Assessing Short-Term Productivity Losses Due to Pm2.5 Exposure Among Industrial Workers in Asaluyeh, Iran Publisher



Alavipoor FS ; Karimi S ; Jafari H ; Hassanvand MS ; Ziyarati MT
Source: Pollution Published:2026

Abstract

This investigation examines the effect of short-term exposure to fine particulate matter (PM2.5) on productivity of workers in the industrial city of Asaluyeh, Iran, with an objective to help develop better air quality control action plans. Because the immediate effects of PM2.5 on the workers in an industrial setting are poorly understood, we collected PM2.5 counts at each of the 6 monitors in 2018 and calculated the lost workdays using log-linear health models. We then isolated the spatial data using Kriging Interpolation and fuzzy logic models (FDEMATEL and FDANP) and consequently generated risk maps in six different areas. We estimated the economic burden by multiplying daily productivity (based on the average wage) with healthcare costs associated with respiratory health. Every 10 µg / m^3 increase in the annual mean concentration of PM2.5 was associated with a 75 increase in workdays lost in Chah Mobarak (relative risk [RR] = 1.75, 95 confidence interval [CI]: 1.6–1.9) and a 61 increase in Shirino (RR = 1.61, 95 CI: 1.5–1.73). Hara and Phases 6, 7 and 8 had no significant effect on workdays lost (RR = 1.0). For the spatial model, we assessed areas identified as very high-risk, which covered a land use footprint of 3,072.96 hectares and legitimate risk variables that were primarily caused by closeness to emission sources. Economic losses equaled 889,200 in productivity losses and 983.76 in health care costs per 10 µg / m^3 increase in Chah Mobarak, and the indirect losses we assigned for examples - reductions in industrial output - were 20 million. These results show the potential effects PM2.5 has on economic issues and health issues in industry served. We suggest specific emission controls that could be fairly easy and inexpensive to implement such as filter systems and live air quality monitoring to improve productive capacity and health of workers. © The Author(s).