Approach to identifying a sudden continuous emission pollutant source based on single sensor with noise. Issue 7 (November 2014)

• Record Type: Journal Article
• Authors: Pang, Liping; Wang, Wei; Qu, Hongquan; Hu, Tao; Zhang, Yu
• Is Part Of: Indoor + built environment. Volume 23:Issue 7(2014)
• Page Start: 955
• Page End: 970
• Publication Date: 2014-11
• Subjects: Ventilated enclosed space -- Pollutant source identification -- Location probability -- Characteristic distance -- Source searching strategy -- Experimental study; Indoor air pollution -- Periodicals; Sick building syndrome -- Periodicals; Buildings -- Environmental engineering -- Periodicals; Environmental health -- Periodicals; 613.5
• DOI: 10.1177/1420326X13490529
• Languages: English
• ISSNs: 1420-326X

Abstract

Accidental pollution events often threaten people's health and lives, and it is necessary to identify a pollutant source rapidly so that prompt actions can be taken. An approach to identify a sudden continuous emission pollutant source based on the sensor measurement noise distribution model is developed in this paper. This approach attempts to locate a source in a steady velocity field while considering the impact of sensor measurement noise. This approach identifies a source by comparing the characteristic distance of the sensor-measured concentration sequence and the multiple hypothetical measured concentration sequences at the sensor position. A location probability function is established to show the location results based on the sensor measurement noise distribution model. Some important influential factors, time interval for identification, source position or sensor position, and mesh size, are discussed in detail. Additionally, a local fine-mesh source search strategy based on prior coarse-mesh location probabilities is presented to improve the efficiency of identification. In order to verify the practical application of the identification approach, an experimental system simulating pollutant transmission is set up and some experiments are conducted. The research results show that the proposed approach is able to identify a sudden pollutant source in a steady flow field by using the measured data with noise.