Abstract
In this paper, a new monitoring alert system for air pollution emergencies is proposed. The proposed system can perform air quality monitoring to provide real-time alerts of an individual event. The system uses two image analysis techniques, namely pixel recognition and haze extraction, for video fire smoke detection. The image analysis process is divided into daytime and nighttime image analyses, which involve the analysis of red-green-blue (RGB) and gray scale images. The images analyzed in this study were captured by the video camera of an air quality monitoring station. Seven fire accidents around a selected industrial park and downtown area were analyzed in detail. Among these accidents, three occurred at daytime, one occurred over 7 days, and three occurred at nighttime. Alert models based on pixel recognition and haze extraction were established. These models incorporated the formulas of haze equivalent (HT(t)) and separated pixels (XT(t)), as well as the threshold equations of haze equivalent (∇H) and separated pixels (∇X). An alert signal is sent to the administrator when HT(t) > ∇H or XT(t) > ∇X. The obtained results indicate that a real-time observation and alert system based on two image analysis techniques can be designed for air quality monitoring without expensive hardware devices. This alert system can be used by administrators to understand the course of a reportable event, especially as evidence for the appraisal of fire accidents. It is recommended that this system be connected to the fire brigades in order to obtain early fire information.
Highlights
The production, transportation, and storage processes in chemical industries are complex; the possibility of severe accidents is relatively high in these industries [1].Most accident escalations in the industry, especially fire events that are not handled in a timely manner, are caused by a domino effect [2]
Seven fire accidents around a selected industrial park and downtown area were analyzed in detail
Images and air quality monitoring data from May 2018 to March 2019 were collected from air quality monitoring stations (AQMSs) of the Environmental Protection Administration (EPA), Taiwan
Summary
The production, transportation, and storage processes in chemical industries are complex; the possibility of severe accidents is relatively high in these industries [1]. Liu et al [22] reported a method based on outdoor imaging to estimate particle matter concentration. Their method involves using six image features (the position of the sun, date, time, geographic information, and weather conditions) to predict the PM2.5 index. Wang et al [25] processed color images obtained with a general camera to estimate the real-time particulate mass concentration. Their method exhibited a correlation coefficient of 0.8219 and a mean square error of. This study assessed the monitoring performance achieved when integrating two image analysis techniques for developing a real-time observation and alert system for reportable events. The proposed system can be used to perform damage assessment and chronological construction for a reportable event
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