Quantitative microbial risk assessment and sensitivity analysis for workers exposed to pathogenic bacterial bioaerosols under various aeration modes in two wastewater treatment plants


Wastewater treatment plants (WWTPs) could emit a large amount of bioaerosols containing pathogenic bacteria. Assessing the health risks of exposure to these bioaerosols by using quantitative microbial risk assessment (QMRA) is important to protect workers in WWTPs. However, the relative impacts of the stochastic input variables on the health risks determined in QMRA remain vague. Hence, this study performed a Monte Carlo simulation-based QMRA case study for workers exposing to S. aureus or E. coli bioaerosols and a sensitivity analysis in two WWTPs with various aeration modes. Results showed that when workers equipped without personal protective equipment (PPE) were exposed to S. aureus or E. coli bioaerosol in the two WWTPs, the annual probability of infection considerably exceeded the U.S. EPA benchmark (≤10E-4 pppy), and the disease burden did not satisfy the WHO benchmark (≤10E-6 DALYs pppy) (except exposure to E. coli bioaerosol for disease health risk burden). Nevertheless, the use of PPE effectively reduced the annual infection health risk to an acceptable level and converted the disease health risk burden to a highly acceptable level. Referring to the sensitivity analysis, the contribution of mechanical aeration modes to the variability of the health risks was absolutely dominated in the WWTPs. On the aeration mode that showed high exposure concentration, the three input exposure parameters (exposure time, aerosol ingestion rate, and breathing rate) had a great impact on health risks. The health risks were also prone to being highly influenced by the various choices of the dose-response model and related parameters. Current research systematically delivered new data and a novel perspective on the sensitivity analysis of QMRA. Then, management decisions could be executed by authorities on the basis of the results of this sensitivity analysis to reduce related occupational health risks of workers in WWTPs.

Authors: Yan-Huan Chen, Cheng Yan, Ya-Fei Yang, Jia-Xin Ma
; Full Source: The Science of the total environment 2020 Oct 1;755(Pt 2):142615. doi: 10.1016/j.scitotenv.2020.142615.