Measuring the Short-Term Emission Rates of Particles in the “Personal Cloud” with Different Clothes and Activity Intensities in a Sealed Chamber

You, Ruoyu; Cui, Weicheng; Chen, Chun; Zhao, Bin

doi:10.4209/aaqr.2012.03.0061

Cited by 91 publications

(69 citation statements)

References 51 publications

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“…This may be explained by active indoor sources, activities/movements of the occupants and reduced ventilation rates as a result of closed doors and windows, so causing accumulation. Among the indoor sources of organic compounds, submicrometer fragments of paper, skin debris, clothing fibres, cleaning products and waxes may be considered (Schneider, 2008;Weschler, 2012;You et al, 2013). Air exchange rates below 1 h -1 , such as those usually registered in Portuguese classrooms, including the school focused in this study , have been reported to contribute to the enrichment of particulate pollution in indoor environments (Abt et al, 2000;RojasBracho et al, 2000).…”

Section: Resultsmentioning

confidence: 99%

Indoor/Outdoor Relationships between PM10 and Associated Organic Compounds in a Primary School

Alves

Urban

Pegas

et al. 2014

Aerosol Air Qual. Res.

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Airborne particulate matter (PM 10 ) samples were collected daily, indoors and outdoors, in a primary school at Aveiro, Portugal, from February 28 to May 27, 2011. The carbonaceous content (organic and elemental carbon) was determined by a thermo-optical technique. The organic speciation of PM 10 was performed by gas chromatography-mass spectrometry. Mean PM 10 levels of 107 and 36 µg/m 3 were obtained in the schoolroom and outdoors, respectively. On average, organic carbon accounted for 30.0% of the mass of PM 10 indoors, whereas a lower mass fraction of 21.3% was found outdoors. The lack of correlation between indoor and outdoor organic carbon and the much higher indoor levels suggest significant contributions by indoor sources. The most abundant organic compound classes were acids, sugars, polyols and n-alkanes. Infiltration of outdoor particles leads to contamination of the schoolroom with vehicle emissions, as well as emissions related to the burning of biofuel in nearby restaurants and bakeries. However, the much higher indoor concentrations than in the outdoor air for the majority of compounds suggest that the origin of much of the particulate matter is from within the school building (due to school activities and materials, skin debris, microorganisms, and so on), and this also includes the formation of secondary organic aerosols. Based on the concentrations of polycyclic aromatic hydrocarbons, a negligible cancer risk was estimated in relation to the air within the school.

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Section: Resultsmentioning

confidence: 99%

Indoor/Outdoor Relationships between PM10 and Associated Organic Compounds in a Primary School

Alves

Urban

Pegas

et al. 2014

Aerosol Air Qual. Res.

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“…Only a few studies have quantified human emissions of total and biological airborne particles with a high temporal and particlesize resolution. [13][14][15] You et al 13 identified a positive correlation between the vigour of human activity and the emission rates of total airborne particles. Bhangar et al 14,15 used a laserinduced fluorescence-based ultraviolet aerodynamic particle sizer (UV-APS) to quantify human emission rates of fluorescent biological aerosol particles and reported that human skin and clothes are important sources indoors.…”

Section: Accepted Articlementioning

confidence: 99%

Emission rates and the personal cloud effect associated with particle release from the perihuman environment

2017

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Inhalation exposure to elevated particulate matter levels is correlated with deleterious health and well-being outcomes. Despite growing evidence that identifies humans as sources of coarse airborne particles, the extent to which personal exposures are influenced by particle releases near occupants is unknown. In a controlled chamber, we monitored airborne total particle levels with high temporal and particle-size resolution for a range of simulated occupant activities. We also sampled directly from the subject's breathing zone to characterize exposures. A material-balance model showed that a sitting occupant released 8 million particles/h in the diameter range 1-10 µm. Elevated emissions were associated with increased intensity of upper body movements and with walking. Emissions were correlated with exposure, but not linearly. The personal PM 10 exposure increment above the roomaverage levels was 1.6-13 µg/m 3 during sitting, owing to spatial heterogeneity of particulate matter concentrations, a feature that was absent during walking. The personal cloud was more discernible among larger particles, as would be expected for shedding from skin and clothing. Manipulating papers and clothing fabric was a strong source of airborne particles. An increase in personal exposure was observed owing to particle mass exchange associated with a second room occupant. Keywords Human emissions, Personal exposure, Activity type, Particle size distribution, Crosscontamination, Particle sources Accepted ArticleThis article is protected by copyright. All rights reserved. Practical implicationsBetter understanding of the nature, scope and significance of human personal clouds is valuable for enhanced prediction and control of personal exposure, which is important in relation to how indoor air quality influences human health. The results of this work are of potential use in indoor air quality models and for improved ventilation design. The work also contributes to a better understanding of human occupants as sources of airborne particles, which could strengthen the design and interpretation of future air pollution exposure studies.

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“…Indoor exposure to particles is especially important, because people spend most of their time indoors (Klepeis et al 2001). The sources of indoor particles include ambient particles (Chen and Zhao 2011), cigarette smoking (Spengler et al 1981), cooking (Ozkaynak et al 1996), human body emission (You et al 2013), human exhalation (Nicas et al 2005), and ozone reactions (Weschler 2006). Many studies assume that particle concentrations are uniform indoors, which may lead to significant errors in personal exposure assessments (Li et al 2005).…”

Section: Introductionmentioning

confidence: 99%

Accelerating the Lagrangian Method for Modeling Transient Particle Transport in Indoor Environments

Chen

Liu

Lin

et al. 2015

Aerosol Science and Technology

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Computational fluid dynamics (CFD) with the Lagrangian method has been widely used in predicting transient particle transport in indoor environments. The Lagrangian method calculates the trajectories of individual particles on the basis of Newton's law. Statistically speaking, a large number of particles are needed in the calculations in order to ensure accuracy. Traditionally, modelers have conducted an independence test in order to find a reasonable value for this particle number. However, the unguided process of an independence test can be highly time-consuming when no simple method is available for estimating the necessary particle number. Therefore, this investigation developed a method for estimating the necessary particle number in the Lagrangian method. Furthermore, the computing cost of the Lagrangian method is positively associated with the particle number. If this number is too large, the computing cost may not be affordable. Thus, this study proposed the superimposition and time-averaging methods to reduce the necessary particle number. This investigation designed multiple cases to verify the proposed methods. The verification results show that the estimation method can provide the necessary particle number with a reasonable magnitude. Moreover, the superimposition method can reduce the necessary particle number when the particle source duration is relatively long. On the other hand, the time-averaging method can reduce the necessary particle number by up to 30 times. When compared with experimental data, predictions of transient particle transport in indoor environments by the combined Lagrangian, superimposition, and time-averaging method with the estimated particle number are reasonably accurate.

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Measuring the Short-Term Emission Rates of Particles in the “Personal Cloud” with Different Clothes and Activity Intensities in a Sealed Chamber

Cited by 91 publications

References 51 publications

Indoor/Outdoor Relationships between PM10 and Associated Organic Compounds in a Primary School

Indoor/Outdoor Relationships between PM10 and Associated Organic Compounds in a Primary School

Emission rates and the personal cloud effect associated with particle release from the perihuman environment

Accelerating the Lagrangian Method for Modeling Transient Particle Transport in Indoor Environments

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