TROPOMI NO2 in the United States: A Detailed Look at the Annual Averages, Weekly Cycles, Effects of Temperature, and Correlation With Surface NO2 Concentrations.
Daniel L GoldbergSusan C AnenbergGaige Hunter KerrArash MoheghZifeng LuDavid G StreetsPublished in: Earth's future (2021)
Observing the spatial heterogeneities of NO2 air pollution is an important first step in quantifying NOX emissions and exposures. This study investigates the capabilities of the Tropospheric Monitoring Instrument (TROPOMI) in observing the spatial and temporal patterns of NO2 pollution in the continental United States. The unprecedented sensitivity of the sensor can differentiate the fine-scale spatial heterogeneities in urban areas, such as emissions related to airport/shipping operations and high traffic, and the relatively small emission sources in rural areas, such as power plants and mining operations. We then examine NO2 columns by day-of-the-week and find that Saturday and Sunday concentrations are 16% and 24% lower respectively, than during weekdays. We also analyze the correlation of daily maximum 2-m temperatures and NO2 column amounts and find that NO2 is larger on the hottest days (>32°C) as compared to warm days (26°C-32°C), which is in contrast to a general decrease in NO2 with increasing temperature at moderate temperatures. Finally, we demonstrate that a linear regression fit of 2019 annual TROPOMI NO2 data to annual surface-level concentrations yields relatively strong correlation (R 2 = 0.66). These new developments make TROPOMI NO2 satellite data advantageous for policymakers and public health officials, who request information at high spatial resolution and short timescales, in order to assess, devise, and evaluate regulations.
Keyphrases
- air pollution
- particulate matter
- public health
- lung function
- electronic health record
- big data
- risk assessment
- liquid chromatography
- heavy metals
- magnetic resonance
- randomized controlled trial
- magnetic resonance imaging
- healthcare
- drinking water
- reactive oxygen species
- single molecule
- municipal solid waste
- life cycle
- machine learning
- cystic fibrosis
- physical activity
- data analysis
- human health
- health information
- computed tomography
- deep learning