Nitrogen dioxide (NO2) is emitted during high temperature combustion from anthropogenic and natural sources. Human exposure to high NO2 concentrations causes cardiovascular and respiratory illnesses. The EPA operates ground monitors across the U.S. which take hourly measurements of NO2 concentrations, providing precise measurements for assessing human pollution exposure but with sparse spatial distribution. Satellite-based instruments capture NO2 amounts through the atmospheric column with global coverage at regular spatial resolution, but do not directly measure surface NO2. This study compares regression methods using satellite NO2 data from the TROPospheric Ozone Monitoring Instrument (TROPOMI) to estimate annual surface NO2 concentrations in varying geographic and land use settings across the continental U.S. We then apply the best-performing regression models to estimate surface NO2 at 0.01o by 0.01o resolution, and we term this estimate as quasi-NO2 (qNO2). qNO2 agrees best with measurements at suburban sites (cross-validation (CV) R2 = 0.72) and away from major roads (CV R2 = 0.75). Among U.S. regions, qNO2 agrees best with measurements in the Midwest (CV R2 = 0.89) and agrees least in the Southwest (CV R2 = 0.65). To account for the non-Gaussian distribution of TROPOMI NO2, we apply data transforms, with the Anscombe transform yielding highest agreement across the continental U.S. (CV R2 = 0.78). The interpretability, minimal computational cost, and health relevance of qNO2 facilitates use of satellite data in a wide range of air quality applications.

The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition. The resulting reductions in anthropogenic activity represent an unprecedented event that yields a glimpse into a future where emissions to the atmosphere are reduced. While air pollutants and greenhouse gases share many common anthropogenic sources, there is a sharp difference in the response of their atmospheric concentrations to COVID-19 emissions changes due in large part to their different lifetimes. Here, we discuss two key takeaways from modeling and observational studies. First, despite dramatic declines in mobility and associated vehicular emissions, the atmospheric growth rates of greenhouse gases were not slowed. Second, it demonstrated empirically that the response of atmospheric composition to emissions changes is heavily modulated by factors including carbon cycle feedbacks to CH4 and CO2, background pollutant levels, the timing and location of emissions changes, and climate feedbacks on air quality.

Most countries around the world including the United States took actions to control COVID-19 spread that lead to an abrupt shift in human activity. On-road NOx emissions from light and heavy-duty vehicles decreased by 9% to 19% between February and March at the onset of the lockdown period in the middle of March in most of the US; between March and April, the on-road NOx emissions dropped further by 8% to 31% when lockdown measures were the most stringent. These precipitous drops in NOx emissions correlated well with tropospheric NO2 column amount observed by the Sentinel 5 Precursor TROPOspheric Monitoring Instrument (S5P TROPOMI). Furthermore, the changes in TROPOMI tropospheric NO2 across the continental U.S. between 2020 and 2019 correlated well with changes in on-road NOx emissions (r = 0.68) but correlated weakly with changes in emissions from the power plants (r = 0.35). At the height of lock-down related unemployment in the second quarter of 2020, the NO2 values decreased at the rate of 0.8 µmoles/m2 per unit percentage increase in the unemployment rate. Despite the lifting of lockdown measures, parts of the US continued to have ~20% below normal on-road NOx emissions. To achieve this new normal urban air quality in the US, continuing remote work policies that do not impede economic growth may become one of the many options.

From the heated debates over the airborne transmission of the novel coronavirus to the abrupt Earth system changes caused by the sudden lockdowns, the dire circumstances resulting from the coronavirus disease 2019 (COVID-19) pandemic has brought the field of GeoHealth to the forefront of visibility in science and policy. The pandemic has inadvertently provided an opportunity to study how human response has impacted the Earth system, how the Earth system may impact the pandemic, and the capacity of GeoHealth to inform real-time policy. The lessons learned throughout our responses to the COVID-19 pandemic are shaping the future of GeoHealth.

The World Health Organization (WHO) recently reduced its health guideline for Nitrogen dioxide (NO 2) to annual and 24-hr means of 10 µg/m 3 (5.3 ppb) and 25 µg/m 3 (13.3 ppb). NO 2 is a criteria air pollutant that varies spatiotemporally at fine resolutions due to its relatively short lifetime (~hours) and current models have limited ability to capture this variation. To advance global exposure estimates, we created a daily global land use regression (LUR) model with 50 x 50 m 2 spatial resolution using 5.7 million daily air monitor averages collected from 8,250 monitor locations. In cross-validation, the model captured 47%, 59%, and 63% of daily, monthly, and annual global NO 2 variation. Daily, monthly, and annual root mean square error were 6.8, 5.0, and 4.4 ppb and absolute bias were 46%, 30%, and 21%, respectively. The final model has 11 variables, including road density and built environments with fine (30 m or less) spatial resolution and meteorological and satellite data with daily temporal resolution. Major roads and satellite-based estimates of NO 2 were consistently the strongest predictors in all regions. Daily model estimates from 2005-2019 are available 1 and can be used for global risk assessments and health studies, particularly in countries without NO 2 monitoring. Short synopsis: This is the first global NO 2 model with daily temporal and 50m spatial resolution, valuable for capturing NO 2 variation.