Various advanced wastewater treatment methods to remove microplastics and prevent transmission of SARS-CoV-2 to airborne microplastics.

Microplastics (MPs) and SARS-CoV-2 interact due to their widespread presence in our environment and affect the virus' behaviour indoors and outdoors. Therefore, it is necessary to study the interaction between MPs and SARS-CoV-2. The environmental damage caused by MPs is increasing globally. Emerging pollutants may adversely affect organisms, especially sewage, posing a threat to human health, animal health, and the ecological system. A significant concern with MPs in the air is that they are a vital component of MPs in the other environmental compartments, such as water and soil, which may affect human health through ingesting or inhaling. This work introduces the fundamental knowledge of various methods in advanced water treatment, including membrane bioreactors, advanced oxidation processes, adsorption, etc., are highly effective in removing MPs; they can still serve as an entrance route due to their constantly being discharged into aquatic environments. Following that, an analysis of each process for MPs' removal and mitigation or prevention of SARS-CoV-2 contamination is discussed. Next, an airborne microplastic has been reported in urban areas, raising health concerns since aerosols are considered a possible route of SARS-CoV-2 disease transmission and bind to airborne MP surfaces. The MPs can be removed from wastewater through conventional treatment processes with physical processes such as screening, grit chambers, and pre-sedimentation.

Supporting an Expert-centric Process of New Product Introduction with Statistical Machine Learning

Industries that sell products with short-term or seasonal life cycles must regularly introduce new products. Forecasting the demand for New Product Introduction (NPI) can be challenging due to the fluctuations of many factors such as trend, seasonality, or other external and unpredictable phenomena (e.g., COVID-19 pandemic). Traditionally, NPI is an expert-centric process. This paper presents a study on automating the forecast of NPI demands using statistical Machine Learning (namely, Gradient Boosting and XGBoost). We show how to overcome shortcomings of the traditional data preparation that underpins the manual process. Moreover, we illustrate the role of cross-validation techniques for the hyper-parameter tuning and the validation of the models. Finally, we provide empirical evidence that statistical Machine Learning can forecast NPI demand better than experts. © Authors.