Current and Future Impacts of Wildfires on PM 2.5, Health, and Policy in the Rocky Mountains

Transcription

1 Current and Future Impacts of Wildfires on PM 2.5, Health, and Policy in the Rocky Mountains Yang Liu, Ph.D. STAR Grants Kick-off Meeting Research Triangle Park, NC April 5, 2017

2 Motivation The Rocky Mountains Region has seen increased frequency of large wildfires, a longer fire season and dramatic increases in average fire size in recent years. Climate models predict the most increased fire activity to occur in this region Changes in wildfire emissions and their interaction with meteorological drivers of PM pollution is an understudied area Epidemiologic information on the health effects of wildfire smoke is still limited in the literature

3 Study Objectives Goal: investigate the climate effects on PM 2.5 and O 3, with an emphasis on wildfires in the Rocky Mountains Region Aims 1. Estimate daily PM 2.5 and O 3 levels in Colorado in by fusing ground observations, satellite data and CMAQ simulations 2. Derive fire-season AQ C-R functions for ED visits and acute hospitalizations

4 Study Objectives 3. Conduct CESM/WRF/CMAQ simulations for ; (funding permits) and to evaluate how climate affects the spatiotemporal patterns of PM 2.5 and O 3 4. Using results from Aim 2 and 3, estimate the impacts of future wildfires on air quality, population health, and public health decision making in the Rocky Mountains Region.

5 Project Team Emory Yang Liu (PI): satellite data applications, exposure assessment Howard Chang: biostatistician, RCM bias correction U. Nevada at Reno Matt Strickland: epidemiologist U. Tennessee at Knoxville Joshua Fu: CESM/WRF/CMAQ modeling, dynamical downscaling US Forest Service Yongqiang Liu: fire potential calculation, climate impact on wildfires

6 Yr 1 Tasks Aims 1 & 2 Evaluation of the impact of different emissions inventories on CTM predictions in Colorado Preliminary CMAQ runs for Preliminary exposure model development Preliminary epidemiologic study

7 Evaluation of Emissions Inventories Three sets of WRF-Chem runs in June 2012 from NCAR FINN v km vs. QFED v km FINN v km vs. FINN v1.5 4 km FINN uses MODIS 1 km active fire spots under relatively cloud-free conditions while QFED uses MODIS fire radiative power (FRP) FINN and QFED also use different land cover type data

8 Preliminary Results Simulated PM 2.5 levels vs. EPA observations r between QFED results and obs = 0.55 r between FINN 12 km results and obs = 0.27 Odds ratios from conditional logistic regression on respiratory endpoints (i.e., asthma and wheeze) FINN 12 km and QFED 12 km results generated statistically significant and similar results in different age groups FINN 4 km results generated noisier effect estimates and null results for ages 65+ group

9 Preliminary Findings QFED appears to be the more appropriate fire emissions inventory in our study domain Higher spatial resolution does not necessarily yield more accurate results.

10 Yr 1 Tasks CMAQ Modeling Colorado State (CO) Colorado (CO) simulations from UTK Horizontal resolution: 12 km Vertical resolution: 25 layers 70 (x) 55 (y) 25 (z) grid cells 2011 NEI Initial/boundary conditions (IC/BC) from GEOS-Chem outputs using the same 2011 NEI and GEOS-Chem IC/BC Meteorological inputs (MCIP) 2011: from US EPA directly : WRF 3.8.1

11 Simulation Period Determination CONUS monthly distribution of major fires (>50K acres) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Suggested simulation period excluding CA, OR, and WA monthly distribution of major fires Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Suggested simulation period Fire months: April October Data from Dr. Yongqiang Liu, USFS

12 WRF Evaluations WRF v3.8.1 Criteria: mean bias (MB) between ±0.5 (Zhang et al., 2014) Procedures: daily MB for each station station-averaged daily MB stationaveraged monthly MB 2011 Monthly MB Month TEMPERATURE ±0.5 K WIND SPEED ±0.5 m/s Monthly MB Month TEMPERATURE ±0.5 K WIND SPEED ±0.5 m/s Monthly MB Month TEMPERATURE ±0.5 K WIND SPEED ±0.5 m/s Monthly MB Month TEMPERATURE ±0.5 K WIND SPEED ±0.5 m/s

13 2011 CMAQ Preliminary Runs PM 2.5 Appel et al. (2016) 2011 Summer 2011 Spring 2011 Fall Seasonal MB (μg/m 3 ) CMAQ AQS Spring: April, May Summer: June August Fall: September, October We evaluated AQS daily data for model performance check Results are comparable to Appel et al. (2016)

14 2012 CMAQ Preliminary Runs PM Spring 2012 Summer 2012 Fall Seasonal mean bias (μg/m 3 ): CMAQ AQS We evaluated AQS daily data for model performance check Summer results are significantly low Spring: April, May Summer: June August Fall: September, October

15 2011 CMAQ Preliminary Runs O 3 Appel et al. (2016) 2011 Summer 2011 Spring 2011 Fall Seasonal MB (ppbv) CMAQ AQS Spring: April, May Summer: June August Fall: September, October We evaluated AQS daily data for model performance check Results are comparable to Appel et al. (2016)

16 2012 CMAQ Preliminary Runs O Spring 2012 Summer 2012 Fall Seasonal mean bias (ppbv): CMAQ AQS We evaluated AQS daily data for model performance check Model performance needs improvement Spring: April, May Summer: June August Fall: September, October

17 Next Steps Preliminary Bayesian fusion modeling of fire season PM 2.5 and O 3 SSSSSSSSSS 1: YY ss, tt = αα 0 ss, tt + αα 1 ss, tt XX ss, tt + ββzz ss, tt + εε(ss, tt) SSSSSSSSSS 2: YY ss, tt = kk ww(ss, tt) ff kk [YY ss, tt ] Satellite data input: MAIAC 1 km AOD Expected outcome: 4 km resolution daily PM 2.5 and O 3 exposure estimates with complete coverage in space and time

18 Next Steps Refine exposure model and finalize epidemiologic study to generate C-R functions Start CESM/WRF/CMAQ climate model runs Develop preliminary bias correction schemes for the downscaled climate model output