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Analysis of Airborne Particulate Matter (PM(2.5)) over Hong Kong Using Remote Sensing and GIS
Airborne fine particulates (PM(2.5); particulate matter with diameter less than 2.5 μm) are receiving increasing attention for their potential toxicities and roles in visibility and health. In this study, we interpreted the behavior of PM(2.5) and its correlation with meteorological parameters in Ho...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3435953/ https://www.ncbi.nlm.nih.gov/pubmed/22969323 http://dx.doi.org/10.3390/s120606825 |
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author | Shi, Wenzhong Wong, Man Sing Wang, Jingzhi Zhao, Yuanling |
author_facet | Shi, Wenzhong Wong, Man Sing Wang, Jingzhi Zhao, Yuanling |
author_sort | Shi, Wenzhong |
collection | PubMed |
description | Airborne fine particulates (PM(2.5); particulate matter with diameter less than 2.5 μm) are receiving increasing attention for their potential toxicities and roles in visibility and health. In this study, we interpreted the behavior of PM(2.5) and its correlation with meteorological parameters in Hong Kong, during 2007–2008. Significant diurnal variations of PM(2.5) concentrations were observed and showed a distinctive bimodal pattern with two marked peaks during the morning and evening rush hour times, due to dense traffic. The study observed higher PM(2.5) concentrations in winter when the northerly and northeasterly winds bring pollutants from the Chinese mainland, whereas southerly monsoon winds from the sea bring fresh air to the city in summer. In addition, higher concentrations of PM(2.5) were observed in rush hours on weekdays compared to weekends, suggesting the influence of anthropogenic activities on fine particulate levels, e.g., traffic-related local PM(2.5) emissions. To understand the spatial pattern of PM(2.5) concentrations in the context of the built-up environment of Hong Kong, we utilized MODerate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Thickness (AOT) 500 m data and visibility data to derive aerosol extinction profile, then converted to aerosol and PM(2.5) vertical profiles. A Geographic Information Systems (GIS) prototype was developed to integrate atmospheric PM(2.5) vertical profiles with 3D GIS data. An example of the query function in GIS prototype is given. The resulting 3D database of PM(2.5) concentrations provides crucial information to air quality regulators and decision makers to comply with air quality standards and in devising control strategies. |
format | Online Article Text |
id | pubmed-3435953 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-34359532012-09-11 Analysis of Airborne Particulate Matter (PM(2.5)) over Hong Kong Using Remote Sensing and GIS Shi, Wenzhong Wong, Man Sing Wang, Jingzhi Zhao, Yuanling Sensors (Basel) Article Airborne fine particulates (PM(2.5); particulate matter with diameter less than 2.5 μm) are receiving increasing attention for their potential toxicities and roles in visibility and health. In this study, we interpreted the behavior of PM(2.5) and its correlation with meteorological parameters in Hong Kong, during 2007–2008. Significant diurnal variations of PM(2.5) concentrations were observed and showed a distinctive bimodal pattern with two marked peaks during the morning and evening rush hour times, due to dense traffic. The study observed higher PM(2.5) concentrations in winter when the northerly and northeasterly winds bring pollutants from the Chinese mainland, whereas southerly monsoon winds from the sea bring fresh air to the city in summer. In addition, higher concentrations of PM(2.5) were observed in rush hours on weekdays compared to weekends, suggesting the influence of anthropogenic activities on fine particulate levels, e.g., traffic-related local PM(2.5) emissions. To understand the spatial pattern of PM(2.5) concentrations in the context of the built-up environment of Hong Kong, we utilized MODerate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Thickness (AOT) 500 m data and visibility data to derive aerosol extinction profile, then converted to aerosol and PM(2.5) vertical profiles. A Geographic Information Systems (GIS) prototype was developed to integrate atmospheric PM(2.5) vertical profiles with 3D GIS data. An example of the query function in GIS prototype is given. The resulting 3D database of PM(2.5) concentrations provides crucial information to air quality regulators and decision makers to comply with air quality standards and in devising control strategies. Molecular Diversity Preservation International (MDPI) 2012-05-25 /pmc/articles/PMC3435953/ /pubmed/22969323 http://dx.doi.org/10.3390/s120606825 Text en © 2012 by the authors; licensee MDPI, Basel, Switzerland This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Article Shi, Wenzhong Wong, Man Sing Wang, Jingzhi Zhao, Yuanling Analysis of Airborne Particulate Matter (PM(2.5)) over Hong Kong Using Remote Sensing and GIS |
title | Analysis of Airborne Particulate Matter (PM(2.5)) over Hong Kong Using Remote Sensing and GIS |
title_full | Analysis of Airborne Particulate Matter (PM(2.5)) over Hong Kong Using Remote Sensing and GIS |
title_fullStr | Analysis of Airborne Particulate Matter (PM(2.5)) over Hong Kong Using Remote Sensing and GIS |
title_full_unstemmed | Analysis of Airborne Particulate Matter (PM(2.5)) over Hong Kong Using Remote Sensing and GIS |
title_short | Analysis of Airborne Particulate Matter (PM(2.5)) over Hong Kong Using Remote Sensing and GIS |
title_sort | analysis of airborne particulate matter (pm(2.5)) over hong kong using remote sensing and gis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3435953/ https://www.ncbi.nlm.nih.gov/pubmed/22969323 http://dx.doi.org/10.3390/s120606825 |
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