Mak, Kai Long, Loh, Wai Keung Anthony, Kwan, Chun Kit Wilson, Ma, Ka Yee (2024) Roadside Vehicle Particulate Vertical Dispersion Model for Friction Course Materials: A Case of Hong Kong. Trends in Applied Sciences Research, . ISSN 1819-3579. E-ISSN 2151-7908. (KAR id:107049)
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Official URL: https://doi.org/10.3923/tasr.2024.170.179 |
Abstract
The rapid urban development has caused various pollution in Hong Kong.
However, the current measures adopted are aimed at controlling the surface level emission, while the
vertical dispersion of pollutants is less investigated. This research project aims to identify the vertical
dispersion patterns of particulate matter and noise emitted from road traffic and their decay rates with
increasing vertical distance from the source and examine the possible correlation between traffic noise
frequency levels and vehicle-emitted particulate. Materials and Methods: Three sets of equipment have
been installed at three different heights on building facades perpendicular to the road surface, facing
traffic to monitor PM concentrations (PM1, PM2.5, PM4.25 and PM10), noise frequencies and other
environmental data namely temperature, relative humidity and wind speed. Results: The study anticipates
uncovering a positive relationship between vehicular particulate matter emissions and traffic-related noise
on lower floors, specifically at an 800 Hz noise frequency. Analysis of the three-dimensional plots indicates
that pollutant concentrations are highest at lower levels. Notably, PM1, PM2.5 and PM4.25 demonstrate
relatively high R-squared values (PM1 = 0.674, PM2.5 = 0.649 and PM4.25 = 0.538), indicating a satisfactory
fit of these models to the data. Conclusion: By highlighting the often-overlooked vertical transmission
of particulate matter and noise from vehicles, this research contributes to a deeper understanding of air
and noise pollution levels in high-rise urban environments. These insights hold the potential to inform
future urban planning initiatives aimed at enhancing public health outcomes.
Item Type: | Article |
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Subjects: | Q Science > Operations Research - Theory |
Divisions: | Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems |
Funders: | University of Kent (https://ror.org/00xkeyj56) |
Depositing User: | Ricky Mak |
Date Deposited: | 30 Aug 2024 11:07 UTC |
Last Modified: | 30 Aug 2024 11:07 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/107049 (The current URI for this page, for reference purposes) |
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