Forecasting Particulate Matter and Other Airborne Pollutants at a Sub-City Scale with Satellite, Model, and Ground-Observation Data in Google Earth Engine
Clients
National Aeronautics and Space Administration (NASA)
Air quality management and protection of public health requires accurate forecasts of air quality at the local scale. Global models provide critical forecasts, but the results typically only provide coarse spatial resolution. The local relevance of such forecasts can be improved by using other complementary data sources. Nathan Pavlovic, Daniel King, and Alan Chan at Sonoma Technology are working with researchers at NASA’s Global Modeling and Assimilation Office to develop tools to forecast particulate matter and nitrogen dioxide at a sub-city scale. This multiyear project has implemented Google Earth Engine Cloud Processing to fuse data from satellite, model, and ground-observation sources to produce high-resolution gridded forecasts with improved accuracy. Key features of the new tool include: </p>
<ul><li>High-resolution information about the spatial distribution of pollutants at sub-city scales and at an hourly temporal frequency. </li>
<li>Enhanced forecasting resolution and accuracy, especially for regionally transported pollutants. </li>
<li>Assessment of the regional representativeness of regulatory air quality monitoring networks. </li>
<li>Evaluation of the accuracy of low-cost sensor data and their suitability as a supplement to regulatory air quality monitoring networks. </li>
<li>Support for analysis of how local phenomena such as coastal breeze effects, industrial emissions, and regulatory decisions are captured via the fusion of available information sources, or whether other local information might be needed.</li></ul></p>
Having developed the forecasting tool, the project team will work with end users in cities in South America, Africa, and the United States to validate the use of the results in local air quality management. At the end of the project, the resulting technology will be transitioned to the U.S. Environmental Protection Agency and the United Nations Environment Programme for sustained use and to expand the impact of the fused air quality forecast.
<ul><li>High-resolution information about the spatial distribution of pollutants at sub-city scales and at an hourly temporal frequency. </li>
<li>Enhanced forecasting resolution and accuracy, especially for regionally transported pollutants. </li>
<li>Assessment of the regional representativeness of regulatory air quality monitoring networks. </li>
<li>Evaluation of the accuracy of low-cost sensor data and their suitability as a supplement to regulatory air quality monitoring networks. </li>
<li>Support for analysis of how local phenomena such as coastal breeze effects, industrial emissions, and regulatory decisions are captured via the fusion of available information sources, or whether other local information might be needed.</li></ul></p>
Having developed the forecasting tool, the project team will work with end users in cities in South America, Africa, and the United States to validate the use of the results in local air quality management. At the end of the project, the resulting technology will be transitioned to the U.S. Environmental Protection Agency and the United Nations Environment Programme for sustained use and to expand the impact of the fused air quality forecast.
Air Quality
Applied Research
Data Management
Emissions
Exposure
Health
Measurements
Public Outreach
Nathan R. Pavlovic
Nathan
R.
Pavlovic
Lead Geospatial Data Scientist / R Resource Coordinator
npavlovic@sonomatech.com
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