Three-State Air Quality Study (3SAQS) Three-State Data Warehouse (3SDW) WestJumpAQMS Lessons Learned and SAQS Platform Development University of North Carolina (UNC-IE) Cooperative Institute for Research in the Atmosphere (CIRA) ENVIRON International Corporation (ENVIRON) November 1, 2013

2 Outline Review the 3SAQS activities for 2011 platform and projections WestJumpAQMS lessons learned for the 3SAQS modeling protocol Expectations for the SAQS modeling work Preliminary outline and schedule for SAQS modeling

3 Plans for 2011 platform Start with development of new protocols for meteorology, emissions, and air quality modeling – Include warehousing and analysis of data in the 3SDW – Projections for emissions – Seeking engagement with stakeholders in this process Use NEI as the basis of the 3SAQS 2011 emissions data – Include updates for 3SAQS Base 2008 ancillary data – Targeted comparisons between NEI2011 and 3SAQS 2008 inventory in the 3 states: largest sources by county/sector/SCC – Remove/replace key sectors: oil & gas, fires, biogenic

4 Plans for 2011 platform 2018 projections off NEI2011 for the 3SAQS 2020 projection modeling – Target changes in the 3 states and compare to changes from the 2008 modeling/projections 3SAQS 2011 Phase 2 O&G projections for 2020 – Supplemental, targeted surveys for important sources with greatest uncertainties – Include information from existing RMPs – Projections to multiple future years from 2011 Periodic calls through the process to update and engage 3SAQS stakeholders

5 Applicable Lessons Learned from WestJumpAQMS WestJumpAQMS was a very large Photochemical Grid Modeling (PGM) study involving three modeling centers, 100s of Terabytes of data and 100s of stakeholders The study encountered numerous issues that had to be overcome whose information could benefit other studies – For example, 3SDW/3SAQS, CARMMS. etc. Chapter 8 of WestJumpAQMS Final Report describes some of the Lessons Learned that is summarized in this presentation

6 Emissions Waiting for the NEI2008v2.0 adversely impacted the project – EPA Strongly encouraged WestJumpAQMS to wait for 2008 NEIv2.0 – Not using 2008 NEIv1.5 (May 2011) and waiting for 2008 NEIv2.0 (April 2012) compromised the WestJumpAQMS – Compressed schedule, lost time and resources waiting – Reduction in detail in source apportionment runs However, in using NEIv2.0 WestJumpAQMS used the most up-to-date emissions available – EPA’s refinement and automation in NEI procedures should make early NEI versions much higher quality than in past

7 Oil and Gas (O&G) Emissions Inventory (EI) Outside of WRAP Phase III and Permian Basins relied on NEIv2.0 for O&G emissions – 2008 NEIv2.0 only includes reported (permitted large point) sources and missing lots of O&G emissions – Similar studies to WRAP Phase III O&G EI development should be considered for the rest of the nation Surveys for WRAP Phase III emissions mainly based on 2006 Baseline conditions – Many changes in O&G development activities since then, such as widespread use of hydraulic fracturing – Incomplete emissions in Phase III O&G EIs Emissions from evaporation ponds Fracing and completions In-field mobile source emissions (P3 Study) Need for consistent identifiers for sources in tribal and non-tribal areas

8 On-Road Mobile Sources Use of SMOKE-MOVES recommended in future as provides day-specific hourly emissions – Coding/script optimizations greatly improves the speed of emissions factor mode processing – Found issues with MOVES inventory-mode monthly emissions due to assumed weekday/weekend day distribution Use spatial surrogates and temporal profiles that are based on locally collected information

9 Electrical Generating Unit (EGU) Point Sources Use hourly Continuous Emissions Monitoring (CEMs) data for large EGU point sources from – CEMs data collected for acid rain trading program to show compliance with the emissions cap – When CEMs malfunctions (missing data), data replaced to maximum emissions – WestJumpAQMS replaced the maximum emissions during missing CEM hours with typical seasonal hourly emissions when the source was operating

Ammonia Emissions WestJumpAQMS convened an Ammonia Emissions Workgroup to identify improvements (see Memo): – CMU model sufficient – Recommend add bidirectional NH3 flux model to CAMx – Recommend review and update EFs – Recommend collect spatial and activity information on CAFOS and fertilizer applications for model years – Recommend use meteorology in temporal adjustments 10

11 Fire Emissions Final WestJumpAQMS simulations based on the 2008 fire inventory from DEASCO3 study Preliminary runs used the 2008 Fire Inventory from NCAR (FINN) – Early on WestJumpAQMS compared 2008 FINN with 2008 SMARTFIRE (BlueSky) from the 2008 NEI and found: FINN was more complete spatially (e.g., Can/Mex) and chemically (see Emissions Technical Memorandum No. 5) FINN and SMARTFIRE has ~2x the emissions in DEASCO3 – Mainly due to higher emissions for large fires – Exacerbates PM overestimation bias when fire plumes impact PM monitors

12 Canada Emissions WestJumpAQMS used 2006 Environment Canada (EC) emissions inventory – No process information (e.g., SCCs) for point sources so had to use default temporal allocation (24/7) 2008 EC points came speciated for CB05 – According to the 2008 EC emissions file, a Fugitive Dust Transport Factor (FDTF) of 0.25 was applied for dust sources However, in reality no such FDTF was applied so dust emissions in Canada are overstated by a factor of ~4 Alberta Environment is working on a 2010 inventory that should be used for future modeling studies: – Should reach out to EC and other Provinces for emission updates Check high CH4 emissions in central provinces

13 Mexico Emissions Last comprehensive emissions inventory for Mexico was for 1999 WestJumpAQMS used a 2008 Mexico emissions inventory published in 2009 that was projected from the 1999 inventory It seems like time for a Mexico emissions inventory update – INE has an inventory developed in-house – Marty Wolf at ERG is looking for funding to update the MNEI

14 Emissions Modeling (SMOKE) SMOKE emissions modeling was split into numerous streams of emissions by major source category – Easy to identify misplaced emissions For example, county swapping of O&G emissions in San Juan and Sandoval Counties, NM – Easier to QA/QC – Facilitates tagging for Source Apportionment Merging numerous “un-merged” emission files can take a long time – Stored on different disks in network – Needs to be taken into account for model run times With fires and off-shore CMV there are lots of point sources Will continue to use this approach for 3SAQS

15 WRF Meteorological Modeling Very large 4 km IMW Domain – ~339 x ~530 – ~8 processor years per run – Ended up using just a small portions of 4 km WRF domain in PGM modeling – Limited sensitivity tests – Large database management 15

16 WRF Meteorological Modeling /12/4 km WRF modeling – ~9 Tb disk storage – All models run on redundant data storage (RAID-5) – Long project schedule necessitated data be archived Silent Corruption of WRF output – Errors corrupted WRF output files Same file size, valid numbers, just wrong values All processors worked, CAMx stopped due to unrealistic variables Screened files with range checks to find bad files and re-ran WRF for offending periods Recommendation: Make external backups of critical model files ASAP after run completions as data can get corrupted 16

17 Model Performance Evaluation Fundamental differences in Modeled and Measurement definitions for PM species – Need better mapping between the two to account for measurement artifacts e.g., blank correction Diagnostic Sensitivity Tests: WestJumpAQMS had limited opportunity due to emissions time crunch – Lots of WRF sensitivity tests – Limited PGM sensitivity tests: CB05 vs. CB6 chemistry MOZART vs. GEOS-Chem CONUS BCs FINN vs. DEASCO3 Fires 17

18 Source Apportionment Modeling Computationally Demanding – Had to make sacrifices due to compressed schedule Role of Transport in Western U.S. – For ozone international and stratospheric intrusion (i.e., CONUS domain BCs) very important in western U.S. typically half or more of the total ozone on high ozone days – For PM international transport also important (e.g., spring Asian dust events) – Interstate transport for ozone and PM also important using CSAPR-type thresholds analysis Need to keep 36 km CONUS when doing source apportionment so BCs clearly represent international transport and stratospheric ozone intrusion 18

19 Use of MATS with Source Apportionment Using MATS to bring closure across all Source Group contributions to Design Values introduced an “Unexplained” portion – For ozone we can limit “Unexplained” portion by using modeling results for just the grid cell containing the monitor instead of an array of grid cells – For Annual PM 2.5 need to separate and label PM components not covered in Source Apportionment modeling: Blank Corrections, SOA, SS, PBW – For 24-Hour PM 2.5 same as above for annual plus several options: Average across top 2% days Weighted quarterly contributions by top 2% days Other? 19

20 Source Apportionment Visualization Source apportionment generates lots of information Electronic spreadsheets (Excel) developed that allow users to drill down into SA results – User can tailor results using own labels and scales as desired – How to assure integrity of the data with downloaded Excel files? – Migrate to web-based tools for 3SDW/3SAQS Data Archiving in 3SDW 20

21 Expectations for Complete 2011 base and projection modeling early in the year Bring 3SDW online with 2008 and 2011-based modeling data Routine distribution and archival/cataloging of NEPA and regional modeling studies Cumulative impacts modeling and analysis Winter ozone case study Convene 3SAQS working groups and use feedback to improve modeling process and products

22 Preliminary Outline Modeling protocol development – met, emissions, and air quality modeling – Extends and builds on 2008 modeling protocol – Data, modeling process, and evaluation protocol WRF 2011 modeling and MPE at 36/12/4-km SMOKE 2011 modeling at 36/12/4-km – Requires NEI2011 and 2011 O&G inventories from Phase I work SMOKE future year (2020 target year?) modeling at 36/12/4-km – Requires analysis of EPA projections off of 2008 and 2011 and applicability to 3SAQS – Completion of Phase II O&G inventories – Emissions projection curves for each future year from 2018  2025 (correct end year?) CAMx 2011 and future year modeling at 36/12-km – Requires decision on BCs (GEOS-Chem or MOZART) – Base year runs, source apportionment, cumulative impacts, other? Upload results into 3SDW and distribute to stakeholders

23 Schedule of SAQS and 3SDW Activities 3SAQS 3SAQS WRF Base SMOKE and MEGAN modeling; complete 3SAQS Phase I oil & gas inventory Complete WRF and SMOKE 2011 modeling and MPE 2011 Base CAMx simulation Begin future year (2020) emissions processing NovemberDecemberJanuaryFebruaryMarch 3SDW Develop and test warehouse infrastructure Beta release of 3SDW to limited set of use-case testers Launch 3SDW with 2008 results Begin uploading data from other NEPA projects into the warehouse

24 Schedule of SAQS and 3SDW Activities 3SAQS 2011 cumulative impacts CAMx simulation 2011-based future year CAMx simulations AprilMayJuneJuly 3SDW Populate 3SDW with 2011 modeling results Populate 3SDW with future year modeling results