Abstract
The assessment of the health risks of volatile organic compounds (VOCs) emitted from landfills via dispersion model is crucial but also challenging because of remarkable variations in their emissions and meteorological conditions. This study used a probabilistic approach for the assessment of the health risks of typical VOCs by combining artificial neural network models for emission rates and a numerical dispersion model enhanced by probability analysis. A total of 8753 rounds of simulation were performed with distributions of waste compositions and the valid hourly meteorological conditions for 1 year. The concentration distributions and ranges of the typical health-risky VOCs after dispersion were analyzed with 95% probability. The individual and cumulative non-carcinogenic risks of the typical VOCs were acceptable with all values less than 1 in the whole study domain. For individual carcinogenic risks, only ethylbenzene, benzene, chloroform, and 1, 2-dichloroethane at extreme concentrations showed minor or moderate risks with a probability of 0.1%–1% and an impact distance of 650–3000 m at specific directions. The cumulative carcinogenic risks were also acceptable at 95% probability in the whole study domain, but exceeded 1 × 10−6 or even 1 × 10−4 at some extreme conditions, especially within the landfill area. The vertical patterns of the health risks with height initially increased, and then decreased rapidly, and the peak values were observed around the height of the emission source. The dispersion simulation and health risk assessment of the typical health-risky VOCs enhanced by Monte Carlo can accurately reflect their probabilistic dispersion patterns and health risks to surrounding residents from both spatial and temporal dimensions. With this approach, this study can provide important scientific basis and technical support for the health risk assessment and management of landfills.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.