How microplastics interact with food chain: a short overview of fate and impacts.

Microplastics as one of the ubiquitous contaminants have recently attracted attentions. Microplastics have the potential to impact the social-ecological environment. Accordingly, negating adverse effects on the environment necessitates scrutinizing physical and chemical characteristics of microplastics, emission sources, effects on the ecological environment, contaminated food chains especially human food web, and the impacts on human health. Microplastics are defined as very small plastic particles with a size smaller than 5 mm, which come in heterogeneous colors depending on their emission source and are composed of thermoplastics and thermosets. These particles based on their emission source are classified into primary and secondary microplastics. These particles diminish the quality of terrestrial, aquatic and air environments, which directly impact the habitats and trigger disruptions in plants and wild life. The adverse effects of these particles are multiplied when adsorbing to toxic chemicals. Moreover, these particles have the potential to be transmitted in organisms and human food chain. Due to the fact that the retention time in the body of organisms is longer than the time elapsed from ingestion to excretion, microplastic bioaccumulation occurs in the food webs.

Contribution of Bayesian networks as a robust tool in risk assessment under sustainability considerations, a case study of Bandarabbas refinery.

Background and purpose: Refineries are among the industrial centers that supply the energy and raw materials to downstream industries. To achieve sustainable development goals, creating appropriate balance between economical and environmental goals has always been the focus of managers and policy makers in the societies. Bayesian Network model has become a robust tool in the field of risk assessment and uncertainty management in refineries. The focus of this research is to prioritizing different units from the point of view of social and ecological aspects for facilitating the decision making process in the context of waste material treatment in Bandarabbas refinery in line with the sustainable development goals. Materials and methods: The methodology of this research is based on risk assessment with the aid of Bayesian Networks. To this end, first material flow analysis of the processes procured risk identification, subsequently influence diagram and Bayesian Network structure were designed. After completing conditional probability tables, finally risk factors were prioritized. What is more, sensitivity analysis of the model performed by applying three approaches namely predictive, diagnostic, and considering only one risk. Conclusion: According to the risk assessment results, Amine treatment and Fuel units were classified as the most significant risk factors, whereas Pipelines and Plant air & instrument air system were identified as the most environmental friendly units. In addition, sensitivity analysis of the model provided appropriate framework to shed some light on the circumstances of determining dominant risk factors whether only one or concurrently all of the endpoints are evaluated.