...
首页> 外文期刊>Mining Engineering >Helmet-CAM: Strategically minimizing exposures to respirable dust through video exposure monitoring
【24h】

Helmet-CAM: Strategically minimizing exposures to respirable dust through video exposure monitoring

机译:Helmet-CAM:通过视频曝光监测,战略性地最大限度地减少可吸入灰尘的暴露

获取原文
获取原文并翻译 | 示例
           

摘要

Exposure to respirable crystalline silica (RCS) remains a serious health hazard to the U.S. mining workforce who are potentially exposed to RCS [1,2] as various ore bodies are drilled, blasted, hauled by truck, crushed, screened/sized and transported to their destinations. To examine a method for assessing RCS exposure using a NIOSH-developed video exposure monitoring (VEM) technology (referred to as Helmet-CAM), video and respirable dust concentration data were collected on 80 miners across seven unique mining sites. The data were then collated and partitioned using a thresholding scheme to determine exposures that were in excess of 10 times the mean exposure for that worker. Focusing on these short-duration, high-magnitude exposures can provide insight into implementing controls and interventions that can dramatically lower the employee's overall average exposure. In 19 of the 80 cases analyzed, it was found that exposure could be significantly lowered by 20 percent or more by reducing exposures that occur during just 10 minutes of work per eight-hour shift. This approach provides a method to quickly analyze and determine which activities are creating the greatest health concerns. In most cases, once identified, focused control technologies or behavioral modifications can be applied to those tasks.
机译:暴露于可吸入的结晶二氧化硅(RCS)仍然是对美国采矿劳动力的严重健康危害,他们可能暴露于RCS [1,2]作为钻孔,喷砂,被卡车牵引,粉碎,筛选/大小和运输他们的目的地。为了检查使用Niosh开发的视频曝光监测(VEM)技术(称为头盔凸轮)进行评估RCS曝光的方法,在七个独特的采矿部位的80名矿工上收集视频和可吸入粉尘浓度数据。然后使用阈值方案来整理和分区数据以确定该工人的平均暴露的10倍的暴露。重点关注这些短期,高幅度曝光可以深入了解实施控制和干预,这些控制和干预措施可以大大降低员工的整体平均曝光。在80例中分析的19例中,发现暴露可能通过减少每8小时班次仅10分钟的工作期间发生的暴露而显着降低20%或更高。这种方法提供了一种快速分析和确定哪些活动正在创造最大的健康问题的方法。在大多数情况下,一旦被识别,聚焦的控制技术或行为修改可以应用于这些任务。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号