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  • ItemOpen 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).
  • ItemOpen Access
    Data associated with "Interpersonal relationships drive successful team science: an exemplary case-based study"
    (Colorado State University. Libraries, 2020) Love, Hannah; Cross, Jennifer; Fosdick, Bailey; Crooks, Kevin; VandeWoude, Susan; Fisher, Ellen
    Team science, or collaborations between groups of scientists with varying expertise, is required for researching solutions to complex problems of the 21st century. Despite the essential need for such transdisciplinary interactions, knowledge about training scientists and developing personal mastery, a set of principles and practices necessary for team learning, also referred to as the science of team science (SciTS) in productive team interactions is still in its nascent stages. This article reports on a longitudinal case study of an exemplary scientific team and evaluates the following question: How do scientists enhance their productivity through participation in transdisciplinary teams? Through a focused SciTS study applying mixed methods, including social network surveys, participant observation, focus groups, interviews, and historical social network data, we found that the interactions of an international, transdisciplinary scientific team trained scientists to become experts in their field, helped the team develop personal mastery, advanced their scientific productivity, and fulfilled the land grant mission. The team’s processes and practices to train new scientists propelled new ideas, collaborations, and research outcomes over a 15-year period. This case study highlights that in addition to specific scientific discoveries, scientific progress benefits from developing and forming interpersonal relationships among scientists from diverse disciplines.
  • ItemOpen 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.
  • ItemOpen Access
    Successful process evaluation provides insight into team development and goal attainment: science of team science
    (Colorado State University. Libraries, 2019) Love, Hannah; Fosdick, Bailey; Cross, Jeni; Fisher, Ellen; Suter, Meghan; Egan, Dinaida
    The Science of Team Science (SciTS) emerged as a field of study because scientists are increasingly charged with solving complex and large-scale societal, health, and environmental challenges. The SciTS field seeks to develop both methods for assessing teams and a knowledge base of effective practices in team science. What makes interdisciplinary scientific teams successful? Many early studies of team science success drew on existing data like bibliometrics and patent applications to examine the patterns of successful teams. However, these metrics have several shortcomings: they can only be used to characterize teams that were successful enough to produce publications, patents or grant proposals; and their creation lags years behind team formation. Studies which rely exclusively on existing data are not able explain the differences between successful and unsuccessful teams in their formation, interaction, and development. This study asks the questions: "How are team processes and interactions related to goal accomplishment in transdisciplinary teams? Can process metrics be used to predict team success and team outcomes?" This study aims to fill the gap in SciTS literature by longitudinally observing eight scientific transdisciplinary teams and correlating process metrics to outcome metrics. From 2015 through 2017, we used participant observation, informal interviews, turn-taking assessments, and social network surveys to follow teams through their first two years of formation. We then examined which metrics of team interaction and team processes are correlated with traditional team-defined outcome metrics such as conference presentations, grant proposals, journal articles, and invention disclosures. We found that the strength of relationships, role of women, and even participation were the biggest predictors of team success. We discuss how process evaluation can be used to assess team success in the early stages of team development and which measures are more strongly associated with team success.
  • ItemOpen 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.
  • ItemOpen Access
    Improved estimation of the radius of gyration from small-angle x-ray scattering data
    (Colorado State University. Libraries, 2015) Alsaker, Cody; Breidt, F. Jay; van der Woerd, Mark J.
    Small-angle X-ray scattering (SAXS) is an experimentally simple technique that provides access to low-resolution information about biological macromolecules in solution. We here provide R code and example data sets for a new algorithm that produces accurate and precise values for the radius of gyration, Rg, of a particle. Theory states that the information derived from the lowest scattering angles can be used to estimate Rg. The value Rg is a fundamental structural parameter that is related to a molecule's size and shape. The original algorithm implemented with the R code estimates Rg with a reliable variance estimate and with higher precision than the classical method. A bias-variance criterion is minimized to determine the optimal number of data points to calculate Rg. After accounting for correlation in the data, least squares regression is used to estimate the radius of gyration and an accurate variance estimate. The software also supports the use of replicate data. Use of the code and examples is described in README.pdf.