A Multi-Analysis Approach for Estimating Regional Health Impacts from the 2017 Northern California Wildfires

Journal Article · 03 July 2021 · Journal of the Air and Waste Management Association

DOI:https://doi.org/10.1080/10962247.2021.1891994· OSTI ID:1819116

O'Neill, Susan ^[1]; Diao, Minghui ^[2]; Raffuse, Sean ^[3]; Al-Hamdan, Mohammad ^[4]; Barik, Muhammad ^[5]; Jia, Yiqin ^[6]; Reid, Steve ^[6]; Zou, Yufei ^[7]; Tong, Daniel ^[8]; West, Jason ^[9]; Wilkins, Joseph ^[10]; Marsha, Amy ^[1]; Freedman, Frank ^[11]; Vargo, Jason ^[12]; Larkin, Narasimhan ^[13]; Alvarado, Ernesto ^[10]; Loesche, Patti ^[10]

---

1. US Forest Service
2. San Jose State University
3. University of California, Davis
4. Universities Space Research Association, National Space Science & Technology Center
5. Universities Space Research Association at NASA Marshall Space Flight Center, National Space Science
6. Bay Area Air Quality Management District
7. BATTELLE (PACIFIC NW LAB)
8. George Mason Universitiy
9. University of North Carolina
10. University of Washington
11. San Francisco State University
12. California Department of Public Health
13. USDA Forest Service

In the evening of October 8 and early hours of October 9, 2017, high winds in Northern California downed trees and power lines, igniting some of the most devastating wildfires the state had seen, and within hours unhealthy air quality impacted millions of people. We simulated these air quality conditions using fire detection information from the MODIS, VIIRS, and GOES-16 ABI satellite instruments, and applying a set of three WRF–CMAQ simulations, one data fusion, and three machine learning methods. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ air quality modeling system. Interestingly, this approach did not necessarily improve results compared to the baseline case, which used a default time profile. However, this approach was key to simulating the initial 12-hr explosive fire activity and smoke impacts. The WRF-CMAQ simulations compared well with observational data for the October 8-15 time period and tended to overestimate concentrations October 16-20. To improve these results, we applied three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across the WRF-CMAQ, data fusion, and machine learning datasets, the best Pearson correlation was 0.5. The data fusion and machine learning results were biased low and WRF-CMAQ results were biased high. Finally, we applied the optimized PM2.5 exposure estimate in a short-term exposure-response function. Total estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to wildland fire smoke.

• Research Organization: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1819116
• Report Number(s): PNNL-SA-159422
• Journal Information: Journal of the Air and Waste Management Association, Vol. 71, Issue 7
• Country of Publication: United States
• Language: English