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Investigating indoor concentrations of PM10 in an underground loading dock in Malaysia

Abualqumboz, M.S. and mohammed, nurul izma and Malakahmad, Dr, Amirhossein, Dr and Nazif, A.N. (2017) Investigating indoor concentrations of PM10 in an underground loading dock in Malaysia. Air Quality, Atmosphere and Health, 10 (2). pp. 147-159.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Risky air contaminants including PM10 can accumulate inside underground confined loading docks because of the enclosed nature and limited contacts of loading docks with ambient air. Exposure to PM10 can increase morbidity and mortality rates. Hence, this study aimed to investigate and model PM10 concentrations in an underground loading dock located at Kuala Lumpur city center, Malaysia. For this purpose, a real-time air quality monitoring instrument was used for measuring PM10 concentrations for 20 consecutive weeks starting from November 8, 2014. After that, the Statistical Package for Social Sciences (SPSS) software was used to analyze measured PM10 concentrations through series of statistical analyses, whereas MATLAB R2013a was employed for developing prediction models of future PM10 concentrations. Moreover, PM10 temporal variation was examined using time series plots. The results showed that short-term PM10 concentrations did not exceed the Malaysian indoor allowable limit of 150 μg/m3. Despite that, PM10 had 8 probability of exceedance of WHO standard concentration of 50 μg/m3. This indicates that the occupants will be under the risk of prolonged exposure to PM10 even at low concentrations. The results confirmed a strong correlation between PM10 concentrations and diesel-powered vehicles flow. Contrarily, the flow of gasoline-powered vehicles was poorly correlated. Finally, future daily-averaged PM10 concentrations were predicted for the three weekdays that followed the measurement period using single exponential smoothing. The obtained accuracy was at 70 of measured PM10 concentrations. Future hourly-averaged PM10 concentrations were estimated using single linear regression with an accuracy of 53 . © 2016, Springer Science+Business Media Dordrecht.

Item Type:Article
Impact Factor:cited By 1
Departments / MOR / COE:Division > Academic > Faculty of Engineering > Civil Engineering
ID Code:19590
Deposited By: Ahmad Suhairi
Deposited On:20 Apr 2018 07:12
Last Modified:20 Apr 2018 07:12

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