Year-round changes in tropospheric nitrogen dioxide caused by COVID-19 in China using satellite observation.

The lockdown policy deals a severe blow to the economy and greatly reduces the nitrogen oxides (NOx) emission in China when the coronavirus 2019 spreads widely in early 2020. Here we use satellite observations from Tropospheric Monitoring Instrument to study the year-round variation of the nitrogen dioxide (NO2) tropospheric vertical column density (TVCD) in 2020. The NO2 TVCD reveals a sharp drop, followed by small fluctuations and then a strong rebound when compared to 2019. By the end of 2020, the annual average NO2 TVCD declines by only 3.4% in China mainland, much less than the reduction of 24.1% in the lockdown period. On the basis of quantitative analysis, we find the rebound of NO2 TVCD is mainly caused by the rapid recovery of economy especially in the fourth quarter, when contribution of industry and power plant on NO2 TVCD continues to rise. This revenge bounce of NO2 indicates the emission reduction of NOx in lockdown period is basically offset by the recovery of economy, revealing the fact that China's economic development and NOx emissions are still not decoupled. More efforts are still required to stimulate low-pollution development.

<span contenteditable="false" data-cke-magic-line="1" style="width: 695px;height: 0px;padding: 0px;margin: 0px;display: block;z-index: 9999;color: rgb(255,255,255);font-size: 0px;line-height: 0px;position: absolute;border-top: 1px dashed rgb(255,0,0);user-select: none;left:-2px;top: 64.2px;"><span style="color:#ffffff"> </span><span style="color:#ffffff"> </span><span style="color:#ffffff">?</span></span>Estimating daily ground-level NO2 concentrations over China based on TROPOMI observations and machine learning approach

Nitrogen dioxide (NO2) is an important target for monitoring atmospheric quality. Deriving ground-level NO2 concentrations with much finer resolution, it requires high-resolution satellite tropospheric NO2 column as input and a reliable estimation algorithm. This paper aims to estimate the daily ground-level NO2 concentrations over China based on machine learning models and the TROPOMI NO2 data with high spatial resolution. In this study, four tree-based algorithm machine learning models, decision trees (DT), gradient boost decision tree (GBDT), random forest (RF) and extra-trees (ET), were used to estimate ground-level NO2 concentrations. In addition to considering many influencing factors of the ground-level NO2 concentrations, we especially introduced simplified temporal and spatial information into the estimation models. The results show that the extra-trees with spatial and temporal information (ST-ET) model has great performance in estimating ground-level NO2 concentrations with a cross-validation R-2 of 0.81 and RMSE of 3.45 mu g/m(3) in test datasets. The estimated results for 2019 based on the ST-ET model achieves a satisfactory accuracy with a cross-validation R-2 of 0.86 compared with the other models. Through time-space analysis and comparison, it was found that the estimated high-resolution results were consistent with the ground observed NO2 concentrations. Using data from January 2020 to test the prediction power of the models, the results indicate that the ST-ET model has a good performance in predicting ground-level NO2 concentrations. Taking four ground-level NO2 concentrations hotspots as examples, the estimated ground-level NO2 concentrations and ground-based observation data during the coronavirus disease (COVID-19) pandemic were lower compared with the same period in 2019. The findings offer a solid solution for accurately and efficiently estimating ground-level NO2 concentrations by using satellite observations, and provide useful information for improving our understanding of the regional atmospheric environment.

Effect of COVID-19-induced lockdown on NO2 pollution using TROPOMI and ground-based CPCB observations in Delhi NCR, India.

The present study investigates the reduction in nitrogen dioxide (NO2) levels using satellite-based (Sentinel-5P TROPOMI) and ground-based (Central Pollution Control Board) observations of 2020. The lockdown duration, monthly, seasonal and annual changes in NO2 were assessed comparing the similar time period in 2019. The study also examines the role of atmospheric parameters like wind speed, air temperature, relative humidity, solar radiation and atmospheric pressure in altering the monthly and annual values of the pollutant. It was ascertained that there was a mean reduction of ~ 61% (~ 66.5%), ~ 58% (~ 51%) in daily mean NO2 pollution during lockdown phase 1 when compared with similar period of 2019 and pre-lockdown phase in 2020 from ground-based (satellite-based) measurements. April month with ~ 57% (~ 57%), summer season with ~ 48% (~ 32%) decline and an annual reduction of ~ 20% (~ 18%) in tropospheric NO2 values were observed (p < 0.001) compared to similar time periods of 2019. It was assessed that the meteorological parameters remained almost similar during various parts of the year in 2019 and 2020, indicating a negligent role in reducing the values of atmospheric pollution, particularly NO2 in the study area. It was concluded that the halt in anthropogenic activities and associated factors was mainly responsible for the reduced values in the Delhi conglomerate. Similar work can be proposed for other pollutants to holistically describe the pollution scenario as an aftermath of COVID-19-induced lockdown.

Tracing out the effect of transportation infrastructure on NO2 concentration levels with Kernel Density Estimation by investigating successive COVID-19-induced lockdowns.

This study aims to investigate the effect of transportation infrastructure on the decrease of NO2 air pollution during three COVID-19-induced lockdowns in a vast region of France. For this purpose, using Sentinel-5P satellite data, the relative change in tropospheric NO2 air pollution during the three lockdowns was calculated. The estimation of regional infrastructure intensity was performed using Kernel Density Estimation, being the predictor variable. By performing hotspot-coldspot analysis on the relative change in NO2 air pollution, significant spatial clusters of decreased air pollution during the three lockdowns were identified. Based on the clusters, a novel spatial index, the Clustering Index (CI) was developed using its Coldspot Clustering Index (CCI) variant as a predicted variable in the regression model between infrastructure intensity and NO2 air pollution decline. The analysis revealed that during the three lockdowns there was a strong and statistically significant relationship between the transportation infrastructure and the decline index, CCI (r = 0.899, R2 = 0.808). The results showed that the largest decrease in NO2 air pollution was recorded during the first lockdown, and in this case, there was the strongest inverse correlation with transportation infrastructure (r = -0.904, R2 = 0.818). Economic and population predictors also explained with good fit the decrease in NO2 air pollution during the first lockdown: GDP (R2 = 0.511), employees (R2 = 0.513), population density (R2 = 0.837). It is concluded that not only economic-population variables determined the reduction of near-surface air pollution but also the transportation infrastructure. Further studies are recommended to investigate other pollutant gases as predicted variables.

Chances in satellite retrievals of atmospheric composition over eastern China during the 2020 COVID-19 lockdowns

We examined daily level-3 satellite retrievals of Atmospheric Infrared Sounder (AIRS) CO, Ozone Monitoring Instrument (OMI) SO2 and NO2, and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) over eastern China to understand how COVID-19 lockdowns affected atmospheric composition. Changes in 2020 were strongly dependent on the choice of background period since 2005 and whether trends in atmospheric composition were accounted for. Over central east China during the 23 January-8 April lockdown window, CO in 2020 was between 3 % and 12 % lower than average depending on the background period. The 2020 CO was not consistently less than expected from trends beginning between 2005 and 2016 and ending in 2019 but was 3 %-4 % lower than the background mean during the 2017-2019 period when CO changes had flattened Similarly for AOD, 2020 was between 14 % and 30 % lower than averages beginning in 2005 and 14 %-17 % lower compared to different background means beginning in 2016. NO2 in 2020 was between 30 % and 43 % lower than the mean over different background periods and between 17 % and 33 % lower than what would be expected for trends beginning later than 2011. Relative to the 2016-2019 period when NO2 had flattened, 2020 was 30 %-33 % lower. Over southern China, 2020 NO2 was between 23 % and 27 % lower than different background means beginning in 2013, the beginning of a period of persistently lower NO2. CO over southern China was significantly higher in 2020 than what would be expected, which we suggest was partly because of an active fire season in neighboring countries. Over central east and southern China, 2020 SO2 was higher than expected, but this depended strongly on how daily regional values were calculated from individual retrievals and reflects background values approaching the retrieval detection limit. Future work over China, or other regions, needs to take into account the sensitivity of differences in 2020 to different background periods and trends in order to separate the effects of COVID-19 on air quality from previously occurring changes or from variability in other sources.

Nitrogen dioxide decline and rebound observed by GOME-2 and TROPOMI during COVID-19 pandemic.

Since its first confirmed case in December 2019, coronavirus disease 2019 (COVID-19) has become a worldwide pandemic with more than 90 million confirmed cases by January 2021. Countries around the world have enforced lockdown measures to prevent the spread of the virus, introducing a temporal change of air pollutants such as nitrogen dioxide (NO2) that are strongly related to transportation, industry, and energy. In this study, NO2 variations over regions with strong responses to COVID-19 are analysed using datasets from the Global Ozone Monitoring Experiment-2 (GOME-2) sensor aboard the EUMETSAT Metop satellites and TROPOspheric Monitoring Instrument (TROPOMI) aboard the EU/ESA Sentinel-5 Precursor satellite. The global GOME-2 and TROPOMI NO2 datasets are generated at the German Aerospace Center (DLR) using harmonized retrieval algorithms; potential influences of the long-term trend and seasonal cycle, as well as the short-term meteorological variation, are taken into account statistically. We present the application of the GOME-2 data to analyze the lockdown-related NO2 variations for morning conditions. Consistent NO2 variations are observed for the GOME-2 measurements and the early afternoon TROPOMI data: regions with strong social responses to COVID-19 in Asia, Europe, North America, and South America show strong NO2 reductions of ∼ 30-50% on average due to restriction of social and economic activities, followed by a gradual rebound with lifted restriction measures. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11869-021-01046-2.

Impact of COVID-19 outbreak on tropospheric NO2 pollution assessed using Satellite-ground perspectives observations in India.

The global outbreak of Novel Corona Virus 2019 (SARS-CoV-2) has made worldwide lockdown including India since March 24, 2020. The current research aims at the improvements of nitrogen dioxide (NO2) during the COVID-19 lockdown in India. This research has been done using both the open source data sets taken from satellite and ground based for better analysis. For the satellite-based analysis, the Sentinel 5 Precauser's Tropospheric NO2 from the European Space Agency and for the ground-based numeric data sets from Central Pollution Control Board (CPCB) has been used. During the COVID-19 disease, outbreak the world has set in quarantine and as an overcome air quality improved in Asian countries after national lockdown, the average NO2 rates plummeted calculated by 40-50%. Similarly, it dramatically decreased in Asia during the COVID-19 pandemic quarantine period. The basic statistical patterns of the NO2 concentration spectrum of historical data sets (2018-2020) bi-weekly showed during October to March were seen higher in each year. Related with National Ambient Air Quality Standards of mean of NO2 in India our result shown in the NO2 levels fall in 21 µg/m3 during the national lockdown, from the Central Pollution Control Board's air quality standards it almost decreased 50% of the hourly mean in India. This caused by the sudden restriction to the development of manufacturing and the transportations which ultimately minimized the fossil fuel burning which cause the most of the NO2 releases to the atmosphere. Nowadays, people are aware about comparatively prosperous future with clear blue skies and uses of renewable energy sources from the nature.

Non-negligible impacts of clean air regulations on the reduction of tropospheric NO2 over East China during the COVID-19 pandemic observed by OMI and TROPOMI.

We study the variation of tropospheric NO2 vertical column densities (TropNO2VCDs) over East China during the 2005-2020 lunar new year (LNY) holiday seasons to understand factors on the reduction of tropospheric NO2 during the outbreak of COVID-19 in East China using Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) observations. TropNO2VCDs from OMI and TROPOMI reveal sharp reductions of 33%-72% during 2020 LNY holiday season and the co-occurring outbreak of COVID-19 relative to the climatological mean of 2005-2019 LNY holiday seasons, and 22%-67% reduction relative to the 2019 LNY holiday season. These reductions of TropNO2VCD occur majorly over highly polluted metropolitan areas with condensed industrial and transportation emission sources. COVID-19 control measures including lockdowns and shelter-in-place regulations are the primary reason for these tropospheric NO2 reductions over most areas of East China in 2020 LNY holiday season relative to the 2019 LNY holiday season, as COVID-19 control measures may explain ~87%-90% of tropospheric NO2 reduction in Wuhan as well as ~62%-89% in Beijing, Yangtze River Delta (YRD) and Sichuan Basin areas. The clean air regulation of China also contributes significantly to reductions of tropospheric NO2 simultaneously and is the primary factor in the Pearl River Delta (PRD) area, by explaining ~56%-63% of the tropospheric NO2 reduction there.