Combustion phenomena in biomass gasifier cookstoves

2016 Summer.Includes bibliographical references.Approximately 2.8 billion people (~40% of the global population) rely on solid fuels, such as wood, charcoal, agricultural residues, and coal, for cooking. Exposure to emissions resulting from incomplete combustion of solid fuels leads to many adverse health impacts. These health impacts have motivated the development of solid-fuel cookstoves that reduce user exposure to carbon monoxide (CO) and fine particulate matter (PM2.5). In recent years, rating systems and emission rate targets for solid-fuel cookstove performance have been proposed. The aspirational targets included in these systems (e.g., Tier 4 in the ISO IWA tiers) have encouraged the development of cookstoves that reduce emissions of CO and PM2.5 by more than 50% and 95%, respectively, compared to a baseline three-stone fire. In a top-lit up draft (TLUD) gasifier cookstove, solid biomass fuel is gasified and the resulting gaseous fuel is mixed with secondary air above the fuel bed to produce the flame that heats the cooking surface. Household biomass cookstoves that utilize gasifier designs have attracted interest due to their demonstrated ability to emit less CO and PM2.5 per unit of energy delivered to the cooking surface than other cookstove designs. Unfortunately, highly variable performance has also been observed among gasifier cookstoves, and some have been found to emit more CO and PM2.5 than a three-stone fire. Accordingly, three studies were conducted to: (1) identify the sources of the observed variability; (2) characterize the manner in which stove design, fuel properties, and operating mode influenced performance; (3) gain insight into how secondary air velocity affected fuel-air mixing and the flame dynamics in the secondary combustion zone; and (4) evaluate whether or not the reductions in emission rates that are sought could be achieved with the TLUD design. In the first study, five natural draft TLUD design configurations were tested with two fuels (corn cobs and Lodgepole pine pellets) to investigate the variability in performance that had been observed in previous studies. The results indicated that stove design, fuel type, and operator behavior all influenced emissions. Four of the five configurations exhibited lower emissions when fueled with Lodgepole pine pellets than when fueled with corn cobs. Furthermore, large transient increases in CO emission rates were observed when stoves were refueled during operation by adding fresh biomass on top of the hot char bed that was left behind after the previous batch of fuel had gasified. An energy balance model was also developed, using temperature data collected from thermocouples mounted on each configuration, to identify the factors that contributed the most to sub-unity efficiency. The results illustrated that up to 60% of the energy input to the stove as fuel could be left over as char at the end of the test, and whether or not the energy in this char was subtracted from the energy in the fuel consumed during the test when calculating the thermal efficiency of a given configuration had a large effect on the calculated efficiency value. The manner in which cookstove design, fuel properties, and operator behavior affected TLUD performance was investigated in more detail in a second study. Seventeen different stove geometries, 4 primary air flow rates, 4 secondary air flow rates, 5 secondary air temperatures, 4 fuel moisture contents, and 4 different sfuel types were tested in a modular test bed using a procedure specifically designed to capture the low emissions observed during normal operation and the high emissions observed during refueling and char burnout. The lowest high-power emissions measured during normal operation were 1.6 g/MJd-1 CO (90% confidence interval (CI) = 1.1-2.1) and 18 mg/MJd-1 PM2.5 (90% CI = 17-19). These values were well below the Tier 4 targets of 8 g/MJd-1 CO and 41 mg/MJd-1 PM2.5, but post-refueling emissions were always above the Tier 4 targets. Higher secondary air velocities resulted in lower emissions. Changes in fuel type influenced the composition of the producer gas entering the secondary combustion zone during normal operation and sometimes resulted in order of magnitude changes in PM2.5 emissions. Temperature measurements taken in the fuel bed indicated that the stove operated as an inverted downdraft gasifier during normal operation and as a conventional updraft gasifier after refueling. Overall, the results suggest that efforts aimed at reducing users' exposure to CO and PM2.5 emissions from solid fuel combustion need to take fuel type and operator behavior, in addition to stove design, into consideration. The third study was designed to investigate the effects of secondary air velocity on the fuel-air mixing process and flame dynamics in the secondary combustion zone by employing high-speed imaging techniques. Images of OH* chemiluminescence, acetone (which served as a fuel tracer) planar laser-induced fluorescence (PLIF), and OH PLIF were collected at multi-kHz repetition rates in a burner designed to generate a two-dimensional replica of the secondary combustion zone in a gasifier cookstove. This burner featured two opposed planar jets that formed an inverse non-premixed flame in which the air and fuel were in cross flow. Images were collected for various air and fuel velocities. Regular deflecting oscillation of the jets, which has been reported previously for isothermal, non-reacting, unconfined opposed planar jets, was observed in some cases but appeared to be suppressed by convection in the vertical direction and buoyancy effects in other cases. The acetone PLIF images revealed that a high air jet velocity resulted in more extensive mixing of the air and fuel below the height of air injection. As a result, the reaction zone was located further below the top of the burner in comparison to the low air velocity case. These results suggest that higher air jet velocities may lead to lower emissions from gasifier cookstoves as a result of better fuel-air mixing and a lower reaction front location that allows more time for CO and PM to be oxidized before reactions are quenched by the cold cooking surface; however, the literature suggests that unconfined opposed axisymmetric jets do not exhibit deflecting oscillation behavior and, as a result, there are limitations associated with the use of opposed planar jets as a model for the secondary air jets in a gasifier cookstove

Service Learning Without Borders – Turning Peanut Shells to Fuel Briquettes in the Gambia

The need of firewood in the Gambia is leading to rapid deforestation. An engineering student team in our program was funded to convert peanut shells, an abundant agricultural waste from the country, into fuel briquette. By consulting the local contacts, the students developed a series of pressing devices and processes for the purpose. Then they compared the strength, burning rate and duration of burning of the briquettes, as well the difficulties to obtain binder and process the material. They finally settled to an easy to follow recipe and a very simple device to press the loose shells to briquettes. In the January of 2012, a student team went to 8 remote villages in rural Gambia. They demonstrated the briquetting process to the local people. The team was warmly received and all villages agreed to try out the method so they could preserve the dwindling forest while supporting the growing community

Kitchen Concentrations of Fine Particulate Matter and Particle Number Concentration in Households Using Biomass Cookstoves in Rural Honduras

Cooking and heating with solid fuels results in high levels of household air pollutants, including particulate matter (PM); however, limited data exist for size fractions smaller than PM2.5 (diameter less than 2.5 μm). We collected 24-h time-resolved measurements of PM2.5 (n = 27) and particle number concentrations (PNC, average diameter 10–700 nm) (n = 44; 24 with paired PM2.5 and PNC) in homes with wood-burning traditional and Justa (i.e., with an engineered combustion chamber and chimney) cookstoves in rural Honduras. The median 24-h PM2.5 concentration (n = 27) was 79 μg/m3 (interquartile range [IQR]: 44–174 μg/m3); traditional (n = 15): 130 μg/m3 (IQR: 48–250 μg/m3); Justa (n = 12): 66 μg/m3 (IQR: 44–97 μg/m3). The median 24-h PNC (n = 44) was 8.5 × 104 particles (pt)/cm3 (IQR: 3.8 × 104–1.8 × 105 pt/cm3); traditional (n = 27): 1.3 × 105 pt/cm3 (IQR: 3.3 × 104–2.0 × 105 pt/cm3); Justa (n = 17): 6.3 × 104 pt/cm3 (IQR: 4.0 × 104–1.2 × 105 pt/cm3). The 24-h average PM2.5 and particle number concentrations were correlated for the full sample of cookstoves (n = 24, Spearman ρ: 0.83); correlations between PM2.5 and PNC were higher in traditional stove kitchens (n = 12, ρ: 0.93) than in Justa stove kitchens (n = 12, ρ: 0.67). The 24-h average concentrations of PM2.5 and PNC were also correlated with the maximum average concentrations during shorter-term averaging windows of one-, five-, 15-, and 60-min, respectively (Spearman ρ: PM2.5 [0.65, 0.85, 0.82, 0.71], PNC [0.74, 0.86, 0.88, 0.86]). Given the moderate correlations observed between 24-h PM2.5 and PNC and between 24-h and the shorter-term averaging windows within size fractions, investigators may need to consider cost-effectiveness and information gained by measuring both size fractions for the study objective. Further evaluations of other stove and fuel combinations are needed

Effects of operational mode on particle size and number emissions from a biomass gasifier cookstove

<p>Interest in the size distribution of particles emitted from biomass cookstoves stems from the hypothesis that exposure to ultrafine particles is more detrimental to human health than exposure to accumulation mode or other size regimes. Previous studies have reported that gasifier cookstoves emit smaller particles than other cookstove designs under steady operating conditions. In the present study, the number size distribution of particles emitted from a forced-air gasifier cookstove was measured at 1 Hz as the stove transitioned between several steady and transient operating modes. During normal operation, when the stove functioned as a top-lit updraft gasifier, the distribution was bimodal, with peaks at 10 nm and 40 nm, when a pot of water was on the stove. The distribution became unimodal with a peak at 10 nm when the pot was removed. Once the fuel bed had completely gasified and the secondary flame extinguished, the concentration of particles increased and the peak in number concentration shifted to approximately 80 nm. After refueling, when the stove operated as a conventional updraft gasifier, the peak in number concentration decreased to 10 nm. When the secondary flame extinguished a second time, the peak in number concentration increased to approximately 100 nm before decreasing to 20 nm during the char burn-out phase. These results demonstrate that changes in operational mode influence the combustion process and produce distinct changes in the size distribution and rate of particle emissions.</p> <p>Copyright © 2018 American Association for Aerosol Research</p

Dataset associated with "Effects of aerosol type and simulated aging on performance of low-cost PM sensors"

These data were collected during a study on the performance of low-cost particulate matter (PM) sensors. All data were collected in an indoor laboratory at Colorado State University in Fort Collins, Colorado, USA between 2019-07-02 and 2019-10-06. The files associated with this dataset include: (1) time-averaged PM mass concentrations reported by the low-cost sensors during each steady-state test point included in the study, (2) time-averaged particle number concentrations reported by the low-cost sensors during each steady-state test point included in the study, (3) time-averaged particle size distribution data measured using an Scanning Mobility Particle Sizer (SMPS) during each steady-state test point included in the study, (4) time-averaged particle size distribution data measured using an Aerodynamic Particle Sizer (APS) Spectrometer during each steady-state test point included in the study, (5) real-time particle size distribution data measured using an APS during an experiment in which the low-cost sensors were exposed to very high Arizona road dust concentrations for 18 hours, (6) PM2.5 concentrations recorded at one-minute intervals by a Tapered Element Oscillating Microbalance (TEOM) during all experiments conducted during the study, (7) PM concentrations recorded at one-minute intervals by a DustTrak during an experiment in which the low-cost sensors were exposed to very high Arizona road dust concentrations for 18 hours, (8) data associated with all gravimetric filter samples of PM collected during the study, (9) real-time data recorded by the low-cost PM sensors during an experiment in which the sensors were exposed to very high Arizona road dust concentrations for 18 hours, (10) all of the raw data recorded by the low-cost PM sensors during the study, and (11) all of the raw data recorded by a DustTrak DRX 8533 during the study.Studies that characterize the performance of low-cost particulate matter (PM) sensors are needed to help practitioners understand the accuracy and precision of the mass and number concentrations reported by different models. We evaluated Plantower PMS5003, Sensirion SPS30, and Amphenol SM-UART-04L PM sensors in the laboratory by exposing them to: (1) four different polydisperse aerosols (ammonium sulfate, Arizona road dust, NIST Urban PM, and wood smoke) at concentrations ranging from 10 to 1000 μg m-3, (2) hygroscopic and hydrophobic aerosols (ammonium sulfate and oil) in an environment with varying relative humidity (15% to 90%), (3) polystyrene latex spheres (PSL) ranging from 0.1 to 2.0 μm in diameter, and (4) extremely high concentrations of Arizona road dust (18-hour mean total PM = 33000 μg m-3; 18-hour mean PM2.5 = 7300 μg m-3). Linear models relating PMS5003- and SPS30-reported PM2.5 concentrations to TEOM-reported ammonium sulfate concentrations up to 1025 μg m-3, nebulized Arizona road dust concentrations up to 540 μg m-3, and NIST Urban PM concentrations up to 330 μg m-3 had R2 ≥ 0.97; however, an F-test identified a significant lack of fit between the model and the data for each sensor/aerosol combination. Ratios of filter-derived to PMS5003-reported PM2.5 concentrations were 1.4, 1.7, 1.0, 0.4, and 4.3 for ammonium sulfate, nebulized Arizona road dust, NIST Urban PM, wood smoke, and oil mist, respectively. For SPS30 sensors, these ratios were 1.6, 2.1, 2.1, 0.6, and 2.2, respectively. Collocated PMS5003 sensors were less precise than collocated SPS30 sensors when measuring ammonium sulfate, nebulized Arizona road dust, NIST Urban PM, oil mist, or PSL. Our results indicated that particle count data reported by the PMS5003 were not reliable. The number size distribution reported by the PMS5003 (a) did not agree with APS data and (b) remained roughly constant whether the sensors were exposed to 0.1 μm PSL, 0.27 μm PSL, 0.72 μm PSL, 2.0 μm PSL, or any of the other laboratory-generated aerosols. The size distribution reported by the SPS30 did not always agree with APS data either, but did shift towards larger particle sizes when the sensors were exposed to 0.72 PSL, 2.0 μm PSL, oil mist, or Arizona road dust from a fluidized bed generator. The proportions of PM mass assigned as PM1, PM2.5, and PM10 by all three sensor models shifted as the PSL size increased. After the sensors were exposed to high concentrations of Arizona road dust for 18 hours, PM2.5 concentrations reported by SPS30 sensors remained consistent, whereas 3/8 PMS5003 sensors and 2/7 SM-UART-04L sensors began reporting erroneously high values.This work was funded by grants OH010635 and OH010662 from the National Institute for Occupational Safety and Health within the US Centers for Disease Control

Dataset associated with "Laboratory evaluation of low-cost PurpleAir PM monitors and in-field correction using co-located portable filter samplers"

This dataset consists of data collected during two laboratory evaluations of PurpleAir monitors and one field deployment of PurpleAir monitors co-located with portable filter samplers.The pre-deployment laboratory evaluation took place on 2018-08-20. The post-deployment laboratory evaluation took place on 2018-12-17. The goals of these evaluations were to: (a) assess whether the PurpleAir monitors responded linearly to NIST Urban Particulate Matter concentrations ranging from approximately 0 to 75 micrograms per cubic meter, (b) obtain laboratory-derived gravimetric correction factors for fine particulate matter (PM2.5) concentrations reported by PurpleAir monitors, (c) determine whether the response of the PurpleAir monitors to NIST Urban Particulate Matter changed over the duration of the field deployment, and (d) evaluate the precision of co-located PurpleAir monitors.The field deployment took place in Fort Collins, Colorado, USA between 2018-10-22 and 2018-12-06. The goals of the field deployment were to: (a) determine whether gravimetric correction factors derived from periodic co-locations with portable filter samplers (called "ASPEN boxes") improved the accuracy of 72-hour average PM2.5 concentrations reported by PurpleAir monitors (relative to conventional PM2.5 filter samplers operated at 16.7 L/min) and (b) compare 72-hour average PM2.5 concentrations measured using portable filter samplers and conventional filter samplers.The files associated with this dataset include: (1) the raw data recorded by the PurpleAir monitors during the two laboratory evaluations and the field deployment; (2) the raw data recorded by a tapered element oscillating microbalance (TEOM) during the two laboratory evaluations; (3) the raw data recorded by the ASPEN boxes during the field deployment; (4) a summary file describing the time-averaged concentrations reported by the PurpleAir monitors and the TEOM during the discrete concentration steps that comprised each laboratory evaluation; and (5) a summary file describing the average PM2.5 concentrations measured using the PurpleAir monitors, ASPEN boxes, and conventional filter samplers at each field site during each 72-hour sample period.Low-cost aerosol monitors can provide more spatially- and temporally-resolved data on ambient fine particulate matter (PM2.5) concentrations than are typically available from regulatory monitoring networks; however, low-cost monitors—which do not measure PM2.5 mass directly and tend to be sensitive to variations in particle size and refractive index—sometimes produce inaccurate concentration estimates. We investigated laboratory- and field-based approaches for calibrating low-cost PurpleAir monitors against gravimetric filter samples. First, we investigated the linearity of the PurpleAir response to NIST Urban PM and derived a laboratory-based gravimetric correction factor. Then, we co-located PurpleAir monitors with portable filter samplers at 15 outdoor sites spanning a 3×3-km area in Fort Collins, CO, USA. We evaluated whether PM2.5 correction factors derived from periodic co-locations with portable filter samplers improved the accuracy of PurpleAir monitors (relative to reference filter samplers operated at 16.7 L/min). We also compared 72-hour average PM2.5 concentrations measured using portable and reference filter samplers. Both before and after field deployment, the coefficient of determination for a linear model relating NIST Urban PM concentrations measured by a tapered element oscillating microbalance and the PurpleAir monitors (PM2.5 ATM) was 0.99; however, an F-test identified a significant lack of fit between the model and the data. The laboratory-based correction factor did not translate to the field. Correction factors derived in the field from monthly, weekly, semi-weekly, and concurrent co-locations with portable filter samplers increased the fraction of 72-hour average PurpleAir PM2.5 concentrations that were within 20% of the reference concentrations from 15% (for uncorrected measurements) to 45%, 59%, 56%, and 70%, respectively. Furthermore, 72-hour average PM2.5 concentrations measured using portable and reference filter samplers agreed (bias ≤ 20% for 71% of samples). These results demonstrate that periodic co-location with portable filter samplers can improve the accuracy of 72-hour average PM2.5 concentrations reported by PurpleAir monitors.This work was funded by the National Oceanic and Atmospheric Administration under grant no. 1305M218CNRMW0048

Dataset associated with "Design and Testing of a Low-Cost Sensor and Sampling Platform for Indoor Air Quality"

These data were collected during a study in which nine prototypes of a low-cost sensor and sampling platform---which was called the "Home Health Box" and was designed to measure concentrations of CO2, CO, NO2, O3, PM2.5, and PM10 in indoor air---were collocated with reference (i.e., research- and regulatory-grade) CO2, CO, NO2, O3, and PM2.5 monitors in the kitchen of a home in Fort Collins, Colorado, USA for one week in October 2020. The files associated with this dataset include: (1) raw CO2 concentrations measured using a LI-COR Biosciences LI-820 CO2 Gas Analyzer (the reference CO2 monitor); (2) raw CO concentrations logged by two TSI Incorporated QTrak 7575-X Indoor Air Quality Monitors with model 982 probes (the reference CO monitors); (3) raw PM2.5 concentrations logged by a ThermoFisher Scientific 1405 Tapered Element Oscillating Microbalance (the reference PM2.5 monitor); (4) the pre- and post- sampling masses of 37-mm diameter polytetrafluoroethylene filters used to sample PM2.5 and PM10 with the nine Home Health Boxes and one Access Sensor Technologies ASPEN box; (5) a log of activities that took place inside the home during the experiment; (6) calibration coefficients provided by the manufacturer (Alphasense) of the low-cost electrochemical CO, NO2, and O3 sensors used in the Home Health Boxes; (7) coefficients of linear mixed calibration models fit to relate CO2 concentrations reported by the low-cost nondispersive infrared (NDIR) sensors used in the Home Health Boxes to reference CO2 concentrations; (8) coefficients of linear mixed calibration models fit to relate data recorded by the low-cost electrochemical CO, NO2, and O3 sensors used in the Home Health Boxes to reference CO, NO2, and O3 concentrations; (9) processed time-series CO2, CO, NO2, O3, and PM2.5 concentration data obtained from the Home Health Boxes as well as the reference monitors; (10) raw NO and NOx concentrations measured using a Thermo Environmental Instruments Model 42C Trace Level Chemiluminescence NO-NO2-NOx Analyzer (the reference NO2 monitor); (11) raw O3 concentrations measured using a Thermo Environmental Instruments Model 49C UV Photometric O3 Analyzer (the reference O3 monitor); (12) all raw data logged by the nine Home Health Boxes; (13) raw data logged by a Access Sensor Technologies ASPEN box installed outside the home.Americans spend most of their time indoors at home, but comprehensive characterization of in-home air pollution is limited by the cost and size of reference-quality monitors. We assembled small "Home Health Boxes" (HHBs) to measure indoor PM2.5, PM10, CO2, CO, NO2, and O3 concentrations using filter samplers and low-cost sensors. Nine HHBs were collocated with reference monitors in the kitchen of an occupied home in Fort Collins, Colorado, USA for 168 h while wildfire smoke impacted local air quality. When HHB data were interpreted using gas sensor manufacturers' calibrations, HHBs and reference monitors (a) categorized the level of each gaseous pollutant similarly (as either low, elevated, or high relative to air quality standards) and (b) both indicated that gas cooking burners were the dominant source of CO and NO2 pollution; however, HHB and reference O3 data were not correlated. When HHB gas sensor data were interpreted using linear mixed calibration models derived via collocation with reference monitors, root-mean-square error decreased for CO2 (from 408 to 58 ppm), CO (645 to 572 ppb), NO2 (22 to 14 ppb), and O3 (21 to 7 ppb); additionally, correlation between HHB and reference O3 data improved (Pearson's r increased from 0.02 to 0.75). Mean 168-h PM2.5 and PM10 concentrations derived from nine filter samples were 19.4 micrograms per cubic meter (6.1% relative standard deviation [RSD]) and 40.1 micrograms per cubic meter (7.6% RSD). The 168-h PM2.5 concentration was overestimated by PMS5003 sensors (median sensor/filter ratio = 1.7) and underestimated slightly by SPS30 sensors (median sensor/filter ratio = 0.91)

Dataset associated with "Effects of fuel moisture content on emissions from a rocket-elbow cookstove"

This dataset includes cookstove emissions data per energy delivered and per mass of fuel burned. Emissions data on a per-energy-delivered basis have units of grams per megajoule delivered. Emissions data on a per-mass-of-fuel-burned basis have units of grams per kilogram of fuel. These data were collected in a laboratory cookstoves testing facility at Colorado State University. The data file includes the following columns: 1. test_id - test ID (used internally to identify test replicates), 2. date - date on which the test replicate was conducted, 3. stove - name of the stove model tested, 4. fuel - name of the fuel, 5. fuel_shape - shape of the fuel, either "Milled" or "Split", 6. mc_level - moisture level of the fuel, either "Low", "Medium", or "High", 7. fuel_mc_dry - the moisture content of the fuel on a dry mass basis, in percent, 8. pollutant - name of the pollutant, 9. value - value of the emissions metric, 10. units - units associated with the emission metric, either "g_MJd" or "g_pol_kg_fuel".Exposure to air pollution from solid-fuel cookstoves is a leading risk factor for premature death; however, the effect of fuel moisture content on air pollutant emissions from solid-fuel cookstoves remains poorly constrained. The objective of this work was to characterize emissions from a rocket-elbow cookstove burning wood at three different moisture levels (5%, 15%, and 25% on a dry mass basis). Emissions of CO2, carbon monoxide (CO), methane, formaldehyde, acetaldehyde, benzene, toluene, ethylbenzene, xylenes, fine particulate matter (PM2.5), elemental carbon (EC), and organic carbon (OC) were measured. Emission factors (EFs; g·MJdelivered-1) for all pollutants, except CO2 and EC, increased with increasing fuel moisture content: CO EFs increased by 84%, benzene EFs increased by 82%, PM2.5 EFs increased by 149%, and formaldehyde EFs increased by 216%. Both modified combustion efficiency and the temperature at the combustion chamber exit decreased with increasing fuel moisture, suggesting that the energy required to vaporize water in the fuel led to lower temperatures in the combustion chamber and lower gas-phase oxidation rates. These results illustrate that changes in fuel equilibrium moisture content could cause EFs for pollutants such as PM2.5 and formaldehyde to vary by a factor of two or more across different geographic regions.This work was supported by grant ES023688 from the National Institute for Environmental Health Sciences.This work was supported by grant RD83543801 from the U.S. Environmental Protection Agency

Data collected during the pilot campaign of the citizen-enabled aerosol measurements for satellites (CEAMS) network in northern Colorado

Files include log files with all output from the AMOD device (including multi-wavelength aerosol optical depth values) for each participant (AD0**txt). To protect the privacy of participants, all GPS location data has been truncated in log files. Log files are separated by participant using unique alphanumeric identifier (bp###). Also included are files with aerosol mass (CEAMS Gravimetric and BC Results.xlsx) and elemental composition (CEAMS XRF Results.csv). Blank filters are noted in the files. Filters can be linked to log files by the log file name (last three numbers in log file name).These measurement data were collected by participants using the Aerosol Mass and Optical Depth (AMOD) sampler during a pilot campaign for the Citizen-Enabled Aerosol Measurements for Satellites (CEAMS) network in Fall/Winter 2017 in northern Colorado. Data include multi-wavelength aerosol optical depth, filter mass and composition, and optical particle mass concentrations (for a subset of the files).This work was funded by NASA grant NNX17AF94A and 80NSSC18M0120