Jason Kang, Software Engineer, NASA-JPL * Develop an Analytic Center Framework (ACF) for Air Quality in support of the NASA AIST air quality technology innovation effort, Air Quality Analytics Center Framework (AQACF) * Harmonize air quality data sets, models, and algorithms to facilitate analysis and projections of air quality across those sources. * Demonstrate analysis application area will focus on air pollution in large cities (e.g., Los Angeles) * Generalize framework to facilitate analyses for air quality applications more broadly Coordinate with other AIST-18 efforts including GEOS-Chem, GCHP, Predicting What We Breathe, etc.

1. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
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Air Quality Analytic Center Framework
Thomas Huang1, Alex Dunn1, Sina Hasheminassab1, Olga Kalashnikova1, Jason Kang1,
Kyo Lee1, Thomas Loubrieu1, Kevin Marlis1, Jessica Neu1, Joe Roberts1
Randall Martin2, Liam Bindle2, Daniel Jacob3, Lucas Estrada3
Jeanne Holm4, Mohammad Pourhomayoun5, Chisato Calvert6, Dawn Comer4
Daven Henze7, Muhammad Omar Nawaz7
Chaowei Yang8, Qian Liu8, Hai Lan8, Anusha Srirenganathanmalarvizhi8
1NASA Jet Propulsion Laboratory, 2Washington University in St Louis, 3Harvard
University, 4City of Los Angeles, 5California State University, Los Angeles, 6OpenAQ,
7University of Colorado Boulder, 8George Mason University
Clearance Number CL#22-4025

2. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
• Funded by the NASA Earth Science Technology Office’s Advanced Information Systems
Technology (AIST) Program
• Develop an Analytic Collaborative Framework (ACF) for Air Quality in support of the NASA
AIST air quality technology innovation effort (a.k.a. Air Quality Analytic Collaborative
Framework (AQACF))
• Harmonize air quality data sets, models, and algorithms to facilitate analysis and
projections of air quality across those sources.
• Demonstrate analysis application area by focusing on air pollution in large cities (e.g., Los
Angeles)
• Generalize framework to facilitate analyses for air quality applications more broadly
• Establish an integrated cloud platform for interactive, on-demand data analysis and
visualization
• Integrate AIST AQ Investments for advanced scenario-based simulation and prediction
• High Performance GEOS-Chem (GCHP)
• GEOS-Chem Adjoint Model Simulations of Air Pollution Health Effects
• Predicting What We Breathe (PWWB)
• Air Quality Prediction Product Generation
Motivation
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3. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Satellite Observations
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4. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Satellite Data
KANG/JPL © 2022. All rights reserved. NASA AIST 4

5. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
In Situ Observations
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6. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
• An Analytic Collaborative Framework
(ACF) to provide an environment for
conducting a science investigation
• Enables the confluence of resources for
that investigation
• Tailored to the individual study area
(physical ocean, sea level, etc.)
• Harmonizes data, tools and
computational resources to permit the
research community to focus on the
investigation
• Scale computational and data infrastructures
• Shift towards integrated data analytics
• Algorithms for identifying and extracting
interesting features and patterns
• Customers and Stakeholders
• Scientists from various disciplines
• Data centers
• Policymakers
Analytic Collaborative Framework
Goals and Objectives
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Apache SDAP High-level System Architecture

7. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Goal: Enabling Next Generation of Science Analysis Tools and Services
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Through Professional Open Source!

8. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Enabling Development of Interactive Analysis Applications
The NASA Sea Level Data Analysis Tool
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Desktop Mobile

9. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
The Evolution of Apache SDAP
9
https://sdap.apache.org
T. Huang, 2020: “Why Build a Castle
When You Can Create a Community
- Advancing Satellite Data Analysis
through Professional Open Source”
ApacheCon @Home 2020 Keynote
THUANG/JPL © 2022. All rights reserved. NASA AIST

10. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
• Built on the Apache SDAP’s federated ACF architecture
• Multi-cloud and Multi-computing
• Commercial Cloud (AWS)
• Private Cloud (OpenStack)
• High-Performance Computing
• ACF integration with provisioned ML service (i.e., AWS
SageMaker)
• Portable, vendor-neutral service API for application
integration
• Streamline data onboarding
• Interactive and on-demand analysis
• Scenario-based numerical simulation and ML prediction
• Architecture that is extensible to support Science-Driven
Actionable Prediction such as dynamic data acquisition or
instrument re-tasking (i.e., New Observing Strategies (NOS)
and Digital Twins)
AQ ACF Architecture
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11. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Notebook Example: Data Harmonization
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PM2.5 Area-Averaged Time Series
PM2.5 Time Averaged Map PM2.5 Visualization

12. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
AQ Visualization Platform
THUANG/JPL © 2022. All rights reserved. NASA AIST 12
During California Wildfires 2018
Visualize Surface PM2.5 on Jupyter Visualize Surface PM2.5 on GIS Webapp

13. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Numerical Model Integration
From Cloud to HPC – Integration with High Performance GEOS-Chem (GCHP)
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Streamline job submission, Cloud-optimized data analysis and image generation

14. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Use Case: California EV Mandate 2035

15. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
• The state of California plans to ban the sale of new gasoline-powered cars
by 2035
• Requires 35% of new vehicles sold in CA to be electric by 2026.
• Will increase to 68% in 2030 and 100% in 2035
• These actions are estimated to achieve a more than 35% reduction in
greenhouse gas emissions and an 80% improvement in NOx emissions
from cars.
• Questions
• How does air pollution in CA respond to changing vehicle
emissions, and what are the impacts on human health and
exposure?
• How can air quality modeling data and remote sensing
observations be used to present answers to these types of
questions?
California EV Mandate 2035 (a.k.a. CAL2035)
THUANG/JPL © 2022. All rights reserved. NASA AIST 15

16. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
1. Simulate pollutant concentrations (PM2.5, O3, and NO2) using the GEOS-Chem forward model for 2011.
2. Include satellite-derived data for PM2.5 and NO2 to improve resolution and correct for model biases.
3. Compare PM2.5, O3, and NO2 concentrations to ground level observations to characterize remaining biases.
4. Incorporate population data and a mask file to isolate pollution in Los Angeles (denoted as the cost-functions).
5. Run the adjoint model which calculates sensitivities of pollution to emissions.
6. Following the completion of the adjoint model scale sensitivities so that the sum of all contributions is equivalent to
the cost-functions and put into netCDF files for tool (e.g., la_sens_pm.nc).
7. Calculate health impacts for all three pollutants using frameworks outlined in the Global Burden of Disease Study
2019 (GBD 2019) and (Achakulwisut et al., 2019).
8. Divide health impact contributions by pollutant exposure contributions assuming linearity in the health impact
calculation to create health scaling factors (e.g., la_health_pm.nc).
9. Convert daily National Emission Inventory (NEI) emissions files into single files for each sector (e.g.,
nei_emis_onroad.nc).
CAL2035 Workflow
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17. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
• Assumptions
• Consider impact of emissions changes using constant (2011) meteorology, resent day population and mortality rates.
• Response of pollution to emissions is linear across the proposed emission changes.
• Response in the health impact calculation with respect to exposure levels is linear across the proposed exposure level changes.
• Evaluation
• Air pollutant concentrations are compared to ground level observations (see figure) with generally low biases and strong correlations.
• Air quality model uncertainties have been discussed in our previous study (Nawaz et al., 2021); they are typically smaller than
epidemiological uncertainty.
• Uncertainty in the health impact assessment is discussed in the Global Burden of Disease Study (GBD 2019) supplemental and in
(Achakulwisut et al., 2019).
CAL2035 Progress
THUANG/JPL © 2022. All rights reserved. NASA AIST 17
All major US
cities
C40 US cities
Observations

18. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
• Air quality exposure and health impact source attribution for Los Angeles
calculated from satellite-derived PM2.5 (van Donkelaar et al., 2021) and
TROPOMI NO2 using the GEOS-Chem adjoint model (Henze et al., 2007;
Nawaz et al., 2021)
• Jupyter notebook delivered to AQACF demonstrating interactive policy
impact tool: air pollution and health response to user-defined vehicle
emissions reduction scenarios
• The notebook has been deployed on the JPL JupyterHub for validation
and demonstration
• Additional work is needed to improve data cloud-optimized management
and access
Notebook Scenario: California EV Mandate 2035
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Pollution Health Impacts in Los Angeles
At 35% PM2.5 Reduction from Transport

19. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Use Case: Backlog at Ports of LA & Long Beach

20. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
• Pandemic-related supply chain issues led to major backlog
of containerships at ports of LA and Long Beach reached an
all-time high in 2021
• To alleviate the situation, both Ports and Union Pacific
Railroad Company switched to 24/7 operation mode
• According to some estimates, by November 2021, the
overall containership emissions alone resulted in an
increase of 20 tons per day (tpd) of NOx and 0.5 tpd of PM
in the Los Angeles Basin, which are equivalent to the
exhaust emissions from almost 100,000 Class 8 diesel
trucks.
Backlogs at Ports of LA & Long Beach
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21. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
• Ships backlog and ports’ activities reached all-time high records in Nov and Dec 2021.
• During the same period, LA Basin experienced persistent high pollution events which could be, at least in part, attributed to emissions from
containerships, ports activities, and associated goods movements (e.g., trucks, trains, warehouses, etc.)
Backlogs and Potential AQ Impacts
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22. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Notebook Example: Federated ACF
Backlogs at Ports of LA and Long Beach
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Proxy from https://digitaltwin.jpl.nasa.gov to
https://aq-sdap.stcenter.net

23. National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
• Loubrieu et al., “Cloud-Optimized Data Service for Multispectral and Hyperspectral Observations,” American Geophysical Union Fall Meeting,
New Orleans, LA, December 13-17, 2021.
• Huang et al., “AQACF: A Platform for Air Quality Analysis, Visualization, and Prediction,” American Geophysical Union Fall Meeting, New
Orleans, LA, December 13-17, 2021.
• Perez et al., “Development of a Cloud-based Data Match-Up Service (CDMS) in Support of Ocean Science and Applications,” American
Geophysical Union Fall Meeting, New Orleans, LA, December 13-17, 2021.
• Huang et al., “Integrated Digital Earth Analysis System,” American Geophysical Union Fall Meeting, New Orleans, LA, December 13-17, 2021.
• Huang, T., “Enable the Earth Observation Digital Transformation,” ESA Living Planet Symposium, Bonn, Germany, May 23-27, 2022.
• Huang, T., From Professional Analytics Platform to Earth System Digital Twin, 2021 NASA ESD Analysis and Information Products Working
Group, November 08, 2021.
• Huang et al., “An Advanced Open-Source Platform for Air Quality Analysis, Visualization, and Prediction,” IEEE International Geoscience and
Remote Sensing Symposium, Kuala Lumpur, Malaysia, July 17-22, 2022.
• Huang et al., “An Earth System Digital Twin for Flood Prediction and Analysis,” IEEE International Geoscience and Remote Sensing
Symposium, Kuala Lumpur, Malaysia, July 17-22, 2022.
Recent Conferences and Publications
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