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A data-driven approach for optimal design of integrated air quality monitoring network in a chemical cluster. / Zhu, Zhengqiu; Chen, Bin; Qiu, Sihang; Wang, Rongxiao; Wang, Yiping; Ma, Liang; Qiu, Xiaogang.

In: Royal Society Open Science, Vol. 5, No. 9, 180889, 2018, p. 1-21.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Zhu, Z, Chen, B, Qiu, S, Wang, R, Wang, Y, Ma, L & Qiu, X 2018, 'A data-driven approach for optimal design of integrated air quality monitoring network in a chemical cluster' Royal Society Open Science, vol. 5, no. 9, 180889, pp. 1-21. https://doi.org/10.1098/rsos.180889

APA

Vancouver

Author

Zhu, Zhengqiu ; Chen, Bin ; Qiu, Sihang ; Wang, Rongxiao ; Wang, Yiping ; Ma, Liang ; Qiu, Xiaogang. / A data-driven approach for optimal design of integrated air quality monitoring network in a chemical cluster. In: Royal Society Open Science. 2018 ; Vol. 5, No. 9. pp. 1-21.

BibTeX

@article{af9c1089b77947c0b21318ec64a7d155,
title = "A data-driven approach for optimal design of integrated air quality monitoring network in a chemical cluster",
abstract = "The chemical industry is of paramount importance to the world economy and this industrial sector represents a substantial income source for developing countries. However, the chemical plants producing inside an industrial district pose a great threat to the surrounding atmospheric environment and human health. Therefore, designing an appropriate and available air quality monitoring network (AQMN) is essential for assessing the effectiveness of deployed pollution-controlling strategies and facilities. As monitoring facilities located at inappropriate sites would affect data validity, a two-stage data-driven approach constituted of a spatio-temporal technique (i.e. Bayesian maximum entropy) and a multi-objective optimization model (i.e. maximum concentration detection capability and maximum dosage detection capability) is proposed in this paper. The approach aims at optimizing the design of an AQMN formed by gas sensor modules. Owing to the lack of long-term measurement data, our developed atmospheric dispersion simulation system was employed to generate simulated data for the above method. Finally, an illustrative case study was implemented to illustrate the feasibility of the proposed approach, and results imply that this work is able to design an appropriate AQMN with acceptable accuracy and efficiency.",
keywords = "air quality monitoring network, atmospheric dispersion simulation system, Bayesian maximum entropy, multi-objective optimization model",
author = "Zhengqiu Zhu and Bin Chen and Sihang Qiu and Rongxiao Wang and Yiping Wang and Liang Ma and Xiaogang Qiu",
year = "2018",
doi = "10.1098/rsos.180889",
language = "English",
volume = "5",
pages = "1--21",
journal = "Royal Society Open Science",
issn = "2054-5703",
publisher = "The Royal Society",
number = "9",

}

RIS

TY - JOUR

T1 - A data-driven approach for optimal design of integrated air quality monitoring network in a chemical cluster

AU - Zhu, Zhengqiu

AU - Chen, Bin

AU - Qiu, Sihang

AU - Wang, Rongxiao

AU - Wang, Yiping

AU - Ma, Liang

AU - Qiu, Xiaogang

PY - 2018

Y1 - 2018

N2 - The chemical industry is of paramount importance to the world economy and this industrial sector represents a substantial income source for developing countries. However, the chemical plants producing inside an industrial district pose a great threat to the surrounding atmospheric environment and human health. Therefore, designing an appropriate and available air quality monitoring network (AQMN) is essential for assessing the effectiveness of deployed pollution-controlling strategies and facilities. As monitoring facilities located at inappropriate sites would affect data validity, a two-stage data-driven approach constituted of a spatio-temporal technique (i.e. Bayesian maximum entropy) and a multi-objective optimization model (i.e. maximum concentration detection capability and maximum dosage detection capability) is proposed in this paper. The approach aims at optimizing the design of an AQMN formed by gas sensor modules. Owing to the lack of long-term measurement data, our developed atmospheric dispersion simulation system was employed to generate simulated data for the above method. Finally, an illustrative case study was implemented to illustrate the feasibility of the proposed approach, and results imply that this work is able to design an appropriate AQMN with acceptable accuracy and efficiency.

AB - The chemical industry is of paramount importance to the world economy and this industrial sector represents a substantial income source for developing countries. However, the chemical plants producing inside an industrial district pose a great threat to the surrounding atmospheric environment and human health. Therefore, designing an appropriate and available air quality monitoring network (AQMN) is essential for assessing the effectiveness of deployed pollution-controlling strategies and facilities. As monitoring facilities located at inappropriate sites would affect data validity, a two-stage data-driven approach constituted of a spatio-temporal technique (i.e. Bayesian maximum entropy) and a multi-objective optimization model (i.e. maximum concentration detection capability and maximum dosage detection capability) is proposed in this paper. The approach aims at optimizing the design of an AQMN formed by gas sensor modules. Owing to the lack of long-term measurement data, our developed atmospheric dispersion simulation system was employed to generate simulated data for the above method. Finally, an illustrative case study was implemented to illustrate the feasibility of the proposed approach, and results imply that this work is able to design an appropriate AQMN with acceptable accuracy and efficiency.

KW - air quality monitoring network

KW - atmospheric dispersion simulation system

KW - Bayesian maximum entropy

KW - multi-objective optimization model

UR - http://www.scopus.com/inward/record.url?scp=85054540574&partnerID=8YFLogxK

UR - http://resolver.tudelft.nl/uuid:af9c1089-b779-47c0-b213-18ec64a7d155

U2 - 10.1098/rsos.180889

DO - 10.1098/rsos.180889

M3 - Article

VL - 5

SP - 1

EP - 21

JO - Royal Society Open Science

T2 - Royal Society Open Science

JF - Royal Society Open Science

SN - 2054-5703

IS - 9

M1 - 180889

ER -

ID: 47134974