Current scientific projects
Using machine learning and satellite data to map fine particulate matter pollution in east Asia
Fine particulate matter (PM2.5) is an air pollutant associated with millions of deaths per year. Measurements of this pollutant are made at surface sites throughout east Asia, but the vast majority of surface area has no available measurements. This means that we can't fully evaluate the impact PM2.5 has on public health, nor do we have a fully view of the atmospheric state of this region. Satellites offer a solution since they image a wide area at once. A geostationary satellite instrument called GOCI has been imaging east Asia 8 times per day. One of the measurements GOCI takes is called Aerosol Optical Depth (AOD), which is a measure of the transparency of the atmosphere. AOD is related to surface PM2.5 but the relationship is complicated; for example, the satellite measures a column of air all at once, and it cannot tell if pollution is high in the sky or on the ground, where humans can inhale it. This project uses a machine learning algorithm called a random forest to link AOD, meteorology, and other data to measured surface PM2.5 in east Asia. The result is a high-resolution, daily dataset that can be used for studies of epidemiology and atmospheric chemistry.
Figure: Daily PM2.5 concentrations during a pollution event in the North China Plain, around Beijing (December 16-21, 2016). Predictions from the random forest algorithm (background, on 6x6 km2 grid scale) are compared to observations made on the ground (circles). We see that the model is able to reproduce even this extreme level of pollution.
Associated publications and presentations
Pendergrass, D.C., D. J. Jacob, Y. J. Oak, J. Lee, M. Kim, J. Kim, S. Lee, S. Zhai, H. Irie, and H. Liao. A continuous 2011-2022 record of fine particulate matter (PM2.5) in East Asia at daily 2-km resolution from geostationary satellite observations: population exposure and long-term trends. Submitted to Atmospheric Chemistry and Physics. Link to PDF.
Pendergrass, D. C., S. Zhai, J. Kim, J-H. Koo, S. Lee, M. Bae, S. Kim, H. Liao, and D. J. Jacob. (2022). Continuous mapping of fine particulate matter (PM2.5) air quality in East Asia at daily 6x6 km2 resolution by application of a random forest algorithm to 2011-2019 GOCI geostationary satellite data. Atmospheric Measurement Techniques, 15, 1075–1091. PDF. Publisher's version (open access). Associated dataset available from Dataverse. Video of 15-minute oral presentation at AGU.
Pendergrass, D. C., D. J. Jacob, Y. Oak, J. Kim, J. Lee, S. Lee, S. Zhai, and H. Liao. High spatiotemporal resolution trends of fine particulate matter (PM2.5) in East Asia inferred from the GOCI geostationary instrument, 2011-2020.
- GEMS science meeting, Jeju, S. Korea, September 2023. Talk. Slides for GEMS talk.
- American Meteorological Society annual meeting, Baltimore, Md., February 2024. Poster. PDF of AMS poster
Pendergrass, D. C., D. J. Jacob, S. Zhai, J. Kim, J-H. Koo, M. Bae, and S. Kim. Continuous Mapping of Fine Particulate Matter (PM2.5) Air Quality in East Asia by Application of a Random Forest Algorithm to GOCI Geostationary Satellite Data.
- American Geophysical Union Fall Meeting, New Orleans, La., December 13, 2021. Talk. Slides. Video of 15-minute oral presentation.
- GEMS science meeting, Seoul, S. Korea, October 2022. Poster. PDF of GEMS poster.
CHEEREIO: An open-source tool for chemical data assimilation
The GEOS-Chem CHEmistry and Emissions REanalysis Interface with Observations (CHEEREIO) is a tool that allows scientists to use observations of pollutants or gases in the atmosphere, such as from satellites or surface stations, to update supercomputer models that simulate the Earth. Other scientists have assembled estimates of emissions of various pollutants from around the world, but our emissions estimates are very uncertain. CHEEREIO uses a model called GEOS-Chem to simulate what the atmosphere would look like if those emissions estimates were correct, and then compares those estimates to the real atmosphere as observed by satellites or equipment on the Earth’s surface. CHEEREIO uses the difference between the model simulation and the real world to update our maps of emissions.
More formally, CHEEREIO is a set of Python and shell scripts that support data assimilation and emissions inversions for arbitrary runs of the GEOS-Chem chemical transport model via an ensemble approach (i.e. without the model adjoint). Code and documentation are available on Github and ReadTheDocs, both linked from the official site. CHEEREIO and all its components are open-source and free to use, forever.
Figure:Schematic of CHEEREIO runtime routines and job control procedures. CHEEREIO is run as an array of m separate jobs on a computational cluster, one for each ensemble member. These m jobs, operating in parallel, alternate between running GEOS-Chem and running the LETKF algorithm for a subset of grid cells, as shown by the light yellow boxes; the m jobs are coordinated by a single job controller shared by the entire ensemble (shown in light red), ensuring that the ensemble remains synchronized. Boxes in blue show data input into CHEEREIO processes.
Associated publications and presentations
Pendergrass, D. C., Jacob, D. J., Nesser, H., Varon, D. J., Sulprizio, M., Miyazaki, K., & Bowman, K. W. (2023). CHEEREIO 1.0: A versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model. Geoscientific Model Development, 16(16), 4793–4810. Link to paper (open access). Link to PDF.
Pendergrass, D. C., D. J. Jacob, H. O. Nesser, D. J. Varon, M. Sulprizio, K. Miyazaki, and K. W. Bowman (2022). CHEEREIO: a generalized, open-source ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model.
- American Geophysical Union Fall Meeting, Chicago, Ill., December 2022. Poster. PDF of AGU poster.
- GEMS science meeting, Seoul, S. Korea, November 2022. Talk. Slides for Seoul talk.
- 10th International GEOS-Chem Meeting (IGC10), St. Louis, Mo., June 2022. Talk. Slides for St. Louis talk.
Other current projects
Writing
I freelance for a variety of outlets, including Harper's and The Guardian. Some of my articles are linked here. Currently, I am at work on (1) a sci-fi novel, and (2) a second book with historian Troy Vettese on economic democracy.
Newsletter
Archived science projects
Extreme value theory applied to environmental science
Extreme value theory is a branch of statistics dedicated to the analysis of outliers and rare events. In environmental science, extremes are sometimes more important than measures of central tendency: for example, bridge builders care more about the maximum height of a river than its mean height. I've used extreme value theory to link winter meteorology in Beijing to severe smog events, which can shut down the city and are a major threat to public health. If you are interested in using this methodology in your own research, send me an email (drew@drewpendergrass.com) and we can collaborate.
Figure: Probability (given in colored contours) for PM2.5 in Beijing to exceed 300 μg m-3 given relative humidity and 850-hPa meridional winds. Actual observations are overlaid on top: green dots represent days that exceeded 300 μg m-3, and black dots represent the rest.
Associated publications and presentations
Pendergrass, D. C., Shen, L., Jacob, D. J., & Mickley, L. J. (2019). Predicting the Impact of Climate Change on Severe Wintertime Particulate Pollution Events in Beijing Using Extreme Value Theory. Geophysical Research Letters, 46(3), 1824–1830. Publisher's version (open access). PDF.
Pendergrass, D. C., L. Shen, D. J. Jacob, and L. J. Mickley. Predicting the impact of climate change on severe winter haze pollution events in Beijing using extreme value theory. American Geophysical Union Fall Meeting, Washington D.C., December 11, 2018. Talk. Slides.
Emissions and concentrations of pollutants in China
Pollutants like tropospheric ozone and fine particulate matter (PM2.5) cause serious public health burdens, especially in eastern China. In response to the severe smog problems, the Chinese government implemented the Action Plan on Air Pollution Prevention and Control in 2013. The plan led to rapid reductions in emissions of ozone precursors like NOx and aerosol precursors like SO2. However, observations show that while PM2.5 has decreased, surface ozone has increased, even though emissions have fallen. This project seeks to understand the PM2.5/ozone tradeoff both in the present day and under various models of the future.
Figure: Air quality changes due to strong NTCF emission reductions in China by midcentury (2045-54); PM2.5 is projected to improve due to modeled emissions mitigation while ozone is projected to worsen.
Associated publications and presentations
Pendergrass, D. C., L.W. Horowitz, and V. Naik. Modeling impact of strong regulation of near-term climate forcers in China on mid-21st century air quality and climate using the GFDL-ESM4 coupled model. American Geophysical Union Fall Meeting, San Francisco, Calif., December 13, 2019. Talk. Slides.
Other archived projects
Log on wheels
A hot new startup that sells logs on wheels for personal transporation.
Half-Earth Socialism
A book from Verso, co-authored with environmental historian Troy Vettese, on how we can get out of our current ecological mess. Published in April 2022. You can order it from the publisher here.
Musical Composition
I am a composer of modern and experimental music, though I haven't written much in a while. You can learn more about my compositions on my music website and you can listen to my works on my SoundCloud.
Alchemy
Alchemy is a single-player military strategy game written in Java. It makes liberal use of statistical analysis of maps and resource distributions to make the goal of world domination as fair as possible. Alchemy is highly efficient and is designed to run on virtually any modern computer - more is on the game's website here.
