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1. 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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2. Gridded surface O ₃ , NO _x , and CO abundances for model metrics from the South Korean ground station network

   https://doi.org/10.5194/egusphere-2024-1173  

   Wilson, Calum Patrick; Prather, Michael John (August 2024, EGU)

   Abstract. We present gridded surface air quality datasets over South Korea for three key species – ozone (O3), carbon monoxide (CO), and nitrogen oxides (NOx) during the timeframe of the Korea–US Air Quality (KORUS–AQ) mission (May–June 2016). The tenth degree hourly averaged abundances are constructed from the 300+ air quality network sites using inverse distance weighting with simple declustering. Cross–comparing the interpolated fields against the site data that was used to create them reveals high prediction skill for O3 (80 %) throughout South Korea, and moderate skill (60 %) for CO and NOx on average in densely observed regions after individual mean bias corrections. The gridded O3 and CO interpolations predict the NASA DC–8 observations in the planetary boundary layer (PBL) with high skill (80 %) in the Seoul Metropolitan Area (SMA) after subtracting the mean bias. DC–8 NOx observations were much less predictable on account of consistently negative vertical gradients within the PBL. Our gridded products capture the mean and variability of O3 throughout South Korea, and of CO and surface NOx in most site–dense urban centres (SMA, Cheongju, Gwangju, Daegu, Changwon, and Busan). 

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3. Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis

   https://doi.org/10.2196/27045  

   Zeng, Chengbo; Zhang, Jiajia; Li, Zhenlong; Sun, Xiaowen; Olatosi, Bankole; Weissman, Sharon; Li, Xiaoming (January 2021, Journal of Medical Internet Research)

   null (Ed.)

   Background Population mobility is closely associated with COVID-19 transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive nonpharmaceutical interventions for disease control. South Carolina is one of the US states that reopened early, following which it experienced a sharp increase in COVID-19 cases. Objective The aims of this study are to examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility data to predict daily new cases at both the state and county level in South Carolina. Methods This longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020, in South Carolina and its five counties with the largest number of cumulative confirmed COVID-19 cases. Population mobility was assessed based on the number of Twitter users with a travel distance greater than 0.5 miles. A Poisson count time series model was employed for COVID-19 forecasting. Results Population mobility was positively associated with state-level daily COVID-19 incidence as well as incidence in the top five counties (ie, Charleston, Greenville, Horry, Spartanburg, and Richland). At the state level, the final model with a time window within the last 7 days had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3, 7, and 14 days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9, 14, 28, 20, and 9 days, respectively. The 14-day prediction accuracy ranged from 60.3%-74.5%. Conclusions Using Twitter-based population mobility data could provide acceptable predictions of COVID-19 daily new cases at both the state and county level in South Carolina. Population mobility measured via social media data could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences. 

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4. Examining the competing effects of contemporary land management vs. land cover changes on global air quality

   https://doi.org/10.5194/acp-21-16479-2021  

   Wong, Anthony Y.; Geddes, Jeffrey A. (January 2021, Atmospheric Chemistry and Physics)

   Abstract. Our work explores the impact of two important dimensions of landsystem changes, land use and land cover change (LULCC) as well as directagricultural reactive nitrogen (Nr) emissions from soils, on ozone(O3) and fine particulate matter (PM2.5) in terms of air quality overcontemporary (1992 to 2014) timescales. We account for LULCC andagricultural Nr emissions changes with consistent remote sensingproducts and new global emission inventories respectively estimating theirimpacts on global surface O3 and PM2.5 concentrations as well as Nrdeposition using the GEOS-Chem global chemical transport model. Over thistime period, our model results show that agricultural Nr emissionchanges cause a reduction of annual mean PM2.5 levels over Europe andnorthern Asia (up to −2.1 µg m−3) while increasing PM2.5 levels in India, China and the eastern US (up to +3.5 µg m−3). Land cover changes induce small reductions in PM2.5 (up to −0.7 µg m−3) over Amazonia, China and India due to reduced biogenic volatile organic compound (BVOC) emissions and enhanced deposition of aerosol precursor gases (e.g., NO2, SO2). Agricultural Nr emissionchanges only lead to minor changes (up to ±0.6 ppbv) in annual meansurface O3 levels, mainly over China, India and Myanmar. Meanwhile, ourmodel result suggests a stronger impact of LULCC on surface O3 over the time period across South America; the combination of changes in drydeposition and isoprene emissions results in −0.8 to +1.2 ppbv surfaceozone changes. The enhancement of dry deposition reduces the surface ozone level (up to −1 ppbv) over southern China, the eastern US and central Africa. The enhancement of soil NO emission due to crop expansion also contributes to surface ozone changes (up to +0.6 ppbv) over sub-Saharan Africa. Incertain regions, the combined effects of LULCC and agricultural Nr emission changes on O3 and PM2.5 air quality can be comparable (>20 %) to anthropogenic emission changes over the same time period. Finally, we calculate that the increase in global agricultural Nr emissions leads to a net increase in global land area (+3.67×106km2) that potentially faces exceedance of the critical Nr load (>5 kg N ha−1 yr−1). Our result demonstrates the impacts of contemporary LULCC and agricultural Nr emission changes on PM2.5 and O3 in terms of air quality, as well as the importanceof land system changes for air quality over multidecadal timescales. 

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5. Integrating storm surge modeling with traffic data analysis to evaluate the effectiveness of hurricane evacuation

   https://doi.org/10.1007/s11709-021-0765-1  

   Huang, Wenrui; Yin, Kai; Ghorbanzadeh, Mahyar; Ozguven, Eren; Xu, Sudong; Vijayan, Linoj (December 2021, Frontiers of Structural and Civil Engineering)

   Abstract An integrated storm surge modeling and traffic analysis were conducted in this study to assess the effectiveness of hurricane evacuations through a case study of Hurricane Irma. The Category 5 hurricane in 2017 caused a record evacuation with an estimated 6.8 million people relocating statewide in Florida. The Advanced Circulation (ADCIRC) model was applied to simulate storm tides during the hurricane event. Model validations indicated that simulated pressures, winds, and storm surge compared well with observations. Model simulated storm tides and winds were used to estimate the area affected by Hurricane Irma. Results showed that the storm surge and strong wind mainly affected coastal counties in south-west Florida. Only moderate storm tides (maximum about 2.5 m) and maximum wind speed about 115 mph were shown in both model simulations and Federal Emergency Management Agency (FEMA) post-hurricane assessment near the area of hurricane landfall. Storm surges did not rise to the 100-year flood elevation level. The maximum wind was much below the design wind speed of 150–170 mph (Category 5) as defined in Florida Building Code (FBC) for south Florida coastal areas. Compared with the total population of about 2.25 million in the six coastal counties affected by storm surge and Category 1–3 wind, the statewide evacuation of approximately 6.8 million people was found to be an over-evacuation due mainly to the uncertainty of hurricane path, which shifted from south-east to south-west Florida. The uncertainty of hurricane tracks made it difficult to predict the appropriate storm surge inundation zone for evacuation. Traffic data were used to analyze the evacuation traffic patterns. In south-east Florida, evacuation traffic started 4 days before the hurricane’s arrival. However, the hurricane path shifted and eventually landed in south-west Florida, which caused a high level of evacuation traffic in south-west Florida. Over-evacuation caused Evacuation Traffic Index ( ETI ) to increase to 200% above normal conditions in some sections of highways, which reduced the effectiveness of evacuation. Results from this study show that evacuation efficiency can be improved in the future by more accurate hurricane forecasting, better public awareness of real-time storm surge and wind as well as integrated storm surge and evacuation modeling for quick response to the uncertainty of hurricane forecasting. 

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