Entropy-based air quality monitoring network optimization using NINP and Bayesian maximum entropy.

Haddadi, A; Nikoo, MR; Nematollahi, B; Al-Rawas, G; Al-Wardy, M; Toloo, M; Gandomi, AH

Entropy-based air quality monitoring network optimization using NINP and Bayesian maximum entropy.

Haddadi, A Nikoo, MR Nematollahi, B Al-Rawas, G Al-Wardy, M Toloo, M Gandomi, AH

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Publisher:: SPRINGER HEIDELBERG
Publication Type:: Journal Article
Citation:: Environ Sci Pollut Res Int, 2023, 30, (35), pp. 84110-84125
Issue Date:: 2023-07

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Field	Value	Language
dc.contributor.author	Haddadi, A
dc.contributor.author	Nikoo, MR
dc.contributor.author	Nematollahi, B
dc.contributor.author	Al-Rawas, G
dc.contributor.author	Al-Wardy, M
dc.contributor.author	Toloo, M
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2024-04-02T04:10:09Z
dc.date.available	2023-06-11
dc.date.available	2024-04-02T04:10:09Z
dc.date.issued	2023-07
dc.identifier.citation	Environ Sci Pollut Res Int, 2023, 30, (35), pp. 84110-84125
dc.identifier.issn	0944-1344
dc.identifier.issn	1614-7499
dc.identifier.uri	http://hdl.handle.net/10453/177431
dc.description.abstract	Effectual air quality monitoring network (AQMN) design plays a prominent role in environmental engineering. An optimal AQMN design should consider stations' mutual information and system uncertainties for effectiveness. This study develops a novel optimization model using a non-dominated sorting genetic algorithm II (NSGA-II). The Bayesian maximum entropy (BME) method generates potential stations as the input of a framework based on the transinformation entropy (TE) method to maximize the coverage and minimize the probability of selecting stations. Also, the fuzzy degree of membership and the nonlinear interval number programming (NINP) approaches are used to survey the uncertainty of the joint information. To obtain the best Pareto optimal solution of the AQMN characterization, a robust ranking technique, called Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) approach, is utilized to select the most appropriate AQMN properties. This methodology is applied to Los Angeles, Long Beach, and Anaheim in California, USA. Results suggest using 4, 4, and 5 stations to monitor CO, NO2, and ozone, respectively; however, implementing this recommendation reduces coverage by 3.75, 3.75, and 3 times for CO, NO2, and ozone, respectively. On the positive side, this substantially decreases TE for CO, NO2, and ozone concentrations by 8.25, 5.86, and 4.75 times, respectively.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	SPRINGER HEIDELBERG
dc.relation.ispartof	Environ Sci Pollut Res Int
dc.relation.isbasedon	10.1007/s11356-023-28270-w
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	03 Chemical Sciences, 05 Environmental Sciences, 06 Biological Sciences
dc.subject.classification	Environmental Sciences
dc.subject.mesh	Models, Theoretical
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	Environmental Monitoring
dc.subject.mesh	Entropy
dc.subject.mesh	Nitrogen Dioxide
dc.subject.mesh	Air Pollution
dc.subject.mesh	Ozone
dc.subject.mesh	Nitrogen Dioxide
dc.subject.mesh	Ozone
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	Air Pollution
dc.subject.mesh	Environmental Monitoring
dc.subject.mesh	Entropy
dc.subject.mesh	Models, Theoretical
dc.subject.mesh	Models, Theoretical
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	Environmental Monitoring
dc.subject.mesh	Entropy
dc.subject.mesh	Nitrogen Dioxide
dc.subject.mesh	Air Pollution
dc.subject.mesh	Ozone
dc.title	Entropy-based air quality monitoring network optimization using NINP and Bayesian maximum entropy.
dc.type	Journal Article
utslib.citation.volume	30
utslib.location.activity	Germany
utslib.for	03 Chemical Sciences
utslib.for	05 Environmental Sciences
utslib.for	06 Biological Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-02T04:10:08Z
pubs.issue	35
pubs.publication-status	Published
pubs.volume	30
utslib.citation.issue	35

Abstract:

Effectual air quality monitoring network (AQMN) design plays a prominent role in environmental engineering. An optimal AQMN design should consider stations' mutual information and system uncertainties for effectiveness. This study develops a novel optimization model using a non-dominated sorting genetic algorithm II (NSGA-II). The Bayesian maximum entropy (BME) method generates potential stations as the input of a framework based on the transinformation entropy (TE) method to maximize the coverage and minimize the probability of selecting stations. Also, the fuzzy degree of membership and the nonlinear interval number programming (NINP) approaches are used to survey the uncertainty of the joint information. To obtain the best Pareto optimal solution of the AQMN characterization, a robust ranking technique, called Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) approach, is utilized to select the most appropriate AQMN properties. This methodology is applied to Los Angeles, Long Beach, and Anaheim in California, USA. Results suggest using 4, 4, and 5 stations to monitor CO, NO2, and ozone, respectively; however, implementing this recommendation reduces coverage by 3.75, 3.75, and 3 times for CO, NO2, and ozone, respectively. On the positive side, this substantially decreases TE for CO, NO2, and ozone concentrations by 8.25, 5.86, and 4.75 times, respectively.

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http://hdl.handle.net/10453/177431