Dan Costa, Sc.D., DABT National Program Director Air, Climate, and Energy Research Program Office of Research and Development January 5, 2016 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 10 th Semi-Annual (AQAST 10) 2 The Many Dimensions of ACE How does the ACE program develop its research agenda and priorities? 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 low- carbon energy sources3 EPA Strategic Plan Goal 1: Addressing Climate Change and Improving Air Quality EPA Partner-Stakeholder Priorities Implementation Sciences - new measurement technologies (e.g., sensors, satellites); air quality models/tools Emissions Science - oil & gas priority; updating multiple inventories (e.g., CAFOs) 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 4 Drought 2012 Heat Anomaly 5 Preparing for the Future: Evolving the ACE Portfolio 6 ACE Research Objectives 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 Near Road 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 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 FY16-19 Strategic RAP Themes Assess the impacts of climate change on the environment and public health to inform the development of sustainable approaches to prepare for climate change 7 Sustainable Energy and Mitigation Climate Impacts Vulnerability and Adaptation Emissions and Measurements Atmospheric and Integrated Modeling Systems Protecting Environmental Public Health and Wellbeing Develop innovative technologies and approaches to characterize source emissions and ambient air pollutants Develop and apply air quality and cross-media models to support regulatory and community-based decisions 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 Assess the environmental impacts and those factors affecting energy sectors choices from extraction to end-use Next Generation Air Monitoring New technology revolutionizing regional, community, fence- line, personal monitoring - 1 st 3 Google listings (note EPA) EPAs Air Sensor Toolbox on the web along with a prototype testing platform Promoting community science, outreach and education ACE Clean Air Res Ctrs working to link satellite data with health outcomes Working with NOAA, NASA, NSF to relate satellite-based air quality data Mobile monitoring for geospatial mapping of pollutants (GMAP) 8 Village Green park bench monitors air quality Jointly funded Innovation Project with NIEHS Furthering development, evaluation, and assimilation of new technologies to complement and enhance air quality assessment and forecasting A New Air Pollution Monitoring and Modeling Paradigm 9 DISCOVER AQ - Denver, CO July-August NASA P-3B (in situ meas.) In situ profiling of aerosols and trace gases over surface measurement sites 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 King Air (Remote sensing) Continuous mapping of aerosols with HSRL and trace gas columns with ACAM Systematic and concurrent observation of column- integrated, surface, and vertically- resolved distributions of aerosols and trace gases relevant to air quality as they evolve throughout the day DISCOVER-AQ: AOD Column Measurements Used to Predict Daily PM : Collaborative work with NASA and Martins Dalhousie University Daily predictions of PM 2.5 by scaling satellite AOD and surface based bias adjustment Model captured spatial varying relation of AOD and PM 2.5, , 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 PM 2.5 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/PM 2.5 predictions (Chu et al., 2014, AE) 2015 (Denver) Spatial, autoregressive model predicts daily PM 2.5 using VIIRS AOD data, meteorological variables, and previous day PM 2.5 over the conterminous U.S. In sample and out-of-sample model validation results indicated marginal predictive improvement with AOD in model Bias Corrected for Jun 27, 2005 DISCOVER-AQ: NO 2 Column Measurements Provide Unique Ability to Assess Urban Scale Variability 12 Scaling NO 2 tropospheric column densities by a modeled boundary layer height accounts for up to 75% of the variability between the column and in-situ NO 2 (Knepp et al., J Atmos. Chem. 2013) Derived mixed layer concentrations from ACAM vertical column density NO 2 data at ~1km 2 (Baltimore) NO 2 gradients are strongest in A.M. with spatial correlation of 20 km or less NO 2 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 in translating column-to-surface mixing ratios Assume NO 2 VCD confined to mixing layer (ML) and well mixed Use surface and P-3 data to calc. air number density profiles throughout flight day. Derive ML air density profile along flight track using HRSL ML heights. Estimate ML NO 2 mixing ratio by normalizing ACAM NO 2 VCD with HSRL ML air number density Column Density Mixing Ratio Mixing Height and STAR Grantees: Satellites to Inform Health Studies 13 Several STAR funded projects use satellite data to estimate PM 2.5, NO 2, and temperature for human exposure Specifically, some Clean Air Centers are evaluating how to best use satellite dataClean Air Centers $32 million investment to four centers - thirteen institutions - over five years 14 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) SCAPE (GT / Emory) Modeling Studies 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) 15 The new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm for AOD at 1km resolution shows improved correlation to PM 2.5 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 and suggest that primary particles have decreased more than secondary particles (Lee et al. 2014a) Developed spatially and temporally resolved exposure assessments of NO 2 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) Harvard Studies Intercomparison of Ambient PM 2.5 Models (HSPH / SCAPE) 16 Objective: to better understand various satellite PM2.5 models re exposure assessment and monitoring network design Models under evaluation: SCAPE: (1) Chang's spatial downscaler (2) Liu groups LME-GWR/GAM model (3) Russell groups CMAQ PM2.5 simulation Harvard: Koutrakis groups mixed effects model Satellite data under evaluation: Aqua MODIS C6 10 km dark target AOD NC domain ~600K km 2, 126 EPA stations. Study period: 2006 2008 Recent Outcomes 17 Established the feasibility of moving to ward the vision of the NGAM Improvements in PM 2.5, NO 2, and temperature exposure with satellite data filling gaps in both temp and space. Expanded spatial coverage to improve power in epidemiology studies. Additional Information Air Research:Climate Research:StRAPs for EPA six national research programs, including ACE, available at:action-plans http://www2.epa.gov/research/strategic-research- action-plans Next Generation Air Measuring Research:researchresearch Discover-AQ:18