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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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