The spatial coverage afforded by satellite instruments has opened new areas of investigation by the air quality community, such as for inferring surface pollutant levels, emissions, and trends, and the health effects of specific pollutants (e.g., Streets et al. 2013; Duncan et al. 2014; Seltenrich 2014). There are a number of satellite data products that have proven valuable for these applications, despite some challenges that must be overcome (Martin 2008; Streets et al. 2013; Duncan et al. 2014; and references therein). 

For instance, a fundamental challenge of using these data is the proper “translation” of the observed quantities to more useful surface quantities, such as emissions and concentrations. This translation is called a retrieval algorithm: the method used to convert electromagnetic radiation observed by the satellite instrument to an atmospheric quantity, such as a column density. 

From the NASA Aura Ozone Monitoring Instrument (OMI) spectra, one infers a column density, which is the sum of all molecules from the instrument to the Earth’s surface and typically reported in units of molecules/cm². From a column density, one may infer a surface concentration (e.g., at “nose-level”) or emission flux if the majority of the temporal variation within the column density is associated with near-surface sources. This is the case for NO₂, SO₂, and formaldehyde, as their chemical lifetimes are short and their primary sources are located near the Earth’s surface. 

Continuing refinements to the OMI retrieval algorithms have resulted in column density data products that are now of sufficient maturity to allow for the reliable and quantitative estimation of concentrations, trends, and fluxes of some surface pollutants. This is an achievement given that the OMI team, for instance, was initially uncertain as to whether it was practical to credibly derive these quantities with the early versions of the retrieval algorithms – the method to convert electromagnetic radiation observed by the satellite instrument to an atmospheric quantity, such as a column density. Over the last few decades, new technologies and retrieval algorithm development have led to vastly improved space-borne datasets of air pollutants, though there is still work to be done to continue improving the technology so that satellite data become even more relevant over time for the air quality and health communities.

While satellite data are not perfect for every air quality and health application today, they will continue to evolve and improve over time. Nevertheless, current data can be used quite effectively, even for policy purposes. For instance, recently President Obama created a video using OMI NO₂ data to show that air quality is improving and to indicate that the Clean Air Act is working. The work presented was the subject of NASA press releases in June 2014 and December 2015.

If the observed vertical column density of a particular chemical species at the surface is not greatly perturbed by physical transport or chemical conversion, then to first order it can be presumed that it is closely related to the direct emissions from sources within the observed surface grid. For an isolated point source, it might be assumed that a good relationship could be obtained between emissions and surface concentrations. For area sources, the use of a chemical transport model might be necessary to allow for transport into and out of any grid and for chemical conversion processes. 

Various instruments have been valuable in studying ground-based pollutant emissions. Instruments measuring ultraviolet (UV) or visible wavelengths of light allow the detection of NO₂, SO₂, and small organic molecules, such as formaldehyde and glyoxal, and instruments measuring infrared (IR) wavelengths of light detect CO, methane, and ammonia. Particulate matter (PM) may be inferred via aerosol optical depth (AOD) measurements, but a direct relationship with PM emissions is still elusive (e.g., Duncan et al., 2014, and references therein). 

One of the biggest successes has been the ability to reproduce annual emission trends over the period 2005–2013, particularly of NO_x. This is largely because of the ability to aggregate data over time and space and increase the statistical representativeness. When it comes to quantifying the emissions from an individual source over a short time period, the sparseness of data (a once-daily overpass with some data rejected for the row anomaly, clouds, etc.) becomes a challenge. 

Streets et al. (2013) reviewed the current capability to estimate emissions from space, and in this section we highlight studies of emissions using NASA Aura data that have been published subsequently. NO_x emission sources continue to be the primary focus because of the strength of the OMI signal and therefore its potential to detect low-intensity sources. Applications have included ship emissions (Vinken et al., 2014a), Canadian oil sands (McLinden et al., 2012, 2014), soil emissions (Vinken et al., 2014b), biomass burning (Castellanos et al., 2014), and urban areas (Vienneau et al., 2013; Lamsal et al., 2015). 

Another recent development has been the application of OMI NO_x data to studies of nitrogen deposition flux (Nowlan et al., 2014). Although the SO₂ signal from OMI is two-to-three orders of magnitude weaker than the NO_x signal, statistical data-enhancement techniques have enabled valuable new studies of SO₂ emissions from Canadian oil sands (McLinden et al., 2014); and Fioletov et al. (2013) reviewed the ability of OMI to detect large SO₂ sources worldwide, including power plants, oil fields, metal smelters, and volcanoes. Recent retrieval improvements and new statistical techniques have allowed for the detection of even smaller SO₂ sources (McLinden et al., 2016). 

Work continues on the challenge of developing reliable quantitative relationships between observations and source emissions for, say, large isolated power plants. Previous work had only moderate success in correlating observations with emissions (Kim et al., 2009; Russell et al., 2012; Duncan et al., 2013; Lu et al., 2012). Based on earlier work by Martin et al. (2003) and Beirle et al. (2004), new techniques have now been developed to enhance the predictive power of the single-source relationship by taking into account such factors as chemical lifetime and dispersion lifetime within the framework of high-resolution data oversampling. Such techniques to account for these complex factors have been explored by Beirle et al. (2011), Fioletov et al. (2011), Lamsal et al. (2011), Valin et al. (2013), and de Foy et al. (2014). The greater the sophistication of the technique, however, the more it relies on additional weather data (wind speed and direction) or model calculations to simulate the surface column density, however, which detracts from the usefulness of the satellite observation. Undoubtedly, the availability of hourly observations at higher resolution from a new geostationary satellite would greatly enhance the capability. 

Infrared instruments add capability for detecting sources of methane and ammonia, which are hard to characterize by bottom-up inventory methods. Sources of methane tend to be weak and widely dispersed, causing a problem for quantifying both activity levels and emission factors. Recently, a paper by Kort et al. (2014) received widespread press coverage when a large methane signal was detected in the Four Corners area of the southwestern U.S., presumably originating from oil, gas, and coal-bed methane operations there. The European Space Agency’s (ESA) SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) data were used in this study, though data from this instrument ended in 2012 (a spectrometer measures individual wavelengths of light that are used to identify individual pollutants). Wecht et al. (2012, 2014) have shown the value of using NASA Tropospheric Emission Spectrometer (TES) data in conjunction with Japan Space Agency (JAXA) Greenhouse-gases Observing SATellite (GOSAT) data, the Total Carbon Column Observing Network (TCCON) of ground-based Fourier Transform Spectrometers, and aircraft measurements to constrain methane emissions in California.

In the future, we hope for new satellite instruments to detect emissions from fracking and related natural gas operations. Rather like methane, ammonia emissions are hard to estimate by bottom-up inventory methods, largely because they are very sensitive to agricultural operations and prevailing climate conditions. Beer et al. (2008) and Shephard et al. (2011) first demonstrated the retrieval of ammonia profiles from TES data, and Pinder et al. (2011) demonstrated that spatial and temporal gradients in surface ammonia concentrations could be extracted. A recent data release from the Atmospheric Infrared Sounder (AIRS) holds promise for the detection of ammonia (Warner et al., 2016) — a sounder is an instrument that measures information, such as the vertical distribution of a pollutant, temperature, water vapor, etc. within the atmosphere. Although quantification of emissions is not easy (and may require the use of adjoint chemical transport models) studies suggest that ammonia emissions may be significantly underestimated in currently used emission inventories (Zhu et al., 2013). 

Finally, OMI has been successful in detecting small organic molecules, particularly formaldehyde and glyoxal, and the data have been used to infer natural emissions of isoprene, a key contributor to ozone production in many parts of the world (e.g., Duncan et al., 2009; Duncan et al., 2010; Marais et al., 2014a, 2014b; Zhu et al., 2014). 

Satellite instruments are perched high above the Earth’s surface, affording a “God’s eye” view of the planet’s air pollution. Air quality managers have taken advantage of this view to track the movement of wildfire and dust plumes, which degrade air quality. However, they have used the data far less to estimate trends in pollutants, such as NO₂, SO₂, and AOD, from which PM_2.5 may be inferred. Instead, air quality managers primarily rely on observations from surface monitors. 

Spatial coverage is the primary advantage of data from satellite instruments over surface monitor networks. That is, satellite data fill in the gaps between the sparse surface monitors, thus providing complementary information (e.g., New NASA Images Highlight U.S. Air Quality Improvement, New NASA Satellite Maps Show Human Fingerprint on Global Air Quality). 

For a general overview (written in plain language and it’s free) of using satellite data for air quality applications, please refer to Duncan et al. (2014). 

From the air quality end-user perspective, the key impediment to using satellite data for estimating trends is access to satellite-based surface concentrations, versus the columns that are typically provided by satellites. Inferring surface concentrations from column data takes considerable expertise and time, both of which may be beyond the financial resources of most air quality agencies. Therefore, air quality managers and other stakeholders would benefit from “off the shelf” satellite-based surface pollutant concentration maps at the spatial and temporal resolutions they need to make informed decisions.

There has been considerable effort over the last decade to improve techniques to infer surface concentrations from satellite data, particularly for NO₂ (e.g., Lamsal et al., 2008) and PM_2.5 (e.g., van Donkelaar et al., 2015, 2016). There are issues with inferring surface concentrations and making apples-to-apples comparisons between the satellite data and surface observations. For instance, estimating surface PM_2.5 from satellite AOD data is complicated as it requires knowledge of various factors that influence AOD, such as relative humidity, aerosol composition, and the altitude of the aerosol layer (e.g., Hoff and Christopher, 2009; Duncan et al., 2014, and references therein). 

As another example, the spatial footprint of the satellite data is often large (e.g., 10x10 km²), so that a surface observation may not accurately represent the larger area. This is particularly true for short-lived pollutants, such as NO₂. Nevertheless, the inferred surface concentrations largely agree well with surface observations (e.g., Figure; Duncan et al., 2013, Lamsal et al., 2015). The agreement often improves with temporal averaging as random errors cancel. Consequently, the comparison of monthly, seasonal, and annual means is often favorable, so that satellite data may be used to estimate trends in surface concentrations. 

The end-user should be cautious when inferring surface concentrations or calculating trends. It is important to understand the strengths and limitations of the satellite data for specific applications, 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). The best advice is to take advantage of the many free resources available (see below) and to contact the satellite data developers for guidance on how to use the data correctly. 

Our ability to infer surface concentrations and emissions is expected to be enabled by improvements in the 1) quality of satellite data from upcoming instruments (e.g., ESA TROPOMI, NOAA GOES-R ABI), and 2) ever-evolving techniques to infer surface concentrations from quantities observed by satellite instruments (e.g., van Donkelaar et al., 2015). The temporal and spatial resolutions will be substantially better for some upcoming (2-5 years) instruments, particularly for instruments that will fly in geostationary orbit (a geostationary satellite’s orbital period matches the Earth’s rotational period, so the satellite appears to be motionless to an observer on the Earth’s surface. The data that we propose to use are from current polar-orbiting satellites, overpassing any given location on the Earth’s surface approximately once per day). 

Geostationary platforms collect hourly data over any given location over the Earth’s surface, thus providing high-quality data at unprecedented spatial and temporal resolutions. With current polar-orbiting satellites, clouds often interfere with data collection, so that daily data are often not possible; there will be more opportunities to observe cloud-free scenes throughout the day with upcoming geostationary satellites. The geostationary NASA TEMPO, Korean GEMS, and ESA Sentinel-4 instruments are planned to launch within the next five years and will provide data over North America, East Asia and Europe/North Africa, respectively.

Currently, there are numerous data websites and web tools available to the end-user. The problem is simply that there are too many and it is difficult to navigate, particularly for the uninitiated. Therefore, a few important resources are recommended:

Overview paper - A good place to begin for the uninitiated. Duncan, B., 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., doi:10.1016/j.atmosenv.2014.05.061, 2014.  

ARSET - If it all seems overwhelming, let ARSET help. The NASA ARSET program has many resources for air quality managers, such as the latest on inferring surface PM_2.5 from AOD data.  Check out their webinar page.

Webtools - Play with the data and make your own maps. Table 2 of Duncan et al. (2014) and the ARSET website list free web tools for manipulating and plotting satellite data.

NO₂ trends - U.S. and worldwide trends in NO₂ pollution over the last decade. 

AOD and PM_2.5 overview paper. Hoff, R., and S. Christopher, Remote sensing of particulate pollution from space: have we reached the promised land?, J. Air Waste Manag. Assoc., 59, 6, 645-675.

Ultimately, the best resources for accurately using satellite data for specific applications are the people who develop the retrievals themselves. As a word of caution, the developers are not funded to provide specific analysis or tailored plots for the end-user. Nevertheless, they are the people who know the strengths and limitations of the data for specific applications and are often willing to provide new and improved datasets that aren’t currently publicly available. Their advice will be invaluable. Since there are so many datasets and retrieval algorithm developers, it is best to do a little search via the web for contact information of the appropriate people. 

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