Pyrolysis of waste surgical masks into liquid fuel and its life-cycle assessment.

Pyrolysis of the middle layer of a surgical mask (MLM) and inner and outer layers of a surgical mask (IOM) was performed to assess their potential valorization as waste-to-energy feedstocks, and the characteristics of the resulting products were investigated. Pyrolysis of the main organics in waste surgical masks occurred at a very narrow temperature range of 456-466 °C. The main product was carbon-rich and oxygen-deficient liquid oil with a high heating value (HHV) of 43.5 MJ/kg. From the life-cycle perspective, environmental benefits and advantages of this upcycling approach were verified compared with conventional waste management approaches. This study advocated the potential application of waste surgical masks as feedstocks for fuels and energy, which is beneficial to mitigate plastic pollution and achieve sustainable plastic waste-to-energy upcycling, simultaneously.

A machine learning framework to quantify and assess the impact of COVID-19 on the power sector: An Indian context

As the COVID-19 continues to disrupt the global norms, there is the requirement of modelling frameworks to accurately assess and quantify the impact of the pandemic on the electricity sector and its emissions. In this study, we devise machine learning models to estimate the pandemic induced reduction in electricity consumption based on weather, econometrics, and social-distancing parameters for seven major Indian states. As per our baseline electricity consumption model, we find that the electricity consumption dropped by 15-33% in 2020 (March-May) during the complete lockdown phase, followed by 6-13% (June-August) during the unlock phases and gradually reached the norms by September 2020. As a result, the net CO2 emissions from power generation in 2020 dropped by 7% and 5% compared to 2018 and 2019 respectively. Amidst the ongoing second wave since mid-April 2021, we projected the electricity consumption across states from May-August by accounting for two scenarios. Under the reference and worst-case scenarios, the electricity consumption approximates 106% and 96% of the non-pandemic situation, respectively. The modelling framework developed in this study is purely data-oriented, cross-deployable across spatio-temporal scales and can serve as a valuable tool to inform current and future energy policies amidst and post COVID-19.