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Roadside Vehicle Particulate Vertical Dispersion Model for Friction Course Materials: A Case of Hong Kong

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
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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