Entropy-based air quality monitoring network optimization using NINP and Bayesian maximum entropy.
Air quality
Bayesian maximum entropy (BME)
Fuzzy set theory
Multi-criteria decision-making (MCDM)
Nonlinear interval number programming (NINP)
Transinformation entropy (TE)
Journal
Environmental science and pollution research international
ISSN: 1614-7499
Titre abrégé: Environ Sci Pollut Res Int
Pays: Germany
ID NLM: 9441769
Informations de publication
Date de publication:
Jul 2023
Jul 2023
Historique:
received:
01
02
2023
accepted:
11
06
2023
medline:
24
7
2023
pubmed:
25
6
2023
entrez:
24
6
2023
Statut:
ppublish
Résumé
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, NO
Identifiants
pubmed: 37355508
doi: 10.1007/s11356-023-28270-w
pii: 10.1007/s11356-023-28270-w
doi:
Substances chimiques
Nitrogen Dioxide
S7G510RUBH
Ozone
66H7ZZK23N
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
84110-84125Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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