A document by Olivia Sablan. Click on the document to view its contents.

Estimating surface-level fine particulate matter from satellite remote sensing data can expand the spatial coverage of ground-based monitors. This approach is particularly effective in assessing rapidly changing air pollution events such as wildland fires that often start far away from centralized ground monitors. We developed Deep Neural Network algorithm to bias correct hourly PM2.5 levels informed by GOES-R satellites, NOAA meteorology forecasts, and real-time PM2.5 observations from the Environmental Protection Agency (EPA) via AirNow. The surface-satellite-model collocated datasets for the period of 2020-2021 was used to assess the biases in GOES-GWR PM2.5 against AirNow measurements at hourly and daily scales. Then a deep neural network (DNN) based bias correction algorithm is used to improve the accuracies of GOES-GWR PM2.5. The DNN uses GOES-GWR PM2.5, GOES-R aerosol parameters, and HRRR meteorological fields as input and AirNow PM2.5 is used as target variable. The application of DNN reduced the PM2.5 biases as compared to GOES-GWR estimates. RMSE was also reduced to 6.55 µg/m3 from 8.72 µg/m3 in GOES-GWR estimates. The DNN model was also evaluated on two sets of independent datasets for its robustness. In the first independent dataset for the first half of 2020, ~89% of stations show an increase in correlation (r) and, ~76% and ~62% of stations show a reduction in bias. The IOA and r for the independent data were 0.77 and 0.61 (GWR: 0.68 and 0.53) and RMSE was 4.48 µg/m3 (GWR=6.13 µg/m3) for the same period.

Most countries around the world took actions to control COVID-19 spread that included social distancing, limiting air and ground travel, closing schools, suspending sports leagues, closing factories etc., leading to economic shutdown. The reduced traffic and human movement compared to Business as Usual (BAU) scenario was tracked by Apple and Android cellphone use; the data showed substantial reductions in mobility in most metropolitan areas. For example in Washington D.C., average distance traveled by people was ~13 km and by April when lockdown was in full effect, the distance reduced to ~5 km. Consistent with reduced mobility, air quality as indicated by satellite observations decreased substantially. Granted that year to year variability in weather patterns can have influence on observed NO2 and aerosol concentrations, but the drop in tropospheric nitrogen dioxide (NO2) observed by Sentinel 5P Tropospheric Ozone Monitoring Instrument (TROPOMI) and Suomi NPP Ozone Mapping Profiling Suite (OMPS) observations of NO2 was significant; reductions in observed NO2 were between 15% to 50% between February and April 2020 depending on location and similar reductions in NO2 amount in March and April 2020 compared to March and April 2019. Further, the changes in NO2 across the continental U.S. between 2020 and 2019 correlated well with on-road emissions but did not correlate with changes in emissions from power plants. In the first quarter of 2020, the total amount of NOx emitted on road were 200 times and 7 times larger than that from power plants in LA and NYC, respectively. These findings confirm that power plants are no longer the major source of NO2 in the United States. We also found positive correlation between NO2 and Suomi NPP Visible Infrared Imaging Radiometer Suite (VIIRS) aerosol optical depth measurements in these urban regions indicating common source sectors for NO2 and aerosols/aerosol precursors.

During COVID-19 pandemic in 2020, many cities and areas were shutdown to control the virus spread. The shutdown introduced reduced emissions from vehicles and power plants. In this study, we studied the impact on pollution by analyzing Suomi NPP satellite Visible Infrared Imaging Radiometer Suite (VIIRS) aerosol optical depth (AOD); AOD is a proxy for particulate pollution in the atmosphere. The investigation is performed over several areas and cities around the globe, i.e. China, India, Europe, the United States, New York city, Los Angeles, etc. In general, reductions in AOD compared to the previous years are found in these areas but with some differences. In China, where the pollution in general is high than the other areas, the reduction in AOD is the most obvious. However, in Europe and the United States, the reduction in AOD is less obvious. In India, the effect is in between. Comparing reductions in S5P Tropospheric Ozone Monitoring Instrument (TROPOMI) nitrogen dioxide (NO2) and AOD, we found that they sometimes co-vary and sometimes do not. The possible reason is that NO2 and aerosols do not have the same life time and therefore may not always co-exist at the same time and place. NO2 has a shorter life time and therefore tends to be observed close to the source region. Because of longer life time of aerosols, the aerosols from smoke, dust, and pollution can be transported with long distance and interfere with those from local sources. To tease out the AOD reductions from lockdown compared to business as usual (BAU), AOD data were analyzed using NO2 as a filter. Using this approach we found marginal reductions to particulate pollution in some regions to reductions of up to 20% in other regions. Analysis of particulate pollution in 2020 compared to BAU globally and regionally will be presented.