Research Data

Permanent URI for this collection

https://hdl.handle.net/10217/181884

Browse

Now showing 1 - 20 of 23

Open Access
Supplementary information for: A road damage and life-cycle greenhouse gas comparison of trucking and pipeline water delivery systems for hydraulically fractured oil and gas field development in Colorado
(Colorado State University. Libraries, 2016) Duthu, Ray; Bradley, Thomas
The process of hydraulic fracturing for recovery of oil and natural gas uses large amounts of fresh water and produces a comparable amount of wastewater, much of which is typically transported by truck. Truck transport of water is an expensive and energy-intensive process with significant external costs including roads damages, and pollution. The integrated development plan (IDP) is the industry nomenclature for an integrated oil and gas infrastructure system incorporating pipeline-based transport of water and wastewater, centralized water treatment, and high rates of wastewater recycling. These IDP have been proposed as an alternative to truck transport systems so as to mitigate many of the economic and environmental problems associated with natural gas production, but the economic and environmental performance of these systems have not been analyzed to date. This study presents an quantification of lifecycle GHG emissions and road damages of a generic oil and gas field, and of an oil and gas development sited in the Denver-Julesburg basin in the northern Colorado region of the US. Results demonstrate that a reduction in economic and environmental externalities can be derived from the development of these IDP-based pipeline water transportation systems. IDPs have marginal utility in reducing GHG emissions and road damage when they are used to replace in-field water transport, but can reduce GHG emissions and road damage by factors of as much as 6 and 7 respectively, when used to replace fresh water transport and waste-disposal routes for exemplar Northern Colorado oil and gas fields.
Open Access
Data associated with the manuscript: Investigating diesel engines as an atmospheric source of isocyanic acid in urban areas
(Colorado State University. Libraries, 2017) Jathar, Shantanu H.; Heppding, Christopher; Link, Michael F.; Farmer, Delphine K.; Akherati, Ali; Kleeman, Michael J.; de Gouw, Joost A.; Veres, Patrick R.; Roberts, James M.
Isocyanic acid (HNCO), an acidic gas found in tobacco smoke, urban environments and biomass burning-affected regions, has been linked to adverse health outcomes. Gasoline- and diesel-powered engines and biomass burning are known to emit HNCO and hypothesized to emit precursors such as amides that can photochemically react to produce HNCO in the atmosphere. Increasingly, diesel engines in developed countries like the United States are required to use Selective Catalytic Reduction (SCR) systems to reduce tailpipe emissions of oxides of nitrogen. SCR chemistry is known to produce HNCO as an intermediate product, and SCR systems have been implicated as an atmospheric source of HNCO. In this work, we measure HNCO emissions from an SCR system-equipped diesel engine and, in combination with earlier data, use a three-dimensional chemical transport model (CTM) to simulate the ambient concentrations and source/pathway contributions to HNCO in an urban environment. Engine tests were conducted at three different engine loads, using two different fuels and at multiple operating points. HNCO was measured using an acetate chemical ionization mass spectrometer. The diesel engine was found to emit primary HNCO (3-90 mg kg-fuel-1) but we did not find any evidence that the SCR system or other aftertreatment devices (i.e., oxidation catalyst and particle filter) produced or enhanced HNCO emissions. The CTM predictions compared well with the only available observational data sets for HNCO in urban areas but under-predicted the contribution from secondary processes. The comparison implied that diesel-powered engines were the largest source of HNCO in urban areas. The CTM also predicted that daily-averaged concentrations of HNCO reached a maximum of ~110 pptv but were an order of magnitude lower than the 1 ppbv level that could be associated with physiological effects in humans. Precursor contributions from other combustion sources (gasoline and biomass burning) and wintertime conditions could enhance HNCO concentrations but need to be explored in future work.
Open Access
Dataset associated with "Temporal variability largely explains difference in top-down and bottom-up estimates of methane emissions from a natural gas production region"
(Colorado State University. Libraries, 2018) Vaughn, Timothy L.; Bell, Clay S.; Pickering, Cody, K.; Schwietzke, Stefan; Heath, Garvin, A.; Petron, Gabrielle; Zimmerle, Daniel; Schnell, Russell, C.; Nummedal, Dag
This study is the first to spatially and temporally align top-down and bottom-up methane emission estimates for a natural gas production basin, using multi-scale emission measurements and detailed activity data reporting. We show that episodic venting from manual liquid unloadings, which occur at a small fraction of natural gas well pads, drives a factor-of-two temporal variation in the basin-scale emission rate of a US dry shale gas play. The mid-afternoon peak emission rate aligns with the sampling time of all regional aircraft emission studies, which target well-mixed boundary layer conditions present in the afternoon. A mechanistic understanding of emission estimates derived from various methods is critical for unbiased emission verification and effective GHG emission mitigation. Our results demonstrate that direct comparison of emission estimates from methods covering widely different time scales can be misleading.
Open Access
Supporting data for the manuscript "Modeling the formation and composition of secondary organic aerosol from diesel exhaust using parameterized and semi-explicit chemistry and thermodynamic models"
(Colorado State University. Libraries, 2018) Eluri, Sailaja; Cappa, Christopher D.; Friedman, Beth; Farmer, Delphine K.; Jathar, Shantanu
Laboratory-based studies have shown that combustion sources emit volatile organic compounds that can be photo-oxidized in the atmosphere to form secondary organic aerosol (SOA). In some cases, this SOA can exceed direct emissions of primary organic aerosol (POA). Jathar et al. (2017a) recently reported on experiments that used an oxidation flow reactor (OFR) to measure the photochemical production of SOA from a diesel engine operated at two different engine loads (idle, load), two fuel types (diesel, biodiesel), and two aftertreatment configurations (with and without an oxidation catalyst and particle filter). In this work, we used two different SOA models, the Volatility Basis Set (VBS) model and the Statistical Oxidation Model (SOM), to simulate the formation and composition of SOA for those experiments. Leveraging recent laboratory-based parameterizations, both frameworks accounted for a semi-volatile and reactive POA; SOA production from semi-volatile, intermediate volatility, and volatile organic compounds (SVOC, IVOC and VOC); NOx -dependent parameterizations; multigenerational gas-phase chemistry; and kinetic gas–particle partitioning. Both frameworks demonstrated that for model predictions of SOA mass to agree with measurements across all engine load–fuel–aftertreatment combinations, it was necessary to model the kinetically limited gas–particle partitioning in OFRs and account for SOA formation from IVOCs, which were on average found to account for 70% of the model-predicted SOA. Accounting for IVOCs, however, resulted in an average underprediction of 28% for OA atomic O:C ratios. Model predictions of the gas-phase organic compounds (resolved in carbon and oxygen space) from the SOM compared favorably to gas-phase measurements from a chemical ionization mass spectrometer (CIMS), substantiating the semi-explicit chemistry captured by the SOM. Model–measurement comparisons were improved on using SOA parameterizations corrected for vapor wall loss. As OFRs are increasingly used to study SOA formation and evolution in laboratory and field environments, models such as those developed in this work can be used to interpret the OFR data.
Open Access
Dataset associated with "A laboratory assessment of 120 air pollutant emissions from biomass and fossil fuel cookstoves
(Colorado State University. Libraries, 2018) Bilsback, Kelsey
Cookstoves emit many pollutants that are harmful to human health and the environment. However, most of the existing scientific literature focuses on fine particulate matter (PM2.5) and carbon monoxide (CO). We present an extensive dataset of speciated air pollution emissions from wood, charcoal, kerosene, and liquefied petroleum gas (LPG) cookstoves. One-hundred and twenty gas- and particle-phase constituents—including organic carbon, elemental carbon (EC), ultrafine particles (10-100 nm), inorganic ions, carbohydrates, and volatile/semi-volatile organic compounds (e.g., alkanes, alkenes, alkynes, aromatics, carbonyls, and polycyclic aromatic hydrocarbons [PAHs])—were measured in the exhaust from 26 stove/fuel combinations. We find that improved biomass stoves tend to reduce PM2.5 emissions, however, certain design features (e.g., insulation or a fan) tend to increase relative levels of other co-emitted pollutants (e.g., EC, ultrafine particles, formaldehyde, or PAHs depending on stove type). In contrast, the pressurized kerosene and LPG stoves reduced all pollutants relative to a traditional three-stone fire (≥93% and ≥79%, respectively). Finally, we find that PM2.5 and CO are not strong predictors of co-emitted pollutants, which is problematic because these pollutants may not be indicators of other cookstove smoke constituents (such as formaldehyde and acetaldehyde) that may be emitted at concentrations that are harmful to human health.
Open Access
Data associated with "Simulating secondary organic aerosol in a regional air quality model using the statistical oxidation model – Part 3: Assessing the influence of semi-volatile and intermediate-volatility organic compounds and NOx"
(Colorado State University. Libraries, 2019) Akherati, Ali; Cappa, Christopher D.; Kleeman, Michael J.; Docherty, Kenneth S.; Jimenez, Jose L.; Griffith, Stephen M.; Dusanter, Sebastien; Stevens, Philip S.; Jathar, Shantanu H.
Semi-volatile and intermediate-volatility organic compounds (SVOCs and IVOCs) from anthropogenic sources are likely to be important precursors of secondary organic aerosol (SOA) in urban airsheds, yet their treatment in most models is based on limited and obsolete data or completely missing. Additionally, gas-phase oxidation of organic precursors to form SOA is influenced by the presence of nitric oxide (NO), but this influence is poorly constrained in chemical transport models. In this work, we updated the organic aerosol model in the UCD/CIT (University of California at Davis and California Institute of Technology) chemical transport model to include (i) a semi-volatile and reactive treatment of primary organic aerosol (POA), (ii) emissions and SOA formation from IVOCs, (iii) the NOx influence on SOA formation, and (iv) SOA parameterizations for SVOCs and IVOCs that are corrected for vapor wall loss artifacts during chamber experiments. All updates were implemented in the statistical oxidation model (SOM) that simulates the oxidation chemistry, thermodynamics, and gas–particle partitioning of organic aerosol (OA). Model treatment of POA, SVOCs, and IVOCs was based on an interpretation of a comprehensive set of source measurements available up to the year 2016 and resolved broadly by source type. The NOx influence on SOA formation was calculated offline based on measured and modeled VOC:NOx ratios. Finally, the SOA formation from all organic precursors (including SVOCs and IVOCs) was modeled based on recently derived parameterizations that accounted for vapor wall loss artifacts in chamber experiments. The updated model was used to simulate a 2-week summer 30 episode over southern California at a model resolution of 8 km. When combustion-related POA was treated as semi-volatile, modeled POA mass concentrations were reduced by 15%-40% in the urban areas in southern California but were still too high when compared against "hydrocarbon-like organic aerosol" factor measurements made at Riverside, CA, during the Study of Organic Aerosols at Riverside (SOAR-1) campaign of 2005. Treating all POA (except that from marine sources) to be semi-volatile, similar to diesel exhaust POA, resulted in a larger reduction in POA mass concentrations and allowed for a better model–measurement comparison at Riverside, but this scenario is unlikely to be realistic since this assumes that POA from sources such as road and construction dust are semi-volatile too. Model predictions suggested that both SVOCs (evaporated POA vapors) and IVOCs did not contribute as much as other anthropogenic precursors (e.g., alkanes, aromatics) to SOA mass concentrations in the urban areas (<5% and <15% of the total SOA respectively) as the timescales for SOA production appeared to be shorter than the timescales for transport out of the urban airshed. Comparisons of modeled IVOC concentrations with measurements of anthropogenic SOA precursors in southern California seemed to imply that IVOC emissions were underpredicted in our updated model by a factor of 2. Correcting for the vapor wall loss artifact in chamber experiments enhanced SOA mass concentrations although the enhancement was precursor-dependent as well as NOx-dependent. Accounting for the influence of NOx using the VOC:NOx ratios resulted in better predictions of OA mass concentrations in rural/remote environments but still underpredicted OA mass concentrations in urban environments. The updated model's performance against measurements combined with the results from the sensitivity simulations suggests that the OA mass concentrations in southern California are constrained within a factor of 2. Finally, simulations performed for the year 2035 showed that, despite reductions in VOC and NOx emissions in the future, SOA mass concentrations may be higher than in the year 2005, primarily from increased hydroxyl radical (OH) concentrations due to lower ambient NO2 concentrations.
Open Access
Data collected during the pilot campaign of the citizen-enabled aerosol measurements for satellites (CEAMS) network in northern Colorado
(Colorado State University. Libraries, 2019) Ford, Bonne; Pierce, Jeffrey R.; Wendt, Eric; Long, Marilee; Jathar, Shantanu; Mehaffy, John; Tryner, Jessica; Quinn, Casey; van Zyl, Lizette; L'Orange, Christian; Miller-Lionberg, Daniel; Volckens, John
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).
Open Access
Dataset associated with "Effects of fuel moisture content on emissions from a rocket-elbow cookstove"
(Colorado State University. Libraries, 2019) van Zyl, Lizette; Tryner, Jessica; Bilsback, Kelsey; Good, Nicholas; Hecobian, Arsineh; Sullivan, Amy P.; Zhou, Yong; Peel, Jennifer; Volckens, John
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.
Open Access
Dataset associated with "Laboratory evaluation of low-cost PurpleAir PM monitors and in-field correction using co-located portable filter samplers"
(Colorado State University. Libraries, 2019) Tryner, Jessica; L'Orange, Christian; Mehaffy, John; Miller-Lionberg, Daniel; Hofstetter, Josephine C.; Wilson, Ander; Volckens, John
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.
Open Access
Data associated with "Health and environmental justice implications of retiring two coal‐fired power plants in the southern Front Range region of Colorado"
(Colorado State University. Libraries, 2019) Martenies, Sheena; Akherati, Ali; Jathar, Shantanu; Magzamen, Sheryl
Despite improvements in air quality over the past 50 years, ambient air pollution remains an important public health issue in the United States. In particular, emissions from coal-fired power plants still have a substantial impact on both nearby and regional populations. Of particular concern is the potential for this impact to fall disproportionately on low-income communities and communities of color. We conducted a quantitative health impact assessment to estimate the health benefits of the proposed decommissioning of coal-fired boilers at two electricity generating stations in the Southern Front Range region of Colorado. We estimated changes in exposures to fine particulate matter (PM2.5) and ozone due to reductions in emission using the Community Multiscale Air Quality model and predicted avoided health impacts and related economic values. In addition to estimating health benefits of reduced emissions, we assessed the distribution of these benefits by population-level socioeconomic status using concentration curves. Across the study area, decommissioning the power plants would result in 4 (95% CI: 2 – 6) avoided premature deaths each year due to reduced PM2.5 exposures and greater reductions in hospitalizations and other morbidities. Health benefits resulting from the modeled shutdowns were greatest in areas with lower median incomes, lower percentages of high school graduates, and higher proportions of households with incomes below the poverty line. However, in our study area, we did not observe higher benefits when examining area-level percentage of residents of color, largely due to the distribution of the smaller proportion of the population in the region that identifies as non-White. Our results suggest that decommissioning the power plants in the southern Front Range and replacing them with zero-emissions sources could have broad public health benefits for residents of Colorado, with larger benefits for those that are socially disadvantaged and historically bear greater environmental pollution burdens. These results also suggested that researchers and decision makers need to consider the unique demographics of their study areas to ensure that important opportunities to reduce health disparities associated with point-source pollution.
Open Access
Data set associated with "A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 1: Specifications and testing"
(Colorado State University. Libraries, 2019) Wendt, Eric A.; Quinn, Casey W.; Miller-Lionberg, Daniel D.; L'Orange, Christian; Ford, Bonne; Yalin, Azer P.; Pierce, Jeffrey R.; Jathar, Shantanu; Volckens, John
Globally, fine particulate matter (PM2.5) air pollution is a leading contributor to death, disease, and environmental degradation. Satellite-based measurements of aerosol optical depth (AOD) are used to estimate PM2.5 concentrations across the world, but the relationship between satellite-estimated AOD and ground-level PM2.5 is uncertain. Sun photometers measure AOD from the Earth's surface and are often used to improve satellite data; however, reference-grade photometers and PM2.5 monitors are expensive and rarely co-located. This work presents the development and validation of the Aerosol Mass and Optical Depth (AMOD) sampler, an inexpensive and compact device that simultaneously measures PM2.5 mass and AOD. The AMOD utilizes a low-cost light-scattering sensor in combination with a gravimetric filter measurement to quantify ground-level PM2.5. Aerosol optical depth is measured using optically filtered photodiodes at four discrete wavelengths. Field validation studies revealed agreement within 10% for AOD values measured between co-located AMOD and AErosol RObotics NETwork (AERONET) monitors and for PM2.5 mass measured between co-located AMOD and EPA Federal Equivalent Method (FEM) monitors. These results demonstrate that the AMOD can quantify AOD and PM2.5 accurately at a fraction of the cost of existing reference monitors.
Open Access
Data associated with the manuscript: Influence of single-nanoparticle electrochromic dynamics on the durability and speed of smart windows
(Colorado State University. Libraries, 2019) Sambur, Justin B.; Evans, R. Colby; Ellingworth, Austin; Cashen, Christina J.; Weinberger, C. R.
Nanomaterials have tremendous potential to increase electrochromic smart window efficiency, speed, and durability. However, nanoparticles vary in size, shape, and surface defects, and it is unknown how nanoparticle heterogeneity contributes to particle dependent electrochromic properties. Here, we use single-nanoparticle level electro-optical imaging to measure structure–function relationships in electrochromic tungsten oxide nanorods. Single nanorods exhibit a particle-dependent waiting time for tinting (from 100 ms to 10 s) due to Li-ion insertion at optically inactive surface sites. Longer nanorods tint darker than shorter nanorods and exhibit a Li-ion gradient that increases from the nanorod ends to the middle. The particle-dependent ion-insertion kinetics contribute to variable tinting rates and magnitudes across large-area smart windows. Next, we quantified how particle–particle interactions impact tinting dynamics and reversibility as the nanorod building blocks are assembled into a thin film. Interestingly, single particles tint 4 times faster and cycle 20 times more reversibly than thin films made of the same particles. These findings allow us to propose a nanostructured electrode architecture that optimizes optical modulation rates and reversibility across large-area smart windows.
Open Access
Dataset associated with "Emissions and radiative impacts of sub-10 nm particles from biofuel and fossil fuel cookstoves"
(Colorado State University. Libraries, 2020) Jathar, Shantanu H.; Sharma, Naman; Bilsback, Kelsey R.; Pierce, Jeffrey R.; Vanhanen, Joonas; Gordon, Timothy D.; Volckens, John
Combustion sources have been shown to directly emit particles smaller than 10 nm. The emission of 1-3 nm particles from biofuel or fossil-fuel cookstoves has not been studied previously, nor have the radiative impacts of these emissions been investigated. In this work, emissions (number of particles) were measured during a water boiling test performed on five different cookstoves (three-stone fire, rocket elbow, gasifier, charcoal, and liquified petroleum gas [LPG]) for particle diameters between ~1 and ~1000 nm. We found significant emissions of particles smaller than 10 nm for all cookstoves (>5×1015 # kg-fuel-1). Furthermore, cleaner (e.g., LPG) cookstoves emitted a larger fraction of sub-10 nm particles (relative to the total particle counts) than traditional cookstoves (e.g., three-stone fire). Simulations performed with the global chemical transport model GEOS-Chem-TOMAS that were informed by emissions data from this work suggested that sub-10 nm particles were unlikely to significantly influence number concentrations of particles with diameters larger than 80 nm that can serve as cloud condensation nuclei (CCN) (<0.3%, globally averaged) or alter the cloud-albedo indirect effect (absolute value <0.005 W m-2, globally averaged). The largest, but still relatively minor, localized changes in CCN-relevant concentrations (<10%) and the cloud-albedo indirect effect (absolute value <0.5 W m-2) were found in large biofuel combustion source regions (e.g., Brazil, Tanzania, Southeast Asia) and in the Southern Ocean. Enhanced coagulation-related losses of these sub-10 nm particles at sub-grid scales will tend to further reduce their impact on particle number concentrations and the aerosol indirect effect, although they might still be of relevance for human health.
Open Access
Dataset associated with "Effects of aerosol type and simulated aging on performance of low-cost PM sensors"
(Colorado State University. Libraries, 2020) Tryner, Jessica; Mehaffy, John; Miller-Lionberg, Daniel; Volckens, John
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.
Open Access
PM2.5 and AOD measurements from the Citizen Enabled Aerosol Measurements for Satellites (CEAMS) 2019 Denver deployment
(Colorado State University. Libraries, 2021) Cheeseman, Michael; Ford, Bonne; Pierce, Jeff; Rosen, Zoey; Eric, Wendt; Alex, DesRosiers; Hill, Aaron; L'Orange, Christian; Quinn, Casey; Long, Marilee; Jathar, Shantanu; Volckens, John
Open Access
Dataset associated with "Effect of discrepancies caused by model resolution on model-measurement comparison for surface black carbon"
(Colorado State University. Libraries, 2021) Sun, Tianye; Zarzycki, Colin M.; Bond, Tami C.
Emission constraining studies have relied on comparisons of model against measurements, but the influence of model resolution has not been fully addressed. This work investigates the discrepancies caused by model resolution on model-measurement comparison of surface black carbon for urban and rural monitoring network sites in the U.S. With resolution of 0.5˚, simulated BC concentrations were 106% greater at urban receptors in California than simulations with 2˚ resolution; the overprediction was 30% greater for rural network sites (IMPROVE). This effect could explain 24% to 41% of the total discrepancy in model-measurement comparison for networks in California. For rural sites elsewhere in the U.S., increasing resolution from 2˚ to 0.5˚ results in either over- and under-prediction, with an averaged discrepancy of 6%. Factors describing the model resolution discrepancy for each urban and rural receptor site are tabulated.
Open Access
simpleSOM models for "A Computationally Efficient Model to Represent the Chemistry, Thermodynamics, and Microphysics of Secondary Organic Aerosol (simpleSOM): Model Development and Application to alpha-pinene SOA"
(Colorado State University. Libraries, 2021) Jathar, Shantanu; Cappa, Christopher; He, Yicong; Pierce, Jeffrey; Chuang, Wayne; Bilsback, Kelsey; Seinfeld, John; Zaveri, Rahul; Shrivastava, Manish
Secondary organic aerosols (SOAs) constitute an important fraction of fine-mode atmospheric aerosol mass. Frameworks used to develop SOA parameters from laboratory experiments and subsequently used to simulate SOA formation in atmospheric models make many simplifying assumptions about the processes that lead to SOA formation in the interest of computational efficiency. These assumptions can limit the ability of the model to predict the mass, composition, and properties of SOAs accurately. In this work, we developed a computationally efficient, process-level model named simpleSOM to represent the chemistry, thermodynamic properties, and microphysics of SOAs. simpleSOM simulates multigenerational gas-phase chemistry, phase-state-influenced kinetic gas/particle partitioning, heterogeneous chemistry, oligomerization reactions, and vapor losses to the walls of Teflon chambers. As a case study, we used simpleSOM to simulate SOA formation from the photooxidation of a-pinene. This was done to demonstrate the ability of the model to develop parameters that can reproduce environmental chamber data, to highlight the chemical and microphysical processes within simpleSOM, and discuss implications for SOA formation in chambers and in the real atmosphere. SOA parameters developed from experiments performed in the chamber at the California Institute of Technology (Caltech) reproduced observations of SOA mass yield, O:C, and volatility distribution gathered from other chambers. Sensitivity simulations suggested that multigenerational gas-phase aging contributed to nearly half of all SOAs and that in the absence of vapor wall losses, SOA production in the Caltech chamber could be nearly 50% higher. Heterogeneous chemistry did not seem to affect SOA formation over the short timescales for oxidation experienced in the chamber experiments. Simulations performed under atmospherically relevant conditions indicated that the SOA mass yields were sensitive to whether and how oligomerization reactions and the particle phase state were represented in the chamber experiment from which the parameters were developed. simpleSOM provides a comprehensive, process-based framework to consistently model the SOA formation and evolution in box and 3D models.
Open Access
Dataset associated with "Particle Size Distribution Dynamics Can Help Constrain the Phase State of Secondary Organic Aerosol"
(Colorado State University. Libraries, 2021) He, Yicong; Akherati, Ali; Nah, Theodora; Nga, Ng; Garofalo, Lauren; Farmer, Delphine; Shiraiwa, Manabu; Zaveri, Rahul; Christopher, Cappa; Pierce, Jeff; Jathar, Shantanu
Particle phase state is a property of atmospheric aerosols that has important implications for the formation, evolution, and gas/particle partitioning of secondary organic aerosol (SOA). In this work, we use a size-resolved chemistry and microphysics model (SOM-TOMAS), updated to include an explicit treatment of particle phase state, to constrain the bulk diffusion coefficient (Db) of SOA produced from alpha-pinene ozonolysis. By leveraging data from laboratory experiments performed in the absence of a seed and under dry conditions, we find that the Db for SOA can be constrained (1-5 ×10^-15 cm^2 s^-1 in these experiments) by simultaneously reproducing the time-varying SOA mass concentrations and the evolution of the particle size distribution. Another version of our model that used the predicted SOA composition to calculate the glass transition temperature, viscosity, and, ultimately, Db (~10-15 cm^2 s^-1) of the SOA was able to reproduce the mass and size distribution measurements when we included oligomer formation (oligomers accounted for about a fifth of the SOA mass). Our work highlights the potential of a size-resolved SOA model to constrain the particle phase state of SOA by utilizing historical measurements of the evolution of the particle size distribution.
Open Access
Dataset associated with "Design and Testing of a Low-Cost Sensor and Sampling Platform for Indoor Air Quality"
(Colorado State University. Libraries, 2021) Tryner, Jessica; Phillips, Mollie; Quinn, Casey W.; Neymark, Gabe; Wilson, Ander; Jather, Shantanu H.; Carter, Ellison; Volckens, John
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).
Open Access
Data set associated with “A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 3: Automation and design improvements”
(Colorado State University. Libraries, 2021) Wendt, Eric A.
Atmospheric particulate matter smaller than 2.5 microns in diameter (PM2.5) impacts public health, the environment, and the climate. Consequently, a need exists for accurate, distributed measurements of surface-level PM2.5 concentrations at a global scale. Remote sensing observations of aerosol optical depth (AOD) have been used to estimate surface-level PM2.5 for studies on human health and the Earth system. However, these estimates are uncertain due to a lack of measurements available to validate the derived PM2.5 products, which rely on the ratio of surface PM2.5 to AOD. Traditional monitoring of these two air quality metrics is costly and cumbersome, leading to a lack of surface monitoring networks with high spatial density. In part 1 of this series we described the development and validation of a first-generation device for low-cost measurement of AOD and PM2.5: The Aerosol Mass and Optical Depth (AMODv1) sampler. Part 2 of the series describes a citizen-science field deployment of the AMODv1 device. Here in part 3, we present an autonomous version of the AMOD, known as AMODv2, capable of unsupervised measurement of AOD and PM2.5 at 20-minute time intervals. The AMOD includes a set of four optically filtered photodiodes for multi-wavelength (current version at 440, 500, 675, and 870 nm) AOD, a Plantower PMS5003 sensor for time-resolved optical PM2.5 measurements, and a pump and cyclone system for time-integrated gravimetric filter measurements of particle mass and composition. The AMODv2 uses low-cost motors and sensor data for autonomous sun alignment to provide the semi-continuous AOD measurements. Operators can connect to the AMODv2 over Bluetooth® and configure a sample using a smartphone application. A Wi-Fi module enables real-time data streaming and visualization on our website (csu-ceams.com). We present a sample deployment of 10 AMODv2s during a wildfire smoke event and demonstrate the ability of the instrument to capture changes in air quality at sub-hourly time resolution. We also present the results of an AOD validation campaign where AMODv2s were co-located with AERONET (Aerosol Robotics Network) instruments as the reference method at AOD levels ranging from 0.016-1.59. We observed close agreement between AMODv2s and the reference instrument with mean absolute errors of 0.046, 0.057, 0.026, and 0.033 AOD units at 440 nm, 500 nm, 675 nm, and 870 nm, respectively. We identified individual unit bias as the primary source of error between AMODv2s and reference units and propose re-calibration to mitigate these biases. The AMODv2 is well suited for citizen-science and other high-spatial-density deployments due to its low cost, compact form, user-friendly interface, and high measurement frequency of AOD and PM2.5. These deployments could provide a rich air pollution data set for evaluating remote sensing observations, atmospheric modeling simulations, and provide communities with the information they need to implement effective public health and environmental interventions.

Browse

Browsing Research Data by Issue Date

Results Per Page

Sort Options