Decrease of mobility, electricity demand, and NO2 emissions on COVID-19 times and their feedback on prevention measures

A new perspective to satellite-based retrieval of ground-level air pollution: Simultaneous estimation of multiple pollutants based on physics-informed multi-task learning

Remote sensing of air pollution is essential for air quality management and health risk assessment. Many machine-learning-based retrieval models have been established for estimating various kinds of air pollutants. These methods mainly aimed to estimate a single pollutant (single-pollutant approach). However, different air pollutants interact with each other and are highly correlated. Building a unified model and conducting a joint retrieval of multiple pollutant can make a better use of these connections and improve the model efficiency. This study proposed a physics-informed multi-task deep neural network (phyMTDNN) for the joint retrieval of six main air pollutants, i.e., PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃. The relationships among these pollutants were used to design the physics-informed network structure and loss function. Top-of-atmosphere reflectance which can generate retrieval results at ultrahigh resolution was used as model input. Experiments for mainland China in 2019 showed that the proposed model successfully achieved simultaneous estimation of six air pollutants, with the cross-validated R² for the six pollutants varying from 0.72 to 0.90. The contrast experiments proved that physics-informed network structure contributed to the most of the model performance improvement. Compared to the single-pollutant approach, phyMTDNN ameliorated the model accuracy on traces gases retrieval. Furthermore, the modeling efficiency was largely improved in that a lot of repetitive work was avoided and modeling method was optimized. The proposed new multiple-pollutant retrieval frame can be applied to various fields for multi-variate retrieval or estimation.

Emissions of nitrogen dioxide in the northeast U.S. during the 2020 COVID-19 lockdown

We have quantified the emissions of Nitrogen dioxide (NO2) in the Northeast megalopolis of the United States during the COVID-19 lockdown. The measurement of NO2 emission serves as the indicator for the emission of the group of nitrogen oxides (NOx). Approximately 56% of NO2 emissions in the US are from mobile sources, and the remainder is from stationary sources. Since 2002, clean air regulations have resulted in approximately 5% compound annual reduction of NOx emissions in the US (8.2% in the study area). Therefore, when studying the impact of sporadic events like an epidemic on emissions, it is necessary to account for the persistent reduction of emissions due to policy driven emission reduction measures. Using spaceborne sensors, ground monitors, National Emission Inventory data, and the US Motor Vehicle Emission Simulator, we quantified the reduction of total NOx emissions, distinguishing stationary sources from on-road mobile sources (trucks and automobiles). When considering total NOx emissions (stationary and mobile combined), we find that the pandemic restrictions resulted in 3.4% reduction of total NOx emissions in the study area in 2020. This is compared to (and in addition to) the expected 8.2% policy driven reduction of NOx emissions in 2020. This somewhat low reduction of emissions is because most stationary sources (factories, power plants, etc.) were operational during the pandemic. Truck traffic, a significant source of mobile emissions, also did not decline significantly (average 4.8% monthly truck traffic reduction in the study area between March and August 2020), as they were delivering goods during the lockdown. On the other hand, automobile traffic, responsible for 24% of total NOx emissions, dropped significantly, 52% in April, returning to near normal after 5 months. While the reduction of automobile traffic was significant, especially in the early months of the pandemic, its effect on emissions was relatively insignificant.

Future assessment of the impact of the COVID-19 pandemic on the electricity market based on a stochastic socioeconomic model

This paper proposes a time-series stochastic socioeconomic model for analyzing the impact of the pandemic on the regulated distribution electricity market. The proposed methodology combines the optimized tariff model (socioeconomic market model) and the random walk concept (risk assessment technique) to ensure robustness/accuracy. The model enables both a past and future analysis of the impact of the pandemic, which is essential to prepare regulatory agencies beforehand and allow enough time for the development of efficient public policies. By applying it to six Brazilian concession areas, results demonstrate that consumers have been/will be heavily affected in general, mainly due to the high electricity tariffs that took place with the pandemic, overcoming the natural trend of the market. In contrast, the model demonstrates that the pandemic did not/will not significantly harm power distribution companies in general, mainly due to the loan granted by the regulator agency, named COVID-account. Socioeconomic welfare losses averaging 500 (MR$/month) are estimated for the equivalent concession area, i.e., the sum of the six analyzed concession areas. Furthermore, this paper proposes a stochastic optimization problem to mitigate the impact of the pandemic on the electricity market over time, considering the interests of consumers, power distribution companies, and the government. Results demonstrate that it is successful as the tariffs provided by the algorithm compensate for the reduction in demand while increasing the socioeconomic welfare of the market.

Spatio-Temporal Heterogeneity of the Relationships Between PM2.5 and Its Determinants: A Case Study of Chinese Cities in Winter of 2020

Fine particulate matter (PM2.5) poses threat to human health in China, particularly in winter. The pandemic of coronavirus disease 2019 (COVID-19) led to a series of strict control measures in Chinese cities, resulting in a short-term significant improvement in air quality. This is a perfect case to explore driving factors affecting the PM2.5 distributions in Chinese cities, thus helping form better policies for future PM2.5 mitigation. Based on panel data of 332 cities, we analyzed the function of natural and anthropogenic factors to PM2.5 pollution by applying the geographically and temporally weighted regression (GTWR) model. We found that the PM2.5 concentration of 84.3% of cities decreased after lockdown. Spatially, in the winter of 2020, cities with high PM2.5 concentrations were mainly distributed in Northeast China, the North China Plain and the Tarim Basin. Higher temperature, wind speed and relative humidity were easier to promote haze pollution in northwest of the country, where enhanced surface pressure decreased PM2.5 concentrations. Furthermore, the intensity of trip activities (ITAs) had a significant positive effect on PM2.5 pollution in Northwest and Central China. The number of daily pollutant operating vents of key polluting enterprises in the industrial sector (VOI) in northern cities was positively correlated with the PM2.5 concentration; inversely, the number of daily pollutant operating vents of key polluting enterprises in the power sector (VOP) imposed a negative effect on the PM2.5 concentration in these regions. This work provides some implications for regional air quality improvement policies of Chinese cities in wintertime.

Assessment of the Impact of the Human Coronavirus (COVID-19) Lockdown on the Energy Sector: A Case Study of Sharjah, UAE

The recent coronavirus (COVID-19) pandemic has wreaked havoc on the global economy, causing major shifts in energy use and output patterns. For some countries, this has had a significant effect on energy demand and carbon emissions, at least in the short term. Since the United Arab Emirates is currently exerting many efforts towards sustainability, it is important to assess and understand the impacts of the pandemic and the lockdown measurements on the local energy sectors. Data for this analysis were gathered by the Sharjah Electricity Water & Gas Authority (SEWA) for Sharjah City which is the capital of the Emirate of Sharjah. The changes in electricity after the implementation of quarantine and lockdown-like measures were assessed, and the results indicate that the electric power demand in Sharjah City was reduced in the commercial, industrial, and agricultural sectors, whereas the residential and government sectors witnessed a higher power demand. The overall electricity consumption in the year 2020 was reduced by 1.04% in comparison with previous years including 2016 to 2019. The results of this study indicate that the changes in electricity consumption were minimal in Sharjah City as compared to other cities around the world. However, this paper highlights the importance of governmental response during and after a pandemic, and the possible impacts that lockdowns could potentially have in the energy industry worldwide.

Assessing the potential repercussions of the COVID-19 pandemic on global SDG attainment

The coronavirus disease (COVID-19) pandemic has led to a worldwide lockdown, and this restriction on human movements and activities has significantly affected society and the environment. Some effects might be quantitative, but some might be qualitative, and some effects could prolong immediately and/or persistently. This study examined the consequences of global lockdown for human movement and nitrogen dioxide (NO2) emissions using an air pollution index and dataset and satellite image analyses. We also evaluated the immediate (during lockdown) and persistent (after lockdown) effects of lockdown on achieving the SDGs. Our analysis revealed a drastic reduction in human movement and NO2 emissions and showed that many SDGs were influenced both immediately and persistently due to the global lockdown. We observed the immediate negative impacts on four goals and positive impacts on five goals, especially those concerning economic issues and ecosystem conservation, respectively. The persistent effects of lockdown were likely to be predominantly reversed from their immediate impacts due to economic recovery. The global lockdown has influenced the global community's ability to meet the SDGs, and our analysis provides powerful insights into the status of the internationally agreed-upon SDGs both during and after the COVID-19-induced global lockdown.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43621-021-00067-2.

Impact of COVID-19 pandemic on oil consumption in the United States: A new estimation approach

The COVID-19 pandemic broke the balance of oil supply and demand. Meeting these oil market challenges induced by the pandemic required a more accurate assessment of the impact of the pandemic on oil consumption. The existing measurement of the impact of the pandemic on oil consumption was based on year-over-year calculation. In this work, a new measurement approach based on a comparison of simulated and actual oil consumption was proposed. In this proposed measurement model, the actual oil consumption was from the official statistics, whereas the simulated oil demand came from business-as-usual (without pandemic) scenario simulation. In order to reduce the simulation error, three hybrid simulation approaches were developed by combining the simulation technique and machine learning technique. The mean relative errors of the proposed simulation approaches were between 1.08% and 2.51%, within the high precision level. An empirical research on the US oil consumption was conducted by running the proposed measurement model. Through analyzing the difference between the simulated and real US oil consumption, we found the impact of the epidemic on U.S. oil consumption was obvious in April-May 2020 and January-February 2021. At its worst, the oil decline in the United States reached 973 trillion British thermal units, compared to the state without the epidemic. During the entire survey period (January 2020-March 2021), the US oil consumption under the epidemic was about 18.14% lower than that under the normal epidemic-free situation, which was 5% higher than the 13% inter-annual decline rate reported. This work contributed to understand the impact of the pandemic on oil consumption more comprehensively, and also provided a new approach for analyzing the impact of the pandemic on energy consumption.

COVID-19 Impact on Public Health, Environment, Human Psychology, Global Socioeconomy, and Education

The end of the year 2019 was marked by the introduction of a third highly pathogenic coronavirus, after SARS-CoV (2003) and MERS-CoV (2012), in the human population which was officially declared a global pandemic by the World Health Organization (WHO) on March 11, 2020. Indeed, the pandemic of COVID-19 (Coronavirus Disease 19) has evolved at an unprecedented rate: after its emergence in Wuhan, the capital of the province of Hubei of the People's Republic of China, in December 2019, the total number of confirmed cases did not cease growing very quickly in the world. In this manuscript, we have provided an overview of the impact of COVID-19 on health, and we have proposed different nutrients suitable for infected patients to boost their immune systems. On the other hand, we have described the advantages and disadvantages of COVID-19 on the environment including the quality of water, air, waste management, and energy consumption, as well as the impact of this pandemic on human psychology, the educational system, and the global economy. In addition, we have tried to come up with some solutions to counter the negative repercussions of the pandemic.

How changes in human activities during the lockdown impacted air quality parameters: A review

The health emergency linked to the spread of COVID-19 has led to important reduction in industrial and logistics activities, as well as to a drastic changes in citizens' behaviors and habits. The restrictions on working activities, journeys and relationships imposed by the lockdown have had important consequences, including for environmental quality. This review aims to provide a structured and critical evaluation of the recent scientific bibliography that analyzed and described the impact of lockdown on human activities and on air quality. The results indicate an important effect of the lockdown during the first few months of 2020 on air pollution levels, compared to previous periods. The concentrations of particulate matter, nitrogen dioxide, sulfur dioxide and carbon monoxide have decreased. Tropospheric ozone, on the other hand, has significantly increased. These results are important indicators that can become decision drivers for future policies and strategies in industrial and logistics activities (including the mobility sector) aimed at their environmental sustainability. The scenario imposed by COVID-19 has supported the understanding of the link between the reduction of polluting emissions and the state of air quality and will be able to support strategic choices for the future sustainable growth of the industrial and logistics sector.