Underestimated contribution of open biomass burning to terpenoid emissions revealed by a novel hourly dynamic inventory

Terpenoids play a crucial role in atmospheric chemistry, contributing significantly to the formation of ozone and secondary organic aerosol. However, the accurate quantification of terpenoid emissions from biomass burning is currently lacking, leading to underestimated air quality impacts. This study developed a near real-time hourly open biomass burning (OBB) emission inventory named OBEIC, which incorporated geostationary and polar-orbiting satellite fire radiative power. The OBEIC inventory provided emission estimates of 69 terpenoids, categorized into four groups, at an hourly resolution. Monoterpenes were the dominant contributors to the total emissions, accounting for 58 % of the total terpenoid emissions from OBB. Notably, only 24 % of the total monoterpenes emitted from OBB were accounted for by α-pinene and β-pinene, indicating the importance of quantifying emissions of other monoterpene species such as limonene and camphene. Additionally, oxygenated terpenoids, which were previously overlooked, contribute to 20 % of total terpenoid emissions from OBB. Diurnally, the emissions of terpenoids were primarily concentrated during the daytime (61 %); however, this study revealed the significance of nighttime emissions (39 %) as well. When compared to the biogenic and anthropogenic emissions, OBB made substantial contributions to nighttime isoprene (99.8 %), monoterpene (66.8 %), and sesquiterpene (61.7 %) emissions where OBB occurs (in 3 km range), suggesting its significant role in nighttime secondary pollutant formation. The methodology developed in this study has the potential to reduce uncertainties in OBB emissions estimation.

Air quality impacts of observationally constrained biomass burning heat flux inputs

Biomass burning is a major contributor to ambient air pollution worldwide, and the accurate characterization of biomass burning plume behavior is an important consideration for air quality models that attempt to reproduce these emissions. Smoke plume injection height, or the vertical level into which the combustion emissions are released, is an important consideration for determining plume behavior, transport, and eventual impacts. This injection height is dependent on several fire properties, each with estimates and uncertainties in terms of historical fire emissions inventories. One such property is the fire heat flux, a fire property metric sometimes used to predict and parameterize plume injection heights in current chemical transport models. Although important for plume behavior, fire heat flux is difficult to predict and parameterize efficiently, and is therefore often held to fixed, constant values in these models, leading to potential model biases relative to real world conditions. In this study we collect observed heat flux estimates from satellite data products for three wildfire events over northern California and use these estimates in a regional chemical transport model to investigate and quantify the impacts of observationally constrained heat fluxes on the modeled injection height and downwind air quality. We find large differences between these observationally derived heat flux estimates and fixed model assumptions, with important implications for modeled behavior of plume dynamics and surface air quality impacts. Overall, we find that using observationally constrained heat flux estimates tends to reduce modeled injection heights for our chosen fires, resulting in large increases in surface particulate matter concentrations. While local wind conditions contribute to variability and additional uncertainties in the impacts of modified plume injection heights, we find observationally constrained heat fluxes to be an impactful and potentially useful tool towards the improvement of emissions inventory assumptions and parameterizations.

Investigation of fire regime dynamics and modeling of burn area over India for the twenty-first century

The characteristics of the vegetation fire (VF) regime are strongly influenced by geographical variables such as regional physiographic settings, location, and climate. Understanding the VF regime is extremely important for managing and mitigating the impacts of fires on ecosystems, communities, and human activities in forest fire-prone regions. The present study thereby aimed to explore the potential effects of the confounding factors on VF in India to offer actionable and achievable solutions for mitigating this concurring environmental issue sustainably. A global burn area (250 m) data (Fire-CCIv5.1) and fire radiative power (FRP) were used to investigate the dynamics of VF across seven different divisions in India. The study also used the maximum and minimum temperatures, precipitation, population density, and intensity of human modification to model forest burn areas (including grassland). The Coupled Model Intercomparison Project-6 (CMIP6) was used to predict the burn area for 2030 and 2050 future climate scenarios. The present study accounted for a sizable increasing trend of VF during 2001-2019 period. The highest increasing trend was found in central India (513 and 343 km2 year-1 in the forest and crop fire, respectively), followed by southern India (364 km2 year-1 in forest fire), and upper Indo-Gangetic plain (128 km2 year-1 in crop fire). The FRP has varied significantly across the divisions, with the north-eastern Himalayas exhibiting the highest FRP hotspot. The maximum and minimum temperatures have the greatest influence on forest fires, according to Random Forest (RF) modeling. The estimated pre-monsoonal burn area for 2050 and 2050 future scenarios suggested a more frequent forest fire occurrence across India, particularly in southern and central India. A comprehensive forest fire control policy is therefore essential to safeguard and conserve forest cover in the regions, affected by forest fire periodically.

Smoke emissions from biomass burning in Central Mexico and their impact on air quality in Mexico City: May 2019 case study

Smoke emissions from biomass burning considerably influence regional and local air quality. Many natural wildfires and agricultural burns occur annually in Central Mexico during the hot, dry season (March to May), potentially leading to air quality problems. Nevertheless, the impact of these biomass burning emissions on Mexico City's air quality has not been investigated in depth. This study examines a severely deteriorated air quality case from 11 to 16 May 2019, during which fine particle concentrations (PM2.5) exceeded the 99th percentile of the available official dataset (2005-2019). Specifically, this work aims to highlight the role of fires and regional pollution in the severe episode observed in Mexico City, identifying the fires that were the sources of regional pollution, the type of fuel burned in those fires, and the dominant atmospheric transport pattern. Biomass burning emissions were calculated for different land cover types using satellite data from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate-Resolution Imaging Spectroradiometer (MODIS). PM2.5 increased by a factor of 2 at some monitoring sites, and ozone concentration increased to 40 % in Mexico City during the poor air quality episode. Our results indicate that over 50 % of the fire activity observed during the 2019 fire season was concentrated in May in Central Mexico. The burning activity was mainly seen over shrubland and forest between 10 and 15 May. Moreover, the fire radiative power analysis indicates that most energy was associated with burning shrubland and forests. Organic carbon emissions were estimated highest on 14 and 15 May, coinciding with the largest number of fires. Back trajectory analysis indicates that enhanced concentration of air pollutants in Mexico City originated from biomass burning detected in neighboring states: Guerrero, Michoacán, and the State of Mexico. Smoke from fires on the specific vegetation cover was advected into Mexico City and contributed to the bad air quality episode. Further meteorological analysis evidenced that the fire intensity and emissions were worsened by low humidity and the late onset of the rainy season in Central Mexico.

Fuel-Type Independent Parameterization of Volatile Organic Compound Emissions from Western US Wildfires

Volatile organic compounds (VOCs) emitted from biomass burning impact air quality and climate. Laboratory studies have shown that the variability in VOC speciation is largely driven by changes in combustion conditions and is only modestly impacted by fuel type. Here, we report that emissions of VOCs measured in ambient smoke emitted from western US wildfires can be parameterized by high- and low-temperature pyrolysis VOC profiles and are consistent with previous observations from laboratory simulated fires. This is demonstrated using positive matrix factorization (PMF) constrained by high- and low-temperature factors using VOC measurements obtained with a proton-transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) on board the NASA DC-8 during the FIREX-AQ (Fire Influence on Regional and Global Environments and Air Quality) project in 2019. A linear combination of high- and low-temperature factors described more than 70% of the variability of VOC emissions of long-lived VOCs in all sampled wildfire plumes. An additional factor attributable to atmospheric aging was required to parameterize short-lived and secondarily produced VOCs. The relative contribution of the PMF-derived high-temperature factor for a given fire plume was strongly correlated with the fire radiative power (FRP) at the estimated time of emission detected by satellite measurements. By combining the FRP with the fraction of the high-temperature PMF factor, the emission ratios (ERs) of VOCs to carbon monoxide (CO) in fresh wildfires were estimated and agree well with measured ERs (r2 = 0.80-0.93).

Impact of satellite AOD data on top-down estimation of biomass burning particulate matter emission

Particulate matter emitted from open biomass burning affects climate, air quality and public health. In the development of remote sensing techniques, top-down methods using satellite observations have become an effective way to estimate particulate matter emissions, but different spatial resolution and coverage of satellite aerosol optical depth (AOD) products introduce great uncertainties. In this work, we assess the differences in total particulate matter (TPM) emission when calculated using different satellite AOD data. To do this, we derive top-down biomass burning TPM emission coefficients (Ce) of Australia based on 1 km and 10 km MODIS AOD products, and compare the results of emission estimation. Using high spatial resolution AOD data, a 90 % decrease of Ce mean value is found in Australia, and the quality of Ce improves by 290 %. When we use 1 km AOD data in place of 10 km AOD data, the estimation of biomass burning TPM emissions in Australia during 2012-2020 drops from 1.08Tg to 0.11Tg; the temporal trends of the two products remain the same. The TPM emission estimates for biome based on higher spatial resolution AOD data in this study are lower than Fire Energetics and Emissions Research (FEERv1) inventories by factors of 0.08-0.2. Our work shows that using satellite AOD products of higher spatial resolution avoids overestimation of biomass burning TPM emissions, and improves the quality of final estimates, providing a possible way to understand biomass burning emission more accurately.

Open biomass burning emissions and their contribution to ambient formaldehyde in Guangdong province, China

Formaldehyde (HCHO) plays a vital role in atmospheric chemistry and O₃ formation. Open biomass burning (OBB) is considered to be an important source of HCHO; however, its quantitative contribution to ambient HCHO remains poorly understood due to the lack of reliable high-resolution emission inventories. In this study, a satellite-based method coupled with local emission factors was developed to estimate the hourly primary emissions of HCHO and volatile organic compound (VOC) precursors from OBB in Guangdong (GD) Province of southern China. Furthermore, the contribution of OBB to ambient HCHO was quantified using the Community Multi-scale Air Quality model. The results suggested that in average OBB emissions contributed 5293 tons of primary HCHO per year, accounting for ~14% of the total anthropogenic HCHO emissions in GD. The ambient HCHO concentration ranged from 0.3 ppbv to 8.7 ppbv during normal days, and from 8 ppbv to 45 ppbv in downwind area during OBB impacted days. The monthly contribution of OBB to local HCHO levels reached up to 50% at locations with frequent fires and over 70% during a forest fire event. Ambient HCHO was heavily affected by primary OBB emissions near the source region and by the oxidation of OBB-emitted VOCs in the downwind area. Secondary HCHO formation from OBB emissions was enhanced during photochemical pollution episodes, especially under conditions of high O₃ and low NO_x. OBB-emitted ethene was identified as the most important VOC precursor of HCHO and contributed to the formation of ~50% of the secondary HCHO. The HCHO formation potential of cropland fires was 26% higher than that of forest fires. Our results suggest that OBB can elevate ambient HCHO levels significantly. Thus, strict control policies on OBB should be implemented, especially for open burning agricultural residues in upwind areas on serious photochemical pollution days.

Airborne Emission Rate Measurements Validate Remote Sensing Observations and Emission Inventories of Western U.S. Wildfires

Carbonaceous emissions from wildfires are a dynamic mixture of gases and particles that have important impacts on air quality and climate. Emissions that feed atmospheric models are estimated using burned area and fire radiative power (FRP) methods that rely on satellite products. These approaches show wide variability and have large uncertainties, and their accuracy is challenging to evaluate due to limited aircraft and ground measurements. Here, we present a novel method to estimate fire plume-integrated total carbon and speciated emission rates using a unique combination of lidar remote sensing aerosol extinction profiles and in situ measured carbon constituents. We show strong agreement between these aircraft-derived emission rates of total carbon and a detailed burned area-based inventory that distributes carbon emissions in time using Geostationary Operational Environmental Satellite FRP observations (Fuel2Fire inventory, slope = 1.33 ± 0.04, r² = 0.93, and RMSE = 0.27). Other more commonly used inventories strongly correlate with aircraft-derived emissions but have wide-ranging over- and under-predictions. A strong correlation is found between carbon monoxide emissions estimated in situ with those derived from the TROPOspheric Monitoring Instrument (TROPOMI) for five wildfires with coincident sampling windows (slope = 0.99 ± 0.18; bias = 28.5%). Smoke emission coefficients (g MJ^-1) enable direct estimations of primary gas and aerosol emissions from satellite FRP observations, and we derive these values for many compounds emitted by temperate forest fuels, including several previously unreported species.

Deterioration of air quality associated with the 2020 US wildfires

The wildfires of August and September 2020 in the western part of the United States were characterized by an unparalleled duration and wide geographical coverage. A particular consequence of massive wildfires includes serious health effects due to short and long-term exposure to poor air quality. Using a variety of data sources including aerosol optical depth (AOD) and ultraviolet aerosol index (UVAI), obtained with the Moderate-Resolution Imaging Spectroradiometer (MODIS), Multi-Angle Implementation of Atmospheric Correction (MAIAC) and Tropospheric Monitoring Instrument (TROPOMI), combined with meteorological information from the European Center for Medium-Range Weather Forecasts (ECMWF) and other supporting data, the impact of wildfires on air quality is examined in the three western US states, California, Oregon, and Washington, and areas to the east. The results show that smoke aerosols not only led to a significant deterioration in air quality in these states but also affected all other states, Canada, and surrounding ocean areas. The wildfires increased the average daily surface concentration of PM_2.5 posing significant health risks, especially for vulnerable populations. Large amounts of black carbon (BC) aerosols were emitted into the atmosphere. AOD and UVAI exceeded 1 and 2 over most of the country. In parts of the three western states, those values reached 3.7 and 6.6, respectively. Moreover, a reanalysis based on MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) showed that the maximum values of BC surface mass concentration during the wildfires were about 370 μg/m³. These various indicators provide a better understanding of the extent of environmental and atmospheric degradation associated with these forest fires.

Reconciling Assumptions in Bottom-Up and Top-Down Approaches for Estimating Aerosol Emission Rates From Wildland Fires Using Observations From FIREX-AQ

Accurate fire emissions inventories are crucial to predict the impacts of wildland fires on air quality and atmospheric composition. Two traditional approaches are widely used to calculate fire emissions: a satellite-based top-down approach and a fuels-based bottom-up approach. However, these methods often considerably disagree on the amount of particulate mass emitted from fires. Previously available observational datasets tended to be sparse, and lacked the statistics needed to resolve these methodological discrepancies. Here, we leverage the extensive and comprehensive airborne in situ and remote sensing measurements of smoke plumes from the recent Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign to statistically assess the skill of the two traditional approaches. We use detailed campaign observations to calculate and compare emission rates at an exceptionally high-resolution using three separate approaches: top-down, bottom-up, and a novel approach based entirely on integrated airborne in situ measurements. We then compute the daily average of these high-resolution estimates and compare with estimates from lower resolution, global top-down and bottom-up inventories. We uncover strong, linear relationships between all of the high-resolution emission rate estimates in aggregate, however no single approach is capable of capturing the emission characteristics of every fire. Global inventory emission rate estimates exhibited weaker correlations with the high-resolution approaches and displayed evidence of systematic bias. The disparity between the low-resolution global inventories and the high-resolution approaches is likely caused by high levels of uncertainty in essential variables used in bottom-up inventories and imperfect assumptions in top-down inventories.

Effect of moisture content and fuel type on emissions from vegetation using a steady state combustion apparatus

Emission measurements are available in the literature for a wide variety of field burns and laboratory experiments, although previous studies do not always isolate the effect of individual features such as fuel moisture content (FMC). This study explores the effect of FMC on gaseous and particulate emissions from flaming and smouldering combustion of four different wildland fuels found across the United States. A custom linear tube-heater apparatus was built to steadily produce emissions in different combustion modes over a wide range of FMC. Results showed that when compared with flaming combustion, smouldering combustion showed increased emissions of CO, particulate matter and unburned hydrocarbons, corroborating trends in the literature. CO and particulate matter emissions in the flaming mode were also significantly correlated with FMC, which had little influence on emissions for smouldering mode combustion, when taking into account the dry mass of fuel burned. These variations occurred for some vegetative fuel species but not others, indicating that the type of fuel plays an important role. This may be due to the chemical makeup of moist and recently live fuels, which is discussed and compared with previous measurements in the literature.

Spatiotemporal Variations and Uncertainty in Crop Residue Burning Emissions over North China Plain: Implication for Atmospheric CO2 Simulation

Large uncertainty exists in the estimations of greenhouse gases and aerosol emissions from crop residue burning, which could be a key source of uncertainty in quantifying the impact of agricultural fire on regional air quality. In this study, we investigated the crop residue burning emissions and their uncertainty in North China Plain (NCP) using three widely used methods, including statistical-based, burned area-based, and fire radiative power-based methods. The impacts of biomass burning emissions on atmospheric carbon dioxide (CO2) were also examined by using a global chemical transport model (GEOS-Chem) simulation. The crop residue burning emissions were found to be high in June and followed by October, which is the harvest times for the main crops in NCP. The estimates of CO2 emission from crop residue burning exhibits large interannual variation from 2003 to 2019, with rapid growth from 2003 to 2012 and a remarkable decrease from 2013 to 2019, indicating the effects of air quality control plans in recent years. Through Monte Carlo simulation, the uncertainty of each estimation was quantified, ranging from 20% to 70% for CO2 emissions at the regional level. Concerning spatial uncertainty, it was found that the crop residue burning emissions were highly uncertain in small agricultural fire areas with the maximum changes of up to 140%. While in the areas with large agricultural fire, i.e., southern parts of NCP, the coefficient of variation mostly ranged from 30% to 100% at the gridded level. The changes in biomass burning emissions may lead to a change of surface CO2 concentration during the harvest times in NCP by more than 1.0 ppmv. The results of this study highlighted the significance of quantifying the uncertainty of biomass burning emissions in a modeling study, as the variations of crop residue burning emissions could affect the emission-driven increases in CO2 and air pollutants during summertime pollution events by a substantial fraction in this region.

The Wildland Fire Heat Budget-Using Bi-Directional Probes to Measure Sensible Heat Flux and Energy in Surface Fires

Sensible energy is the primary mode of heat dissipation from combustion in wildland surface fires. However, despite its importance to fire dynamics, smoke transport, and in determining ecological effects, it is not routinely measured. McCaffrey and Heskestad (A robust bidirectional low-velocity probe for flame and fire application. Combustion and Flame 26:125–127, 1976) describe measurements of flame velocity from a bi-directional probe which, when combined with gas temperature measurements, can be used to estimate sensible heat fluxes. In this first field application of bi-directional probes, we describe vertical and horizontal sensible heat fluxes during the RxCADRE experimental surface fires in longleaf pine savanna and open ranges at Eglin Air Force Base, Florida. Flame-front sensible energy is the time-integral of heat flux over a residence time, here defined by the rise in gas temperatures above ambient. Horizontal flow velocities and energies were larger than vertical velocities and energies. Sensible heat flux and energy measurements were coordinated with overhead radiometer measurements from which we estimated fire energy (total energy generated by combustion) under the assumption that 17% of fire energy is radiated. In approximation, horizontal, vertical, and resultant sensible energies averaged 75%, 54%, and 64%, respectively, of fire energy. While promising, measurement challenges remain, including obtaining accurate gas and velocity measurements and capturing three-dimensional flow in the field.

Interannual and Seasonal Variability of Greenhouse Gases and Aerosol Emissions from Biomass Burning in Northeastern China Constrained by Satellite Observations

Biomass burning is a major source of greenhouse gases (GHGs) and particulate matter (PM) emissions in China. Despite increasing efforts of fire monitoring, it remains challenging to quantify the variability in interannual and seasonal emissions of GHGs and PM from biomass burning. In this study, we investigated the biomass burning emissions in Northeastern China based on fire radiative power (FRP) obtained from the Visible Infrared Imaging Radiometer Suites (VIIRS) active fires datasets during the period 2012 to 2019. Our results showed that the average annual emissions from biomass burning in Northeastern China during 2012–2019 were: 81.6 Tg for CO2, 260.2 Gg for CH4, 5.5 Gg for N2O, 543.2 Gg for PM2.5 and 573.6 Gg for PM10, respectively. Higher levels of GHGs and PM 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 each year: after harvesting (October to November) and before planting (March to April). During this study period, the total fire emissions initially increased and then decreased in a fluctuating pattern, with emissions peaking in 2015, the year when more emission regulations were introduced. Crop straw was the major source of GHGs and PM among all kinds of biomass burning. Following more stringent controls on burning and the utilization of crop straw, the main burning season changed from autumn to spring. The proportion from spring burning increased from 20.5% in 2013 to 77.1% in 2019, with an annual growth rate of 20%. The results of this study demonstrate the effectiveness of regulatory control in reducing GHGs and PM emissions, as well as satellite fire observations as a powerful means to assess such outcomes.

First High-Resolution Emission Inventory of Levoglucosan for Biomass Burning and Non-Biomass Burning Sources in China

Levoglucosan (LG) emitted from non-biomass burning (non-BB) sources has given rise to biased or even unreasonable source identification results when adopting LG as a distinct marker of biomass burning (BB). The estimation of LG emission and its spatiotemporal variation for various sources are the keys to reducing uncertainty. This study first developed a LG emission inventory for China from 25 sub-type sources belonging to eight categories, with a 3 km × 3 km spatial resolution and monthly distribution. The total LG emission in 2014 was 145.7 Gg. Domestic BB and open BB contributed 39.2 and 34.3% of the total emission. Non-BB sources, including municipal solid waste burning (9.7%), firework burning (9.6%), meat cooking (5.4%), domestic coal burning (1.5%), ritual item burning (0.2%), and industrial coal burning (0.1%), contributed to 26.5% of the total emission. LG emission varied spatially and temporally. Non-BB sources have a significant spatiotemporal impact on BB source contributions, even in high BB emission regions or in sowing, harvesting, and winter heating seasons. The local BB contributions have been substantially overestimated by 4.28-369% in previous studies, wherein LG was solely referred to as the BB source. By 2018, LG emission from BB might decrease to 63.9% of its total emission. This high-resolution LG emission inventory can be greatly useful for source identification studies in China. It also supports future research on the modeling of smoke aging and pollution control.

Development of the User Requirements for the Canadian WildFireSat Satellite Mission

In 2019 the Canadian Space Agency initiated development of a dedicated wildfire monitoring satellite (WildFireSat) mission. The intent of this mission is to support operational wildfire management, smoke and air quality forecasting, and wildfire carbon emissions reporting. In order to deliver the mission objectives, it was necessary to identify the technical and operational challenges which have prevented broad exploitation of Earth Observation (EO) in Canadian wildfire management and to address these challenges in the mission design. In this study we emphasize the first objective by documenting the results of wildfire management end-user engagement activities which were used to identify the key Fire Management Functionalities (FMFs) required for an Earth Observation wildfire monitoring system. These FMFs are then used to define the User Requirements for the Canadian Wildland Fire Monitoring System (CWFMS) which are refined here for the WildFireSat mission. The User Requirements are divided into Observational, Measurement, and Precision requirements and form the foundation for the design of the WildFireSat mission (currently in Phase-A, summer 2020).

Biomass Burning in Africa: An Investigation of Fire Radiative Power Missed by MODIS Using the 375 m VIIRS Active Fire Product

Biomass burning plays a key role in the interaction between the atmosphere and the biosphere. The nearly two-decade-old Moderate Resolution Imaging Spectroradiometer (MODIS) active fire product provides critical information (e.g., fire radiative power or FRP) for characterizing fires and estimating smoke emissions. Due to limitations of sensing geometry, MODIS fire detection capability degrades at off-nadir angles and the sensor misses the observation of fires occurring inside its equatorial swath gaps. This study investigates missing MODIS FRP observations using the 375 m Visible Infrared Imaging Radiometer Suite (VIIRS) active fire data across Africa where fire occurs in the majority of vegetation-covered areas and significantly contributes to global biomass-burning emissions. We first examine the FRP relationship between the two sensors on a continental scale and in grids of seven different resolutions. We find that MODIS misses a considerable number of low-intensity fires across Africa, which results in the underestimation of daily MODIS FRP by at least 42.8% compared to VIIRS FRP. The underestimation of MODIS FRP varies largely with grid size and satellite view angle. Based on comparisons of grid-level FRP from the two sensors, adjustment models are established at seven resolutions from 0.05°–0.5° for mitigating the underestimation of MODIS grid FRP. Furthermore, the investigation of the effect of equatorial swath gaps on MODIS FRP observations reveals that swath gaps could lead to the underestimation of MODIS monthly summed FRP by 12.5%. The quantitative information of missing MODIS FRP helps to improve our understanding of potential uncertainties in the MODIS FRP based applications, especially emissions estimation.

A statistical model for predicting PM2.5 for the western United States

A new statistical model for predicting daily ground level fine scale particulate matter (PM_2.5) concentrations at monitoring sites in the western United States was developed and tested operationally during the 2016 and 2017 wildfire seasons. The model is site-specific, using a multiple linear regression schema that relies on the previous day's PM_2.5 value, along with fire and smoke related variables from satellite observations. Fire variables include fire radiative power (FRP) and the National Fire Danger Rating System Energy Release Component index. Smoke variables, in addition to ground monitored PM_2.5, include aerosol optical depth (AOD) and smoke plume perimeters from the National Oceanic and Atmospheric Administration's Hazard Mapping System. The overall statistical model was inspired by a similar system developed for British Columbia (BC) by the BC Center for Disease Control, but it has been heavily modified and adapted to work in the United States. On average, our statistical model was able to explain 78% of the variance in daily ground level PM_2.5. A novel method for implementation of this model as an operational forecast system was also developed and was tested and used during the 2016 and 2017 wildfire seasons. This method focused on producing a continuously-updating prediction that incorporated the latest information available throughout the day, including both updated remote sensing data and real-time PM_2.5 observations. The diurnal pattern of performance of this model shows that even a few hours of data early in the morning can substantially improve model performance. Implications: Wildfire smoke events produce significant air quality impacts across the western United States each year impacting millions. We present and evaluate a statistical model for making updating predictions of fine particulate (PM_2.5) levels during smoke events. These predictions run hourly and are being used by smoke incident specialists assigned to wildfire operations, and may be of interest to public health officials, air quality regulators, and the public. Predictions based on this model will be available on the web for the 2019 western U.S. wildfire season this summer.

Intercomparison of MODIS AQUA and VIIRS I-Band Fires and Emissions in an Agricultural Landscape-Implications for Air Pollution Research

Quantifying emissions from crop residue burning is crucial as it is a significant source of air pollution. In this study, we first compared the fire products from two different sensors, the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG) and Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km fire product (MCD14ML) in an agricultural landscape, Punjab, India. We then performed an intercomparison of three different approaches for estimating total particulate matter (TPM) emissions which includes the fire radiative power (FRP) based approach using VIIRS and MODIS data, the Global Fire Emissions Database (GFED) burnt area emissions and a bottom-up emissions approach involving agricultural census data. Results revealed that VIIRS detected fires were higher by a factor of 4.8 compared to MODIS Aqua and Terra sensors. Further, VIIRS detected fires were higher by a factor of 6.5 than Aqua. The mean monthly MODIS Aqua FRP was found to be higher than the VIIRS FRP; however, the sum of FRP from VIIRS was higher than MODIS data due to the large number of fires detected by the VIIRS. Besides, the VIIRS sum of FRP was 2.5 times more than the MODIS sum of FRP. MODIS and VIIRS monthly FRP data were found to be strongly correlated (r2 = 0.98). The bottom-up approach suggested TPM emissions in the range of 88.19-91.19 Gg compared to 42.0-61.71 Gg, 42.59-58.75 Gg and 93.98-111.72 Gg using the GFED, MODIS FRP, and VIIRS FRP based approaches, respectively. Of the different approaches, VIIRS FRP TPM emissions were highest. Since VIIRS data are only available since 2012 compared to MODIS Aqua data which have been available since May 2002, a prediction model combining MODIS and VIIRS FRP was derived to obtain potential TPM emissions from 2003-2016. The results suggested a range of 2.56-63.66 (Gg) TPM emissions per month, with the highest crop residue emissions during November of each year. Our results on TPM emissions for seasonality matched the ground-based data from the literature. As a mitigation option, stringent policy measures are recommended to curtail agricultural residue burning in the study area.

Time series analysis of fine particulate matter and asthma reliever dispensations in populations affected by forest fires

BACKGROUND

Several studies have evaluated the association between forest fire smoke and acute exacerbations of respiratory diseases, but few have examined effects on pharmaceutical dispensations. We examine the associations between daily fine particulate matter (PM2.5) and pharmaceutical dispensations for salbutamol in forest fire-affected and non-fire-affected populations in British Columbia (BC), Canada.

METHODS

We estimated PM2.5 exposure for populations in administrative health areas using measurements from central monitors. Remote sensing data on fires were used to classify the populations as fire-affected or non-fire-affected, and to identify extreme fire days. Daily counts of salbutamol dispensations between 2003 and 2010 were extracted from the BC PharmaNet database. We estimated rate ratios (RR) and 95% confidence intervals (CIs) for each population during all fire seasons and on extreme fire days, adjusted for temperature, humidity, and temporal trends. Overall effects for fire-affected and non-fire-affected populations were estimated via meta-regression.

RESULTS

Fire season PM2.5 was positively associated with salbutamol dispensations in all fire-affected populations, with a meta-regression RR (95% CI) of 1.06 (1.04-1.07) for a 10 ug/m3 increase. Fire season PM2.5 was not significantly associated with salbutamol dispensations in non-fire-affected populations, with a meta-regression RR of 1.00 (0.98-1.01). On extreme fire days PM2.5 was positively associated with salbutamol dispensations in both population types, with a global meta-regression RR of 1.07 (1.04 - 1.09).

CONCLUSIONS

Salbutamol dispensations were clearly associated with fire-related PM2.5. Significant associations were observed in smaller populations (range: 8,000 to 170,000 persons, median: 26,000) than those reported previously, suggesting that salbutamol dispensations may be a valuable outcome for public health surveillance during fire events.

MODIS derived fire characteristics and aerosol optical depth variations during the agricultural residue burning season, north India

Agricultural residue burning is one of the major causes of greenhouse gas emissions and aerosols in the Indo-Ganges region. In this study, we characterize the fire intensity, seasonality, variability, fire radiative energy (FRE) and aerosol optical depth (AOD) variations during the agricultural residue burning season using MODIS data. Fire counts exhibited significant bi-modal activity, with peak occurrences during April-May and October-November corresponding to wheat and rice residue burning episodes. The FRE variations coincided with the amount of residues burnt. The mean AOD (2003-2008) was 0.60 with 0.87 (+1σ) and 0.32 (-1σ). The increased AOD during the winter coincided well with the fire counts during rice residue burning season. In contrast, the AOD-fire signal was weak during the summer wheat residue burning and attributed to dust and fossil fuel combustion. Our results highlight the need for 'full accounting of GHG's and aerosols', for addressing the air quality in the study area.

Emission reductions from woody biomass waste for energy as an alternative to open burning

Woody biomass waste is generated throughout California from forest management, hazardous fuel reduction, and agricultural operations. Open pile burning in the vicinity of generation is frequently the only economic disposal option. A framework is developed to quantify air emissions reductions for projects that alternatively utilize biomass waste as fuel for energy production. A demonstration project was conducted involving the grinding and 97-km one-way transport of 6096 bone-dry metric tons (BDT) of mixed conifer forest slash in the Sierra Nevada foothills for use as fuel in a biomass power cogeneration facility. Compared with the traditional open pile burning method of disposal for the forest harvest slash, utilization of the slash for fuel reduced particulate matter (PM) emissions by 98% (6 kg PM/BDT biomass), nitrogen oxides (NOx) by 54% (1.6 kg NOx/BDT), nonmethane volatile organics (NMOCs) by 99% (4.7 kg NMOCs/BDT), carbon monoxide (CO) by 97% (58 kg CO/BDT), and carbon dioxide equivalents (CO2e) by 17% (0.38 t CO2e/BDT). Emission contributions from biomass processing and transport operations are negligible. CO2e benefits are dependent on the emission characteristics of the displaced marginal electricity supply. Monetization of emissions reductions will assist with fuel sourcing activities and the conduct of biomass energy projects.