Please use this identifier to cite or link to this item: https://doi.org/10.3390/ijerph14091008
Title: Fine-scale spatial variability of pedestrian-level particulate matters in compact urban commercial districts in Hong Kong
Authors: Shi, Y
Ng, E 
Keywords: environmental risk
health impact
health risk
interpolation
particulate matter
pedestrian
spatial variation
urban area
Hong Kong
human
particulate matter
pedestrian
air pollutant
air pollution
analysis
environmental monitoring
Hong Kong
particle size
particulate matter
pedestrian
procedures
spatial analysis
statistical model
statistics and numerical data
China
Hong Kong
Air Pollutants
Air Pollution
Environmental Monitoring
Hong Kong
Humans
Models, Statistical
Particle Size
Particulate Matter
Pedestrians
Spatial Analysis
Issue Date: 2017
Publisher: MDPI
Citation: Shi, Y, Ng, E (2017). Fine-scale spatial variability of pedestrian-level particulate matters in compact urban commercial districts in Hong Kong. International Journal of Environmental Research and Public Health 14 (9) : 1008. ScholarBank@NUS Repository. https://doi.org/10.3390/ijerph14091008
Rights: Attribution 4.0 International
Abstract: Particulate matters (PM) at the pedestrian level significantly raises the health impacts in the compact urban environment of Hong Kong. A detailed investigation of the fine-scale spatial variation of pedestrian-level PM is necessary to assess the health risk to pedestrians in the outdoor environment. However, the collection of PM data is difficult in the compact urban environment of Hong Kong due to the limited amount of roadside monitoring stations and the complicated urban context. In this study, we measured the fine-scale spatial variability of the PM in three of the most representative commercial districts of Hong Kong using a backpack outdoor environmental measuring unit. Based on the measurement data, 13 types of geospatial interpolation methods were examined for the spatial mapping of PM2.5 and PM10 with a group of building geometrical covariates. Geostatistical modelling was adopted as the basis of spatial interpolation of the PM. The results show that the original cokriging with the exponential kernel function provides the best performance in the PM mapping. Using the fine-scale building geometrical features as covariates slightly improves the interpolation performance. The study results also imply that the fine-scale, localized pollution emission sources heavily influence pedestrian exposure to PM. © 2017 by the authors.
Source Title: International Journal of Environmental Research and Public Health
URI: https://scholarbank.nus.edu.sg/handle/10635/183500
ISSN: 1661-7827
DOI: 10.3390/ijerph14091008
Rights: Attribution 4.0 International
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