Development of numerical tools for characterizing and quantifying biomass cookstove impact, The

2013 Summer.Includes bibliographical references.Biomass cookstove use can be damaging to both human health and the global climate. In an effort to minimize these impacts, numerous programs are working to disseminate improved biomass cookstoves. However, few programs have achieved extensive success towards improving either climate or health. One reason programs have only resulted in limited improvements has been the sector's inability to quantify cookstove performance. A numeric tool has been developed for characterizing biomass cookstove performance. This dissertation documents the development of that tool. The document is comprised of three components: (i) the critical analysis of the uncertainty associated with current methods for cookstove field-testing, (ii) the development and validation of a probabilistic impact model for biomass cookstoves, and (iii) the application of these numerical tools to quantify cookstove impact. Biomass cookstoves have traditionally been evaluated empirically. Cookstoves are tested in both the field and the laboratory, with each approach having advantages and limitations. Neither laboratory nor field testing are sufficient, however, for quantifying cookstove impact. Field-testing provides invaluable data on cookstove use but is limited by the large variability typically seen in the results. Drawing conclusions from field tests is challenging due to this variability. Many groups attempt to address testing variability by increasing the number of test replicates conducted. A numeric model was developed to determine the number of test replicates required to quantify cookstove performance in field settings. Because of the large number of test replicates required to have statistical confidence in field-based data, an improved method of quantifying biomass cookstove performance is needed. Therefore, to address this need a probabilistic Monte Carlo prediction model was developed to quantify cookstove performance. The intention of the model is to serve as a tool for predicting the impact of various cookstove designs. The model integrates various facets of existing cookstove performance knowledge in more a cohesive fashion. Model simulations were compared to experimental studies to validate this approach. Numeric tools are only valuable if they result in useful information; for example, information that allows informed decisions to be made. The potential of numeric models to provide valuable information for cookstove programs has been demonstrated by simulating the performance of multiple cookstove designs. Three improved cookstoves designs have been compared to a traditional three-stone fire. Each design was evaluated for multiple scenarios, use patterns, and locations. The impact of each design (in regard to climate and health) was then quantified and monetized. This exercise yielded two important findings. First, consideration of location and context is critical when comparing the performance of cookstoves. Second, numeric models can be used as highly informative tools to support decision-making in the cookstove sector. Empirical testing is necessary for most technical programs; this is especially true for cookstoves projects. There are aspects of cookstove designs that can only be evaluated experimentally. Examples include whether an individual likes the cookstove, or if the design is appropriate for the specific cooking requirements of a particular community. Physical testing is needed to answer some basic questions such as: Do users find the cookstove intuitive to use? Do they like the color? However, empirical testing is not well-suited to answer every question related to cookstove performance. For example, comparing the climate impact of different cookstove designs is difficult in the field. The work presented demonstrates the potential of numerical models to provide invaluable information to the cookstove sector. The development and validation of these models has been documented. These models can help quantify the impact of current designs and help guide the development of future cookstove programs

A conceptual framework for evaluating cooking systems

PUBLISHED 7 March 2022Tami C Bond, Christian L, Orange, Paul R Medwell, George Sizoomu, Samer Abdelnour, Verena Brinkmann, Philip Lloyd and Crispin Pemberton-Pigot

Internet-based public debate of CCS: lessons from online focus groups in Poland and Spain

This paper makes three contributions to the developing literature on public opinion and understanding of CCS. The first is a discussion of online focus groups as a deliberative method in experimental and perhaps consultative contexts. The second is the role of anchoring and associative reasoning in the development of public opinion of CCS, illustrated through the coincidental timing of the investigation with the Fukushima nuclear accident. The third is a discussion of managing public-facing energy messaging in an age of public access to online information. Two multi-day, online focus groups or "dialogue boards" were held, one in Poland and one in Spain, with participants drawn from regions with active CCS development potential. The nature of the groups led to participants being subject to wider social influence through discussion of the topic off-line. They were also able to research and present evidence on the topic to the group, deepening debate and allowing the emergence of 'experts'. The study illustrates and affirms the importance of trust in message source, the difficulties of challenging pre-existing concerns and opinion and the challenge potentially posed by access to conflicting online information

Dataset of filtration efficiency associated with "Quantifying the health benefits of face masks and respirators to mitigate exposure to severe air pollution"

This dataset includes the average (“mask_efficiency_mean”) and standard deviation (“mask_efficiency_sd”) of measured filtration efficiency for natural-fiber masks, synthetic-fiber mask, surgical mask, and N95 respirator as a function of particle diameter. This dataset is associated with the publication "Quantifying the health benefits of face masks and respirators to mitigate exposure to severe air pollution".Familiarity with the use of face coverings to reduce the risk of respiratory disease has increased during the coronavirus pandemic; however, recommendations for their use outside of the pandemic remains limited. Here, we develop a modeling framework to quantify the potential health benefits of wearing a face covering or respirator to mitigate exposure to severe air pollution. This framework accounts for the wide range of available face coverings and respirators, fit factors and efficacy, air pollution characteristics, and exposure-response data. Our modeling shows that N95 respirators offer robust protection against different sources of air pollution, reducing exposure by more than a factor of 14 when worn with a leak rate of 5%. Synthetic-fiber masks offer less protection with a strong dependence on aerosol size distribution (protection factors ranging from 4.4 to 2.2.), while natural-fiber and surgical masks offer reductions in exposure of 1.9 and 1.7, respectively. To assess the ability of face coverings to provide population-level health benefits to wildfire smoke, we perform a case study for the 2012 Washington state fire season. Our models suggest that although natural-fiber masks offer minor reductions in respiratory hospitalizations attributable to smoke (2-11%) due to limited filtration efficiency, N95 respirators and to a lesser extent surgical and synthetic-fiber masks may lead to notable reductions in smoke-attributable hospitalizations (22-39%, 9-24%, and 7-18%, respectively). The filtration efficiency, bypass rate, compliance rate (fraction of time and population wearing the device) are the key factors governing exposure reduction potential and health benefits during severe air pollution events

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 "Aerosol Emissions from Wind Instruments: Effects of Performer Age, Sex, Sound Pressure Level, and Bell Covers"

This dataset describes participant demographics and aerosol emission rates associated with the CSU Bioaerosol in the Performing Arts Study.Aerosol emissions from wind instruments are a suspected route of transmission for airborne infectious diseases, such as SARS-CoV-2. We evaluated aerosol number emissions (from 0.25 – 35.15 m) from 81 volunteer performers of both sexes and varied age (12 to 63 years) while playing wind instruments (bassoon, clarinet, flute, French horn, oboe, piccolo, saxophone, trombone, trumpet, and tuba) or singing. Measured emissions spanned more than two orders of magnitude, ranging in rate from 8 to 1,400 particless-1, with brass instruments, on average, producing 191% (95% CI: 81-367%) more aerosol than woodwinds. Being male was associated with a 70% increase in emissions (vs. female; 95% CI: 9-166%). Each 1 dBA increase in sound pressure level was associated with a 28% increase (95% CI: 10-40%) in emissions from brass instruments; sound pressure level was not associated with woodwind emissions. Age was not a significant predictor of emissions. The use of bell covers reduced aerosol emissions from three brass instruments tested (trombone, tuba, and trumpet), with average reductions ranging from 53 to 73%, but not for the two woodwind instruments tested (oboe and clarinet). Results from this work can facilitate infectious disease risk management for the performing arts.This work was supported by crowdsourced, philanthropic donations to the School of Music, Theatre, and Dance at Colorado State University