10-Month characterization of the aerosol number size distribution and related air quality and meteorology at the Bondville, IL Midwestern background site

2017 ◽  
Vol 154 ◽  
pp. 348-361 ◽  
Author(s):  
Robert L. Bullard ◽  
Ashish Singh ◽  
Sybil M. Anderson ◽  
Christopher M.B. Lehmann ◽  
Charles O. Stanier
Fibers ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 9
Author(s):  
Ioannis A. Kartsonakis

Nanomaterial is defined a natural, incidental or manufactured material containing particles, in an unbound state, as an aggregate, or as an agglomerate, and where, for 50% or more of the particles in the number size distribution, one or more external dimensions is in the size range 1–100 nm [...]


2015 ◽  
Vol 15 (7) ◽  
pp. 10123-10162 ◽  
Author(s):  
D. C. S. Beddows ◽  
R. M. Harrison ◽  
D. C. Green ◽  
G. W. Fuller

Abstract. Positive Matrix Factorisation (PMF) analysis was applied to PM10 chemical composition and particle Number Size Distribution (NSD) data measured at an urban background site (North Kensington) in London, UK for the whole of 2011 and 2012. The PMF analyses revealed six and four factors respectively which described seven sources or aerosol types. These included Nucleation, Traffic, Diffuse Urban, Secondary, Fuel Oil, Marine and Non-Exhaust/Crustal sources. Diffuse Urban, Secondary and Traffic sources were identified by both the chemical composition and particle number size distribution analysis, but a Nucleation source was identified only from the particle Number Size Distribution dataset. Analysis of the PM10 chemical composition dataset revealed Fuel Oil, Marine, Non-Exhaust Traffic/Crustal sources which were not identified from the number size distribution data. The two methods appear to be complementary, as the analysis of the PM10 chemical composition data is able to distinguish components contributing largely to particle mass whereas the number particle size distribution dataset is more effective for identifying components making an appreciable contribution to particle number. Analysis was also conducted on the combined chemical composition and number size distribution dataset revealing five factors representing Diffuse Urban, Nucleation, Secondary, Aged Marine and Traffic sources. However, the combined analysis appears not to offer any additional power to discriminate sources above that of the aggregate of the two separate PMF analyses. Day-of-the-week and month-of-the-year associations of the factors proved consistent with their assignment to source categories, and bivariate polar plots which examined the wind directional and wind speed association of the different factors also proved highly consistent with their inferred sources.


1981 ◽  
Author(s):  
Birgitta Berglund ◽  
Ulf Berglund ◽  
Thomas Lindvall ◽  
Helene Nicander-Bredberg

Author(s):  
Mona E. Elbashier ◽  
Suhaib Alameen ◽  
Caroline Edward Ayad ◽  
Mohamed E. M. Gar-Elnabi

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and  features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.


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