Particulate Matter

EO satellites measure atmospheric aerosols formed by the combination of particulate matter and air. Particulate matter is classified by its aerodynamic diameter, which determines its risk to human health and the environment. Inhalable coarse particles, designated PM10, are particles with a diameter of 10 μm or less, while the more dangerous fine particles, designated PM2.5, measure 2.5 μm or less. Ultrafine particles (UFPs), with diameters of 100 nm or less, are currently unregulated. 1) 2) 3)

Airborne particulate matter is recognised as a Group 1 carcinogen, reflecting the severe health risks associated with exposure. Natural PM sources such as dust storms and anthropogenic sources such as biomass burning and industrial production can produce high concentrations of aerosols. Aerosols also play a role in the Earth Radiation Budget, and can affect how much energy Earth absorbs or reflects.

Monitoring and forecasting near-surface PM concentrations requires a combination of ground-based measurements, satellite observations, and information from atmospheric chemistry and transport models. Although not yet optimally exploited for PM estimation, spaceborne sensors are capable of measuring many different aspects of PM. Broadband multispectral imagers map the horizontal distribution of the vertically integrated aerosol burden (the total column of aerosol). Multi-angle and polarimetric imagers are capable of discerning particle size and type, hyperspectral imagers provide information on spectral absorption and aerosol speciation, and lidars measure vertical distributions of aerosol along narrow tracks. The Committee on Earth Observation Satellites (CEOS) published a White Paper detailing approaches for generating satellite-informed products and services, particularly for surface PM2.5 monitoring, and making recommendations to strengthen the role of satellites in constraining global PM levels. 4)

Satellite Retrieval Principles for Aerosols

Satellite aerosol measurements are based on Radiative Transfer Theory (RTT), which describes how incoming solar radiation interacts with aerosols, atmospheric gases, and the Earth's surface. Passive sensors, such as radiometers and spectrometers, measure the total radiance reflected back to the Top-of-Atmosphere (TOA) across multiple wavelengths to derive aerosol properties. A challenge in passive remote sensing is accurately and consistently isolating weak signals scattered by aerosols from the stronger, highly variable signal reflected by the Earth's surface, particularly over land. To counter this, two specialised retrieval algorithms are used to generate the aerosol optical depth (AOD): the Dark Target and Deep Blue algorithms. 5) 6)

Retrieval Algorithms

Dark Target

The Dark Target (DT) algorithm was developed for AOD retrieval over spectrally dark surfaces, such as oceans and areas with dense vegetation. To calculate AOD, the algorithm generates masks for clouds, snow, and other bright surfaces at varying resolutions. 7) 8)

Deep Blue

The Deep Blue (DB) algorithm was developed to retrieve AOD over spectrally bright surfaces such as deserts. The Collection 6 (C6) algorithm improved its ability to retrieve AOD over both bright and dark surfaces. DB estimates surface reflectance using the lower reflectivity in the ultraviolet and blue visible wavelengths. The retrieval is typically performed at 1 km resolution by matching the TOA reflectance observed by the satellite to pre-calculated, theoretical values. 9) 10)

To maximise spatial coverage and data accuracy, a simplified merge scheme (SMS) is used to combine DT and DB products, resulting in products such as the merged DTB3K. This merging technique averages the DT and DB retrievals, significantly improving accuracy and reducing bias compared to DT3K, making it useful for air quality studies and research.

Example Products

Related Missions

Copernicus: Sentinel-5 Precursor (Sentinel-5P)

The Copernicus Sentinel-5 Precursor (Sentinel-5P) mission was developed by ESA to provide highly accurate atmospheric data. The satellite carries the Tropospheric Monitoring Instrument (TROPOMI), a push-broom grating spectrometer with a swath of 2600 km. TROPOMI provides daily global coverage of atmospheric trace gases and aerosols, which are used for air quality monitoring, climate forecasting, and tracking the ozone layer. 15)

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Copernicus: Sentinel-4

The Sentinel-4 mission, developed by ESA under the Copernicus programme, is an instrument housed onboard the Meteosat Third Generation (MTG) geostationary satellites, contributing continuous, high-temporal resolution monitoring of atmospheric properties over Europe. Its Ultraviolet, Visible, Near-infrared (UVN) spectrometer is designed to measure AOD, important for the assimilation of data into air quality forecasting models and supporting environmental policy across Europe. 14)

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Joint Polar Satellite System (JPSS)

The Joint Polar Satellite System (JPSS) is a series of environmental satellites developed by NOAA and NASA. JPSS satellites orbit the Earth approximately 14 times a day, providing full global coverage twice daily. Each JPSS satellite carries a variety of instruments, including the Advanced Technology Microwave Sounder (ATMS), the Cross-track Infrared Sounder (CrIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Clouds and the Earth’s Radiant Energy System (CERES). VIIRS is a passive sensor designed to measure aerosol properties. Data from VIIRS is merged with measurements from MODIS and MISR to create highly accurate products required for long-term climate change monitoring, including detailed assessments of AOD trends. 11)

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Tropospheric Emissions: Monitoring of Pollution (TEMPO)

Tropospheric Emissions: Monitoring of Pollution (TEMPO), developed and operated by NASA, is a geostationary satellite instrument mission aiming to monitor major air pollutants over the North American continent. TEMPO provides hourly measurements of atmospheric properties at a Ground Sample Distance of 2.21 x 4.97 km and a spectral resolution of 0.57 nm. The instrument measures variables required for advanced pollution forecasting models, including seven aerosol-sensitive radiance bands, which are used in Machine Learning frameworks to estimate concentrations of PM2.5 particles. The data is used for operational air quality forecasting and real-time public health interventions. 12)

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Terra & Aqua

The Terra and Aqua satellites, developed by NASA, provide long-term data for aerosol monitoring. Onboard both satellites is MODIS, a passive sensor that captures AOD observations at multiple spatial resolutions. MODIS data is important for climate change research as it provides data necessary to measure aerosol radiative effects and identify aerosol properties on a global scale. The Dark Target algorithm, applied over spectrally dark surfaces, makes MODIS data reliable for tracking aerosols over oceans. 11)

The Multi-angle Imaging SpectroRadiometer (MISR) onboard the Terra satellite complements MODIS by observing the same target at multiple viewing angles, capturing additional data on aerosol properties such as shape and size distribution.

Read more: Terra | Aqua

Geostationary Korea Multi-Purpose Satellite–2B (GEO-KOMPSAT-2B)

The Geostationary Environment Monitoring Spectrometer (GEMS), deployed onboard South Korea’s Geostationary Korea Multi-Purpose Satellite-2B (GEO-KOMPSAT-2B), is an instrument that focuses on providing data for air quality monitoring across Asia. GEMS provides hourly data on aerosols, important for tracking particulate matter such as smoke and pollution. 13)

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References  

1) NASA, “Particulate Matter”, URL:https://airquality.gsfc.nasa.gov/particulate-matter

 2) EPA, “Particulate Matter (PM) Basics”, URL: https://www.epa.gov/pm-pollution/particulate-matter-pm-basics

3) ESA, “Using Earth Observation Data for Public Health”, URL: https://gda.esa.int/story/using-earth-observation-data-for-public-health/

4) Kondragunta, Veihelmann, Chatfield et al., "Monitoring Surface PM2.5: An International Constellation Approach to Enhancing the Role of Satellite Observations,” Committee on Earth Observation Satellites, 2022, URL: https://doi.org/10.25923/7snz-vn34 

5) NASA, “Chapter 10 - Remote Sensing Measurements of Aerosol Properties”, URL: https://ntrs.nasa.gov/citations/20230002556

6) NASA, “Introduction to the Theory of Atmospheric Radiative Transfer”, URL: https://ntrs.nasa.gov/api/citations/19860018367/downloads/19860018367.pdf

7) NASA, “Algorithm Intro - Dark Target”, URL: https://darktarget.gsfc.nasa.gov/algorithm-intro

8) NASA, “Introduction - Dark Target”, URL: https://darktarget.gsfc.nasa.gov/

9) NASA, “Deep Blue”, URL: https://earth.gsfc.nasa.gov/climate/data/deep-blue

10) NASA, “MODIS Standard Collection 6.0 Update”, URL: https://atmosphere-imager.gsfc.nasa.gov/documentation/collection-6

11) NASA, “Validation Method”, URL: https://darktarget.gsfc.nasa.gov/validation

12) NASA, “New Instrument to Track Air Pollution Hourly, Shed Light on Disparities”, URL: https://www.nasa.gov/missions/tempo/new-instrument-to-track-air-pollution-hourly-shed-light-on-disparities/

13) EGU, “Geostationary Environment Monitoring Spectrometer (GEMS) polarization characteristics and correction algorithm”, URL: https://amt.copernicus.org/articles/17/145/2024/

14) Sentinel Online, “Sentinel-4”, URL: https://sentinels.copernicus.eu/missions/sentinel-4

15) Sentinel Online, “Sentinel-5P”, URL: https://sentinels.copernicus.eu/copernicus/sentinel-5p