Report: APP CMHS Project 4




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2.5.2. Hazard Identification

National Institute for Occupational Safety and Health (NIOSH), Pittsburgh Research Laboratory, has developed the Hazard Recognition Training Program to teach miners to recognise visual cues that distinguish poor ground conditions. The impetus behind this effort was to test the hypothesis that worker safety, in all segments of the mining industry, can be improved if individual hazard recognition skills are enhanced. Earlier studies have shown that the ability of coal miners to recognise hazards in their work environment was significantly improved through the "degraded" method of training (Figure 7). The degraded-image concept was originally developed and used for military target detection training. Military research has shown that pilots who were trained with less than ideal, or degraded, pictures, were more successful in subsequent identification of targets than those trained using ideal(or "highlighted") pictures of targets. Degraded images are scenes in which the subjects are partially hidden from view, observed from an eccentric angle, viewed through haze to dust, inadequately illuminated, or otherwise obstructed as to camouflage the target.

NIOSH has examined the applicability of formal risk assessment practices to the U.S. mining industry. This research uses case studies at operating mines to identify potential benefits and obstacles to implementation of international methods in risk assessment and management. NIOSH has also undertaken basic research to better characterise the hazards of and means to eliminate methane explosions and large ground collapse events similar to the Sago, Darby and Crandall Canyon disasters of 2006 and 2007. This effort utilises the expertise available at leading U.S. mining universities and government research labs through contracts in coordination with intramural research efforts. Some coal mines in USA are starting to consider or adopt the findings of the NIOSH research.

Figure 7 Safety board showing on-the-job risk assessment: SLAM


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