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

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ID: 87854
2018
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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.
Reference Key
zhu2018aroyal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhu, Zhengqiu;Chen, Bin;Qiu, Sihang;Wang, Rongxiao;Wang, Yiping;Ma, Liang;Qiu, Xiaogang;
Journal Royal Society open science
Year 2018
DOI
10.1098/rsos.180889
URL
Keywords
bayesian maximum entropy air quality monitoring network atmospheric dispersion simulation system multi-objective optimization model

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