A system-scale environmental risk analysis with considering a conceptual conversion from material/energy flow to information flow under uncertainties. (15th December 2021) - Li, Jing; Chen, Yizhong; Peng, He; He, Li
Abstract: A conceptual conversion from material/energy flow to information flow is presented in this study for evaluating network environment analysis (NEA) within the naphthalene-contaminated groundwater ecosystems under stochastic-fuzzy uncertainties. Four components (i.e., vegetation, herbivore, soil microorganism, and carnivore) are considered into the NEA framework for quantifying their direct and integral ecological risks. Carcinogenic risk related to human health concern is also evaluated under four remediation periods. The developed method is then applied to a power plant site. Results reveal that the average naphthalene concentration after pump-and-treat treatment would significantly decrease from 8.672 to 1.232 μg/L when remediation period extends to 10 years. The probabilities of suffering from carcinogenic risk would reach 0.9862, 0.9566, 0.8746, and 0.6142 under different remediation periods. Soil microorganism would receive more input risk than vegetation owing to its higher vulnerability. Although the upper-layer components (such as herbivore and carnivore) are not exposed to risk sources, they would gradually accumulate to a high-level ecological risk through food chains. Sensitivity analysis shows that variations in standard boundaries would have a significant impact on the risks of all components within groundwater ecosystems. This study can offer a novel perspective and methodology for comprehensively assessing the system-scale environment risks. Graphical abstract: Image 1 Highlights: Pathways of risk transmission are illustrated based on a Network Environ Analysis model. Comprehensive environment risks are evaluated under stochastic-fuzzy uncertainties. Impact of remediation actions on risk transfer within groundwater ecosystems is identified. Four representing components are used for characterizing information-based risk flows.