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

PM2.5 anomaly detection for exceptional event demonstrations: A Texas case study

The shifting frontiers of air pollution emission sources contribute to stagnation or reversal of air quality gains across the United States (US). The frequency and possibly duration of Exceptional Events - driven primarily by wildfires and dust storms - have significantly increased in the US over the past decade. Combined with the US Environmental Protection Agency (EPA) final rule strengthening primary annual National Ambient Air Quality Standards (NAAQS) for PM2.5 by 25%, communities will need to reevaluate domestic and international sources of PM2.5.This study applies the Isolation Forest methodology to Exceptional Event demonstrations to flag and evaluate sources of anomalies in large PM2.5 measurement datasets. Focusing on a decade of hourly PM2.5 data measured in seven regions across Texas from 2012-2021 (>3 million data points), we present methods to efficiently flag hourly PM2.5 anomalies with compute times of ~minutes and characterize their spatial impacts as local or (multi-) regional; subsequent evaluation of potential sources of the increase can then be conducted more efficiently in a targeted manner. For a subset of anomalies, we incorporate air mass back trajectories, surface influences, and positive matrix factorization to evaluate potential sources.Our anomaly characterization method separated statistically normal PM2.5 data and enabled differentiation of localized versus larger-scale PM2.5 sources. In addition, our method successfully characterized the Summer 2020 severe Saharan dust intrusions into Texas, as well as the influence of international smoke from Mexico on El Paso's regional air quality.This anomaly flagging and characterization method is promising for assessing the relative importance of sources to anomalies in PM2.5 and other criteria air pollutants for multiple purposes; while this work focuses on its capacity for exceptional event demonstrations, the applicability includes long-term trend analyses from environmental justice analyses of air pollutant exposure to air quality attainment demonstrations.

Near-term impact of COVID-19 pandemic on seniors' crash size and severity

Recent research revealed that COVID-19 pandemic was associated with noticeable changes in travel demand, traffic volumes, and traffic safety measures. Despite the reduction of traffic volumes across the US, several recent studies indicated that crash rates increased across different states during COVID-19 pandemic. Although some recent studies have focused on examining the changes in traffic conditions and crash rates before and during the pandemic, not enough research has been conducted to identify risk factors to crash severity. Even the limited research addressing the contributing factors to crash severity were focused on the pool category of drivers and no insight is available regarding older drivers, one of the most vulnerable groups to traffic collision and coronavirus. Moreover, these studies investigated the early impact of the COVID-19 pandemic mostly using up to three months of data. However, near-term and long-term effects of the COVID-19 pandemic are still unknown on traffic collisions. Therefore, this study aims to contribute to the literature by studying the near-term impact of the COVID-19 pandemic on crash size and severity among older drivers. To this end, a relatively large sample of crash data with senior drivers at fault was obtained and analyzed. To identify the main contributing factors affecting crash outcomes, Exploratory Factor Analysis was conducted on a high-dimension data set to identify potential latent factors which were validated through Confirmatory Factor Analysis. After that, Structural Equation Modeling technique was performed to examine the associations among the identified independent latent factors and the dependent variable. Additionally, SEM model identified the impact of the COVID-19 pandemic on seniors' crash severity. The findings reveal that several latent variables were the significant predictors of crash severity of older drivers including "Driving maneuver & crash location", "Road features and traffic control devices", "Driver condition & behavior", "Road geometric characteristics", "Crash time and lighting", and "Road class" latent factors. The binary variable of "Pandemic" was found to be as highly significant as the last four latent factors mentioned above. This means not only were older drivers more likely to be involved in higher crash size with higher severity level during the pandemic period, but also "Pandemic" was a risk factor to seniors as much as "Driver condition & behavior", "Road geometric characteristics", "Crash time & lighting", and "Road class" factors. The results of this study provide useful insights that may improve road safety among senior drivers during pandemic periods like COVID-19.

The Relationship Between Air Quality, Health Outcomes, and Socioeconomic Impacts of the COVID-19 Pandemic in the US

COVID-19 lockdowns caused significant improvements in air quality in US states where traffic emissions are the main pollution source. In this study, we investigate the socioeconomic impacts of the COVID-19-related lockdowns in states which experienced the greatest changes in air quality, especially among different demographic populations and those with contraindications to health. We administered a 47-question survey and collected 1,000 valid responses in these cities. Our results show that 74% of respondents within our survey sample had some level of concern regarding air quality. In agreement with previous literature, perceptions of air quality were not significantly correlated with measured air quality criteria but rather seemed to be influenced by other factors. Respondents in Los Angeles were the most concerned about air quality followed by Miami, San Francisco, and New York City. However, those from Chicago and Tampa Bay expressed the least amount of concern about air quality. Age, education, and ethnicity were all factors affecting peoples' concerns about air quality. Respiratory conditions, living in proximity to industrial areas, and financial impacts from the COVID-19 lockdowns influenced concerns about air quality. About 40% of the survey sample reported greater concern for air quality during the pandemic, while approximately 50% stated that the lockdown didn't affect their perception. Furthermore, respondents seemed concerned about air quality in general, not a specific pollutant, and are willing to adopt additional measures and more stringent policies to improve air quality in all investigated cities.

Assessment of light-duty versus heavy-duty diesel on-road mobile source emissions using general additive models applied to traffic volume and air quality data and COVID-19 responses

Following the outbreak of the COVID-19 pandemic, several papers have examined the effect of the pandemic response on urban air pollution worldwide. This study uses observed traffic volume and near-road air pollution data for black carbon (BC), oxides of nitrogen (NO_x), and carbon monoxide (CO) to estimate the emissions contributions of light-duty and heavy-duty diesel vehicles in five cities in the continental United States. Analysis of mobile source impacts in the near-road environment has several health and environmental justice implications. Data from the initial COVID-19 response period, defined as March to May in 2020, were used with data from the same period over the previous two years to develop general additive models (GAMs) to quantify the emissions impact of each vehicle class. The model estimated that light-duty traffic contributes 4-69%, 14-65%, and 21-97% of BC, NO_x, and CO near-road levels, respectively. Heavy-duty diesel traffic contributes an estimated 26-46%, 17-63%, and -7-18% of near-road levels of the three pollutants. The estimated mobile source impacts were used to calculate NO_x to CO and BC to NO_x emission ratios, which were between 0.21-0.32 μg m^-3 NO_x (μg m^-3 CO)^-1 and 0.013-0.018 μg m^-3 BC (μg m^-3 NO_x)^-1. These ratios can be used to assess existing emission inventories for use in determining air pollution standards. These results agree moderately well with recent National Emissions Inventory estimates and other empirically-derived estimates, showing similar trends among the pollutants. However, a limitation of this study was the recurring presence of an implausible air pollution impact estimate in 41% of the site-pollutant combinations, where a vehicle class was estimated to account for either a negative impact or an impact higher than the total estimated pollutant concentration. The variations seen in the GAM estimates are likely a result of location-specific factors, including fleet composition, external pollution sources, and traffic volumes.Implications: Drastic reductions in traffic and air pollution during the lockdowns of the COVID-19 pandemic present a unique opportunity to assess vehicle emissions. A General Additive Modeling approach is developed to relate traffic levels, observed air pollution, and meteorology to identify the amount vehicle types contribute to near-road levels of traffic-related air pollutants (TRAPs), which is important for future emission regulation and policy, given the significant health and environmental justice implications of vehicle-related pollution along major roadways. The model is used to evaluate emission inventories in the near-road environment, which can be used to refine existing estimates. By developing a locally data-driven method to readily characterize impacts and distinguish between heavy and light duty vehicle effects, local regulations can be used to target policies in major cities around the country, thus addressing local health disbenefits and disparities occurring as a result of exposure to near-road air pollution.

The Effects of COVID-19 Lockdown on Air Pollutant Concentrations across China: A Google Earth Engine-Based Analysis

To overcome the spread of the severe COVID-19 outbreak, various lockdown measures have been taken worldwide. China imposed the strictest home-quarantine measures during the COVID-19 outbreak in the year 2020. This provides a valuable opportunity to study the impact of anthropogenic emission reductions on air quality. Based on the GEE platform and satellite imagery, this study analyzed the changes in the concentrations of NO2, O3, CO, and SO2 in the same season (1 February-1 May) before and after the epidemic control (2019-2021) for 16 typical representative cities of China. The results showed that NO2 concentrations significantly decreased by around 20-24% for different types of metropolises, whereas O3 increased for most of the studied metropolises, including approximately 7% in megacities and other major cities. Additionally, the concentrations of CO and SO2 showed no statistically significant changes during the study intervals. The study also indicated strong variations in air pollutants among different geographic regions. In addition to the methods in this study, it is essential to include the differences in meteorological impact factors in the study to identify future references for air pollution reduction measures.

Quantifying diurnal changes in NO2 due to COVID-19 stay-at-home orders in New York City

Introduction

Policy responses to the COVID-19 pandemic, such as the NY on Pause stay-at-home order (March 22 - June 8, 2020), substantially reduced traffic and traffic-related air pollution (TRAP) in New York City (NYC). We evaluated the magnitude of TRAP decreases and examined the role of modifying factors such as weekend/weekday, road proximity, location, and time-of-day.

Methods

Hourly nitrogen dioxide (NO₂) concentrations from January 1, 2018 through June 8, 2020 were obtained from the Environmental Protection Agency's Air Quality System for all six hourly monitors in the NYC area. We used an interrupted time series design to determine the impact of NY on Pause on NO₂ concentrations, using a mixed effects model with random intercepts for monitor location, adjusted for meteorology and long-term trends. We evaluated effect modification through stratification.

Results

NO₂ concentrations decreased during NY on Pause by 19% (-3.2 ppb, 95% confidence interval [CI]: -3.5, -3.0), on average, compared to pre-Pause time trends. We found no evidence for modification by weekend/weekday, but greater decreases in NO₂ at non-roadside monitors and weak evidence for modification by location. For time-of-day, we found the largest decreases for 5 am (27%, -4.5 ppb, 95% CI: -5.7, -3.3) through 7 am (24%, -4.0 ppb, 95% CI: -5.2, -2.8), followed by 6 pm and 7 pm (22%, -3.7 ppb, 95% CI: -4.8, -2.6 and 22%, -4.8, -2.5, respectively), while the smallest decreases occurred at 11 pm and 1 am (both: 11%, -1.9 ppb, 95% CI: -3.1, -0.7).

Conclusion

NY on Pause's impact on TRAP varied greatly diurnally. Decreases during early morning and evening time periods are likely due to decreases in traffic. Our results may be useful for planning traffic policies that vary by time of day, such as congestion tolling policies.

Assessment of the dynamics of urban surface temperatures and air pollution related to COVID-19 in a densely populated City environment in East Java

The COVID-19 pandemic that has hit the whole world has caused losses in various aspects. Several countries have implemented lockdowns to curb the spread of the SARS-CoV-2 virus that caused death. However, for developing countries such as Indonesia, it is not suitable for lockdown because it considers the economic recession. Instead, the Large-scale Social Restrictions (LSSR) regulation is applied, the same as the partial lockdown. Thus, it is hypothesized that implementing LSSR that limits anthropogenic activities can reduce heat emissions and air pollution. Utilization of remote sensing data such as Terra-MODIS LST and Sentinel-5P images to investigate short-term trends (i.e., comparison between baseline year and COVID-19 year) in surface temperature, Surface Urban Heat Islands Intensity (SUHII), and air pollution such as NO₂, CO, and O₃ in Malang City and Surabaya City, East Java Province. Spatial downscaling of LST using the Random Forest Regression technique was also carried out to transform the spatial resolution of the Terra-MODIS LST image to make it feasible on a city scale. Raster re-gridding was also implemented to refine the Sentinel-5P spatial resolution. The accuracy of LST spatial downscaling results is quite satisfactory in both cities. Surface temperatures in both cities slightly decreased (below 1 °C) during LSSR was applied (P < 0.05). SUHII in both cities experienced a slight increase in both cities during LSSR. NO₂ gas was reduced significantly (P < 0.05) in Malang City (∼38%) and Surabaya City (∼28%) during LSSR phase due to reduced vehicle traffic and restrictions on anthropogenic activities. However, CO and O₃ gases did not indicate anomaly during LSSR. Moreover, this study provides insight into the correlation between SUHII change and the distribution of air pollution in both cities during the pandemic year. Air temperature and wind speed are also added as meteorological factors to examine their effect on air pollution. The proposed models of spatial downscaling LST and re-gridding satellite-based air pollution can help decision-makers control local air quality in the long and short term in the future. In addition, this model can also be applied to other ecological research, especially the input variables for ecological spatial modeling.

Changes in air quality over different land covers associated with COVID-19 in Turkey aided by GEE

With the increased urbanization, the rise of the manufacturing industry, and the use of fossil fuels, poor air quality is one of the most serious and pressing problems worldwide. The COVID-19 outbreak prompted absolute lockdowns in the majority of countries throughout the world, posing new research questions. The study's goals were to analyze air and temperature parameters in Turkey across various land cover classes and to investigate the correlation between air and temperature. For that purpose, remote sensing data from MODIS and Sentinel-5P TROPOMI were used from 2019 to 2021 over Turkey. A large amount of data was processed and analyzed in Google Earth Engine (GEE). Results showed a significant decrease in NO₂ in urban areas. The findings can be used in long-term strategies for lowering global air pollution. Future research should look at similar investigations in various study sites and evaluate changes in air metrics over additional classes.

NASA Satellite Measurements Show Global-Scale Reductions in Free Tropospheric Ozone in 2020 and Again in 2021 During COVID-19

NASA satellite measurements show that ozone reductions throughout the Northern Hemisphere (NH) free troposphere reported for spring-summer 2020 during the COronaVIrus Disease 2019 pandemic have occurred again in spring-summer 2021. The satellite measurements show that tropospheric column ozone (TCO) (mostly representative of the free troposphere) for 20°N-60°N during spring-summer for both 2020 and 2021 averaged ∼3 Dobson Units (DU) (or ∼7%-8%) below normal. These ozone reductions in 2020 and 2021 were the lowest in the 2005-2021 record. We also include satellite measurements of tropospheric NO2 that exhibit reductions of ∼10%-20% in the NH in early spring-to-summer 2020 and 2021, suggesting that reduced pollution was the main cause for the low anomalies in NH TCO in 2020 and 2021. Reductions of TCO ∼2 DU (7%) are also measured in the Southern Hemisphere in austral summer but are not associated with reduced NO2.

Perspectives from remote sensing to investigate the COVID-19 pandemic: A future-oriented approach

As scientific technology and space science progress, remote sensing has emerged as an innovative solution to ease the challenges of the COVID-19 pandemic. To examine the research characteristics and growth trends in using remote sensing for monitoring and managing the COVID-19 research, a bibliometric analysis was conducted on the scientific documents appearing in the Scopus database. A total of 1,509 documents on this study topic were indexed between 2020 and 2022, covering 165 countries, 577 journals, 5239 institutions, and 8,616 authors. The studies related to remote sensing and COVID-19 have a significant increase of 30% with 464 articles. The United States (429 articles, 28.42% of the global output), China (295 articles, 19.54% of the global output), and the United Kingdom (174 articles, 11.53%) appeared as the top three most contributions to the literature related to remote sensing and COVID-19 research. Sustainability, Science of the Total Environment, and International Journal of Environmental Research and Public Health were the three most productive journals in this research field. The utmost predominant themes were COVID-19, remote sensing, spatial analysis, coronavirus, lockdown, and air pollution. The expansion of these topics appears to be associated with cross-sectional research on remote sensing, evidence-based tools, satellite mapping, and geographic information systems (GIS). Global pandemic risks will be monitored and managed much more effectively in the coming years with the use of remote sensing technology.

COVID-19 Lockdown and the Impact on Mobility, Air Quality, and Utility Consumption: A Case Study from Sharjah, United Arab Emirates

This study presents an analysis of the impact of COVID-19 lockdown on people’s mobility trends, air quality, and utility consumption in Sharjah, United Arab Emirates (UAE). Records of lockdown and subsequent easing measures, infection and vaccination rates, community mobility reports, remotely sensed and ground-based air quality data, and utility (electricity, water, and gas) consumption data were collected and analyzed in the study. The mobility trends reflected the stringency of the lockdown measures, increasing in the residential sector but decreasing in all other sectors. The data showed significant improvement in air quality corresponding to the lockdown measures in 2020 followed by gradual deterioration as the lockdown measures were eased. Electricity and water consumption increased in the residential sector during the lockdown; however, overall utility consumption did not show significant changes. The changes in mobility were correlated with the relevant air quality parameters, such as NO2, which in turn was highly correlated to O3. The study provides data and analysis to support future planning and response efforts in Sharjah. Furthermore, the methodology used in the study can be applied to assess the impacts of COVID-19 or similar events on people’s mobility, air quality and utility consumption at other geographical locations.

Changes in the concentration and composition of urban aerosols during the COVID-19 lockdown

This work investigates the impact of COVID-19 restrictive measures on the mass concentrations of PM1 and PM10, and their chemical components (water-soluble ions, organic and elemental carbon, and major and trace metals) at an urban site in the western Mediterranean. The evolution of gaseous pollutants (NOx, O3 and some volatile organic compounds) was also analyzed. The concentrations measured during the lockdown in 2020 were compared to those obtained during the same period over the preceding five years. The average decrease in the levels of NOx and traffic-related volatile organic compounds was higher than 50 %, while O3 concentrations did not exhibit significant variations during the study period. Our results show that temporal variations in PM1 and PM10 concentrations were strongly affected by the frequency of Saharan dust events. When these episodes were excluded from the analysis period, a 35 % decrease in PM1 and PM10 levels was observed. Traffic restrictions during the lockdown led to important reductions in the concentrations of elemental carbon and metals derived from road dust (e.g. Ca and Fe) and break wear (e.g. Cu). Regarding secondary inorganic aerosols, nitrate showed the largest reductions as a consequence of the drop in local emissions of NOx.

Characterizing the interruption-recovery patterns of urban air pollution under the COVID-19 lockdown in China

The COVID-19 pandemic provides an opportunity to study the effects of urban lockdown policies on the variation in pollutant concentrations and to characterize the recovery patterns of urban air pollution under the interruption of COVID-19 lockdown policies. In this paper, interruption-recovery models and regression discontinuity design were developed to characterize air pollution interruption-recovery patterns and analyze environmental impacts of the COVID-19 lockdown, using air pollution data from four Chinese metropolises (i.e., Shanghai, Wuhan, Tianjin, and Guangzhou). The results revealed the air pollutant interruption-recovery curve represented by the three lockdown response periods (Level I, Level II and Level III) during COVID-19. The curve decreased during Level I (A 25.3%-48.8% drop in the concentration of NO₂ has been observed in the four metropolises compared with the same period in 2018-2019.), then recovered around reopening, but decreased again during Level III. Moreover, the interruption-recovery curve of the year-on-year air pollution difference suggests a process of first decreasing during Level I and gradually recovering to a new equilibrium during Level III (e.g., the unit cumulative difference of NO₂ mass concentrations in Shanghai was 21.7, 22.5, 11.3 (μg/m³) during Level I, II, and III and other metropolises shared similar results). Our findings reveal general trends in the air quality externality of different lockdown policies, hence could provide valuable insights into air pollutant interruption-recovery patterns and clear scientific guides for policymakers to estimate the effect of different lockdown policies on urban air quality.

Google Earth Engine based spatio-temporal analysis of air pollutants before and during the first wave COVID-19 outbreak over Turkey via remote sensing

Air pollution is one of the vital problems for the sustainability of cities and public health. The lockdown caused by the COVID-19 outbreak has become a natural laboratory, enabling to investigate the impact of human/industrial activities on the air pollution. In this study, we investigated the spatio-temporal density of TROPOMI-based nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) products, and MODIS-derived Aerosol Optical Depth (AOD) from January 2019 to September 2020 (also covering the first wave of the COVID-19) over Turkey using Google Earth Engine (GEE). The results showed a significant decrease in NO₂ and AOD, while SO₂ unchanged and had slightly higher concentrations in some regions during the lockdown compared to 2019. The relationship between air pollutants and meteorological parameters during the lockdown showed that air temperature and pressure were highly correlated with air pollutants, unlike precipitation and wind speed. Moreover, Purchasing Managers' Index (PMI) data, indicator of economic/industrial activities, also provided poor correlation with air pollutants. TROPOMI-based NO₂ and SO₂ were compared with station-based pollutants for three sites (suburban, urban, and urban-traffic classes) in Istanbul, revealing 0.83, 0.70 and 0.65 correlation coefficients for NO₂, respectively, while SO₂ showed no significant correlation. Besides, AOD data were validated using two AERONET sites providing 0.86 and 0.82 correlation coefficients. Overall, the satellite-based data provided significant outcomes for the spatio-temporal evaluation of air quality, especially during the first wave of the COVID-19 lockdown.

Spatiotemporal Change of Air-Quality Patterns in Hubei Province—A Pre- to Post-COVID-19 Analysis Using Path Analysis and Regression

Mitigation measures and control strategies relating to the novel coronavirus disease 2019 (COVID-19) have been widely applied in many countries to reduce the transmission of this pandemic disease. China was the first country to implement a strong lockdown policy to control COVID-19 when countries worldwide were struggling to manage COVID-19 cases. However, lockdown causes numerous changes to air-quality patterns due to the low amount of traffic and the decreased human mobility it results in. To study the impact of the strict control measures of the new COVID-19 epidemic on the air quality of Hubei in early 2020, the air-quality monitoring data of Hubei’s four cities, namely Huangshi, Yichang, Jingzhou, and Wuhan, from 2019 to 2021, specifically 1 January to 30 August, was examined to analyze the characteristics of the temporal and spatial distribution. All air-quality pollutants decreased during the active-COVID-19 period, with a maximum decrease of 26% observed in PM10, followed by 23% of PM2.5, and a minimum decrease of 5% observed in O3. Changes in air pollutants from 2017 to 2021 were also compared, and a decrease in all pollutants through to 2020 was found. The air-quality index (AQI) recorded an increase of 2% post-COVID-19, which shows that air quality will worsen in future, but it decreased by 22% during the active-COVID-19 period. A path analysis model was developed to further understand the relationship between the AQI and air-quality patterns. This path analysis shows a strong correlation between the AQI and PM10 and PM2.5, however its correlation with other air pollutants is weak. Regression analysis shows a similar pattern of there being a strong relationship between AQI and PM10 (r2 = 0.97) and PM2.5 (r2 = 0.93). Although the COVID-19 pandemic had numerous negative effects on human health and the global economy, it is likely that the reduction in air pollution and the significant improvement in ambient air quality due to lockdowns provided substantial short-term health benefits. The government must implement policies to control the environmental issues which are causing poor air quality in post-COVID-19.

Declining carbon emission/concentration during COVID-19: A critical review on temporary relief

In December 2019 the deadly pandemic COVID-19 traumatized mankind through its lethal impact. To seize the outbreak, nationwide/region-based lockdown strategies were adopted by most of the COVID-19 affected countries. This in turn resulted in restricted transportation via surface, water, and air, as well as significantly reduced working hours of the industry sectors, so on and so forth. The obvious outcome was a sudden discernible decline in atmospheric adulteration. Accordingly, the anthropogenic emissions at the global and regional/local scales were examined during the lockdown period by several researchers using both or either satellite-based and ground-based monitoring. Among several other air-contaminants, carbon has a dominant toxicological profile causing adverse health effects and thereby attracting researches interest in carbon-release probing during the systematic confinement period imposed by the ruling authorities across the globe. The results of those studies indicated a confirmed decline in carbon emission/concentration making the air more breathable for the period. In this review, the studies related to anthropogenic emissions of carbon during the lockdown period are accounted for by compiling the recently reported data from published articles.

On the impact of the COVID-19 pandemic on air quality in Florida

Since early 2020, the world has faced an unprecedented pandemic caused by the novel COVID-19 virus. In this study, we characterize the impact of the lockdown associated with the pandemic on air quality in six major cities across the state of Florida, namely: Jacksonville, Tallahassee, Gainesville, Orlando, Tampa, and Miami. Hourly measurements of PM2.5, ozone, NO2, SO2, and CO were provided by the US EPA at thirty sites operated by the Florida Department of Environmental Protection during mid-February to mid-April from 2015 through 2020. To analyze the effect of the pandemic, atmospheric pollutant concentrations in 2020 were compared to historic data at these cities during the same period from 2015 to 2019. Reductions in NO2 and CO levels were observed across the state in most cities and were attributed to restrictions in mobility and the decrease in vehicle usage amid the lockdown. Likewise, decreases in O3 concentrations were observed and were related to the prevailing NOx-limited regime during this time period. Changes in concentrations of SO2 exhibited spatial variations, concentrations decreased in northern cities, however an increase was observed in central and southern cities, likely due to increased power generation at facilities primarily in the central and southern regions of the state. PM2.5 levels varied temporally during the study and were positively correlated with SO2 concentrations during the lockdown. In March, reductions in PM2.5 levels were observed, however elevations in PM2.5 concentrations in April were attributed to long-range transport of pollutants rather than local emissions. This study provides further insight into the impacts of the COVID-19 pandemic on anthropogenic sources from vehicular emissions and power generation in Florida. This work has implications for policies and regulations of vehicular emissions as well as consequences on the use of sustainable energy sources in the state.

Improvement of atmospheric pollution in the capital cities of US during COVID-19

The spread of COVID-19 during 2020 impacted the whole world and still affecting the lives of people living in some parts of the world. The spread of this epidemic started in the US in late March 2020 and became a major issue in April due to an outburst of COVID-19 cases. Most of the countries in the world imposed complete to partial lockdown, but in the US, few states imposed lockdowns. Even after the advisory of the various Government department, the mobility data suggest that there was an enhancement (10–15%) in mobility during March 2020. Later sudden drop in mobility was observed during April 2020. The fall in aerosols optical depth (AOD), particulate matter concentration, NO_2, and Ozone are observed along with the positive shifts in the SO₂. In some of the states, AOD shows pronounced decline during May and June (5–40.90%), in the month of  May more than 80% decline was observed compared to the month of June 2020. In the month of April 2020, up to 73.64% decline was observed in NO_2, and 70–99% in the months of May and June 2020. We found a good relationship between the mobility data and improvement in the air quality of the US. The changes were not significant compared to other countries in the world due to scattered lockdown policy, but in the US a pronounced change is observed during April month compared to March and May.

Extracting Road Traffic Volume in the City before and during covid-19 through Video Remote Sensing

Continuous, automatic measurements of road traffic volume allow the obtaining of information on daily, weekly or seasonal fluctuations in road traffic volume. They are the basis for calculating the annual average daily traffic volume, obtaining information about the relevant traffic volume, or calculating indicators for converting traffic volume from short-term measurements to average daily traffic volume. The covid-19 pandemic has contributed to extensive social and economic anomalies worldwide. In addition to the health consequences, the impact on travel behavior on the transport network was also sudden, extensive, and unpredictable. Changes in the transport behavior resulted in different values of traffic volume on the road and street network than before. The article presents road traffic volume analysis in the city before and during the restrictions related to covid-19. Selected traffic characteristics were compared for 2019 and 2020. This analysis made it possible to characterize the daily, weekly and annual variability of traffic volume in 2019 and 2020. Moreover, the article attempts to estimate daily traffic patterns at particular stages of the pandemic. These types of patterns were also constructed for the weeks in 2019 corresponding to these stages of the pandemic. Daily traffic volume distributions in 2020 were compared with the corresponding ones in 2019. The obtained results may be useful in terms of planning operational and strategic activities in the field of traffic management in the city and management in subsequent stages of a pandemic or subsequent pandemics.

Global tropospheric ozone responses to reduced NO x emissions linked to the COVID-19 worldwide lockdowns

Efforts to stem the transmission of coronavirus disease 2019 (COVID-19) led to rapid, global ancillary reductions in air pollutant emissions. Here, we quantify the impact on tropospheric ozone using a multiconstituent chemical data assimilation system. Anthropogenic NO x emissions dropped by at least 15% globally and 18 to 25% regionally in April and May 2020, which decreased free tropospheric ozone by up to 5 parts per billion, consistent with independent satellite observations. The global total tropospheric ozone burden declined by 6TgO3 (∼2%) in May and June 2020, largely due to emission reductions in Asia and the Americas that were amplified by regionally high ozone production efficiencies (up to 4 TgO3/TgN). Our results show that COVID-19 mitigation left a global atmospheric imprint that altered atmospheric oxidative capacity and climate radiative forcing, providing a test of the efficacy of NO x emissions controls for co-benefiting air quality and climate.