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NASA's Fleet of Earth Observing Satellites: Monitoring Our Planet's Health

NASA has a fleet of Earth-observing satellites whose instruments observe our planet's oceans, biosphere, and atmosphere. Several of these satellites have instruments that observe air pollutants around the world. The data collected are being used by air quality managers and researchers studying the impact of air pollution on human health and agriculture. This website is dedicated to providing examples of these applications.

Air Pollution: Magnitude of the Problem

Human Health: Exposure to outdoor air pollution is responsible for an estimated 4 million premature deaths annually with about another 3-4 million resulting from exposure to indoor air pollution; that is, air pollution is responsible for about 1 in 9 deaths worldwide (WHO, 2018;  Cohen et al., 2017). The majority of deaths are associated with fine particulate matter of less than 2.5 µm in width (PM2.5). Exposure to ozone (O3) is associated with about another 250 thousand deaths annually worldwide (Cohen et al., 2017).

Agriculture: The economic impact of crop yield loss due to pollution is significant all over the world. Global crop yield losses for wheat, corn, and soybeans are estimated to range from $11-18 billion annually, with the greatest economic loss estimated to occur in the United States ($3.1 billion). Most losses occur from one pollutant, O3, despite the fact that scientists have been working with farmers for decades to find O3-tolerant species to cultivate. These losses are projected to increase for many regions.

Spatial Coverage is the Primary Advantage of NASA Satellite Data for Health and Air Quality Applications

Historically, air quality data have been collected with in situ surface monitors. The primary strength of an air quality monitor is that high quality data may be collected over a long period of time, which is useful for assessing pollutant trends, developing policy, and studying human health. However, there are a number of limitations when relying on air quality monitors alone. For instance, a large number of monitors is required to adequately capture the spatial heterogeneity of air pollution in urban environments, which is not feasible as current regulatory-grade monitors are expensive to purchase and operate. Even in relatively wealthy countries, the spatial distribution of surface monitors is sparse. Surface monitors are typically nonexistent in many of the world’s countries. Relative to surface air quality monitoring networks, the primary advantage of observing air pollution from space is global spatial coverage.

Scientific researchers have shown the potential of satellite data of air pollutants to be used for a number of applications, such as monitoring air pollution concentrations and trends over a wide spatial area and determining where additional pollution mitigation efforts are needed (Streets et al., 2013; Duncan et al., 2014). For example, all the satellite datasets of air pollutants over the U.S. tell the same story:  the Clean Air Act and other environmental regulations are improving air quality in the Eastern U.S. as discussed in a video by former President Obama.  This story is generally consistent with the trends in EPA surface monitor data.

Before and After: World Nitrogen Dioxide Levels, 2005-2016

Image Credit: 
NASA's Goddard Space Flight Center

The slider above shows satellite data of nitrogen dioxide (NO2) from the Aura Ozone Monitoring Instrument (OMI) data for two years, 2005 and 2016. The data indicate that levels of this pollutant changed significantly over the last decade in many places around the world.  Slide the bar from back and forth to see these changes.  More information on NO2 trends for each region and more than 300 world cities can be found under the "NO2" tab.

New and Exciting Satellites for Health and Air Quality Applications

Several new satellite instruments, that were recently launched or are nearing launch, promise to provide much better data on air pollutants than can be obtained from current satellites.

  • Higher Spatial Resolution: The European Space Agency’s TROPOspheric Monitoring Instrument (TROPOMI; launched in 2017) collects data on NO2, sulfur dioxide (SO2), carbon monoxide (CO), and methane (CH4) at sub-urban spatial resolutions (e.g., a few kilometers) and at resolutions much finer than current instruments.
  • Higher Temporal Resolution: A fleet of satellites in geosynchronous orbit covering major polluted regions of the world will provide much needed information on how air pollutant concentrations and emissions vary throughout the day. (A satellite in geosynchronous orbit will appear to remain in a fixed location in the sky relative to an observer on the ground.) Currently, there are several satellites (e.g., National Oceanic and Atmospheric Administration GOES-R; Japan Space Agency Himawari-8/9) in geosynchronous orbits that provide information on aerosols. Three satellites in geosynchronous orbits are planned to observe NO2, SO2, CO, and CH4 over East Asia (Korean Space Agency Geostationary Environment Monitoring Spectrometer (GEMS)), North America (NASA Tropospheric Emissions: Monitoring Pollution (TEMPO)), and Europe (European Space Agency Sentinel-4). Given the potential of air pollution to increase in the growing world megacities, geosynchronous satellites with similar capabilities are needed over tropical and subtropical land masses as well.
  • A Upcoming NASA Health Mission: The NASA Multi-Angle Imager for Aerosols (MAIA) mission, which has a focus to provide data useful to health researchers, will collect high-resolution data on the properties of aerosols, which may be used to infer surface PM2.5, in several of the world’s megacities.

These new capabilities will improve our ability to infer surface concentrations of air pollutants from satellite data. While it is not currently feasible to infer surface concentrations of O3 from satellite data, surface concentrations of a number of pollutants, such as PM2.5, SO2, and NO2, are inferred using atmospheric models. For example, PM2.5 may be inferred from a quantity observed from space called aerosol optical depth (AOD), which is a measure of the extinction of sunlight by small airborne particles (aerosols). However, there are often large uncertainties associated with these estimates (Duncan et al., 2014 and references therein). Nevertheless, these estimates are proving useful for air quality managers and health researchers as demonstrated on this website. For example, Science writer, Emily Underwood, interviewed a number of HAQAST scientists and describes how they are using satellite data for health applications in her article, entitled "Measuring Air Pollution's Health Impacts from Space"

Building a Comprehensive, Global Observational Network for Air Pollutants

A comprehensive observing strategy for air pollutants includes satellite observations and robust surface monitoring networks, which are necessary to validate and interpret the surface level estimates of pollutant concentrations as inferred from satellite data in conjunction with atmospheric models (NAS, 2018). The world’s population is projected to grow by about two billion by 2050 with much of this growth occurring in tropical and subtropical megacities (e.g., in India, Indonesia, Nigeria, Pakistan; UN, 2017) and the number of premature deaths associated with outdoor air pollution is projected to double by 2050 (Lelieveld et al., 2015). Therefore, particular attention should be paid to developing a comprehensive, yet affordable, observing strategy in tropical and subtropical cities.

Currently, the data quality of low-cost sensors (e.g., hand-held instruments) compared to traditional air quality monitors is too poor to offset the cost savings. At some point in the future, however, technological developments may improve these low-cost sensors to the point that they produce science-quality data, which will allow many cities and countries to “leap frog” technologies and bypass expensive surface monitors. Additionally, low-cost sensors may then prove to be a valuable resource for evaluating satellite-based estimates of surface air pollution concentrations, including in areas without surface observations.

For any robust network, knowledge of the concentrations and sources of a number of pollutants is required to devise effective mitigation strategies for PM2.5 and O3. PM2.5 is directly emitted to the atmosphere, such as in the form of smoke and dust, but it can also form in the atmosphere through chemical reactions that transform gaseous pollutants (e.g., sulfur dioxide (SO2), ammonia (NH3)) to particles (i.e., gas to particle conversion). Nitrogen dioxide (NO2), which is released into the air by burning fossil fuels, is unhealthy to breathe, but, more importantly, knowledge of its concentrations is important as NO2 is a primary ingredient for the formation of unhealthy levels of surface O3.


We urge users of any scientific data to carefully interpret the data, including the data presented in the figures and tables on this website, so that one does not draw erroneous conclusions.  The overall uncertainty associated with the data is a combination of uncertainties associated with the instrument and those introduced during the creation of the data product, which is a multi-step and sometimes imperfect process that may lead to inaccurate data for some areas (e.g., Duncan et al., 2014). In addition, for many places in the world, there are few, if any, independent observations with which to evaluate the satellite data.

Additional Resources

Since many professionals do not have the financial resources to support the processing and correct interpretation of satellite data, which takes computing resources and, more importantly, expertise, NASA has developed a number of resources for new satellite data users.  Please see the sidebar on this page and also click on the "Resources" tab for more information.

For more information, including on how satellite data are being used for health and air quality research and applications and for data user resources, please visit the following websites:


  • Brauer, M. et al., Ambient Air Pollution Exposure Estimation for the Global Burden of Disease 2013. Environ. Sci. Technol. 50, 79-88 (2016).
  • Cohen, A.J., et al., Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. The Lancet 389, 1907-1918, 2017.
  • Duncan, B.N., et al., Satellite data of atmospheric pollution for U.S. air quality applications: Examples of applications, summary of data end-user resources, answers to FAQs, and common mistakes to avoid. Atmos. Environ. 94, 647-662 (2014).
  • Lelieveld, J., J. S. Evans, M. Fnais, D. Giannadaki, A. Pozzer, The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 525, 367-371 (2015).
  • NAS, National Academies of Sciences, Medicine, Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space.  (The National Academies Press, Washington, DC, 2018), pp. 700.
  • Streets, D.G., et al., Emissions estimation from satellite retrievals: A review of current capability. Atmos. Environ. 77, 1011-1042 (2013).