Transcript EPA air research priorities - Atmospheric Chemistry Modeling Group

Air, Climate, & Energy (ACE)
Research Priorities
Preparing for the Future:
Building a Foundation of Science to Support
Policy to Solve Problems
Air Quality Applied Sciences Team
10th Semi-Annual (AQAST 10)
Dan Costa, Sc.D., DABT
National Program Director
Air, Climate, and Energy Research Program
Office of Research and Development
January 5, 2016
The Many Dimensions of ACE
How does the ACE program
develop its research agenda and
priorities?
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EPA Strategic Plan 2014-2018
Goal 1: Addressing Climate Change and Improving Air Quality
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Conduct integrated science assessments of criteria air pollutants and provide new
data and approaches for improving these assessments
Develop credible models and tools to inform sustainable policies, decisions, and
responses to a changing climate by EPA national and regional offices, state, tribal,
and local governments, and others
Conduct research to change the paradigm for air pollution monitoring, with a focus on
lower cost measurements
Develop and evaluate models and decision support tools to integrate multi-media
processes and systems
Develop approaches to assess multi-pollutant exposures and the resulting human
and ecological effects of air pollutant mixtures
Conduct research to inform policies protecting human and ecosystem health in an
evolving energy landscape, including impacts of unconventional oil and gas and lowcarbon energy sources
http://www2.epa.gov/sites/production/files/2014-09/documents/epa_strategic_plan_fy14-18.pdf
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EPA Partner-Stakeholder Priorities
Drought
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2012 Heat Anomaly
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Implementation Sciences - new measurement
technologies (e.g., sensors, satellites); air quality
models/tools
Emissions Science - oil & gas priority; updating
multiple inventories (e.g., CAFO’s)
Public Health/Welfare - multipollutant issues
(e.g., NOx/SOx, near-source risks, exposure
science); translational science for public health
Climate Change Preparedness – assessments
and impacts relevant to adaptation are important
(e.g., cross-media models); mitigation
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Preparing for the Future:
Evolving the ACE Portfolio
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ACE Research Objectives
Near Road
Objective 1: Assess Impacts
Assess human and ecosystem exposures and effects associated
with air pollutants and climate change at individual, community,
regional, and global scales
Objective 2: Prevent and Reduce Emissions
Provide data & tools to develop and evaluate approaches to
prevent and reduce emissions of pollutants to the atmosphere,
particularly environmentally sustainable, cost effective, and
innovative multipollutant and sector-based approaches
Objective 3: Prepare for and Respond to Changes in
Climate & Air Quality
Provide human exposure and environmental modeling, monitoring,
metrics and information needed by individuals, communities, and
governmental agencies to adapt to the impacts of climate change
and make informed public health decisions regarding air quality
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FY16-19 Strategic RAP Themes
Climate Impacts
Vulnerability and
Adaptation
Assess the impacts of climate change on the environment and
public health to inform the development of sustainable approaches
to prepare for climate change
Emissions and
Measurements
Develop innovative technologies and approaches to characterize
source emissions and ambient air pollutants
Atmospheric and
Integrated
Modeling Systems
Develop and apply air quality and cross-media models to support
regulatory and community-based decisions
Protecting
Environmental
Public Health and
Wellbeing
Develop solutions-oriented approaches to assess multipollutant
exposures and resulting human and ecological effects of air
pollutant mixtures to inform policy and public health practices
Sustainable Energy
and Mitigation
Assess the environmental impacts and those factors affecting
energy sectors choices from extraction to end-use
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Next Generation Air Monitoring
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New technology revolutionizing regional, community, fenceline, personal monitoring - 1st 3 Google listings (note EPA)
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EPA’s Air Sensor Toolbox on the web along with a prototype
testing platform
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Promoting community science, outreach and education
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ACE Clean Air Res Ctrs working to link satellite data with
health outcomes
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Working with NOAA, NASA, NSF to relate satellite-based air
quality data
Mobile monitoring for geospatial
mapping of pollutants (GMAP)
“Village Green” park bench monitors air
quality
Jointly funded Innovation Project
with NIEHS
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A New Air Pollution Monitoring and
Modeling Paradigm
Furthering development, evaluation, and assimilation of new technologies
to complement and enhance air quality assessment and forecasting
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DISCOVER AQ - Denver, CO
July-August 2014
Systematic and
concurrent
observation of
columnintegrated,
surface, and
verticallyresolved
distributions of
aerosols and
trace
gases relevant
to air quality as
they evolve
throughout the
day
Ground sites/measurements
• Ambient trace gases and aerosols, primarily
based on EPA FRM/FEM
• Remote sensing of trace gas and aerosol
columns
• Aerosol and Ozone profiles
NASA P-3B (in situ meas.)
In situ profiling of aerosols
and trace gases over
surface measurement sites
NASA King Air (Remote sensing) Continuous
mapping of aerosols with HSRL and trace
gas columns with ACAM
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DISCOVER-AQ: AOD Column
Measurements Used to Predict Daily PM2.5
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2012: Collaborative work with NASA and Martin’s
Dalhousie University
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Bias Corrected for Jun 27, 2005
Daily predictions of PM2.5 by scaling satellite AOD and
surface based bias adjustment
Model captured spatial varying relation of AOD and PM2.5, η,
but not necessarily magnitude of AOD
Method implemented in satellite data stream to AIRNow from
NOAA (van Donkelaar et al., 2012, ES&T)
2014: mid-Atlantic Region (Baltimore)
– Purely measurement-based approach showed
normalizing AOD with haze layer height (i.e., AOD/HLH)
can product reasonable PM2.5 predictions from AOD over
a regional (100 km); results similar to models for scaling
– Characterization of mixed layer height shown to be a key
variable in AOD/PM2.5 predictions (Chu et al., 2014, AE)
2015 (Denver)
– Spatial, autoregressive model predicts daily PM2.5 using
VIIRS AOD data, meteorological variables, and previous
day PM2.5 over the conterminous U.S.
– In sample and out-of-sample model validation results
indicated marginal predictive improvement with AOD in
model
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DISCOVER-AQ: NO2 Column Measurements Provide
Unique Ability to Assess Urban Scale Variability
Column
Density
and
Mixing
Height
Mixing Ratio
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Assume NO2 VCD
confined to mixing layer
(ML) and well mixed
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Use surface and P-3
data to calc. air number
density profiles
throughout flight day.
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Derive ML air density
profile along flight track
using HRSL ML
heights.
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Estimate ML NO2
mixing ratio by
normalizing ACAM NO2
VCD with HSRL ML air
number density
• Scaling NO2 tropospheric column densities by a modeled boundary layer height accounts for up to
≈75% of the variability between the column and in-situ NO2 (Knepp et al., J Atmos. Chem. 2013)
• Derived mixed layer concentrations from ACAM vertical column density NO2 data at ~1km2
(Baltimore)
– NO2 gradients are strongest in A.M. with spatial correlation of 20 km or less
– NO2 hot spots associated with major point sources and mobile sources and are lower in concentration
and more confined in space versus high resolution ( 1 km and 4 km) WRF-CMAQ D-AQ results.
– Mixing layer heights and the vertical distribution of NO2 are likely two of the most controlling variables 12
in translating column-to-surface mixing ratios
STAR Grantees: Satellites to
Inform Health Studies
• Several STAR funded projects use satellite data
to estimate PM2.5, NO2, and temperature for
human exposure
• Specifically, some Clean Air Centers are
evaluating how to best use satellite data
– $32 million investment to four centers - thirteen
institutions - over five years
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SCAPE (GT / Emory)
Modeling Studies
• Satellite-retrieved cloud fraction (CF) and cloud optical
thickness (COT) could be used to help fill the PM2.5
prediction gap (Yu et al., 2015)
• Statistical downscaling using
PM2.5 LUR models with AOD
and techniques for combining
data with different spatial
resolutions increases accuracy
compared to ground
measurements (Chang et al.,
2014)
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Harvard Studies
• The new Multi-Angle Implementation of Atmospheric
Correction (MAIAC) algorithm for AOD at 1km
resolution shows improved correlation to PM2.5 as
measured by 27 EPA ground monitoring stations
(Chudnovsky et al. 2014)
• Introduced a model to expand limited spatial coverage
of MODIS daily air temperature to high resolution in
large geographical areas (Kloog et al. 2014)
• Used satellite AOD to determine location-specific
PM2.5 trends over 2000-2008 and suggest that
primary particles have decreased more than
secondary particles (Lee et al. 2014a)
• Developed spatially and temporally resolved exposure
assessments of NO2 from a combination of satellite
remote sensing and land use regression (Lee et al.,
2014b)
• Examined chronic and acute exposure to air pollution
using fine-scale satellite data to find biases from
predicted air pollution exposures (Alexeeff et al. 2014)
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Intercomparison of Ambient
PM2.5 Models (HSPH / SCAPE)
• Objective: to better
understand various satellite
PM2.5 models re exposure
assessment and monitoring
network design
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Models under evaluation:
NC domain ~600K km 126 EPA
stations. Study period: 2006 – 2008
• SCAPE:
(1) Chang's spatial downscaler
(2) Liu group’s LME-GWR/GAM model
(3) Russell group’s CMAQ PM2.5 simulation
• Harvard: Koutrakis group’s mixed effects model
Satellite data under evaluation:
Aqua MODIS C6 10 km dark target AOD
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Recent Outcomes
• Established the feasibility of moving to ward
the vision of the NGAM
• Improvements in PM2.5, NO2, and temperature
exposure with satellite data filling gaps in both
temp and space.
• Expanded spatial coverage to improve power
in epidemiology studies.
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Additional Information
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Air Research: http://www2.epa.gov/air-research
Climate Research: http://www2.epa.gov/climate-research
StRAPs for EPA six national research programs, including ACE,
available at: http://www2.epa.gov/research/strategic-researchaction-plans-2016-2019
Next Generation Air Measuring Research:
http://www.epa.gov/air-research/next-generation-air-measuringresearch
Discover-AQ: http://www.epa.gov/sciencematters/discover-aq
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