Next Article in Journal
Evaluation of Moist Static Energy in a Simulated Tropical Cyclone
Previous Article in Journal
Customization and Validation of a Regional Climate Model Using Satellite Data Over East Africa
Open AccessArticle

Optimal Design of Air Quality Monitoring Network for Pollution Detection and Source Identification in Industrial Parks

by Zihan Huang 1, Qi Yu 1,2,3, Yujie Liu 1, Weichun Ma 1,2,3 and Limin Chen 1,*
1
Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
2
Big Data Institute for Carbon Emission and Environmental Pollution, Fudan University, Shanghai 200433, China
3
Shanghai Institute of Eco-Chongming (SIEC), No.3663 Northern Zhongshan Road, Shanghai 200062, China
*
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(6), 318; https://doi.org/10.3390/atmos10060318
Received: 11 May 2019 / Revised: 29 May 2019 / Accepted: 4 June 2019 / Published: 11 June 2019
(This article belongs to the Section Air Quality)
Dense air quality monitoring network (AQMN) is one of main ways to surveil industrial air pollution. This paper is concerned with the design of a dense AQMN for H2S for a chemical industrial park in Shanghai, China. An indicator (Surveillance Efficiency, SE) for the long-term performance of AQMN was constructed by averaging pollution detection efficiency (rd) and source identification efficiency (rb). A ranking method was developed by combing Gaussian puff model and Source area analysis for improving calculation efficiency. Candidate combinations with highest score were given priority in the selection of next site. Two existing monitors were suggested to relocate to the west and southwest of this park. SE of optimized AQMN increased quickly with monitor number, and then the growth trend started to flatten when the number reached about 60. The highest SE occurred when the number reached 110. Optimal schemes of AQMNs were suggested which can achieve about 98% of the highest SE, while using only about 60 monitors. Finally, the reason why the highest SE is less than 1 and the variation characteristics of rd and rb were discussed. Overall, the proposed method is an effective tool for designing AQMN with optimal SE in industrial parks. View Full-Text
Keywords: optimal design; surveillance efficiency; Gaussian puff model; source area analysis; odor pollution optimal design; surveillance efficiency; Gaussian puff model; source area analysis; odor pollution
Show Figures

Figure 1

MDPI and ACS Style

Huang, Z.; Yu, Q.; Liu, Y.; Ma, W.; Chen, L. Optimal Design of Air Quality Monitoring Network for Pollution Detection and Source Identification in Industrial Parks. Atmosphere 2019, 10, 318.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop