JOURNAL ARTICLE
Airborne particulate matter exposure in male sugarcane workers at risk for chronic kidney disease in Guatemala.
Published In: Annals of Work Exposures & Health, 2025, v. 69, n. 4. P. 377 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Adgate, John L; Erlandson, Grant; Butler-Dawson, Jaime; Calvimontes-Barrientos, Laura; Amezquita, Luis; Seidel, James; Barnoya, Joaquin; Castro, Colton; Coyoy, Magali; Pérez, Marcos; Dally, Miranda; Krisher, Lyndsay; Jaramillo, Diana; Brindley, Stephen; Newman, Lee S; Schaeffer, Joshua 3 of 3
Abstract
This article focuses on measuring personal breathing zone exposure to particulate matter smaller than 5 microns (PM5) among male sugarcane harvesters in Guatemala, a population at risk for chronic kidney disease of unknown cause (CKDu). The study found that median full-shift personal PM5 concentrations (449 µg/m³) were substantially higher—approximately 3.5 times—than concurrent ambient field concentrations (median 136 µg/m³), indicating that ambient measurements alone underestimate true personal exposure. These elevated exposures are primarily attributed to resuspended dust and ash from burned sugarcane during manual harvesting rather than ambient smoke plumes. The findings underscore the importance of personal monitoring to assess occupational particulate exposure and suggest further research on the toxic constituents of this particulate matter and its potential links to kidney injury and other health outcomes in agricultural workers.
Additional Information
- Source:Annals of Work Exposures & Health. 2025/05, Vol. 69, Issue 4, p377
- Document Type:Article
- Subject Area:Environmental Sciences
- Publication Date:2025
- ISSN:2398-7308
- DOI:10.1093/annweh/wxaf008
- Accession Number:185321011
- Copyright Statement:Copyright of Annals of Work Exposures & Health is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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