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
AN - SCOPUS:85054540574
VL - 5
SP - 1
EP - 21
JO - Royal Society Open Science
JF - Royal Society Open Science
IS - 9
M1 - 180889
ER -