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T1  - Sensitivity Analysis for Predicting Sub-Micron Aerosol Concentrations Based on Meteorological Parameters 
SN  -  /  
UR  - http://hdl.handle.net/10138/323391 
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A1  - Zaidan, Martha A.; Surakhi, Ola; Fung, Pak Lun; Hussein, Tareq 
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PB  -  
Y1  - 2020 
LA  - eng 
AB  - Sub-micron aerosols are a vital air pollutant to be measured because they pose health effects. These particles are quantified as particle number concentration (PN). However, PN measurements are not always available in air quality measurement stations, leading to data scarcity. In order to compensate this, PN modeling needs to be developed. This paper presents a PN modeling framework using sensitivity analysis tested on a one year aerosol measurement campaign conducted in Amman, Jordan. The metho... 
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KW  - particle number concentration; modeling; sensitivity analysis; artificial neural networks; feed-forward neural network; time-delay neural network; PARTICLE NUMBER CONCENTRATIONS; ULTRAFINE PARTICLES; AIR-QUALITY; OZONE CONCENTRATIONS; PM2.5; MODEL; MATTER; CARBON; 114 Physical sciences; 116 Chemical sciences 
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