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

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

Zhu, Zhengqiu (National University of Defense Technology)
Chen, B. (National University of Defense Technology)
Qiu, S. (TU Delft Web Information Systems)
Wang, Rongxiao (National University of Defense Technology)
Wang, Yiping (Naval 902 Factory)
Ma, Liang (National University of Defense Technology)
Qiu, Xiaogang (National University of Defense Technology)

2018

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.

air quality monitoring network
atmospheric dispersion simulation system
Bayesian maximum entropy
multi-objective optimization model

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

https://doi.org/10.1098/rsos.180889

Royal Society Open Science, 5 (9), 1-21

Institutional Repository

journal article

© 2018 Zhengqiu Zhu, B. Chen, S. Qiu, Rongxiao Wang, Yiping Wang, Liang Ma, Xiaogang Qiu