Surveillance efficiency evaluation of air quality monitoring networks for air pollution episodes in industrial parks: Pollution detection and source identification

Abstract

Both air pollution detection and source identification for air pollution episodes are highly desirable for detecting and controlling industrial air pollution. Surveillance of air pollution episodes in industrial parks is the focus of this article. The surveillance in this study consists of air pollution detection and subsequent source identification. The Gaussian puff model is applied to simulate the dispersion of air pollution, and the source area analysis method is used to reconstruct unknown source terms. A case study involving hydrogen sulfide emissions in a typical chemical industrial park is presented. The long-term efficiencies of both pollution detection and source identification of a developing planning of boundary-type air quality monitoring network (AQMN) are evaluated. Five typical scenarios are identified for the evaluation. Moreover, several key factors for the surveillance efficiency variation (i.e., meteorological conditions, monitor number and distance between sources) are discussed. The efficiency of pollution detection increases with the number of monitors. The efficiency of source identification increases with the number of monitors and the distance between sources.