AQAST Tiger Team. Gabriele Pfister

Transcription

1 AQAST Tiger Team Air Quality and Health Impacts from the Colorado Wildfires in June 2012 Gabriele Pfister David Edwards, Louisa Emmons, Arthur Mizzi, Christine Wiedinmyer National Center for Atmospheric Research, Boulder, CO Patrick Reddy and Jane Mitchell Colorado Department of Public Health and Environment, Denver, CO Yang Liu and Matthew Strickland Emory University, Atlanta, GA

2 Event Summer 2012: Colorado experienced one of its most costly and devastating fire seasons Multiple events: High Park Fire (06/09), Waldo Canyon Fire (06/23), Webber Fire (06/21), Flagstaff Fire(06/26), Estimated damage for High Park & Waldo fires: ~ $450M (Rocky Mountain Insurance Information Association) Fort Collins, CO High Park Fire

3 Establish impacts of wildfires on Human health Epidemiological analysis based on hospital admissions and other health data Bring together air quality managers, health authorities and academic and agency scientists Quantify fire impact on AQ Support Exceedance Demonstration Prerequisite Objectives Accurate information about surface AQ and exposure

4 Methodology WRF-Chem over the Western U.S. 5-week 6/1-7/6, 2012 ( summer) 12x12 km 2 down to 4x4 km 2 WRF/DART Met-Assimilation -> provide best possible meteorology Evaluation: satellite and surface observations Challenges: Front Range AQ impacted by Complex topography and flow patterns Diverse sources (urban, agriculture, oil/gas, fires.) Long-range transport Fires: Highly variable source; localized impacts Highly accurate representation of fire impacts needed Limited data set for evaluation

5 Surface: CDPHE/EPA Surface Network (O 3, PM2.5, PM10, NO 2 ) Operational Met Data (NCAR DTC) Research Continuous Operations: NOAA Ozone Sonde Field Campaign: NCAR/NSF/DC-3 (some sampling of high altitude fire plumes) Satellite Data MOPITT CO IASI CO and O 3 OMI NO 2 MODIS AOD MISR AOD and Plume Height CALIOP Extinction Coefficient and Aerosol Type Profiles

6 Project Status Modeling Studies with different emissions & meteorology While overall nudging and assimilation provide similar statistics, data assimilation provides a more balanced simulation and seems to perform slightly better in specific transport events Anthropogenic emissions: NEI 2011 projection high, improvement with 2008 Colorado specific inventory Fire Emissions Improvements Standard FINN emissions too low and area burned underestimated by factor 2 compared to independent data Improvements: Area burned calculations from SMARTFIRE Meeting of Project Collaborators on 16 April in Denver CDPHE, Emory, NCAR Health Study proposal submitted to Emory Institutional Review Board, under review approved as of this morning

7 Project Status Modeling Studies with different emissions & meteorology While overall nudging and assimilation provide similar statistics, data assimilation provides a more balanced simulation and seems to perform slightly better in specific transport events

8 DART/WRF-Chem Met Assimilation Domain-wide Statistics

9 July 4 Transport Event WRF-Chem Nudging RAP 13 km Analysis (Credit P. Reddy, CDPHE) WRF-Chem DART Note: different model versions High ozone conditions: Weak cool front, shallow PBL, warm temperatures, upslope flow, clear skies Did smoke make O 3 worse or reduce it? 17 UTC

10 Project Status Modeling Studies with different emissions & meteorology While overall nudging and assimilation provide similar statistics, data assimilation provides a more balanced simulation and seems to perform slightly better in specific transport events Anthropogenic emissions: NEI 2011 projection high, improvement with 2008 Colorado specific inventory

11 WRF-Chem Emissions Anthropogenic OMI NO 2 WRF-Chem NEI 2011 Tropospheric NO 2 (1e14 cm -2 ) Note: OMI data impacted by row anomaly; Filtering applied based on XTrackQualityFlag; Colorado Inv. Fire * 2 CO Inv. 2008: CDPHE, ENVIRON and Alpine Geophysics

12 WRF-Chem Emissions Anthropogenic EPA CO_EI2008 NEI2011 Adams/Welby (39.83N W) Denver CAMP (39.75 N W) EPA CO_EI2008 NEI2011 NO2 Measurements:

13 Project Status Modeling Studies with different emissions & meteorology While overall nudging and assimilation provide similar statistics, data assimilation provides a more balanced simulation and seems to perform slightly better in specific transport events Anthropogenic emissions: NEI 2011 projection high, improvement with 2008 Colorado specific inventory Fire Emissions Improvements Standard FINN emissions too low and area burned underestimated by factor 2 compared to independent data Improvements: Area burned calculations from SMARTFIRE

14 WRF-Chem Emissions Fire FINN Fire Inventory from NCAR (C. Wiedinmyer) Difference from FINN Default Instead of using MODIS Fire Detections and assuming area burned, used SMARTFIRE output SMARTFIRE Used GEOMAC fire perimeters and HMS fire detections (from MODIS, AVHRR and GOES) Processed together to get daily fire location and area burned Processed by Sean Raffuse (Sonoma Technology) HMS files from Mark Ruminski (NOAA) Land Cover data, emission factors and processing as in default FINN

15 WRF-Chem Emissions Fire FINN/Standard FINN/Smartfire Organic Carbon Fire Emissions 06/01-07/04

16 High Park Fire, CO Date Area (1e3 acres) Contained 06/ % 06/ % 06/ % 06/ % 06/ % 06/ % 06/ %

17 FINN/Std. FINN/Smartfire: High Park Fire Surface PM2.5 Fort-Collins-CSU-E Obs FINN/Standard FINN/Smartfire FINN/Standard FINN/Smartfire Surface PM2.5 All CO sites

18 CALIOP Ext. Coeff. - Vertical Smoke CALIOP : Maria val Martin, CSU CALIOP Extinction Profiles over Colorado

19 Next Steps & Methods Data Assimilation and WRF-Chem Improvements Can modeling alone provide required accuracy for health and exceedance study? Additional Methods: Fusion product from WRF-Chem and other data sources to support epidemiological studies Analysis of residual + model fire tracers

20 Fusion Product Health impacts from wildfire PM, California, Summer 2008 Collaborative Project with Colleen Reid and Mike Jerrett, UC Berkely Land Use Regression models with monitored PM2.5 as dependent variable Predictors: Spatiotemporal: AOD (GASP, MODIS, STI), WRF-Chem Other covariates: Meteorology, land-use characteristics,elevation, traffic metrics, time and space indicators Figures: C. Reid

21 Fusion Product Figures: C. Reid

22 Residual Method Jaffe et al., submitted to ES & T Impact of Wildfires on Ozone Exceptional Events in the Western U.S. Statistical model to estimate MD8A as function of several meteorological and temporal variables (Data set: Jun-Sep ) Statictical Model explains 60%, 52% and 27% of the variability in daily MDA8 for Salt Lake City, UT; Boise, ID; and Reno, NV Observed MDA8 WRF-Chem MDA8 WRF-Chem O 3 Fire Tracer Residual Reno, NV 2008

23 EXTRAS

24 Other Updates

25 Other Updates

26 Other Updates Regional Air Quality Climate Simulations with WRF-Chem 12 years/simulation April-September 12 x 12 km 2 Climate Emissions Chemistry Present 2000 yes Future 2050 RCP 8.5 yes Future 2000 yes Future none no

27 Other Updates Western States Air Resources Council (WESTAR) Wildfire and Ozone Exceptional Events Meeting March 5-6, 2012 in Sacramento, CA Goals: Reach a common understanding of the current state of scientific knowledge about ozone formation from wildfires. Identify the techniques and existing data sources available to state air quality agencies to characterize ozone exceedances as exceptional events focusing on the characteristics of fires in the western states.

28 Other Updates Smoke Modeling Workshop March 28, 2012 in Golden, CO (U.S. Forest Service/Region 2, CDPHE/APCD, Colorado Forest Restoration Institute (CFRI), Southern Rockies Fire Science Network(SRFSN), The Nature Conservancy, and others) Goal: develop a current understanding of smoke models and modeling capabilities, and to develop agreement on how to evaluate applicability of smoke production and dispersal models for use in Colorado. Topics: In what way can models be used to supplement the experience-based model that currently specifies permit conditions issued by the Air Pollution Control Division (APCD), Colorado Department of Public Health and Environment? In what way can models supplement the burner s decision making processes, specifically impacting the go/no-go decision? What needs to be done to evaluate these models? What is the potential of models to measure cumulative smoke impacts from multiple burns?

29 Ash Creek Fire, MT Date Area (1e3 acres) 06/ % 06/ % 06/ % 07/ % 07/ % Contained 07/ %