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1. Investigating the Impacts of the COVID-19 Lockdown on Trace Gases Using Ground-Based MAX-DOAS Observations in Nanjing, China

   https://doi.org/10.3390/rs12233939  

   Javed, Zeeshan ; Wang, Yuhang ; Xie, Mingjie ; Tanvir, Aimon ; Rehman, Abdul ; Ji, Xiangguang ; Xing, Chengzhi ; Shakoor, Awais ; Liu, Cheng ( December 2020 , Remote Sensing)

   null (Ed.)

   The spread of the COVID-19 pandemic and consequent lockdowns all over the world have had various impacts on atmospheric quality. This study aimed to investigate the impact of the lockdown on the air quality of Nanjing, China. The off-axis measurements from state-of-the-art remote-sensing Multi-Axis Differential Optical Absorption Spectroscope (MAX-DOAS) were used to observe the trace gases, i.e., Formaldehyde (HCHO), Nitrogen Dioxide (NO2), and Sulfur Dioxide (SO2), along with the in-situ time series of NO2, SO2 and Ozone (O3). The total dataset covers the span of five months, from 1 December 2019, to 10 May 2020, which comprises of four phases, i.e., the pre lockdown phase (1 December 2019, to 23 January 2020), Phase-1 lockdown (24 January 2020, to 26 February 2020), Phase-2 lockdown (27 February 2020, to 31 March 2020), and post lockdown (1 April 2020, to 10 May 2020). The observed results clearly showed that the concentrations of selected pollutants were lower along with improved air quality during the lockdown periods (Phase-1 and Phase-2) with only the exception of O3, which showed an increasing trend during lockdown. The study concluded that limited anthropogenic activities during the spring festival and lockdown phases improved air quality with a significant reduction of selected trace gases, i.e., NO2 59%, HCHO 38%, and SO2 33%. We also compared our results with 2019 data for available gases. Our results imply that the air pollutants concentration reduction in 2019 during Phase-2 was insignificant, which was due to the business as usual conditions after the Spring Festival (Phase-1) in 2019. In contrast, a significant contamination reduction was observed during Phase-2 in 2020 with the enforcement of a Level-II response in lockdown conditions i.e., the easing of the lockdown situation in some sectors during a specific interval of time. The observed ratio of HCHO to NO2 showed that tropospheric ozone production involved Volatile Organic Compounds (VOC) limited scenarios. 

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2. The Status of Air Quality in the United States during the COVID-19 Pandemic: A Remote Sensing Perspective

   https://doi.org/10.3390/rs13030369  

   Elshorbany, Yasin F. ; Kapper, Hannah C. ; Ziemke, Jerald R. ; Parr, Scott A. ( February 2021 , Remote Sensing)

   null (Ed.)

   The recent COVID-19 pandemic has prompted global governments to take several measures to limit and contain the spread of the novel virus. In the United States (US), most states have imposed a partial to complete lockdown that has led to decreased traffic volumes and reduced vehicle emissions. In this study, we investigate the impacts of the pandemic-related lockdown on air quality in the US using remote sensing products for nitrogen dioxide tropospheric column (NO2), carbon monoxide atmospheric column (CO), tropospheric ozone column (O3), and aerosol optical depth (AOD). We focus on states with distinctive anomalies and high traffic volume, New York (NY), Illinois (IL), Florida (FL), Texas (TX), and California (CA). We evaluate the effectiveness of reduced traffic volume to improve air quality by comparing the significant reductions during the pandemic to the interannual variability (IAV) of a respective reference period for each pollutant. We also investigate and address the potential factors that might have contributed to changes in air quality during the pandemic. As a result of the lockdown and the significant reduction in traffic volume, there have been reductions in CO and NO2. These reductions were, in many instances, compensated by local emissions and, or affected by meteorological conditions. Ozone was reduced by varying magnitude in all cases related to the decrease or increase of NO2 concentrations, depending on ozone photochemical sensitivity. Regarding the policy impacts of this large-scale experiment, our results indicate that reduction of traffic volume during the pandemic was effective in improving air quality in regions where traffic is the main pollution source, such as in New York City and FL, while was not effective in reducing pollution events where other pollution sources dominate, such as in IL, TX and CA. Therefore, policies to reduce other emissions sources (e.g., industrial emissions) should also be considered, especially in places where the reduction in traffic volume was not effective in improving air quality (AQ). 

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3. Quantifying the Impacts of COVID-19 Lockdown and Spring Festival on Air Quality over Yangtze River Delta Region

   https://doi.org/10.3390/atmos12060735  

   Javed, Zeeshan ; Tanvir, Aimon ; Wang, Yuhang ; Waqas, Ahmed ; Xie, Mingjie ; Abbas, Adnan ; Sandhu, Osama ; Liu, Cheng ( June 2021 , Atmosphere)

   The emergence of the novel corona virus and the resulting lockdowns over various parts of the world have substantially impacted air quality due to reduced anthropogenic activity. The objective of this study is to investigate the impact of COVID-19 lockdown and Spring Festival on air quality of four major cities of Yangtze River Delta (YRD) region, including Shanghai, Nanjing, Hefei, and Hangzhou. In situ measurements were taken for nitrogen dioxide (NO2), particulate matter (PM2.5) and ozone (O3). In situ measurements from 1 January to 25 April were taken two years prior to COVID-19 (2018–19), during COVID-19 lockdown (2020), and one year after the COVID-19 (2021). The results indicated that the concentration of NO2 and PM2.5 dropped considerably during the lockdown days compared to normal days while the O3 concentration showed an upsurge. The NO2 showed reduction of about 54% on average during lockdown level 1 in 2020 whereas, PM 2.5 showed reduction of about 36% through the YRD. A substantial drop was observed in concentration of NO2 during the Spring Festival holidays throughout the YRD from 2019 to 2021. 

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4. Visualizing COVID Restrictions: Activity Patterns Before, During, and After COVID-19 Lockdowns in Uttar Pradesh, India

   https://doi.org/10.1177/23780231221117962  

   Varela, Gabriel ; Swanson, Kendal ; Pasquale, Dana K. ; Mohanan, Manoj ; Moody, James W. ( January 2022 , Socius: Sociological Research for a Dynamic World)

   Globally, restrictions implemented to limit the spread of COVID-19 have highlighted deeply rooted social divisions, raising concerns about differential impacts on members of different groups. Inequalities among households of different castes are ubiquitous in certain regions of India. Drawing on a novel data set of 8,564 households in Uttar Pradesh, the authors use radar plots to examine differences between castes in rates of activity for several typical behaviors before, during, and upon lifting strict lockdown restrictions. The visualization reveals that members of all castes experienced comparable reductions in activity rates during lockdown and recovery rates following it. Nonetheless, members of less privileged castes procure water outside the household more often than their more privileged peers, highlighting an avenue of improvement for future public health efforts. 

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5. Automated Machine Learning to Evaluate the Information Content of Tropospheric Trace Gas Columns for Fine Particle Estimates Over India: A Modeling Testbed

   https://doi.org/10.1029/2022MS003099  

   Zheng, Zhonghua ; Fiore, Arlene M. ; Westervelt, Daniel M. ; Milly, George P. ; Goldsmith, Jeff ; Karambelas, Alexandra ; Curci, Gabriele ; Randles, Cynthia A. ; Paiva, Antonio R. ; Wang, Chi ; et al ( March 2023 , Journal of Advances in Modeling Earth Systems)

   India is largely devoid of high‐quality and reliable on‐the‐ground measurements of fine particulate matter (PM_2.5). Ground‐level PM_2.5concentrations are estimated from publicly available satellite Aerosol Optical Depth (AOD) products combined with other information. Prior research has largely overlooked the possibility of gaining additional accuracy and insights into the sources of PM using satellite retrievals of tropospheric trace gas columns. We evaluate the information content of tropospheric trace gas columns for PM_2.5estimates over India within a modeling testbed using an Automated Machine Learning (AutoML) approach, which selects from a menu of different machine learning tools based on the data set. We then quantify the relative information content of tropospheric trace gas columns, AOD, meteorological fields, and emissions for estimating PM_2.5over four Indian sub‐regions on daily and monthly time scales. Our findings suggest that, regardless of the specific machine learning model assumptions, incorporating trace gas modeled columns improves PM_2.5estimates. We use the ranking scores produced from the AutoML algorithm and Spearman’s rank correlation to infer or link the possible relative importance of primary versus secondary sources of PM_2.5as a first step toward estimating particle composition. Our comparison of AutoML‐derived models to selected baseline machine learning models demonstrates that AutoML is at least as good as user‐chosen models. The idealized pseudo‐observations (chemical‐transport model simulations) used in this work lay the groundwork for applying satellite retrievals of tropospheric trace gases to estimate fine particle concentrations in India and serve to illustrate the promise of AutoML applications in atmospheric and environmental research.

    

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