A quantitative model of cookstove variability and field performance: Implications for sample size

L’Orange, Christian; Leith, David; Volckens, John; DeFoort, Morgan

doi:10.1016/j.biombioe.2014.10.031

Cited by 15 publications

(5 citation statements)

References 28 publications

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“…Table 2 presents the summary statistics for the input parameters measured during cooking events for the single‐zone model, as well as the corresponding kitchen concentrations. Ventilation (mean = 17.8 air changes per hour [ACH]; range = 6–73 [ACH]), kitchen volumes (mean = 21.1 m 3 ; range = 5–52 m 3 ), and cooking event durations (mean = 51; range = 7–125 min/event) were generally in line with those used by ISO and WHO, 12,13 as well as other similar modeling exercises 37–39 . Kitchen event concentrations (mean = 886 µg/m 3 ; range of 10–16 161 µg/m 3 for PM 2.5, and mean = 28.6 ppm; range = 0–196 ppm for CO) and 24‐h exposures for PM2.5 (mean = 135 µg/m 3 ; range = 14–686 µg/m 3 ) were reasonable given previous studies' ranges of 24‐h exposures for these fuel user groups 3,40 .…”

Section: Resultssupporting

confidence: 65%

Modeling approaches and performance for estimating personal exposure to household air pollution: A case study in Kenya

et al. 2021

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This study assessed the performance of modeling approaches to estimate personal exposure in Kenyan homes where cooking fuel combustion contributes substantially to household air pollution (HAP). We measured emissions (PM2.5, black carbon, CO); household air pollution (PM2.5, CO); personal exposure (PM2.5, CO); stove use; and behavioral, socioeconomic, and household environmental characteristics (eg, ventilation and kitchen volume). We then applied various modeling approaches: a single‐zone model; indirect exposure models, which combine person‐location and area‐level measurements; and predictive statistical models, including standard linear regression and ensemble machine learning approaches based on a set of predictors such as fuel type, room volume, and others. The single‐zone model was reasonably well‐correlated with measured kitchen concentrations of PM2.5 (R2 = 0.45) and CO (R2 = 0.45), but lacked precision. The best performing regression model used a combination of survey‐based data and physical measurements (R2 = 0.76) and a root mean‐squared error of 85 µg/m3, and the survey‐only‐based regression model was able to predict PM2.5 exposures with an R2 of 0.51. Of the machine learning algorithms evaluated, extreme gradient boosting performed best, with an R2 of 0.57 and RMSE of 98 µg/m3.

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Section: Resultssupporting

confidence: 65%

Modeling approaches and performance for estimating personal exposure to household air pollution: A case study in Kenya

et al. 2021

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“…In addition to those studying the housing stock in broad geographic regions, some studies have focused on homes with specific characteristics (e.g., new homes, energy‐efficient homes, low‐income/public housing; US EPA, ; Zota et al., ). A limited number of studies have characterized ventilation in homes in developing countries (L'Orange et al., and references therein; Williams and Unice, ) (Figure a). The use of solid fuels for cooking and heating, particularly in developing countries, is a leading indoor air quality issue on a global scale, with approximately 4.3 million premature deaths annually attributed to related pollutant exposures (http://www.WHO.int/indoorair/en).…”

Section: Methodsmentioning

confidence: 99%

Indoor inhalation intake fractions of fine particulate matter: review of influencing factors

et al. 2015

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Exposure to fine particulate matter (PM 2.5 ) is a major contributor to the global human disease burden. The indoor environment is of particular importance when considering the health effects associated with PM 2.5 exposures because people spend the majority of their time indoors and PM 2.5 exposures per unit mass emitted indoors are two to three orders of magnitude larger than exposures to outdoor emissions. Variability in indoor PM 2.5 intake fraction ( ), which is defined as the integrated cumulative intake of PM 2.5 per unit of emission, is driven by a combination of building-specific, human-specific, and pollutant-specific factors. Due to a limited availability of data characterizing these factors, however, indoor emissions and intake of PM 2.5are not commonly considered when evaluating the environmental performance of product life cycles. With the aim of addressing this barrier, a literature review was conducted and data characterizing factors influencing were compiled. In addition to providing data for the calculation of in various indoor environments and for a range geographic regions, this paper discusses remaining limitations to the incorporation of PM 2.5 -derived health impacts into life cycle assessments and makes recommendations regarding future research. Accepted ArticleThis article is protected by copyright. All rights reserved. Practical ImplicationsThis paper reviews and summarizes the factors that influence indoor inhalation intake fraction of fine particulate matter, with a focus on primary particle emissions indoors. It provides valuable data for the calculation of indoor inhalation intake fraction for a range of indoor environments and contributes to the effort to incorporate PM 2.5 -derived health impacts into life cycle assessment.

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“…The formal packaging of a device, its fuel, its ignition, and its operation as a system that meets a set of household needs may encourage more realism in laboratory tests and emission projection scenarios. Although the changes we propose account for some causes of variability, they do not assist in confirming improvements when performance is highly variable . Reducing the performance variability itself, rather than seeking to distinguish improvements in a highly variable population, could be a worthy focus for stove programs.…”

Section: Resultsmentioning

confidence: 96%

“…Although the changes we propose account for some causes of variability, they do not assist in confirming improvements when performance is highly variable. 45 Reducing the performance variability itself, rather than seeking to distinguish improvements in a highly variable population, could be a worthy focus for stove programs. Fuel−device combinations with low variability are most likely to achieve significant improvements in environment and human health.…”

Section: Environmental Science and Technologymentioning

confidence: 99%

Field Emission Measurements of Solid Fuel Stoves in Yunnan, China Demonstrate Dominant Causes of Uncertainty in Household Emission Inventories

Thompson

Weyant

et al. 2019

Environ. Sci. Technol.

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Emission factors of carbon monoxide (CO), particulate matter (PM 2.5 ), organic carbon (OC), and elemental carbon (EC), as well as combustion efficiency and particle optical properties were measured during 37 uncontrolled cooking tests of residential stoves in Yunnan Province, China. Fuel mixtures included coal, woody biomass, and agricultural waste. Compared to previously published emission measurements of similar stoves, these measurements have higher CO and PM 2.5 emission factors. Real-time data show two distinct burn phases: a devolatilization phase after fuel addition with high PM 2.5 emissions and a solid-fuel combustion phase with low PM 2.5 emissions. The average emission factors depend on the relative contributions of these phases, which are affected by the services provided by the stoves. Differences in stove and fuel characteristics that are not represented in emission inventories affect the variability of emission factors much more than do the type of solid fuel or stove. In developing inventories with highly variable sources such as residential solid-fuel combustion, we suggest that (1) all fuels should be accounted for, not just the primary fuel; (2) the household service provided should be emphasized rather than specific combinations of solid fuels and devices; and (3) the devolatilization phase should be explicitly measured and represented.

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A quantitative model of cookstove variability and field performance: Implications for sample size

Cited by 15 publications

References 28 publications

Modeling approaches and performance for estimating personal exposure to household air pollution: A case study in Kenya

Modeling approaches and performance for estimating personal exposure to household air pollution: A case study in Kenya

Indoor inhalation intake fractions of fine particulate matter: review of influencing factors

Field Emission Measurements of Solid Fuel Stoves in Yunnan, China Demonstrate Dominant Causes of Uncertainty in Household Emission Inventories

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