Fine-Scale Spatial Variability of Pedestrian-Level Particulate Matters in Compact Urban Commercial Districts in Hong Kong
Bibliographic Data
| ID | 15501003 |
|---|---|
| Authors | Yuan Shi (0000-0003-2675-986X, Chinese University of Hong Kong, corresponding author), Edward Ng (0000-0002-2483-9922, Chinese University of Hong Kong) |
| Year | 2017 |
| Volume | 14 |
| Issue | 9 |
| Pages | 1008-1008 |
| Publication date | 2017-09-03 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph14091008 |
| PMID | 28869527 |
| OpenAlex | W2751418483 |
| Language | EN |
| Citations received | 1 |
| References cited | 51 |
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 PM 2.5 and PM 10 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
Air pollution · Cartography · Context (archaeology · Geography · Geospatial analysis · Interpolation (computer graphics · Multivariate interpolation · Pedestrian · Remote sensing · Scale (ratio · Spatial analysis · Spatial contextual awareness · Spatial ecology · Spatial variability · Statistics · Air Quality and Health Impacts · Computer Science · Environmental Science · Mathematics · Noise Effects and Management · Urban Transport and Accessibility
Policies and technical guidelines for urban planning of high-density cities – air ventilation assessment (AVA) of Hong Kong
Spatial Interpolation Methods
Individual exposure estimates may be erroneous when spatiotemporal variability of air pollution and human mobility are ignored
Hong Kong
The Uncertain Geographic Context Problem
Geospatial Estimation of Individual Exposure to Air Pollutants
| Citations per year | 0.2 |
|---|---|
| Citation span | 2021 - 2021 (1) |
| Highly cited | No |