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利用可穿戴摄像机和个人吸入暴露测量在加纳非正规电子废物回收场的工人中推导时间-活性数据。

Derivation of Time-Activity Data Using Wearable Cameras and Measures of Personal Inhalation Exposure among Workers at an Informal Electronic-Waste Recovery Site in Ghana.

机构信息

Department of Epidemiology, University of Michigan, Washington Heights, Ann Arbor, MI, USA.

Department of Environmental Health Sciences, University of Michigan, Ann Arbor, MI, USA.

出版信息

Ann Work Expo Health. 2019 Oct 11;63(8):829-841. doi: 10.1093/annweh/wxz056.

Abstract

OBJECTIVES

Approximately 2 billion workers globally are employed in informal settings, which are characterized by substantial risk from hazardous exposures and varying job tasks and schedules. Existing methods for identifying occupational hazards must be adapted for unregulated and challenging work environments. We designed and applied a method for objectively deriving time-activity patterns from wearable camera data and matched images with continuous measurements of personal inhalation exposure to size-specific particulate matter (PM) among workers at an informal electronic-waste (e-waste) recovery site.

METHODS

One hundred and forty-two workers at the Agbogbloshie e-waste site in Accra, Ghana, wore sampling backpacks equipped with wearable cameras and real-time particle monitors during a total of 171 shifts. Self-reported recall of time-activity (30-min resolution) was collected during the end of shift interviews. Images (N = 35,588) and simultaneously measured PM2.5 were collected each minute and processed to identify activities established through worker interviews, observation, and existing literature. Descriptive statistics were generated for activity types, frequencies, and associated PM2.5 exposures. A kappa statistic measured agreement between self-reported and image-based time-activity data.

RESULTS

Based on image-based time-activity patterns, workers primarily dismantled, sorted/loaded, burned, and transported e-waste materials for metal recovery with high variability in activity duration. Image-based and self-reported time-activity data had poor agreement (kappa = 0.17). Most measured exposures (90%) exceeded the World Health Organization (WHO) 24-h ambient PM2.5 target of 25 µg m-3. The average on-site PM2.5 was 81 µg m-3 (SD: 94). PM2.5 levels were highest during burning, sorting/loading and dismantling (203, 89, 83 µg m-3, respectively). PM2.5 exposure during long periods of non-work-related activities also exceeded the WHO standard in 88% of measured data.

CONCLUSIONS

In complex, informal work environments, wearable cameras can improve occupational exposure assessments and, in conjunction with monitoring equipment, identify activities associated with high exposures to workplace hazards by providing high-resolution time-activity data.

摘要

目的

全球约有 20 亿工人在非正规环境中工作,这些环境存在大量因危险暴露而产生的风险,且工作任务和时间安排也各不相同。现有的职业危害识别方法必须适应无监管和具有挑战性的工作环境。我们设计并应用了一种从可穿戴摄像机数据中客观推导时间活动模式的方法,并将图像与工人在非正式电子废物(电子废物)回收现场的个人吸入暴露于特定粒径颗粒物(PM)的连续测量结果相匹配。

方法

在加纳阿克拉的阿格博格布洛西电子废物场,142 名工人在总共 171 个班次中佩戴了配备可穿戴摄像机和实时粒子监测器的采样背包。在下班采访中收集了 30 分钟分辨率的自我报告的时间活动回忆。每分钟收集图像(N=35588)和同时测量的 PM2.5,并进行处理以识别通过工人访谈、观察和现有文献确定的活动。生成了活动类型、频率和相关 PM2.5 暴露的描述性统计数据。kappa 统计量衡量自我报告和基于图像的时间活动数据之间的一致性。

结果

基于基于图像的时间活动模式,工人主要拆解、分类/装载、焚烧和运输电子废物材料以进行金属回收,活动持续时间变化很大。基于图像和自我报告的时间活动数据一致性较差(kappa=0.17)。大多数测量的暴露量(90%)超过世界卫生组织(WHO)24 小时环境 PM2.5 目标值 25 µg m-3。现场平均 PM2.5 为 81 µg m-3(SD:94)。在焚烧、分类/装载和拆解过程中,PM2.5 浓度最高(分别为 203、89 和 83 µg m-3)。在与工作无关的长时间活动期间,超过 88%的测量数据中的 PM2.5 暴露量也超过了世卫组织标准。

结论

在复杂的非正规工作环境中,可穿戴摄像机可以改善职业暴露评估,并与监测设备结合使用,通过提供高分辨率的时间活动数据来识别与工作场所危害高暴露相关的活动。

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