Electrospun nanofibers for efficient adsorption of heavy metals from water and wastewater

Heavy metals (HMs) are persistent and toxic environmental pollutants that pose critical risks toward human health and environmental safety. Their efficient elimination from water and wastewater is essential to protect public health, ensure environmental safety, and enhance sustainability. In the recent decade, nanomaterials have been developed extensively for rapid and effective removal of HMs from water and wastewater and to address the certain economical and operational challenges associated with conventional treatment practices, including chemical precipitation, ion exchange, adsorption, and membrane separation. However, the complicated and expensive manufacturing process of nanoparticles and nanotubes, their reduced adsorption capacity due to the aggregation, and challenging recovery from aqueous solutions limited their widespread applications for HM removal practices. Thus, the nanofibers have emerged as promising adsorbents due to their flexible and facile production process, large surface area, and simple recovery. A growing number of chemical modification methods have been devised to promote the nanofibers\u27 adsorption capacity and stability within the aqueous systems. This paper briefly discusses the challenges regarding the effective and economical application of conventional treatment practices for HM removal. It also identifies the practical challenges for widespread applications of nanomaterials such as nanoparticles and nanotubes as HMs adsorbents. This paper focuses on nanofibers as promising HMs adsorbents and reviews the most recent advances in terms of chemical grafting of nanofibers, using the polymers blend, and producing the composite nanofibers to create highly effective and stable HMs adsorbent materials. Furthermore, the parameters that influence the HM removal by electrospun nanofibers and the reusability of adsorbent nanofibers were discussed. Future research needs to address the gap between laboratory investigations and commercial applications of adsorbent nanofibers for water and wastewater treatment practices are also presented

An investigation of stormwater quality variation within an industry sector using the self-reported data collected under the stormwater monitoring program

Storm runoff pollutants are among the major sources of surface water impairments, globally. Despite several monitoring programs and guidance on stormwater management practices, there are many streams still impaired by urban runoff. This study evaluates an industry sector’s pollutant discharge characteristics using the self-reported data collected under Tennessee Multi Sector Permit program. The stormwater pollutant discharge characteristics were analyzed from 2014 to 2018 for an industry sector involving twelve facilities in West Tennessee, USA. The data analysis revealed the presence of both organic and inorganic contaminants in stormwater samples collected at all twelve industrial facilities, with the most common metals being magnesium, copper, and aluminum. The principal component analysis (PCA) was applied to better understand the correlation between water quality parameters, their origins, and seasonal variations. Furthermore, the water quality indexes (WQIs) were calculated to evaluate the stormwater quality variations among studied facilities and seasons. The results demonstrated slight variations in stormwater WQIs among the studied facilities ranging from “Bad” to “Medium” quality. The lowest seasonal average WQI was found for spring compared to the other seasons. Certain limitations associated with the self-reported nature of data were identified to inform the decision makers regarding the required future changes

Robust design of a multi-objective closed-loop supply chain by integrating on-time delivery, cost, and environmental aspects, case study of a Tire Factory

Rising population and vehicle use resulted in an exponential growth of used tires generation which could cause significant disposal and environmental challenges. Thus, implementation of appropriate recycling or disposal practices has emerged as the critical challenge for the industries aiming to improve their environmental sustainability while satisfy the customers’ needs and maintain the economic benefits. In this study, a multi-level closed-loop supply chain network is developed under deterministic and uncertain conditions to maximize the time delivery, and minimize total costs, and environmental impacts under the uncertainty of some parameters. A three-objective mixed integer linear programming model is proposed. The delivery time of purchased materials from suppliers to the manufacturing plants is maximized using the first objective function. The network overall profitability is maximized using the second objective function, and the negative environmental impacts are minimized using the third objective function. Furthermore, to overcome the innate uncertainty of the models’ parameters, the Soyster and Mulvey approaches are applied. The developed model is implemented in a case study of a Tire Company. The results revealed the efficiency of on-time delivery considerations on the selection of internal and external suppliers. Applying the VIKOR method demonstrated the better performance of Soyster method compare to the Mulvey method. The results highlighted the performance of the proposed model in which the decision makers were enabled to decide among different types of suppliers (internal and external) based on the time delivery, total cost, and environmental impact

Development of sustainable and resilient healthcare and non-cold pharmaceutical distribution supply chain for COVID-19 pandemic: a case study

Purpose: This study evaluated the influence of the coronavirus pandemic on the healthcare and non-cold pharmaceutical care distribution supply chain. Design/methodology/approach: The model involves four objective functions to minimize the total costs, environmental impacts, lead time and the probability of a healthcare provider being infected by a sick person was developed. An improved version of the augmented e-constraint method was applied to solve the proposed model for a case study of a distribution company to show the effectiveness of the proposed model. A sensitivity analysis was conducted to identify the sensitive parameters. Finally, two robust models were developed to overcome the innate uncertainty of sensitive parameters. Findings: The result demonstrated a significant reduction in total costs, environmental impacts, lead time and probability of a healthcare worker being infected from a sick person by 40%, 30%, 75% and 54%, respectively, under the coronavirus pandemic compared to the normal condition. It should be noted that decreasing lead time and disease infection rate could reduce mortality and promote the model\u27s effectiveness. Practical implications: Implementing this model could assist the healthcare and pharmaceutical distributors to make more informed decisions to minimize the cost, lead time, environmental impacts and enhance their supply chain resiliency. Originality/value: This study introduced an objective function to consider the coronavirus infection rates among the healthcare workers impacted by the pharmaceutical/healthcare products supply chain. This study considered both economic and environmental consequences caused by the coronavirus pandemic condition, which occurred on a significantly larger scale than past pandemic and epidemic crises