How NASA and Google are teaming up to understand and analyze air quality around the world
By Karin Tuxen-Bettman, PhD, Program Manager, Google Earth Outreach
Nicholas Clinton, PhD, Developer Advocate, Google Earth Engine
Argyro Kavvada, PhD, Program Lead, Sustainable Development Goals, Applied Sciences Program, NASA Earth Science Division
Air quality and its impact on health and climate is a global problem, but much of the job of improving air quality rests on local governments. To show the scale of this problem: an estimated seven million people die around the world every year from the effects of air pollution. Many communities want to take action, but need accurate and up-to-date local air quality maps to better understand the impact and develop solutions.
This challenge is why Google is expanding its collaboration with NASA’s Earth Science Division, specifically its Global Modeling and Assimilation Office (GMAO), to help improve local air pollution monitoring and forecasts. The timing couldn’t be more urgent: almost 99% of the globe’s population breathes air that exceeds WHO air quality limits. We’ll use several datasets, from stationary and mobile air quality monitors on the ground, to generate high-resolution air quality estimates that improve NASA air quality forecasts.
Air quality maps with better space and time granularity
Air quality maps can be useful tools for taking action on climate-related issues in urban areas, because these insights can show where communities and city governments should focus their attention — like in neighborhoods with the largest pollution sources. To achieve maps with this level of detail, we need data and maps that are granular in both space and show insights over periods of time.
As a first step for this project, we’re excited to share two NASA datasets new to the Earth Engine Data Catalog, which will be available to our users, and NASA will also leverage for its air quality forecasts:
- Goddard Earth Observing System Composition Forecasting system (GEOS-CF) is developed by NASA GMAO. NASA will use these datasets in Earth Engine to conduct research downscaling methods to improve GEOS-CF’s spatial resolution from 25km, increasing the ability to make decisions based on the data. Access one of GEOS-CF datasets here, and see a code sample in Earth Engine. This dataset shows a daily five-day forecast of nitrogen dioxide (NO2) and other pollutants.
- Modern-Era Retrospective analysis for Research and Applications (MERRA-2) produces global atmospheric datasets showing data from 1980-present. NASA will use these data to conduct downscaling methods, which are algorithms to improve MERRA-2’s spatial resolution from 50 km, increasing the ability to make decisions based on the data. Access one of MERRA datasets here, and see a code sample in Earth Engine. It’s an hourly time-averaged collection consisting of assimilated aerosol diagnostics.
Ground air quality measurements
We also plan to add more ground air quality measurements, such as Google’s Project Air View data, to the Earth Engine Data Catalog. The Project Air View dataset includes the entire four-year California data collection, comprising 42 million measurement locations with multiple pollutants and other parameters measured at each location.
Finally, NASA and Google will study downscaling methods in Earth Engine. We plan to combine GEOS-CF NO2 data with data from datasets, such as TROPOMI, MAIAC, and ground air quality measurements, to increase the resolution of the daily NO2 maps. With higher resolution and granularity, the maps could be used to get a clearer picture of the air quality conditions on the ground every day. Increasing spatial resolution can help infer ties between pollutant exposure and health effects, and highlight potential disparities in between neighborhoods or within cities.
If you’re not familiar with Google’s air quality projects, visit our Earth Outreach page. We’ll also share news about the status of projects here, so visit our Medium page often.
