Developing an early-warning system for air quality prediction and assessment of cities in China

Wang, J; Zhang, X; Guo, Z; Lu, H

Developing an early-warning system for air quality prediction and assessment of cities in China

Wang, J Zhang, X Guo, Z Lu, H

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Publication Type:: Journal Article
Citation:: Expert Systems with Applications, 2017, 84 pp. 102 - 116
Issue Date:: 2017-10-30

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Field	Value	Language
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Zhang, X	en_US
dc.contributor.author	Guo, Z	en_US
dc.contributor.author	Lu, H https://orcid.org/0000-0001-5655-0237	en_US
dc.date.available	2020-05-25T19:02:43Z
dc.date.issued	2017-10-30	en_US
dc.identifier.citation	Expert Systems with Applications, 2017, 84 pp. 102 - 116	en_US
dc.identifier.issn	0957-4174	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124149
dc.description.abstract	© 2017 Elsevier Ltd Air quality has received continuous attention from both environmental managers and citizens. Accordingly, early-warning systems for air pollution are very useful tools to avoid negative health effects and develop effective prevention programs. However, developing robust early-warning systems is very challenging, as well as necessary. This paper develops a reliable and effective early-warning system that consists of air quality prediction and assessment modules. In the prediction module, a hybrid forecasting method is developed for predicting pollutant concentrations that effectively estimates future air quality conditions. In developing this proposed model, we suggest the use of a back propagation neural network algorithm, combined with a probabilistic parameter model and data preprocessing techniques, to address the uncertainties involved in future air quality prediction. Meanwhile, a pre-analysis is implemented, primarily by using optimized distribution functions to examine and analyze statistical characteristics and emission behaviors of air pollutants. The second method, which is developed as part of the second module, is based on fuzzy set theory and the Analytic Hierarchy Process, and it performs air quality assessments to provide a clear and intelligible description of air quality conditions. Using data from the Ministry of Environmental Protection of China and six stages of air quality classification levels, specifically good, moderate, lightly polluted, moderately polluted, heavily polluted and severely polluted, two cities in China, Chengdu and Hangzhou, are used as illustrative examples to verify the effectiveness of the developed early-warning system. The results demonstrate that the proposed methods are effective and reliable for use by environmental supervisors in air pollution monitoring and management.	en_US
dc.relation.ispartof	Expert Systems with Applications	en_US
dc.relation.isbasedon	10.1016/j.eswa.2017.04.059	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Developing an early-warning system for air quality prediction and assessment of cities in China	en_US
dc.type	Journal Article
utslib.citation.volume	84	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	84	en_US

Abstract:

© 2017 Elsevier Ltd Air quality has received continuous attention from both environmental managers and citizens. Accordingly, early-warning systems for air pollution are very useful tools to avoid negative health effects and develop effective prevention programs. However, developing robust early-warning systems is very challenging, as well as necessary. This paper develops a reliable and effective early-warning system that consists of air quality prediction and assessment modules. In the prediction module, a hybrid forecasting method is developed for predicting pollutant concentrations that effectively estimates future air quality conditions. In developing this proposed model, we suggest the use of a back propagation neural network algorithm, combined with a probabilistic parameter model and data preprocessing techniques, to address the uncertainties involved in future air quality prediction. Meanwhile, a pre-analysis is implemented, primarily by using optimized distribution functions to examine and analyze statistical characteristics and emission behaviors of air pollutants. The second method, which is developed as part of the second module, is based on fuzzy set theory and the Analytic Hierarchy Process, and it performs air quality assessments to provide a clear and intelligible description of air quality conditions. Using data from the Ministry of Environmental Protection of China and six stages of air quality classification levels, specifically good, moderate, lightly polluted, moderately polluted, heavily polluted and severely polluted, two cities in China, Chengdu and Hangzhou, are used as illustrative examples to verify the effectiveness of the developed early-warning system. The results demonstrate that the proposed methods are effective and reliable for use by environmental supervisors in air pollution monitoring and management.

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http://hdl.handle.net/10453/124149