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From air to airway: Dynamics and risk of inhalable bacteria in municipal solid waste treatment systems.
- Source :
-
Journal of hazardous materials [J Hazard Mater] 2023 Oct 15; Vol. 460, pp. 132407. Date of Electronic Publication: 2023 Aug 26. - Publication Year :
- 2023
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Abstract
- Municipal solid waste treatment (MSWT) system emits a cocktail of microorganisms that jeopardize environmental and public health. However, the dynamics and risks of airborne microbiota associated with MSWT are poorly understood. Here, we analyzed the bacterial community of inhalable air particulates (PM <subscript>10</subscript> , n = 71) and the potentially exposed on-site workers' throat swabs (n = 30) along with waste treatment chain in Shanghai, the largest city of China. Overall, the airborne bacteria varied largely in composition and abundance during the treatment (P < 0.05), especially in winter. Compared to the air conditions, MSWT-sources that contributed to 15 ∼ 70% of airborne bacteria more heavily influenced the PM <subscript>10</subscript> -laden bacterial communities (PLS-SEM, β = 0.40, P < 0.05). Moreover, our year-span analysis found PM <subscript>10</subscript> as an important media spreading pathogens (10 <superscript>4</superscript> ∼ 10 <superscript>8</superscript> copies/day) into on-site workers. The machine-learning identified Lactobacillus and Streptococcus as pharynx-niched featured biomarker in summer and Rhodococcus and Capnocytophaga in winter (RandomForest, ntree = 500, mtry = 10, cross = 10, OOB = 0%), which closely related to their airborne counterparts (Procrustes test, P < 0.05), suggesting that MSWT a dynamic hotspot of airborne bacteria with the pronounced inhalable risks to the neighboring communities.<br />Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2023 Elsevier B.V. All rights reserved.)
- Subjects :
- Humans
China
Dust
Machine Learning
Solid Waste
Bacteria
Subjects
Details
- Language :
- English
- ISSN :
- 1873-3336
- Volume :
- 460
- Database :
- MEDLINE
- Journal :
- Journal of hazardous materials
- Publication Type :
- Academic Journal
- Accession number :
- 37651934
- Full Text :
- https://doi.org/10.1016/j.jhazmat.2023.132407