Session Presentations

Ece Ari*, William. H. Battye, and Viney P. Aneja

Wildfires (bushfires) are connected with Australia's ecology and culture. This is due to highly flammable biota and shortfall of precipitation in Australia. These wildfires affect air quality and emit PM2.5. In the past century, the occurrence of wildfires has increased. Form November 2019 to January 2020 Southeast Australia faced devastating wildfires. This study analyzes air quality impact associated with Australia wildfires that occurred during 29th December 2019 to 4th of January 2020. The emission of PM2.5 was analyzed using satellite products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Fire Information for Resource Management System and the WORLDVIEW system. Daily calculated PM2.5 emissions range from 24*106 kg/day to 265*106 kg/day. Using the HYSPLIT model evaluation determined the impact of PM2.5 in Tasmania, New Zealand, and inner Australia during this time.

Ricardo Morales, Karen Ballesteros, Juan Felipe Espinosa, Fernando Garcia

Smoke from biomass burning (BB) deteriorates air quality and negatively impacts human health. However, precisely quantifying their impact is not straightforward, as many anthropogenic and natural sources concurrently influence atmospheric composition at a given location. Therefore, abrupt declines in anthropogenic emissions, such as those resulting from sharply reduced traffic during SARS-COV-2 related lockdowns during the first semester of 2020, provided unique natural experiments to uncover hitherto underestimated effects on air quality. In Colombia full lock-downs caused historically low traffic volumes, as well as reduced industrial activity caused by both disruptions in the supply chain as well as historically low demand from March 20 to April 26 in Bogota. However, during the SARS-COV-2 lock-down an air quality environmental emergency was declared in Bogota from March 5 to April 3. Despite the strict lock-down measures already in place in Colombia, severe air pollution episodes continued, in tandem with massive BB events in Northern South America (NSA). This new evidence, enabled by the unprecedented reduction in local mobile and industrial sources, highlights the teleconnections between fires linked to expanding agricultural frontiers and deforestation, and air quality in distant cities. In this work we used the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) to estimate the PM2.5 contribution ( PM2.5) from open BB during a full lock-down in Colombia, and quantified diverse short-term effects associated with BB. Three nested modeling domains cover the northern half of South America with 120, 126, and 132 grid cells, at a horizontal resolution of 27, 9, and 3 km, centered in Colombia are used for domain 1 (D01), domain 2 (D02), and domain 3 (D03), respectively. Forty-one vertical levels are used, spaced to give higher resolution in the boundary layer. NCEP-FNL products are used for meteorological boundary and initial conditions for D01. D02 and D03 boundary and initial conditions were passed down from parent domain. Gas-phase pollutants are processed using MOZART chemical mechanism. Aerosols are described with the two-moment four size-bins sectional aerosol scheme MOSAIC. Anthropogenic emissions from EDGAR are used, and diurnal variation profiles were applied to manufacturing, transformations industry, and road transportation. Local emission inventory of Bogota was merged with EDGAR in D03. We applied estimated emissions reductions during the lockdowns to local emissions inventory and used 31.2% of PM from re-suspension, 20% of VOCs and NOx, and 35% of CO from road transportation sector during March 15 to March 28, period before full lock-down measures were applied. Base on Inter-American Development Bank additional reduction factors of 87% for transportation sector, and 9% for industry and transformation sectors were applied for three domains after March 20. Biogenic emissions from MEGAN were included in all simulations. BB emission inventory from GFED4s was mapped to three simulations domains and was chemically speciated to MOZART chemical mechanism. Simulation results showed that model underestimates by 50% BB aerosols concentration regionally. Additionally, it was estimated higher mortality in densely populated areas, especially over Colombia with about 70% of cases in NSA. Our sensitivity analysis suggest that BB aerosols can be responsible for thousands of excess hospital emergency visits associated with respiratory diseases.

Stephanie Cleland, J. Jason West, Yiqin Jia, Stephen Reid, Sean Raffuse, Susan O'Neill, Ana Rappold, Marc Serre

Exposure to wildfire smoke causes a range of adverse health outcomes. This health risk in combination with a predicted increase in the frequency and severity of wildfires due to climate change suggest the importance of accurately estimating smoke concentrations and quantifying the health impacts of smoke exposure. While chemical transport models (CTMs) and the spatial interpolation of observations are often used to assess smoke exposure, geostatistical methods can combine surface observations with modeled and satellite-derived concentrations to produce more accurate exposure estimates during wildfires.

Here we use a novel approach to estimate ground-level PM_2.5 during the October 2017 California wildfires. We use the Constant Air Quality Model Performance (CAMP) and Bayesian Maximum Entropy (BME) methods to bias-correct and fuse together three concentration datasets: permanent and temporary monitoring stations, a CTM, and satellite observations. Four different BME space/time kriging and data fusion methods were evaluated for accuracy. We then used the most accurate PM_2.5 estimations in a health risk assessment to calculate the excess respiratory, cardiovascular, and asthma hospital admissions attributable to exposure to fire-originated PM_2.5.

All BME methods produce more accurate estimates than the standalone CTM and satellite products, emphasizing the importance of combining multiple datasets to estimate smoke exposure. Performing a non-linear bias-correction on the modeled concentrations, via CAMP, notably improves accuracy. The data fusion of observations with the CAMP-corrected CTM provides the best overall PM_2.5 estimate (R²=0.73), especially in station-scarce regions. Including satellite data does not improve overall performance. Using these ground-level PM_2.5 estimations, we estimate approximately 60,000 people were exposed to very unhealthy air (daily average PM_2.5 150.5 μg/m³) and 15.3 million people were exposed to concentrations greater than the EPA's 24-hour PM_2.5 standard, 35 μg/m³. We further estimate that smoke exposure during the fires caused 260 (95% CI: 124, 435), 73 (95% CI: -11, 171), and 28 (95% CI: 19, 85) excess respiratory, cardiovascular, and asthma hospital admissions, respectively.

Archana Dayalu

Matthew J. Alvarado

Biomass burning in Central America can significantly impact air quality in the United States. Specifically, the agricultural fire season in Mexico's Yucatan Peninsula (Yucatan), extending from April to May, has been linked to multiple air pollution events in and around the US Gulf Coast states. Yet, characterizing regions impacted by biomass burning remains an ongoing challenge. In this study we explored different methods for determining the presence and amount of brown carbon aerosol (BrC) to identify biomass burning smoke intrusions into the Houston-Galveston-Brazoria (HGB) area from the Yucatan. We derive Absorption Angstrom Exponent (AAE) and Extinction Angstrom Exponent (EAE) using NASA's Ozone Monitoring Instrument (OMI) measurements of aerosol optical depth at 1. We use the AAE/EAE ratio in the 354nm-388nm wavelength window to establish pixels likely characterized by high BrC and, therefore, biomass burning smoke. We analyzed data for 99 days between 2005 and 2020 with approximately half the days corresponding to a known or suspected smoke intrusion into the region. Using a k-means clustering algorithm, we find that pixels corresponding to AAE values of 4.5 (SD=0.4) and EAE values of 1.4 (SD=0.1) are likely impacted by BrC aerosol. Our analysis is supported by airmass trajectories modeled by NOAA's HYSPLIT model and a suite of smoke and fire maps. Our analysis provides a method for stakeholders to identify smoke-impacted pixels and provide likely attribution to smoke source. Future development will include finer resolution measurements from the NASA TEMPO mission, scheduled for launch in 2022.

Samantha Faulstich, Xia Sun, A. Grant Schissler, Matthew J. Strickland, Heather A. Holmes,

Understanding the impacts of wildfire smoke on nearby communities requires knowing the amount and composition of emissions released by the wildfire into the atmosphere. This may seem easily measurable using the vast network of ground-based air quality monitoring sites, but these instruments capture pollution from all sources, making it difficult to isolate pollution exclusively from wildfire smoke. Fire emissions inventories can be used to estimate smoke emissions from an individual wildfire by using empirical data to model fire behavior. Outputs from the fire emissions inventories can also be used in atmospheric transport models, like CMAQ, a chemical transport model, or HySplit, a Lagrangian particle trajectory calculator that simulates smoke plume transport and dispersion. Emissions from a wildfire can be impacted by many variables, including fire behavior, weather, fuel characteristics (amount, type, moisture content), and combustion type (i.e., flaming vs. smoldering). Variability in any of these factors leads to differences in the chemical speciation and amount of gasses and aerosols released into the atmosphere by a wildfire. Each fire emissions inventory models these variables as inputs to the emissions model, but each inventory adopts a different method or dataset to determine these variables, leading to different wildfire smoke emissions estimates from each inventory. Evaluating the differences between inventories is crucial to understanding their effects on modeling wildfire emissions, and how these differences are propagated through smoke plume dispersion modeling used to determine downwind smoke plume concentrations.

Determining the differences between fire emissions inventories and how their contrasting methods of input variable modeling can impact second order modeling of other air quality characteristics requires a direct comparison. Four fire emissions inventories will be compared and discussed: the Missoula Fire Lab Emission Inventory (MFLEI), Global Fire Emissions Database (GFED) version 4s, the Fire INventory from NCAR (FINN), and the Wildland Fire Emissions Information System (WFEIS). Results of these fire emissions inventories for the entire year of 2013 and a single, large fire that impacted the Reno, Nevada area (the Yosemite Rim Fire) will be presented. Factors such as burned area, primary emissions of PM2.5 and CO, and the average daily emissions rate are compared to highlight differences amongst the fire emissions inventories. To aid in the comparison of these emissions inventories, a Bayesian single level model was developed for each fire emissions inventory, using data from the fire emissions inventories as prior information. These models are compared, allowing for understanding of what each fire emissions captures as a distribution curve. Results show that each fire emissions inventory had a similar amount of burned area for both 2013 as a whole and the Yosemite Rim Fire, but primary emissions and the average daily emissions rate differed greatly between fire emissions inventories. Results from the Bayesian single level model show that MFLEI had less variability in the model than other fire emissions inventories. The primary PM2.5 emissions concentrations from each of these fire emissions inventories were used in HySplit to investigate how variations in fire emissions inventory modeling methods can impact smoke plume transport modeling. These comparisons will be used to inform the selection of the fire emissions inventory used for a broader project related to the human health impacts of wildfire smoke in the Reno area.

Cenlin He, Piyush Bhardwaj, Rajesh Kumar

Fine particulate matter (PM_2.5) continues to be a major air quality problem in the U.S., especially during wildfires. To improve the PM_2.5 forecasts during fire seasons, this study develops a chemical data assimilation system by coupling the Weather Research and Forecasting (WRF) model, the Community Multiscale Air Quality (CMAQ) model, and the community Gridpoint Statistical Interpolation (GSI) system to assimilate MODIS and GOES aerosol optical depth (AOD) retrievals. The WRF-CMAQ modeling system follows the EPA model configuration and uses theNational Emissions Inventory (NEI), with a horizontal grid spacing of 12 x 12 km2. The background covariance matrix used in the assimilation is generated by contrasting two simulations using different WRF physics configurations and anthropogenic and biomass burning emissions. We select the 2018 summer fire season as a case study considering data availability and quality of GOES AOD retrievals. We find that the WRF simulation generally captures the meteorological fields. Before assimilation, the WRF-CMAQ first- and second-day forecasts significantly underestimates AOD (mean bias of -0.1 and correlation of < 0.2) compared with MODIS and GOES retrievals, and surface PM_2.5 concentrations (mean bias of -1.3 and correlation of 0.1) compared with the EPA AirNow in-situ network measurements. With the assimilation of MODIS AOD at 15 Z, 18 Z, and 21 Z (UTC) every day, the model forecasts substantially improve AOD (mean bias of -0.05 and correlation of > 0.7) and surface PM_2.5 (mean bias of 0.2 and correlation of 0.2), with similar improvements for the first- and second-day forecasts. The diurnal cycles of surface PM_2.5 forecasts are largely improved particularly during afternoon and night. The assimilation of GOES AOD every 3 hours on each day shows similar but slightly smaller improvements in AOD and PM2.5 forecasts, likely due to the relatively less accurate AOD retrievals of GOES than MODIS. We are also evaluating model forecasts against fire-related field campaign (e.g., WE-CAN) measurements, and quantifying the effect of assimilating MODIS and GOES AOD together to investigate the benefit gained from using the high temporal resolution of geostationary satellite data.

Youhua Tang1,2, Patrick Campbell1,2, Pius Lee1, Daniel Tong1,2,3, Barry Baker1,2, Rick Saylor4, Jeff McQueen5, Jianping Huang5,6 , Ho-Chun Huang5,6, Li Pan5,6, Ivanka Stajner5, Shobha Kondragunta7, Xiaoyang Zhang8, Dorothy Koch9, Jose Tirado9,10, and Youngsun Jung9

1. NOAA Air Resources Laboratory (ARL), College Park, MD. 2. Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD. 3. Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA. 4. NOAA ARL, Oak Ridge, TN. 5. NOAA National Centers for Environmental Prediction (NCEP), College Park, MD 6. I.M. Systems Group Inc., Rockville, MD 7. NOAA NESDIS/STAR 8. South Dakota State University, Brookings, SD 9. NOAA NWS/STI 10. Eastern Research Group, Inc (ERG)

The NOAA Air Resources Laboratory (ARL) has led research and pre-implementation testing of the National Air Quality Forecasting Capability (NAQFC) since 2003. Field campaigns represent collection of large amount of in-situ data critical for verification and pin-pointing deficiencies of the chemical and/or physical processes modeled in NAQFC. We will share past experiences how large campaigns had enriched the development and improvement of the NAQFC. In particular, how they enabled incremental and process-specific improvements in NAQFC. Discussion will be focusing on similar benefits of utilizing high quality observation from the Firex-AQ campaign to pin-point short-comings in the research version of NAQFC (dubbed as the --- version) which provided real-time forecasting support for Firex-AQ. The NAQFC- consists of NOAA's Geophysical Fluid Dynamics Laboratory based FV3 dynamic core driven Global Forecast System (GFS) as the meteorological model and US EPA's CMAQ5.0.2 as its Chemical Transport Model. The Premaq module serves as the interface processor between GFS and CMAQ. This study high-lighted wild fires associated emissions, transport and removal of pollutants due to depositions. During the campaign wild fire associated particulate matter (PM) emissions resulted in spikes of large surface level PM concentrations. Our NAQFC- based forecast provided to the campaign managers evidence-based information to assist them in making deployment decisions. We will share lessons learned on how NAQFC- captured the episodic wild fire events by utilizing surface, remotely sensed, and in-situ flight data in its multiple platform data verification. We will provide examples on how sector-wise evaluation can identify specific NAQFC- deficiencies.

Yunyao Li1, Daniel Tong1,2,3, Matthew Alcarado4,Benjamin Brown-Steiner4,Pius Lee3, Siqi Ma1

1 Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA, USA.

2 Center for Spatial Science and Systems, George Mason University, Fairfax, VA, USA

3 NOAA Air Resources Laboratory, College Park, MD, USA.

4 Atmospheric and Environmental Research, Lexington, MA, USA.

Biomass burning releases a vast amount of aerosols into the atmosphere, often leading to severe air quality and health problems. The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency's (EPA) Office of Research and Development (ORD). In this study, two improvements related to wildfire emission transport are added to the CMAQ V5.3.1. First, we added the Sofiev et al. (2012) plume scheme which is based on FRP to the CMAQ model. The original plume rise scheme in the CMAQ is base on Briggs (1969). Previous study shows that the difference in the calculated plume injection height caused great differences in the pollution transport and downwind pollution concentration. Second, we added biomass burning (BB) intermediate-volatile organic compounds (IVOC) emission to the CMAQ fire emission input data, and put two new IVOC related chemistry reactions into the cb6r3_ae6_aq mechanism. The improved model is used to simulate FIREX-AQ and Camp Fire cases with the blended Global Biomass Burning Emissions Product from MODIS, VIIRS, and geostationary satellites (GBBEPx). For the Camp Fire case, after adding the IVOC and related chemistry reactions to the model, the O3 mixing ratio is reduced by 50%, PM2.5 concentration is reduced by 10%, and NOx is increased by 1 ppbv in the downwind region compared to the simulation results using original CMAQ model.

Jeffrey M Vukovich, USEPA OAQPS

Currently diurnal profiles being used for prescribed fires will be reviewed. A methodology for updating these profiles using state permit data and satellite remote sensing data will be described. Lastly, results and recommendations for updated profiles will be discussed.

Cheng-En Yang, Joshua S. Fu, Xinyi Dong, Yongqiang Liu, and Yang Liu

Wildland fires have been a well-known issue that causes severe public health threats and property damage over the Western United States (WUS) especially in recent years. Rising surface temperatures, drier soil moisture, and lower atmospheric humidity as a result of increasing anthropogenic emissions have induced a more fire-prone environment in WUS. In this presentation, we will illustrate how future air quality in WUS would be influenced by wildfires through a series of CMAQ simulations. during the 2050-2059 fire seasons. Through controlling two different wildfire emission data sets, one from an empirical fire model driven by meteorological conditions from the U.S. Forest Serviceand the other one based on socio-economic activities from the International Institute for Applied Systems Analysis, we will demonstrate the projections of ozone and PM_2.5 concentration changes at state- and city-level due to wildfires, which is essential to quantify air quality changes for health impact researches and to decision-making strategies for environmental management.

Hongmei Zhao, Guangyi Yang, Daniel Q. Tong, Xuelei Zhang, Aijun Xiu

Biomass burning is a major source of particulate matter (PM) and reactive trace gases emissions in China. Especially in the post-harvest season in the Northeastern China, open field crop residue burning and regional haze happened frequently in the past few years. In this study, we developed a near-real-time biomass burning emission inventory based on fire radiative power (FRP) obtained from the Visible Infrared Imaging Radiometer Suites (VIIRS) active fires datasets, and quantified the contribution of open biomass burning to surface PM2.5 (particulate matter with diameter less than 2.5 m) concentrations using air quality modeling. Higher levels of aerosol and pollutant gases emissions were concentrated in the Songnen Plain and Sanjiang Plain, the main grain producing areas in this region, and were associated with dense fire points. There were two emission peaks observed: after harvesting (October to November) and before planting (March to April). Furthermore, modeling results showed that open biomass burning contributed to 52.7% of PM2.5 concentrations during a regional haze episode over Northeastern China. The burning ban enforced in 2018 have caused the PM2.5 concentrations decreased by 48.1% during the post-harvest season over this region. Results of this study demonstrate the effectiveness of regulatory control in reducing fire emissions and lowering region-wide PM2.5 concentration.

Shupeng Zhu¹,Dabo Guan^2,4, Daoping Wang³, Michael MacKinnon¹, Guannan Geng⁵, Qiang Zhang⁴, Heran Zheng⁶, Tianyang Lei⁴, Peng Gong⁴, and Steven J. Davis⁷

¹ Advanced Power and Energy Program, University of California, Irvine, Irvine, CA, USA.

² The Bartlett School of Construction and Project Management, University College London, London, UK.

³ School of Urban and Regional Science, Shanghai University of Fiance and Economics, Shanghai, China.

⁴ Department of Earth System Science, Tsinghua University, Beijing, China.

⁵ State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China.

⁶ School of International Development, University of East Anglia, Norwich, UK.

⁷ Department of Earth System Science, University of California, Irvine, Irvine, CA, USA.

Recent increases in the frequency and scale of wildfires worldwide have raised concerns about the influence of climate change and associated socio-economic costs. In the western U.S., the hazard of wildfire has been increasing for decades. Between 1972 and 2018, the annual burned area in California increased fivefold, due at least in part to anthropogenic climate change. Over the same period, the state's population and the economy grew by 92% and about 440%, respectively. The combination of these trends has led to escalating impacts of wildfire on human well-being that reached new highs during the extremely large and destructive fires of 2017 and 2018. However, most previous estimates of these impacts have focused on direct damages: numbers of structures destroyed, the value of destroyed infrastructure, and lives lost. Here, we use a combination of physical, epidemiological, and economic models to comprehensively estimate the economic impacts of California wildfires in 2018, including the value of destroyed and damaged capital, the health costs related to air pollution exposure, and indirect losses due to broader economic disruption cascading along with regional and national supply chains. To better estimate the wildfire-related air quality impact, a systematic method is developed in a combination with both ground and satellite observation, the GFED4 fire emissions database, the GEOS-Chem air quality model, and the BenMAP health-economic tool. We find that wildfire damages in 2018 totaled $148.5 (126.1-192.9) billion (roughly 1.5% of California's annual GDP), with $27.7 billion (19%) in capital losses, $32.2 billion (22%) in health costs, and $88.6 billion (59%) in indirect losses. Our results reveal that the majority of economic impacts related to California wildfires may be indirect and often affect industry sectors and locations distant from the fires (e.g., 52% of the indirect losses, 31% of total losses, in 2018 were outside of California). Our findings and methods provide new information for decision-makers tasked with protecting lives, and key production sectors and reducing the economic damages of future wildfires.