Energy and environmental sustainability of waste personal protective equipment (PPE) treatment under COVID-19.

Combating the COVID-19 pandemic has raised the demand for and disposal of personal protective equipment in the United States. This work proposes a novel waste personal protective equipment processing system that enables energy recovery through producing renewable fuels and other basic chemicals. Exergy analysis and environmental assessment through a detailed life cycle assessment approach are performed to evaluate the energy and environmental sustainability of the processing system. Given the environmental advantages in reducing 35.42% of total greenhouse gas emissions from the conventional incineration and 43.50% of total fossil fuel use from landfilling processes, the optimal number, sizes, and locations of establishing facilities within the proposed personal protective equipment processing system in New York State are then determined by an optimization-based site selection methodology, proposing to build two pre-processing facilities in New York County and Suffolk County and one integrated fast pyrolysis plant in Rockland County. Their optimal annual treatment capacities are 1,708 t/y, 8,000 t/y, and 9,028 t/y. The proposed optimal personal protective equipment processing system reduces 31.5% of total fossil fuel use and 35.04% of total greenhouse gas emissions compared to the personal protective equipment incineration process. It also avoids 41.52% and 47.64% of total natural land occupation from the personal protective equipment landfilling and incineration processes.

Effect of COVID-19 virus on reducing GHG emission and increasing energy generated by renewable energy sources: A brief study in Malaysian context.

Coronavirus 2019 (COVID-19) has globally affected the human mortality rate and economic history of the modern world. According to the World Health Organization, COVID-19 has caused a severe threat to the health of the vulnerable groups, notably the elderly. There is still some disagreements regarding the source of the virus and its intermediate host. However, the spread of this disease has caused most countries to enforce strict curfew laws and close most industrial and recreational centres. This study aims to show the potential positive effects of COVID-19 on the environment and the increase of renewable energy generation in Malaysia. To prevent the spread of this disease, Malaysia enacted the Movement Control Order (MCO) law in March 2020. Implementation of this law led to a reduction in environmental pollution, especially air pollution, in this country. The greenhouse gases (GHG) emission , which was 8 Mt CO2 eq. from January 2020 to March 2020, reduced to <1 Mt CO2 eq. for April and May. The reduction of GHG emission and pollutant gases allowed more sunlight to reach photovoltaic panels, hence increasing the renewable energy generation.

The relationship between air pollution and COVID-19-related deaths: An application to three French cities.

Being heavily dependent to oil products (mainly gasoline and diesel), the French transport sector is the main emitter of Particulate Matter (PMs) whose critical levels induce harmful health effects for urban inhabitants. We selected three major French cities (Paris, Lyon, and Marseille) to investigate the relationship between the Coronavirus Disease 19 (COVID-19) outbreak and air pollution. Using Artificial Neural Networks (ANNs) experiments, we have determined the concentration of PM2.5 and PM10 linked to COVID-19-related deaths. Our focus is on the potential effects of Particulate Matter (PM) in spreading the epidemic. The underlying hypothesis is that a pre-determined particulate concentration can foster COVID-19 and make the respiratory system more susceptible to this infection. The empirical strategy used an innovative Machine Learning (ML) methodology. In particular, through the so-called cutting technique in ANNs, we found new threshold levels of PM2.5 and PM10 connected to COVID-19: 17.4 µg/m3 (PM2.5) and 29.6 µg/m3 (PM10) for Paris; 15.6 µg/m3 (PM2.5) and 20.6 µg/m3 (PM10) for Lyon; 14.3 µg/m3 (PM2.5) and 22.04 µg/m3 (PM10) for Marseille. Interestingly, all the threshold values identified by the ANNs are higher than the limits imposed by the European Parliament. Finally, a Causal Direction from Dependency (D2C) algorithm is applied to check the consistency of our findings.