Global Air Quality Forecast
Despite the substantial negative impacts of air pollution, many countries lack reliable air quality information due to the prohibitive expense of establishing and maintaining the necessary infrastructure. A global air quality model forecasting system can provide air quality information to everyone.The NASA Global Modeling and Assimilation Office (GMAO) develops and maintains the GEOS system of models, which has a suite of capabilities including simulating weather, climate, chemistry-climate interactions, and now air pollution. They assimilate satellite and in situ data of some variables that affect weather and air quality into the GEOS model. The assimilation of these data improves the simulation of weather and air quality by providing the best representation of the atmosphere at the start of the forecast period.
⇒ Visualize the NASA composition forecasts for your city using FLUID.
Forecast System Description
Forecasting and Risk Communication: Air Pollution in Your City
Forecasted air pollution values of PM2.5, NO2, O3, and SO2 are available for your city up to five days in advance. A health-based air quality index is also available to help with local risk communication. These values can be specifically tailored to your city by sharing local air pollution and daily health data. [more details]
Please contact Bryan Duncan (email@example.com) and Kevin Cromar (firstname.lastname@example.org) to learn more about accessing forecasted air pollution and health-based air quality index values. They can also help you learn how this information can be specifically tailored to the local conditions in your city.
Potential Applications: an Air Quality Index (AQI)
The GEOS-CF output could be used to generate an AQI for cities around the world. For example, the animation below shows the Canadian HAQI as applied to the output.
Health Air Quality Index (HAQI) calculated using the GEOS-CF model output for the time period October 10-23, 2017. The HAQI comprises of surface concentrations of NO2, PM2.5, and O3, using the formulation by Stieb et al. (2008). Values range from 0 (white, good) to 10 (hazardous air quality conditions). The time frame captures extensive wild fires over western Canada, California and Portugal, as well as tropical storm Ophelia (October 15-16) transporting highly polluted air masses to the British Isles. Animation credit: Dr. Emma Knowland, NASA GMAO.
Evaluating the Performance of the Air Quality Forecasts
The GEOS-FP and GEOS-CF models tend to perform well as compared to surface observations where air pollutant emissions from automobiles, industry, and power generation are characterized well, such as in places like the U.S. and Western Europe.
The models also tend to perform well at simulating wildfire and agricultural smoke events that affect whole regions, in large part to the assimilation of satellite data of aerosol optical depth and weather-related variables. For instance, the animation shows the transport of smoke from seasonal agricultural fires over India and PM2.5 levels in Delhi. The surface PM2.5 data from an air quality monitor at the U.S. Embassy. Karambelas et al. (2018) estimate that in northern India 463, 200 adults die prematurely each year from PM2.5 and that 37,800 adults die prematurely each year from O3.
Modeled surface concentrations of particulate matter with diameter of less than 2.5 microns (PM2.5), expressed in units of microgram per m3, for Delhi, India between February 1-28, 2018 (top panel). GEOS-CF model 1-day hindcast and five day-forecasts are performed once a day. These concentration time series are shown in the bottom panel. Modeled concentrations are shown in dark red (hindcast) to yellow (5-day forecast). Corresponding observations (from the OpenAQ database) are shown in black. Animation credit: Dr. Christoph Keller, NASA GMAO.
However, for most of the world's cities, urban emissions are not well characterized and there are little or no publicly-available air quality data (e.g., OpenAQ) with which to evaluate the air quality forecasts. The animation shows that the model tends to over-predict observed PM2.5 from the U.S. Embassy monitor in Jakarta. Therefore, we are beginning to work with Indonesian scientists to benefit from their local expertise to, for instance, improve the pollutant emissions used in the model, to understand the unique meteorological variations that may occur there, and to access their air quality data, which are not publicly-available. We are also working with Brazilian scientists to improve the forecasts for Rio de Janeiro.
Modeled surface concentrations of particulate matter with diameter of less than 2.5 microns (PM2.5), expressed in units of microgram per m3, for Jakarta, Indonesia between Oct 26 and Nov 10, 2017 (top panel). GEOS-CF model 1-day hindcast and five day-forecasts are performed once a day. These concentration time series are shown in the bottom panel. Modeled concentrations are shown in dark red (hindcast) to yellow (5-day forecast). Corresponding observations (from the OpenAQ database) are shown in black. Animation credit: Dr. Christoph Keller, NASA GMAO.
HAQAST Participants: Dr. Bryan Duncan (email@example.com; NASA)
Stakeholder Partners: In discussions with US Army Public Health Center, City of Rio de Janeiro, IBM, UNICEF, WRI, etc.