Satellite Products, the AQAST process, and EPA/OAQPS Air Quality Management Needs

Transcription

1 Satellite Products, the AQAST process, and EPA/OAQPS Air Quality Management Needs Rich Scheffe, Kirk Baker, Carey Jang, Pat Dolwick, James Kelly, Norm Possiel, Venkatesh Rao, Heather Simon (ack: J. Szykman, T. Keating) Office of Air Quality Planning and Standards, U.S. EPA Atlanta, Georgia, December 3, 2014

2 Topics Satellite Support for Air Quality Management AQAST Support for Air Quality Management Emphasis on data and tool development Priorities for Air Quality Management Field Studies: Filling a regulatory need Future directions

3 Satellite Support for Air Quality Management Highly Tangible and Supportive Characterization of wild and prescribed fires for emissions inventories LANDSAT data for WRF and CMAQ surface properties Sea surface temperatures for WRF integration Radiation data for in-line photolysis and biogenic emission models Exceptional event assessments (e.g., 6/2012 WY O3) Constraining global emissions estimates/atmospheric characterization (hemispheric CMAQ) Tangible and mildly supportive NO2 Trends analyses PM data gap filling (e.g., AIRNOW) Potentially extremely supportive contingent on signal attributes NH3 and HCHO characterization O/G emissions characterization Less tangible and less supportive Mortality studies GEOCAPE will elevate relevancy

4 AQAST Support for Air Quality Management (unquantified human capital) Numerous examples of past AQAST projects that have (or will) directly or indirectly assisted EPA OAQPS air quality management (partial list): Model estimates of background ozone and its components Tools and datasets to support exceptional event determinations (WY O3) Improvements to model inputs or algorithms (e.g., DYNAMO project) Development of tools and training for greater use of satellite data in model applications Development of tools to enable linking emissions changes w/ global radiative forcing (Climate and Clean Air Coalition, CCAC) ARSET training RTP, 10/2014; EPA now needs to invest effort to take advantage of satellite products to effectively match satellite retrievals with gridded model S/T/C attributes.

5 Emphasis on data and tool development From OAQPS perspective, AQAST applications are most valuable when projects result in specific findings that can be easily translated into improved air quality modeling via better inputs, algorithms, or analytical methods Expensive to provide snapshots of a problem or issue that need to be continually updated More cost effective to build tools that can be used more than once to take better advantage of continually updated modeling and emissions inventories Case example N deposition Tiger Team (singled out due to familiarity) Primary outcome - informative analyses regarding geographical scope of nitrogen deposition Provide broad directional guidance and insights, unlikely to be directly used in EPA regulatory application Preferred outcome tool development (e.g., CMAQ adjoint version or response surface model) to execute internally similar attribution analyses with modeling platforms (emissions and meteorological inputs) commensurate with ongoing regulatory analyses Example of related research product directly used by EPA: Hybrid deposition data from NADP total deposition science committee (TDEP,

6 The image part with relationship ID rid2 was not found in the file. TDEP Total Hybrid Deposition Estimates annual averages, Attributes Common platform for EPA rule assessments Recent modeling inputs and emissions Inter agency collaboration EPA strength modeling Observations multiple agencies Welfare targets/critical loads USDA, NPS, USGS Highly vetted approach through intensive collaboration under NADP structure gap not taken on by regulatory community EPA scientists are PIs Guaranteed ownership reduction of redundant efforts

7 Priorities for Air Quality Management Applications that incorporate the unique capabilities of earth science products which may be unfamiliar to the AQ management community Improve bottom-up emission inventories or related products such as sector specific spatial surrogates, temporal profiles, etc. Tools and pre-formatted datasets Examples being satellite PAR translation tool, WHIPS, and pygeos (not an AQAST supported product but maybe should be!) Likely need to build in project time to translate the end product from research phase to applications

8 Priorities for Air Quality Management Regions with significant AQ problems with little data for basing AQ management decisions (e.g., Salt Lake PM2.5 & Uintah basin,pre-2012, O3) Many of these areas are in the western US and most are data sparse meaning satellites and field study data is critically important to understand source-receptor and precursor/secondary pollutant relationships HTAP2/Western States Air Quality & Modeling workshop in mid-may 2015 in Boulder, CO to discuss background O3 and other western AQ issues (week after DISCOVER-AQ mtg) Characterization of highly variable sources that are gradually increasing their fraction of relative atmospheric loading Agriculture sector Fires o/g

9 Field campaigns and EPA Measurement programs and air quality Routine regulatory monitoring - EPA Routine visibility, deposition, general characterization NPS, EPA, USGS, DOE, USDA, Industry Human exposure studies EPA, Industry, Academia Field campaigns supporting model diagnosis/process development NASA, NOAA, NCAR, NSF Sensors epa!

10 Field Studies: Filling a regulatory need Field studies help fill some of the data gap between routine monitor networks and (relatively) coarse satellite data EPA is strongly dependent on this inter-federal agency collaboration for continued improvement of air quality and emissions modeling platforms Using DISCOVER-AQ and other field studies such as CALNEX/CARES and SOAS to better understand air quality, measurement methods (FRM vs. FEMs), and for model performance evaluation Value in model performance evaluation with intent to improve the most recent regulatory emission inventory; value lost with outdated inputs and lack of direct bottom-up improvements One and done analysis with outdated emissions or emissions using generic inputs not terribly useful and generally ends up creating confusion Search for more efficient updates (EPA support?)

11 The image part with relationship ID rid2 was not found in the file. The image part with relationship ID rid2 was not found in the file. The image part with relationship ID rid2 was not found in the file. Model Performance Evaluation: EPA currently using winter San Joaquin Valley DISCOVER-AQ 2013 case (among others) CMAQ 4 km resolution with 35 layers and 2011/2013 emissions (2011 National emission inventory) Model is underestimating PM2.5 ammonium nitrate at Fresno Model is predicting high PM2.5 ammonium nitrate but may be spatially displaced Could be due to meteorology or emissions; likely a combination of both 11

12 The image part with relationship ID rid2 was not found in the file. The image part with relationship ID rid2 was not found in the file. Anticipated application Preliminary source apportionment (CMAQ ISAM at 4 km grid res.) run done as proof of concept Tracked broad sector contribution to precursors (e.g. NOX below) and PM2.5 ammonium nitrate ion (example below) Sectors tracked for contribution include onroad, nonroad, area, agricultural operations, oil & gas, and point sources Onroad contribution to NOX Onroad contribution to PM2.5 nitrate ion Spatial plots show maximum sector contribution on February 5,

13 Future Direction Research challenge in shifting regulatory focus toward climate (balancing climate and air quality issues) Initial climate regulatory action is emissions based Similar to Acid rain and NAPAP Emissions target program without air quality or deposition targets lead to rapid decline in research after 1990 CAA EPA engagement in post-aqast Renewing MOA between NASA and EPA (expiring in 2015) Bridge to TEMPO/GEOCAPE

14 Concluding Notes These are observations benefitting from hindsight AQAST Design is well constructed and responsive Lessons learned from execution that may or may not lead to enhanced value AQAST flexibility has demonstrated value Background O3, O/G Collaboration benefits for EPA Maximized in field campaigns Associated process level insights human resource capital Potential benefits in tool development Challenge to maximize satellite based products, field studies, modeling efforts to provide critically needed data for areas with poor air quality (e.g. western U.S.) Less funding so we need to maximize returns on this process Longer term benefits in tool development and emission inventory improvements Level of engagement (partner) leading to ownership cradle-to-grave interaction increases value 2 - way issue Probable Value to EPA Global Regional Urban Local* Data Tool Development Applications EPA mission probability * Strong research need, tailored toward NASA strength EPA Collaboration Need