Yam Code

New Paste Browse

https://www.selleckchem.com/products/epz020411.html © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.The inhalation of particulate matter (PM) is a significant health risk associated with reduced life expectancy due to increased cardio-pulmonary disease and exacerbation of respiratory diseases such as asthma and pneumonia. PM originates from natural and anthropogenic sources including combustion engines, cigarettes, agricultural burning, and forest fires. Identifying the source of PM can inform effective mitigation strategies and policies, but this is difficult to do using current techniques. Here we present a method for identifying PM source using excitation emission matrix (EEM) fluorescence spectroscopy and a machine learning algorithm. We collected combustion generated PM2.5 from wood burning, diesel exhaust, and cigarettes using filters. Filters were weighted to determine mass concentration followed by extraction into cyclohexane and analysis by EEM fluorescence spectroscopy. Spectra obtained from each source served as training data for a convolutional neural network (CNN) used for source identification in mixed samples. This method can predict the presence or absence of the three laboratory sources with an overall accuracy of 89% when the threshold for classifying a source as present is 1.1 μg/m3 in air over a 24-hour sampling time. The limit of detection for cigarette, diesel and wood are 0.7, 2.6, 0.9 μg/m3, respectively, in air assuming a 24-hour sampling time at an air sampling rate of 1.8 liters per minute. We applied the CNN algorithm developed using the laboratory training data to a small set of field samples and found the algorithm was effective in some cases but would require a training data set containing more samples to be more broadly applicable.Contamination of mangrove ecosystems, including those of the Red Sea area, has caused serious concern globally. Spatial distribution of heavy metals and their bioaccu

Paste Settings

Paste Title : [Optional]

Paste Folder : [Optional]

Syntax Highlighting : [Optional]

Paste Expiration : [Optional]

Paste Status : [Optional]

Password : [Optional]

Description: [Optional]

Tags: [Optional]

Encrypt Paste (?)

You are currently not logged in, this means you can not edit or delete anything you paste. Sign Up or Login

Paste Settings

Site Languages