a 1024 x 1024 slow-scan CCD camera (model 679, Gatan Inc.).

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