Research on the emission reduction effects of carbon trading mechanism on power industry: plant-level evidence from China

PurposeCarbon trading mechanism has been adopted to foster the green transformation of the economy on a global scale, but its effectiveness for the power industry remains controversial. Given that energy-related greenhouse gas emissions account for most of all anthropogenic emissions, this paper aims to evaluate the effectiveness of this trading mechanism at the plant level to support relevant decision-making and mechanism design.Design/methodology/approachThis paper constructs a novel spatiotemporal data set by matching satellite-based high-resolution (1 × 1 km) CO2 and PM2.5 emission data with accurate geolocation of power plants. It then applies a difference-in-differences model to analyse the impact of carbon trading mechanism on emission reduction for the power industry in China from 2007 to 2016.FindingsResults suggest that the carbon trading mechanism induces 2.7% of CO2 emission reduction and 6.7% of PM2.5 emission reduction in power plants in pilot areas on average. However, the reduction effect is significant only in coal-fired power plants but not in gas-fired power plants. Besides, the reduction effect is significant for power plants operated with different technologies and is more pronounced for those with outdated production technology, indicating the strong potential for green development of backward power plants. The reduction effect is also more intense for power plants without affiliation relationships than those affiliated with particular manufacturers.Originality/valueThis paper identifies the causal relationship between the carbon trading mechanism and emission reduction in the power industry by providing an innovative methodology for identifying plant-level emissions based on high-resolution satellite data, which has been practically absent in previous studies. It serves as a reference for stakeholders involved in detailed policy formulation and execution, including policymakers, power plant managers and green investors.

SURFACE URBAN HEAT ISLAND IN MOSCOW DURING THE COVID-19 PANDEMIC LOCKDOWN IN 2020

The influence of the COronaVIrus Disease 2019 (COVID-19) pandemic lockdown (the period of strict quarantine measures) in the spring of 2020 on the ‘Surface Urban Heat Island' (SUHI) geographical phenomenon in Moscow has been studied. For this purpose, we used the measurements of the surface temperature TS made by Moderate Resolution Imaging Spectroradiometer (MODIS) radiometer installed on Terra and Aqua satellites. As a result, TS during the 2020 lockdown, both in the city and surrounding rural zone, was found lower than at the same calendar time in the previous 20 years due to the relatively cold spring. The SUHI intensity as the difference between TS inside Moscow and the surrounding rural zone around it during the lockdown was also lower than usual (on average in the previous 20 years), but this decrease is relatively small and nonsignificant. The Normalized Difference Vegetation Index (NDVI) in Moscow and Moscow region during the lockdown was close to its usual values, but the leaf area index (LAI) was significantly lower than its average values in the previous 20 years. Thus, the weakening of the SUHI during the lockdown in 2020 was caused mostly by lower heat loss due to transpiration in the rural zone. This was associated with the slowdown in vegetation development as a result of the cold spring. Besides, an additional possible reason was the reduction of human activity due to the collapse of many anthropogenic heat sources in the city. According to long-term MODIS data, the SUHI intensity in Moscow and the surface temperature in Moscow region, as well as the NDVI and LAI values, do not demonstrate statistically significant long-term trends in the spring season over the past 21 years, despite climate changes. In spring, during faster snow melting in cities, when it still persists in the rural zone, the SUHI intensity can be record high (up to 8 ºC). © 2022, Russian Geographical Society. All rights reserved.

Analysis and forecasting of air quality index based on satellite data.

OBJECTIVE: The air quality index (AQI) forecasts are one of the most important aspects of improving urban public health and enabling society to remain sustainable despite the effects of air pollution. Pollution control organizations deploy ground stations to collect information about air pollutants. Establishing a ground station all-around is not feasible due to the cost involved. As an alternative, satellite-captured data can be utilized for AQI assessment. This study explores the changes in AQI during various COVID-19 lockdowns in India utilizing satellite data. Furthermore, it addresses the effectiveness of state-of-the-art deep learning and statistical approaches for forecasting short-term AQI. MATERIALS AND METHODS: Google Earth Engine (GEE) has been utilized to capture the data for the study. The satellite data has been authenticated against ground station data utilizing the beta distribution test before being incorporated into the study. The AQI forecasting has been explored using state-of-the-art statistical and deep learning approaches like VAR, Holt-Winter, and LSTM variants (stacked, bi-directional, and vanilla). RESULTS: AQI ranged from 100 to 300, from moderately polluted to very poor during the study period. The maximum reduction was recorded during the complete lockdown period in the year 2020. Short-term AQI forecasting with Holt-Winter was more accurate than other models with the lowest MAPE scores. CONCLUSIONS: Based on our findings, air pollution is clearly a threat in the studied locations, and it is important for all stakeholders to work together to reduce it. The level of air pollutants dropped substantially during the different lockdowns.

COVID-19 lockdown improves air quality in Morocco

Several measures have been taken to mitigate the effects of the COVID-19 pandemic. In this context, almost all non-essential activities in Morocco have been halted since March 20, 2020. From that date, Morocco announced the lockdown for one month and it was extended until June 10, 2020. The main objective of this paper is to study the effects of the lockdown measures on air quality, by analyzing dust PM2.5, NO2, and O3. The dust PM2.5 analysis was carried out from 2016 to 2020. NO2 and O3 analysis was carried out in 2019 and 2020. This study, which is based on satellite data from TROPOMI Sentinel 5P and MERRA, has shown that Morocco has experienced an improvement in air quality during the lockdown. A significant reduction in surface dust PM2.5 and tropospheric NO2 was observed (-10%,-4%, respectively on average). The total column of ozone recorded a slight increase on average of around 1%. Moreover, we demonstrate that a significant part of particulate pollution and NO2 emissions are incoming mainly from the northern and northern-eastern borders of Morocco. © 2022 Korean Society of Environmental Engineers.

Satellite Data Investigation for Change Estimation During COVID Era by Fusing Pixel and Object-Based Technique

In this investigation, an innovative combination of pixel-based change detection technique and object-based change detection technique is explored with the satellite images of Holy Masjid al-Haram, Saudi Arabia. The gray-level co-occurrence matrix (GLCM) method is used to quantify the texture of the remote sensing data through the texture classification approach on the satellite data in this work. GLCM produces results of the texture quantification in normalized form. Thus, applying a texture classification scheme on the satellite data is impressive to observe. Later maximum likelihood image classification approach is used for classification purposes. The classified information is categorized into four different classes. The kappa coefficient’s value and the overall accuracy for the pre- COVID classified study area are 0.6532 and 76.38%, respectively. During COVID, the classified study area presents the kappa coefficient and the overall accuracy of 0.7631 and 82.18%, respectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Air Quality Analysis During COVID-19 Utilizing Satellite Data

Human health is severely endangered by the novel coronavirus (COVID-19). It is viewed as the worst global health threat humans have faced since the second world war and the WHO recognized it as a pandemic on March 11, 2020. This pandemic led several nations to adopt statewide lockdowns, while the industrial, construction, and transportation activities in several nations were disrupted, which lead to a significant shift in air pollutants. The lockdown, however, significantly impacted the environment and air quality in distinct cities. There are numerous ground stations deployed by pollution control organizations to monitor and collect the air pollutants data, but it is not feasible to set up a ground station in every city. In places where ground stations are not available for data collection, Google Earth Engine (GEE) satellite captured data can be used for data analysis. This study aimed to analyze the changes in air pollutants during the different lockdowns in India, such as nitrogen dioxide(NO2), sulfur dioxide(SO2), and carbon monoxide(CO) that contribute significantly to air pollution. In India, lockdowns were imposed during different periods of 2020, 2021, and 2022, according to COVID-19 waves. The air pollutants data during different waves have been analyzed and compared with the pre-COVID year (2019) data for the same duration. According to the study results, N O2 and S O2 were drastically reduced, but only a minor reduction in CO. Delhi, Jaipur, Ahmedabad, and Mumbai were among the major cities that saw the largest reduction, which was up to 60%. © 2022 IEEE.

Spatial effect of anti-COVID measures on land surface temperature (LST) in urban areas: A case study of a medium-sized city

This case study investigates the magnitude and nature of the spatial effect generated by the anti-COVID measures on land surface temperature (LST) in the city of Targu Mures (Marosvasarhely), Romania. The measures were taken by the Romanian government during the state of emergency (March 16 - May 14, 2020) due to the SARS-CoV-2 coronavirus pandemic. The study shows that - contrary to previous studies carried out on cities in China and India in most of the urban areas of Marosvasarhely LST has increased in the period of health emergency in 2020 concerning the large average of the years 2000-2019. Remote sensing data from the MODIS and the Landsat satellites show. that MODIS data, having a moderate spatial (approximately 1 km) but good temporal resolution (daily measurements), show a temperature increase of +0.78 degrees C, while Landsat data, having better spatial (30 m) but lower temporal resolution, show an even greater increase, +2.36 degrees C in the built-up areas. The difference in temperature increase is mainly due to the spatial resolution difference between the two TIR band sensors. The LST anomaly analysis performed with MODIS data also shows a positive anomaly increase of 1 degrees C. However, despite this increase, with the help of the hotspot-coldspot analysis of the Getis-Ord Gi* statistic we were able to identify 46 significant coldspots that showed a 1- 2 degrees C decrease of LST in April 2020 compared to the average of the previous years in April. Most of these coldspots correspond to factory areas, public transport epicenters, shopping centers, industrial polygons. and non-residential areas. This shows that anti-COVID measures in the medium-sized city of Marosvasarhely had many effects on LST in particular areas that have links to the local economy, trade. and transport. Paired sample t-test for areas identified with LST decrease shows that there is a statistically significant difference in the average LST observed before and after anti-COVID measures were applied. MODIS-based LST is satisfactory for recognizing patterns and trends at large or moderate geographical scales. However, for a hotspot-coldspot analysis of the urban heat islands, it is more suitable to use Landsat data.

Investigating The Effect of Covid Induced Lockdown On Land Surface Temperature Over Ahmedabad City

The present study focuses over Ahmedabad City of Gujarat State, India for the time period 1st March to 30th June comprising of the Pre-Lockdown Phase (PLP), the National Lockdown Phase - 1 (NLP1) and the Unlock Phase - 1 (ULP1). We have considered this time period over the years 2019, 2020 and 2021 to explore the effect of COVID induced lockdown on LST and understanding its variation. Satellite data acquired from AQUA - MODIS with a spatial and temporal resolution of 1 Km and 1-2 days respectively was used for the analysis of the LST. The average LST over Ahmedabad was 314.18 K, 311.79 K and 315.67 K for PLP over the years 2019, 2020 and 2021. For NLP1 the average LST over those years were 321.68 K, 318.73 K and 319.39 K respectively. And for the ULP1 the average LST over those years were 319.87 K, 314.07 K and 312.19 K respectively. We observe a 2.38 %, 2.22 % and 1.17 % increase in LST from the PLP to NLP1 during the years 2019, 2020 and 2021. The increase of LST during the NLP1 in 2020 showed that as the pollution decreased, the active elements that were present in the atmosphere which caused disturbance to the sensor on the satellite while calculating LST were reduced and we got a brighter top of surface. The decrease in LST from 2019 levels for the ULP1 is also observed indicating the effects of lockdown and onset of monsoon in 2020 and 2021. © 2021 IEEE.

Using satellite images of nighttime lights to predict the economic impact of COVID-19 in India.

The outbreak of COVID-19 in early 2020 heralded a deep global recession not seen since the Second World War. With entire nations in lockdown, burgeoning economies of countries like India plunged into a downward spiral. The conventional instruments of estimating the short-term economic impact of a pandemic is limited, and as a result, it is challenging to implement timely monetary policies to mitigate the financial impact of such unforeseen events. This study investigates the promise of using nighttime images of lights on Earth, also known as nightlight (NTL), captured by the Visible Infrared Imaging Radiometer Suite (VIIRS) instrumentation onboard the Suomi National Polar-Orbiting Partnership (Suomi NPP) satellite mission to measure the economic cost of the pandemic in India. First, a novel data processing framework was developed for a recently released radiance dataset known as VNP46A1, part of NASA's Black Marble suite of NTL products. Second, the elasticity of nightlight to India's National Gross Domestic Product (GDP) was estimated using panel regression followed by machine learning to predict the Year-over-Year (YoY) change in GDP during Fiscal Year (FY) 2020Q1 (Apr-Jun, 2020). Electricity consumption, known to closely track economic output and precipitation were included as additional features to improve model performance. A strong relationship between both electricity usage and nightlight to GDP was observed. The model predicted a YoY contraction of 24% in FY2020Q1, almost identical to the official GDP decline of 23.9% later announced by the Indian Government. Based on the findings, the study concludes that nightlight along with electricity usage can be invaluable proxies for estimating the cost of short-term supply-demand shocks such as COVID-19, and should be explored further.

The method of GF-7 satellite laser altimeter on-orbit geometric calibration without field site

On-orbit geometric calibration without field site is a key problem for future multi-beam laser altimetry satellites. In view of the linear system full waveform laser altimeter loaded on the GF-7 satellite, a non-field step by step calibration method based on terrain and waveform matching is proposed. Based on the analysis of the characteristics of the GF-7 satellite laser altimeter, a rigorous geometric positioning model is constructed. The field-free on orbit geometric calibration test is carried out by using the open topographic reference data and the basic geographic information of DOM and LiDAR DSM in a certain area, which has greatly improved the accuracy of the laser altimetry data. With this method, during the first half of 2020, the calibration parameter configuration and data processing of GF-7 satellite laser altimeter was not affected, even the field calibration can't be implemented due to the negative impact of the COVID-19. The accuracy is compared with the field calibration results after the COVID-19, and the results show that the plane error of the non-field calibration is 11.597±3.693 m and the minimum value is 7.115 m. The elevation accuracy of flat area is better than 0.3 m, although it is slightly lower than the results of field calibration, it can basically meet the requirements of 1: 10 000 elevation control points. © 2022, Surveying and Mapping Press. All right reserved.

NIGHTTIME LIGHT EMISSIONS EXPLAIN THE DECLINE IN NO2 DURING A COVID-19-INDUCED TOTAL LOCKDOWN IN FRANCE

Referring to a total lockdown due to COVID-19 in Metropolitan France, this study investigates the geospatial correlation between nighttime light emission and the relative change of NO2 air pollution (dNO(2) %). To address the research problem, near-surface NO2 data and nighttime light data were implemented. Stable night lights were obtained for a long period on average (2014-2019) using Day-Night Band (DNB) data from the Visible Infrared Imaging Radiometer Suite (VIIRS). The relative change in tropospheric NO2 was calculated using Sentinel-5P satellite data from the Tropospheric Monitoring Instrument (TROPOMI). The dNO(2) calculation was performed considering an equivalent reference period (April 2019) to the major lockdown period in France (April 2020). The correlation between the variables DNB nighttime lights and dNO(2) was tested with a statistical T-test. The findings revealed an intense phenomenon of decreasing NO2 air pollution in France (decreases by -25% to -50%). Decreases < -50% were mainly recorded in the greater Paris metropolitan area, in Alsace, and other locations. The results showed a strong and statistically significant inverse geospatial correlation between the two variables under anti-COVID-19 control measures. The higher was the emission of nighttime lights, the higher was the degree of tropospheric NO2 decrease in the regions of France (R-2 =0.72). It is concluded that employing remote sensing techniques, DNB nighttime light is a reliable indicator to estimate the degree of air decontamination. DNB as an independent variable is recommended for future research on changes in the concentration of other pollutant gases.

Learnings from COVID-19 Forced Lockdown on Regional Air Quality and Mitigation Potential for South Asia

South Asia is a hotspot of air pollution with limited resilience and hence, understanding the mitigation potential of different sources is critically important. In this context the country lockdown initiated to combat the COVID-19 pandemic (during March and April 2020 that is the pre-monsoon season) provides an unique opportunity for studying the relative impacts of different emission sources in the region. Here, we analyze changes in levels of air quality species across the region during selected lockdown periods using satellite and in-situ datasets. This analysis compares air quality levels during the lockdown against pre-lockdown conditions as well as against regional long-term mean. Satellite derived AOD, NO2, and CO data indicates an increase of 9.5%, 2%, and 2.6%, respectively, during the 2020 lockdown period compared to pre-lockdown over the South Asia domain. However, individual country statistics, urban site data, and industrial grid analysis within the region indicate a more varied picture. Cities with high traffic loads reported a reduction of 12–39% in columnar NO2 during lockdown, in-situ PM2.5 measurements indicate a 23–56% percent reduction over the country capitals and columnar SO2 has an approximate reduction of 50% over industrial areas. In contrast, pollutant emissions from natural sources e.g., from biomass burning were observed to be adversely affecting the air quality in this period potentially masking expected lockdown related air quality improvements. This study demonstrates the need for a more nuanced and situation specific understanding of sources of air pollutants (anthropogenic and natural) and for these sources to be better understood from the local to the regional scale. Without this deeper understanding, mitigation strategies cannot be effectively targeted, wasting limited resources as well as risking unintended consequences both for the atmosphere and how mitigation action is perceived by the wider public. © The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

ESA Business Applications Ambassador Platform for Italy COVID-19 experience: tools for accelerating the Italian industrial progress and innovation

The New Space Economy has radically changed the role of the Space sector. In both Europe and Italy, the Space sector is playing a key role in accelerating economic growth, permeating the industrial ecosystem and fostering the development of innovation. The purpose of this paper is to give an overview of the activities carried out by the ESA Business Applications and InCubed+ Ambassador Platform for Italy (AP-IT) and to highlight the strategy followed in the 2020-2021 biennium, given the global and national economic difficulties of this timeframe. Indeed, AP-IT, part of the ESA Space Solutions network, aims to facilitate the development of innovation by assuming the role of enabler and promoter of the use of Integrated Applications, supporting start-ups and SMEs interested in accessing the ESA Business Applications programme and promoting the ESA InCubed+ one by organising training courses on the exploitation of hyperspectral data in conjunction with other emerging technologies such as Machine Learning, Artificial Intelligence, and Big Data. Although the 2020-2021 timeframe has been strongly influenced by the COVID-19 pandemic, this has created an opportunity to develop new innovative tools based on Space assets, favouring for example the creation of key solutions such as connectivity support for remote regions, digitisation of education, and innovative ideas aimed at counteracting the repercussions of the pandemic. Specifically, the first public call to support society against the epidemic Space in Response to COVID-19 outbreak in Italy of the ESA Business Applications programme proposed by ASI, in agreement with the Italian Minister for Technological Innovation and Digitisation, received more than 100 proposals. Italy has allocated EUR 10M and about 80% of the received proposals have been funded, exceeding expectations in terms of both quality and quantity. In this context, the vision of AP-IT is to continue to contribute to the creation of an entrepreneurial ecosystem in order to facilitate interactions between investors, stakeholders and new entrepreneurs, shaping the connective tissue between the Space sector and the rest of industrial sectors in Italy, supporting start-ups from seed stage to exit. Copyright © 2021 by the International Astronautical Federation (IAF). All rights reserved.

Quantifying urban, industrial, and background changes in NO2 during the COVID-19 lockdown period based on TROPOMI satellite observations

The COVID-19 lockdown had a large impact on anthropogenic emissions of air pollutants and particularly on nitrogen dioxide (NO2). While the overall NO2 decline over some large cities is well-established, understanding the details remains a challenge since multiple source categories contribute. In this study, a new method of isolation of three components (background NO2, NO2 from urban sources, and NO2 from industrial point sources) is applied to estimate the impact of the COVID-19 lockdown on each of them. The approach is based on fitting satellite data by a statistical model with empirical plume dispersion functions driven by a meteorological reanalysis. Population density and surface elevation data as well as coordinates of industrial sources were used in the analysis. The tropospheric NO2 vertical column density (VCD) values measured by the Tropospheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor over 261 urban areas for the period from 16 March to 15 June 2020 were compared with the average VCD values for the same period in 2018 and 2019. While the background NO2 component remained almost unchanged, the urban NO2 component declined by -18 % to -28 % over most regions. India, South America, and a part of Europe (particularly, Italy, France, and Spain) demonstrated a-40 % to -50 % urban emission decline. In contrast, the decline over urban areas in China, where the lockdown was over during the analysed period, was, on average, only -4.4±8 %. Emissions from large industrial sources in the analysed urban areas varied greatly from region to region from -4.8±6 % for China to -40±10 % for India. Estimated changes in urban emissions are correlated with changes in Google mobility data (the correlation coefficient is 0.62) confirming that changes in traffic were one of the key elements in the decline in urban NO2 emissions. No correlation was found between changes in background NO2 and Google mobility data. On the global scale, the background and urban components were remarkably stable in 2018, 2019, and 2021, with averages of all analysed areas all being within ±2.5 % and suggesting that there were no substantial drifts or shifts in TROPOMI data. The 2020 data are clearly an outlier: in 2020, the mean background component for all analysed areas (without China) was -6.0%±1.2 % and the mean urban component was -26.7±2.6 % or 20σ below the baseline level from the other years.

Satellite Data and Epidemic Cartography: A Study of the Relationship Between the Concentration of NO2 and the COVID-19 Epidemic

Satellite data are widely used to study the spatial component of epidemics: to monitor their evolution, to create epidemiological risk maps and predictive models. The improvement of data quality, not only in technical terms but also of scientific relevance and robustness, represents in this context one of the most important aspects for health information technology that can make further significant and useful progress in monitoring and managing epidemics. In this regard, this paper intends to address an issue that is not always adequately considered in the use of satellite data for the creation of maps and spatial models of epidemics, namely the preliminary verification of the level of spatial correlation between remote sensing environmental variables and epidemics. Specifically, we intend to evaluate the contribution of exposure to the pollutant nitrogen dioxide (NO2) on the spatial spread of the virus and the severity of the current COVID infection. © 2022, The Author(s).

Global to local impacts on atmospheric CO2from the COVID-19 lockdown, biosphere and weather variabilities

The worldwide lockdown in response to the COVID-19 pandemic in year 2020 led to an economic slowdown and a large reduction in fossil fuel CO2 emissions (Le Quéré 2020 Nat. Clim. Change 10 647-53, Liu 2020 Nat. Commun. 11);however, it is unclear how much it would slow the increasing trend of atmospheric CO2 concentration, the main driver of climate change, and whether this impact can be observed considering the large biosphere and weather variabilities. We used a state-of-the-art atmospheric transport model to simulate CO2, and the model was driven by a new daily fossil fuel emissions dataset and hourly biospheric fluxes from a carbon cycle model forced with observed climate variability. Our results show a 0.21 ppm decrease in the atmospheric column CO2 anomaly in the Northern Hemisphere latitude band 0-45 N in March 2020, and an average of 0.14 ppm for the period of February-April 2020, which is the largest decrease in the last 10 years. A similar decrease was observed by the carbon observing satellite GOSAT (Yokota et al 2009 Sola 5 160-3). Using model sensitivity experiments, we further found that the COVID and weather variability are the major contributors to this CO2 drawdown, and the biosphere showed a small positive anomaly. Measurements at marine boundary layer stations, such as Hawaii, exhibit 1-2 ppm anomalies, mostly due to weather and the biosphere. At the city scale, the on-road CO2 enhancement measured in Beijing shows a reduction by 20-30 ppm, which is consistent with the drastically reduced traffic during the COVID lockdown. A stepwise drop of 20 ppm during the city-wide lockdown was observed in the city of Chengdu. The ability of our current carbon monitoring systems in detecting the small and short-lasting COVID signals at different policy relevant scales (country and city) against the background of fossil fuel CO2 accumulated over the last two centuries is encouraging. The COVID-19 pandemic is an unintended experiment. Its impact suggests that to keep atmospheric CO2 at a climate-safe level will require sustained effort of similar magnitude and improved accuracy, as well as expanded spatiotemporal coverage of our monitoring systems. © 2021 The Author(s). Published by IOP Publishing Ltd.

Exploring the linkage between seasonality of environmental factors and COVID-19 waves in Madrid, Spain.

Like several countries, Spain experienced a multi wave pattern of COVID-19 pandemic over more than one year period, between spring 2020 and spring 2021. The transmission of SARS-CoV-2 pandemics is a multi-factorial process involving among other factors outdoor environmental variables and viral inactivation.This study aims to quantify the impact of climate and air pollution factors seasonality on incidence and severity of COVID-19 disease waves in Madrid metropolitan region in Spain. We employed descriptive statistics and Spearman rank correlation tests for analysis of daily in-situ and geospatial time-series of air quality and climate data to investigate the associations with COVID-19 incidence and lethality in Madrid under different synoptic meteorological patterns. During the analyzed period (1 January 2020-28 February 2021), with one month before each of three COVID-19 waves were recorded anomalous anticyclonic circulations in the mid-troposphere, with positive anomalies of geopotential heights at 500 mb and favorable stability conditions for SARS-CoV-2 fast diffusion. In addition, the results reveal that air temperature, Planetary Boundary Layer height, ground level ozone have a significant negative relationship with daily new COVID-19 confirmed cases and deaths. The findings of this study provide useful information to the public health authorities and policymakers for optimizing interventions during pandemics.

The Impact of COVID-19 on Weather Forecasts: A Balanced View.

Aircraft reports are an important source of information for numerical weather prediction (NWP). From March 2020, the COVID-19 pandemic resulted in a large loss of aircraft data but despite this it is difficult to see any evidence of significant degradation in the forecast skill of global NWP systems. This apparent discrepancy is partly because forecast skill is very variable, showing both day-to-day noise and lower frequency dependence on the mean state of the atmosphere. The definitive way to cleanly assess aircraft impact is using a data denial experiment, which shows that the largest impact is in the upper troposphere. The method used by Chen (2020, https://doi.org/10.1029/2020gl088613) to estimate the impact of COVID-19 is oversimplistic. Chen understates the huge importance of satellite data for modern weather forecasts and raises more alarm than necessary about a drop in forecast accuracy.

Characterization of the aerosol chemical composition during the COVID-19 lockdown period in Suzhou in the Yangtze River Delta, China.

To control the spread of COVID-19, rigorous restrictions have been implemented in China, resulting in a great reduction in pollutant emissions. In this study, we evaluated the air quality in the Yangtze River Delta during the COVID-19 lockdown period using satellite and ground-based data, including particle matter (PM), trace gases, water-soluble ions (WSIs) and black carbon (BC). We found that the impacts of lockdown policy on air quality cannot be accurately assessed using MODIS aerosol optical depth (AOD) data, whereas the tropospheric nitrogen dioxide (NO2) vertical column density can well reflect the influences of these restrictions on human activities. Compared to the pre-COVID period, the PM2.5, PM10, NO2, carbon monoxide (CO), BC and WSIs during the lockdown in Suzhou were observed to decrease by 37.2%, 38.3%, 64.5%, 26.1%, 53.3% and 58.6%, respectively, while the sulfur dioxide (SO2) and ozone (O3) increased by 1.5% and 104.7%. The WSIs ranked in the order of NO3- > NH4+ > SO42- > Cl- > Ca2+ > K+ > Mg2+ > Na+ during the lockdown period. By comparisons with the ion concentrations during the pre-COVID period, we found that the ions NO3-, NH4+, SO42-, Cl-, Ca2+, K+ and Na+ decreased by 66.3%, 48.8%, 52.9%, 56.9%, 57.9% and 76.3%, respectively, during the lockdown, in contrast to Mg2+, which increased by 30.2%. The lockdown policy was found to have great impacts on the diurnal variations of Cl-, SO42-, Na+ and Ca2+.

Effect of lockdown due to SARS COVID-19 on aerosol optical depth (AOD) over urban and mining regions in India.

The Severe Acute Respiratory Syndrome-COronaVIrus Diseases 2019 (SARS-COVID-19) pandemic has posed a serious threat to human health (death) and substantial economic losses across the globe. It was however presumed that extreme preventive measures of entire lockdown in India might have reduced the air pollution level and therefore decreased the aerosol optical depth (AOD). The Moderate Resolution Imaging Spectroradiometer (MODIS)-based Multi-angle Implementation of Atmospheric Correction (MAIAC) daily AOD product was deployed to investigate the change in AOD level during lockdown phases across the Indian Territory as compared to the long-term mean AOD level (2000-2019) of the same periods. The key findings of the study revealed that AOD level over the Indian Territory is greatly reduced (~45%) during the lockdown periods as compared to the long-term mean AOD level (2000-2019). Furthermore, a noteworthy negative AOD anomaly (~6 to 37%) was observed across the four metropolitan cities in India during the entire lockdown period (25th March to 15th May 2020). However, coal mining regions of the various coalfields in India showed a positive anomaly (~+11 to 40%) during the lockdown periods due to ongoing mining operations. In a nutshell, the study results indicated a huge drop in the AOD level over Indian Territory during lockdown periods. It is expected that the pandemic can influence some policy decisions to propose air pollution control methods. Lockdown events possibly may play a crucial role as a potential solution for air pollution abatement in the future. It may not be uncommon in future when the governments may implement deliberately selective lockdowns at pollution hotspots to control the pollution level.