A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires

O’Neill, Susan; Diao, Minghui; Raffuse, S. M.; Al-Hamdan, Mohammad; Barik, M. G.; Jia, Yunfeng; Reid, Stephen; Zou, Yufei; Tong, Daniel; West, J. Jason; Wilkins, Joseph L.; Marsha, Amy; Freedman, F.; Vargo, Jason; Larkin, Narasimhan K.; Alvarado, Ernesto; Loesche, Patti

doi:10.1080/10962247.2021.1891994

Cited by 32 publications

(29 citation statements)

References 85 publications

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“…For example, nocturnal emissions from the 2013 CA Rim Fire determined by an inverse modeling method constrained by airborne in‐situ CO were a factor of 10 times higher than a climatological diurnal cycle would predict (Saide et al., 2015). Furthermore, for WRF–Community Multiscale Air Quality Modeling System simulations of the October 2017 N. CA fires, it was necessary to use 5‐min temporal resolution GOES‐R FRP to shape the diurnal cycle of hourly emissions in order to reproduce smoke impacts from the rapidly increasing fire activity in the first 12 hr after initiation (O’Neill et al., 2021). Nevertheless, forecasting the temporal variability of fire emissions remains a significant challenge for current operational atmospheric models.…”

Section: Resultsmentioning

confidence: 99%

Quantifying Carbon Monoxide Emissions on the Scale of Large Wildfires

Béla

Kille

McKeen

et al. 2022

Geophysical Research Letters

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The October 2017 fires near Santa Rosa in northern California (N. CA) were the second most destructive fires to date in California 12 , killing 44 people 13 , destroying nearly 9,000 structures 13,14 , with reported losses of over $10 billion 14 , and resulting in the highest particulate matter (PM2.5) levels recorded in the Bay Area since 1999 15 . The Tubbs fire, the largest of the Oct. 2017 N. CA fires, which devastated the city of Santa Rosa, was started by a private electrical system 16 , and was associated with an intense terrain-induced downslope windstorm 17 . Such windstorms, commonly known as "Santa Ana" or "Diablo" winds, can be very destructive when driving fires and are projected with climate change to extend the fire season later into the fall and winter 17 .The Oct. 2017 N. CA fires are an example of fires with anthropogenic ignition sources and their subsequent air quality impacts that are likely to increase due to a warmer and drier climate in the Western U.S. during the 21 st century 18,19,20,21 . These trends combined with rising numbers of humans living in the urban-wildland interface are likely to result in increases in population exposure to fire activity 22 and poor air quality episodes 23 . Long-term exposure to elevated PM2.5 may increase human susceptibility to respiratory diseases such as coronavirus (COVID-19) 24,25 .The University of Colorado Airborne Solar Occultation Flux (CU AirSOF) instrument was deployed to quantify the emission fluxes of the N. CA wildfires on Oct. 10, 2017. CU AirSOF consists of a Fourier Transform Spectrometer installed on a research aircraft, and uses a digital fast solar tracker to point directly at the sun to measure the total CO column above the aircraft at mid-infrared wavelengths 26,27 . The SOF method has been used to quantify emissions from area sources by mass balance 28,29,30 , but its potential to study wildfires remains largely unexplored. The CU AirSOF instrument is a unique prototype, and optimized to quantify wildfire emissions due to

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Section: Resultsmentioning

confidence: 99%

Quantifying Carbon Monoxide Emissions on the Scale of Large Wildfires

Béla

Kille

McKeen

et al. 2022

Geophysical Research Letters

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“…For retrospective analyses when assumptions can be better specified, the system performs with Pearson correlation of about 0.65 and a positive bias of 7-9 µg/m 3 . Data assimilation improves these results (O'Neill et al, 2021;Zou et al, 2019) but with a negative bias tendency (O'Neill et al, 2021). Analyses such as these helped justify the need for new coherent multi-faceted observational campaigns to help advance the state of science in these areas, such as the Fire and Smoke Model Evaluation Experiment (Brown et al, 2014;Prichard et al, 2019).…”

Section: Us Bluesky Smoke Modeling Framework and Smoke Prediction Systemmentioning

confidence: 99%

Profiles of Operational and Research Forecasting of Smoke and Air Quality Around the World

O’Neill

Xian²,

Flemming

et al. 2022

Preprint

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Biomass burning has shaped many of the ecosystems of the planet and for millennia humans have used it as a tool to manage the environment. When widespread fires occur, the health and daily lives of millions of people can be affected by the smoke, often at unhealthy to hazardous levels leading to a range of short-term and long-term health consequences such as respiratory issues, cardiovascular issues, and mortality. It is critical to adequately represent and include smoke and its consequences in atmospheric modeling systems to meet needs such as addressing the global climate carbon budget and informing and protecting the public during smoke episodes. Many scientific and technical challenges are associated with modeling the complex phenomenon of smoke. Variability in fire emissions estimates has an order of magnitude level of uncertainty, depending upon vegetation type, natural fuel heterogeneity, and fuel combustion processes. Quantifying fire emissions also vary from ground/vegetation-based methods to those based on remotely sensed fire radiative power data. These emission estimates are input into dispersion and air quality modeling systems, where their vertical allocation associated with plume rise, and temporal release parameterizations influence transport patterns, and, in turn affect chemical transformation and interaction with other sources. These processes lend another order of magnitude of variability to the downwind estimates of trace gases and aerosol concentrations. This chapter profiles many of the global and regional smoke prediction systems currently operational or quasi-operational in real time or near-real time. It is not an exhaustive list of systems, but rather is a profile of many of the systems in use to give examples of the creativity and complexity needed to simulate the phenomenon of smoke. This chapter, and the systems described, reflect the needs of different agencies and regions, where the various systems are tailored to the best available science to address challenges of a region. Smoke forecasting requirements range from warning and informing the public about potential smoke impacts to planning burn activities for hazard reduction or resource benefit. Different agencies also have different mandates, and the lines blur between the missions of quasi-operational organizations (e.g. research institutions) and agencies with operational mandates. The global smoke prediction systems are advanced, and many are self-organizing into a powerful ensemble, as discussed in section 2. Regional and national systems are being developed independently and are discussed in sections 3-5 for Europe (11 systems), North America (7 systems), and Australia (3 systems). Finally, the World Meteorological Organization (WMO) effort (section 6) is bringing together global and regional systems and building the Vegetation Fire and Smoke Pollution Advisory and Assessment Systems (VFSP-WAS) to support countries with smoke issues and who lack resources.

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“…But with very few exceptions, satellite data provide little to no vertical information directly. Modeling of smoke transport and exposure is challenging for a number of reasons, including uncertainties in emissions, plume injection heights and model resolution ( Lu 2016;O'Neill 2021;Ye 2021). It is possible to measure unique smoke markers, such as acetonitrile (CH3CN) (Singh et al 2012;Chandra et al 2020), but these measurements are not routinely performed at surface sites, and even common tracer like acetonitrile also have some anthropogenic sources (Huangfu et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Technical note: Use of PM<sub>2.5</sub> to CO ratio as a tracer of wildfire smoke in urban areas

Jaffe

Schnieder²,

Inouye³

2022

Preprint

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Abstract. Wildfires, and the resulting smoke, are an increasing problem in many regions of the world. However, identifying the contribution of smoke to pollutant loadings in urban regions can be challenging at lower concentrations due to the presence of the usual array of anthropogenic pollutants. Here we propose a method using the difference in PM to CO emission ratios between smoke and typical urban pollution. For smoke, emission ratios of PM2.5 to CO are between 200–300 µg m−3 ppb−1, whereas typical urban sources have an emission ratio that is lower by a factor of 4–10. This gives rise to the possibility of using this ratio as an indicator of smoke extent. We use observations a regulatory surface monitoring sites in Sparks, NV, for the period of May–September 2018–2021. During this time, there were many smoke-influenced periods from numerous California wildfires that burned during this period. Using a PM / CO ratio of 30, we can split the data into smoke-influenced and no-smoke periods. We then develop a Monte Carlo simulation, tuned to local conditions, to derive a set of PM2.5 / CO values that can be used to identify smoke influence in urban areas. From the simulation, we find that a smoke enhancement ratio of 140 µg m−3 ppb−1 best fits the observations, which is significantly lower than the ratio observed in fresh smoke plumes. The most likely explanation for this difference is greater loss of PM2.5 during dilution and transport to warmer surface layers. We find that the PM2.5 / CO ratio in urban areas is an excellent indicator of smoke and should prove to be useful to identify biomass burning influence on the policy relevant concentrations of both PM2.5 and O3. Using the results of our Monte Carlo simulation, this ratio can also quantify the influence of smoke on urban PM2.5.

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A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires

Cited by 32 publications

References 85 publications

Quantifying Carbon Monoxide Emissions on the Scale of Large Wildfires

Quantifying Carbon Monoxide Emissions on the Scale of Large Wildfires

Profiles of Operational and Research Forecasting of Smoke and Air Quality Around the World

Technical note: Use of PM<sub>2.5</sub> to CO ratio as a tracer of wildfire smoke in urban areas

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A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires

Cited by 32 publications

References 85 publications

Quantifying Carbon Monoxide Emissions on the Scale of Large Wildfires

Quantifying Carbon Monoxide Emissions on the Scale of Large Wildfires

Profiles of Operational and Research Forecasting of Smoke and Air Quality Around the World

Technical note: Use of PM&lt;sub&gt;2.5&lt;/sub&gt; to CO ratio as a tracer of wildfire smoke in urban areas

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Technical note: Use of PM<sub>2.5</sub> to CO ratio as a tracer of wildfire smoke in urban areas