Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/7420
Title: | An integrated web-based air pollution decision support system – a prototype |
Authors: | Leung, Yee Prof. LEUNG Kwong Sak Wong, Man-Hong Mak, Terrence Cheung, Kwan-Yau Lo, Leung-Yau Yi, Wei-Ying Dong, Yuan-Lin |
Issue Date: | 2018 |
Source: | International Journal of Geographical Information Science,2018, vol. 32(9), pp. 1787-1814 |
Journal: | International Journal of Geographical Information Science |
Abstract: | To efficiently and effectively monitor and mitigate air pollution in the urban environment, it is of paramount importance to integrate into a unified whole air pollutant concentration databases coming from different sources including the ground-based stations, mobile sensors, remote sensing, atmospheric-chemical-transport models and social media for the analysis and unraveling of the complex air pollution processes in space and time. This study constructs and implements for the first time a prototype of the fully integrated air pollution decision support system (APDSS) that put together in an integrated manner all relevant multi-scale, multi-type and multi-source data for decision-making on urban air pollution. The prototype contains the main system that handles the multi-source, multi-type and multi-scale databases, queries, visualization and data mining algorithms and the integrated modules that individually and holistically capitalize on the power of the ground-based stations, ground and aerial mobile sensors, satellite-borne remote-sensing technologies, atmospheric-chemical-transport models and social media. It renders a solid scientific foundation and system development methodology for the study of the spatiotemporal air pollution profiles crucial to the mitigation of urban air pollution. Real-life applications of the prototype are employed to illustrate the functionality of the APDSS. |
Type: | Peer Reviewed Journal Article |
URI: | http://hdl.handle.net/20.500.11861/7420 |
DOI: | 10.1080/13658816.2018.1460752 |
Appears in Collections: | Applied Data Science - Publication |
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