ABSTRACT
The outbreak of COVID-19 has caused concerns globally. To reduce the rapid transmission of the virus, strict city lockdown measures were conducted in different regions. China is the country that takes the earliest home-based quarantine for people. Although normal industrial and social activities were suspended, the spread of virus was efficiently controlled. Simultaneously, another merit of the city lockdown measure was noticed, which is the improvement of the air quality. Contamination levels of multiple atmospheric pollutants were decreased. However, in this work, 24 and 14 air fine particulate matter (PM2.5) samples were continuously collected before and during COVID-19 city lockdown in Linfen (a typical heavy industrial city in China), and intriguingly, the unreduced concentration was found for environmentally persistent free radicals (EPFRs) in PM2.5 after normal life suspension. The primary non-stopped coal combustion source and secondary Cu-related atmospheric reaction may have impacts on this phenomenon. The cigarette-based assessment model also indicated possible exposure risks of PM2.5-bound EPFRs during lockdown of Linfen. This study revealed not all the contaminants in the atmosphere had an apparent concentration decrease during city lockdown, suggesting the pollutants with complicated sources and formation mechanisms, like EPFRs in PM2.5, still should not be ignored. © 2022
ABSTRACT
Since the containment measures placed in several countries to deal with Covid-19 pandemic, air and noise pollution has been significantly reduced, but what about soil pollution and greenhouse gas emissions from waste management? Covid-19 has given a break to the earth by immobilizing a very large part of the world economy, industrial activity, and transport and by having an important modification on the Moroccan consumer behaviour. This demographic evolution and change of consumption do not infect the resources, but are a source of degradation and pollution of the different environmental components. Among these sources, we find the production of household and similar waste. Our objective is to rate the impact of this pandemic on waste production, in Ajdir landfill, El Hoceima, which reflects the activity of the Moroccan citizen (between March 20 and April 27, 2020), and the initiatives that have been taken and launched to solve the problems at the level of each province through the establishment of a provincial master plan for the management of household and similar waste. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
COVID-19 has plagued the world, one of which is Indonesia. During the COVID-19 pandemic, all anthropogenic activities are limited, including activities that cause air pollution, such as transportation and industrial activities. Nitrogen Dioxide (NO2) is one of the parameters of air pollution which has the main source of human activity. Therefore, this study aims to analyze the effect of the COVID-19 pandemic on changes in NO2 gas concentrations in the Yogyakarta Special Province. This study uses Sentinel 5-P satellite imagery data obtained through cloud computing on Google Earth Engine (GEE) to obtain NO2 gas concentration values. The results showed that there was a 3.7% decrease in the concentration of NO2 gas before and after the COVID-19 pandemic. The correlation result between the number of COVID-19 cases and the concentration of NO2 gas is 0.39, which means it has a weak correlation. © 2022 IEEE.
ABSTRACT
Non-technical summary. As we consider a transition to a low-carbon future, there is a need to examine the mineral needs for this transformation at a scale reminiscent of the Green Revolution. The efficiency gains of the agrarian transition came at ecological and social costs that should provide important lessons about future metal sourcing. We present three options for a Mineral Revolution: status quo, incremental adaption and revolutionary change. We argue that a sustainable Mineral Revolution requires a paradigm shift that considers wellbeing as a purpose and focuses on preserving natural capital.Technical summary. As we consider a transition to a low-carbon future, there is a need to examine the mineral needs for this transformation at a scale reminiscent of the Green Revolution. The efficiency gains of the agrarian transition came at ecological and social costs that can also provide important lessons about the Mineral Revolution. We lay out some of the key ways in which such a mineral revolution can be delineated over temporal scales in a paradigm shift that considers wellbeing as a purpose and focuses on preserving natural capital. These prospects are conceptually presented as three pathways that consider the status quo, incremental adaption and revolutionary change as a means of planning more effectively for a low-carbon transition.Social media summary. Sourcing metals sustainably will require to consider wellbeing as a purpose and to preserve natural capital.
ABSTRACT
In the present study, the atmospheric concentrations of Carbon Monoxide (CO) over India during COVID-19 (2020) were studied by comparing it with 2019 and 2021. COVID-19 has created an undesirable impact all over the world. However, as a blessing in disguise, these measures have a positive effect on the environment due to closing the mass gathering places. The work has undergone using the TROPOMI instrument, on-board Sentinel-5 Precursor. The results, evidence that human activities like transportation in Delhi, Industrial activities near Indo-Gangetic Plain have sharply fallen during the lockdown phase. On Contrary, there is a sharp increment in the area of Thermal power plants being coal-based. On the whole, the mean concentration of CO over India has minimal change due to long lifetime (1~2 months), indicating the duration of the (68 days) lockdown did not capture prompt and short-term atmospheric change. © 2021 IEEE.
ABSTRACT
The pandemics outbreak of Covid-19 in the world has made society and industrial activities very dynamic. The operating power plant must prepare to fulfil the fluctuating electricity demand from the load dispatcher. Hence, predicting the electrical power output is important to give the accuracy to maximize the profit and minimize losses. This paper discusses and predicts the half-hourly electrical output of Paiton Coal-Fired Power Station Unit 1 by develops many predictive models using five different machine learning regression methods. The five parameters that affect the electrical power output are used in the dataset, such as main steam flow, total coal flow, primary airflow, secondary airflow, and vacuum condenser pressure. These input and target variables as the dataset were collected over one year. The dataset is sorted and observed. Then, the best prediction model is sought for predicting electrical power output. Thus, the best performance of the best subset, which contains a complete set of input variables, has been analyzed using the most accurate machine learning algorithm, which is the random forest, with R-squared of 0.996. © 2021 IEEE.