Black Carbon Exposure Characteristics in Diesel Engine Vehicle-related Jobs - Sangjun Choi; Ju-Hyun Park; Won Kim; Seung Won Kim; Kyong-Hui Lee; Taejin Chung; Jihoon Park; Seung-Hun Ryu; Jungah Shin; Dong-Hee Koh; Dong-Uk Park (Aerosol and Air Quality Research)
Abstract We aimed to characterize the black carbon (BC) exposure from eight types of jobs involving diesel engine vehicles—namely, machinery operation, forklift operation, automobile assembly, garbage collection, garbage truck driving, delivery, toll operation, and crane operation—across seven industries. The workers chosen for this study (N = 106) measured their exposure every minute using an AE51 microAethalometer affixed to a microcyclone and also recorded their time-activity patterns and whereabouts in detail during working hours. We then categorized and analyzed 71,987 of these 1-min observations based on four potential exposure determinants: the operation of a diesel engine vehicle (yes or no), proximity to the source of BC (near or far), location of the workplace (indoor or outdoor), and type of work (moving or stationary). Among the participants, the geometric mean (GM) exposure was highest for forklift operators in indoor environments (9.5 µg m−3), followed by toll operators (GM = 7.4 µg m−3), machinery operators (GM = 7.4 µg m−3), and garbage collectors (GM = 5.5 µg m−3). After accounting for the random effects of the individual workers and working dates (evaluated per occupation) as well as the fixed effects of the determinants and their pairwise interaction terms (p < 0.001), we found that all four of the determinants significantly differed in their associated levels of BC exposure. In particular, working near instead of far from diesel engine equipment doubled the average level of exposure. Additionally, upon investigating different combinations of the determinants, we identified proximity to diesel exhaust sources and indoor working conditions as the main factors of BC exposure. Thus, installing diesel particulate filters on diesel engine vehicles effectively reduces exposure. Our findings potentially contribute to the development of a model that predicts the level of exposure for various types of jobs.