Field measurement of natural ventilation rate in an idealised full-scale building located in a staggered urban array: Comparison between tracer gas and pressure-based methods

Gough, Hannah; Luo, Zhiwen; Halios, Christos; King, Marco‐Felipe; Noakes, Catherine; Grimmond, C. S. B.; Barlow, Janet F.; Hoxey, R.P.; Quinn, Andrew

doi:10.1016/j.buildenv.2018.03.055

Cited by 65 publications

(32 citation statements)

References 49 publications

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“…Results for six measurements are shown in Table 5. The slope of linear dependence of the corrected CO2 concentration increases with higher occupant density and ACH, but when CO2 gains are calculated from this slope similar values are obtained in all cases from 0.0043 to 0.0048 L/s, and agree with the theoretical value of Table 1 for young university students (18)(19)(20)(21)(22)(23)(24)(25) year old) with sedentary activity (M = 1.3 MET) with similar male and female contribution. The study of the method reproducibility in the workshop classroom, shows a high discrepancy between the results of both measurements (0.044 vs. 0.048 L/s).…”

Section: Resultssupporting

confidence: 83%

“…Thus a proper ventilation design as well as its regulation during operation process is crucial, with an optimal air flow that allows reasonable energy consumption in buildings also to established, in times of an adequate CO 2 concentration, as well as maintaining a proper microclimate for occupants of the building.There are different methods for estimation of building ventilation, including ACHs and air flow. A large part of them is based in the measurement of CO 2 produced by occupants as a tracer gas [21][22][23]. This type of methods needs the knowledge of CO 2 gain from people.…”

mentioning

confidence: 99%

“…There are different methods for estimation of building ventilation, including ACHs and air flow. A large part of them is based in the measurement of CO 2 produced by occupants as a tracer gas [21][22][23]. This type of methods needs the knowledge of CO 2 gain from people.…”

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confidence: 99%

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Carbon Dioxide Human Gains—A New Approach of the Estimation

Rodero

Krawczyk

2019

Sustainability

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Human health is dependent on the Indoor Air Quality (IAQ) of residential and public buildings, where people spend a substantial amount of time. Part of IAQ parameters, like temperature or humidity influence the thermal comfort of users, whereas too high carbon dioxide concentration (CO 2 ) could cause various complaints or diseases. In buildings like offices and schools, where we have a brush with a high density of users, the main source of CO 2 is simply people. The type of their activity brings higher or lower carbon dioxide gains, that must be taken into account to design and properly use room ventilation, allowing recommended CO 2 levels not to be exceeded. This paper presents an approach to marking human CO 2 generation off by using an experimental method. The method was verified based on measuring results of six test series conducted in different types of rooms at Bialystok University of Technology (Poland) during lectures, meetings, projects and laboratories. Carbon dioxide gains were comparable with an average value of 0.0045 L/s, which corresponds to theoretical CO 2 generation rates that are symptomatic of males and females, between 16 and 30 years old, with low physical activity.Sustainability 2019, 11, 7128 2 of 12 dioxide concentration, is not considered particularly dangerous as such its high level could result in a room occupants becoming drowsy, fatigued and with insufficient ability to concentrate. A review of the literature conducted by Johnsona et al., [8] showed that there was an association between low-level exposure to CO 2 beginning at 700 ppm and building-related symptoms, whereas respiratory symptoms were indicated in children in a case of indoor CO 2 concentrations higher than 1000 ppm. However it was not possible to eliminate other causes of health problems. As highlighted by Meciarova et al., [9], CO 2 is not considered a pollutant of concern, but as an indicator of how well other people-related pollutants are controlled-particularly odor-causing compounds. As underlined by Lu et al., [6], the concentration of CO 2 in office buildings is primarily dependent on occupant density and ventilation rates. It is worth noting that CO 2 concentration is often found above recommendations in buildings with high people density and insufficient ventilation, especially schools [10][11][12][13][14][15][16][17]. Problems with CO 2 concentration in residential buildings were discussed by Mainka et al., [18].Nowadays, the ASHRAE (American Society of Heating, Refrigerating and Air Conditioning Engineers) standard [19] recommends indoor CO 2 concentrations less than 700 ppm above the outdoor concentration, giving also a guideline of 1000 ppm. Thus, the CO 2 recommended level is significantly lower than several years ago. As analyzed by Persily et al., [20] in 1981 the CO 2 limit of 4500 mg/m 3 (2500 ppm) was proposed in an appendix, while in 1989 the value was decreased by 60% to 1800 mg/m 3 (1000 ppm) and such a level was maintained in the 1999 and 2001 versions of the Standard 62.Sufficiently high venti...

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Section: Resultssupporting

confidence: 83%

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confidence: 99%

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Carbon Dioxide Human Gains—A New Approach of the Estimation

Rodero

Krawczyk

2019

Sustainability

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“…This method gave an infiltration rate of 0.078 ± 0.002 (R 2 = 0.91) air changes per hour, consistent with the rubber-sealed windows and minimal air gaps around the door. The value is approximate, given air exchange rates can differ over time due to differences in temperature, wind direction and wind speed [39].…”

Section: Methodsmentioning

confidence: 99%

Using EEG to characterise drowsiness during short duration exposure to elevated indoor Carbon Dioxide concentrations

Snow

Boyson

Felipe-King

et al. 2018

Preprint

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1 Abstract: Drowsiness which can affect work performance, is often elicited through self-2 reporting. This paper demonstrates the potential to use EEG to objectively quantify changes to 3 drowsiness due to poor indoor air quality. Continuous EEG data was recorded from 23 4 treatment group participants subject to artificially raised indoor CO 2 concentrations (average 5 2,700 ± 300 ppm) for approximately 10 minutes and 13 control group participants subject to 6 the same protocol without additional CO 2 (average 830 ± 70 ppm). EEG data were analysed 7 for markers of drowsiness according neurophysiological methods at three stages of the 8 experiment, Baseline, High CO 2 and Post-Ventilation. Treatment group participants' EEG data 9 yielded a closer approximation to drowsiness than that of control group participants during the 10 High CO 2 condition, despite no significant group differences in self-reported sleepiness. Future 11 work is required to determine the persistence of these changes to EEG over longer exposures 12 and to better isolate the specific effect of CO 2 on drowsiness compared to other environmental 13 or physiological factors. 14 15 16 17 18 Practical implications: 19 This study introduces EEG as a potential objective indicator of the effect of indoor 20 environmental conditions upon drowsiness 21  Participants exposed to 2,700 ppm for 10 minutes showed greater evidence of a 22 progression towards drowsiness (as measured by EEG) than that of participants who 23 received the same protocol without additional CO 2 (mean 830 ± 70 ppm), despite 24 similar ratings of subjective sleepiness. 25 Subjective and objectively measured indications of drowsiness were reduced following 26 ventilation of the room. Future work could explore the potential of regular ventilation 27 episodes in knowledge work spaces to retain alertness. 3 28 29 Introduction 30 Being a product of human respiration, carbon dioxide (CO 2 ) increases in indoor spaces when 31 ventilation of the space is insufficient to replace stale air [1,2]. CO 2 is thus a useful indicator 32 of ventilation and, by extension air quality indoors, in occupied spaces [3,4]. A large body of 33 literature exists relating poor ventilation to mild health symptoms [2,5-7] and lowered 34 cognitive performance [4,8-10]. Office-realistic levels of CO 2 are reported to be typically < 35 3,000 ppm, but whether CO 2 itself negatively impacts cognitive performance, or whether other 36 pollutants such as volatile organic compounds (VOCs), including human bio-effluents, are 37 responsible, is still unclear [11,12]. Human performance effects have been recorded in studies 38 both where CO 2 is accompanied by human bio-effluents (e.g. the CO 2 concentration is a 39 product of poor ventilation in occupied spaces) [4,13,14] and where pure CO 2 gas is added to 40 a room to achieve steady-state concentrations [12,13,[15][16][17][18].41 At a room concentration of 3,000 ppm, human bio-effluents are found to cause an increase in 42 respired -end-tidal-CO 2 (ETCO 2 ), increased b...

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“…The tracer decay measurement methodology was followed, in compliance with ISO 16000-8 [18,40], by using CO 2 as tracer gas. The CO 2 concentration and all the environmental conditions were monitored at established time intervals in each survey.…”

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confidence: 99%

Mean Age of Air in Natural Ventilated Buildings: Experimental Evaluation and CO2 Prediction by Artificial Neural Networks

Buratti

Palladino

2020

Applied Sciences

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The mean age of air (MAA) is one of the most useful parameters in evaluating indoor air quality in natural ventilated buildings. Its evaluation is generally based on the CO2 monitoring within the environment; however, other methods can be found in the literature, but they have not always led to satisfactory results. In this context, the present paper is focused on two main topics: the effect of the windows airtightness and of the environmental conditions on MAA and the application of artificial neural network (ANN) for the CO2 prediction within the room. Two case studies (case study 1 located in Terni and case study 2 located in Perugia) were investigated, which differ in geometric dimensions (useful area, volume, window area) and in airtightness of windows. The indoor and outdoor environmental conditions (air temperature, pressure, relative humidity, air velocity, and indoor CO2 concentration) were monitored in 33 experimental campaigns, in four room configurations: open door-open window (OD-OW); closed door-open window (CD-OW); open door-closed window (OD-CW); closed door-closed window (CD-CW). Tracer decay methodology, according to ISO 16000-8:2007 standard, was compiled during all the experimental campaigns. A feedforward ANN, able to simulate the indoor CO2 concentration within the rooms, was then implemented; the monitored environmental conditions (air temperature, pressure, relative humidity, and air velocity), the geometric dimensions (useful area, volume, window area), and the airtightness of windows were provided as input data, while the CO2 concentration was used as target. In particular, data of 19 experimental campaigns were provided for the training process of the network, while 14 were only used for testing the reliability of ANN. The CO2 concentration predicted by ANN was then used for the MAA calculation in the four room configurations. Experimental results show that MAA of case study 2 is always higher, in all the examined configurations, due to the higher airtightness characteristics of the window and to the higher volume of the room. When the difference between indoor and outdoor temperature increases, the MAA increases too, in almost all the investigated configurations. Finally, the CO2 concentration predicted by ANN was compared with experimental data; results show a good accuracy of the network both in CO2 prediction and in the MAA calculation. The predicted CO2 concentration at the beginning of experimental campaigns (time step 0) always differs less than 2% from experimental data, while a mean percentage difference of −18.8% was found considering the maximum CO2 concentration. The MAA calculated using the predicted CO2 of ANN was greater than the one obtained from experimental data, with a difference in the 0.5–1.3 min range, depending on the configuration. According to the results, the developed ANN can be considered an alternative and valuable tool for a preliminary evaluation of MAA.

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Field measurement of natural ventilation rate in an idealised full-scale building located in a staggered urban array: Comparison between tracer gas and pressure-based methods

Cited by 65 publications

References 49 publications

Carbon Dioxide Human Gains—A New Approach of the Estimation

Carbon Dioxide Human Gains—A New Approach of the Estimation

Using EEG to characterise drowsiness during short duration exposure to elevated indoor Carbon Dioxide concentrations

Mean Age of Air in Natural Ventilated Buildings: Experimental Evaluation and CO2 Prediction by Artificial Neural Networks

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