sustainability
Article Carbon Dioxide Human Gains—A New Approach of the Estimation
Antonio Rodero 1 and Dorota Anna Krawczyk 2,*
1 School of Engineering Sciences of Belmez, University of Cordoba, 14071 Córdoba, Spain; [email protected] 2 Faculty of Civil and Environmental Engineering, Bialystok University of Technology, 15-351 Bialystok, Poland * Correspondence: [email protected]; Tel.: +48-85-797-995-926
Received: 28 October 2019; Accepted: 6 December 2019; Published: 12 December 2019
Abstract: 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 (CO2) 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 CO2 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 CO2 levels not to be exceeded. This paper presents an approach to marking human CO2 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 CO2 generation rates that are symptomatic of males and females, between 16 and 30 years old, with low physical activity.
Keywords: indoor air quality; CO2 concentration; CO2 gains per person; human CO2 generation; ventilation; sick building symptoms (SBS); physical activity; occupation
1. Introduction Indoor Air Quality (IAQ) is an important indicator in the sustainability analysis of a building, included in social benefit and usability categories [1], but also in economic aspects, due to its possible influence on the ventilation of the building and the corresponding energy consumption. Maintaining a proper quality of indoor air in buildings is very important for their users. There are lots of factors that influence IAQ, as shown in [1]: including physical, chemical, and biological parameters as well as inadequate ventilation, indoor sources of air pollution like HVAC (heating, ventilation and air conditioning) systems, building equipment, furnishings, and human activities. As described in the literature [2–5], a long-term staying in an environment with a bad IAQ results in the occurrence of different complaints: headaches, eyes and skin irritation, nausea, dizziness etc. The main reason for the poor IAQ in buildings is the high CO2 concentration in rooms, produced by the high human occupancy and inadequate ventilation. The sick building syndrome (SBS) symptoms noted among the users of multi-story building exhibited a strong association with carbon dioxide (CO2) concentrations, that during the research were in a range between 467 to 2800 ppm [6]. Authors found that tiredness and dizziness were associated with CO2 level, CO2 concentrations was irrelevant to respiratory, eye or skin symptoms. On the other hand, results described by Apte et al., [7] indicated association between CO2 concentration and the SBS symptoms like sore throat, stuffy nose, chest tightness and wheezing. Therefore, even though carbon
Sustainability 2019, 11, 7128; doi:10.3390/su11247128 www.mdpi.com/journal/sustainability 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 CO2 beginning at 700 ppm and building-related symptoms, whereas respiratory symptoms were indicated in children in a case of indoor CO2 concentrations higher than 1000 ppm. However it was not possible to eliminate other causes of health problems. As highlighted by Meciarova et al., [9], CO2 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 CO2 in office buildings is primarily dependent on occupant density and ventilation rates. It is worth noting that CO2 concentration is often found above recommendations in buildings with high people density and insufficient ventilation, especially schools [10–17]. Problems with CO2 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 CO2 concentrations less than 700 ppm above the outdoor concentration, giving also a guideline of 1000 ppm. Thus, the CO2 recommended level is significantly 3 lower than several years ago. As analyzed by Persily et al., [20] in 1981 the CO2 limit of 4500 mg/m (2500 ppm) was proposed in an appendix, while in 1989 the value was decreased by 60% to 1800 mg/m3 (1000 ppm) and such a level was maintained in the 1999 and 2001 versions of the Standard 62. Sufficiently high ventilation is needed to remove air pollutants, including CO2, and avoid health problems. However, high ventilation and ACH (air change rates per hour) increases the energy consumption of the building. 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 CO2 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 CO2 produced by occupants as a tracer gas [21–23]. This type of methods needs the knowledge of CO2 gain from people. A better knowledge of this parameter will allow us to have better information about the ventilation and air renovation rate in the building and improve the design and regulation of the ventilation systems. Currently, the CO2 gains from people are estimated from a metabolic approach (see Section2). In this approach, CO2 gains depend on age, gender, activity and metabolism. In a big group with diverse age and gender composition, the variability of occupants could affect to the accuracy of results. Uncertainties of activity and metabolism can have even greater influence as evidenced by Batterman et al., [24]. Batterman et al., also studied the influence of prior activity on the method. So, a method is needed to obtain more realistic estimates of CO2 gains and which could also be representative for each condition. To the best knowledge of the authors, contrary to the widely established methods based on metabolic calculations, there is a lack of experimental methods that can be used with the purpose of obtaining direct representatives CO2 gains from people in buildings and allow the obtained results to be compared with the theoretical metabolic values in each condition. This work presents a new experimental method for the determination of this CO2 gain based on measurements of the dependence of CO2 concentration on time. The method was tested in six educational rooms with different size, occupancy and age. Results are compared with metabolic values given in the literature.
2. Theoretical CO2 Gains from People The changes of carbon dioxide concentration in buildings occupied by many users are mainly influenced by CO2 gains from people, that depend on their activity. Sustainability 2019, 11, 7128 3 of 12
As shown in the literature [24–26], a metabolic rate depends on many factors, for example: temperature and its variation, gender or garbing type. According to Ainsworth et al., [27] we can estimate the CO2 gains from people from the Equation (1):
g[L/s] = 0.000569 RQ BMR M (1) where: RQ is the respiratory ratio, BMR is a basal metabolic rate in MJ/day that is the amount of energy that one’s body needs to accomplish its most basic life-sustaining functions, and M is the metabolic rate in MET [28,29]. Table1 shows the BMR and CO 2 gains from a person in L/s for adults with ages between 16 and 60 years old in sedentary activity, M = 1.3 MET.
Table 1. Basal metabolic rate (BMR) and CO2 gains from 16 to 60 years old (y.o.) people with a metabolic rate of M = 1.3 MET.
Age 16–21 (y.o.) 21–30 y.o. 30–40 y.o. 40–50 y.o. 50–60 y.o BMR = 7.77 BMR = 8.24 BMR = 7.83 BMR = 8.00 BMR = 7.95 Male MJ/day MJ/day MJ/day MJ/day MJ/day g = 0.0049 L/s g = 0.0052 L/s g = 0.00495 L/s g = 0.0050 L/s g = 0.0050 L/s BMR = 6.12 BMR = 6.49 BMR = 6.08 BMR = 6.16 BMR = 6.17 Female MJ/day MJ/day MJ/day MJ/day MJ/day g = 0.0039 L/s g = 0.0041 L/s g = 0.0038 L/s g = 0.0039 L/s g = 0.0039 L/s Similar Contribution g = 0.0044 L/s g = 0.00465 L/s g = 0.0044 L/s g = 0.00445 L/s g = 0.00445 L/s (Male+Female) Note: y.o. stands for year-old.
Some authors use RQ = 0.85, while others, following recommendations given in papers [8,24] and assuming RQ = 0.83, use an expression for CO2 volumetric generation rate g for school age children (the Equation (2)): g = 1.4304 S MET (2) where S is the Dubois surface area in m2, depending on height and weight, and MET is the metabolic rate per unit of surface area in W/m2 (1.4 for children and 1.7 for adults). Factors influencing MET value and errors in its estimations were discussed by Kozey et al., [30] and Plowman and Smith [31]. CO2 gains from children were estimated as 0.216–0.217 L/min, while the values for adult males and females were found to be 0.500 and 0.442 L/min, respectively [8]. It is worth noting, that as presented by Qi et al., [32], widely used methods require good estimates of human CO2 generation rates because some indicators in the equations are not valid. Results of their research on Chinese people at positions of quiet sitting and relaxed standing, showed an over-prediction of CO2 generation rates, that could be corrected with a factor of 0.75 for Chinese females and of 0.85 for Chinese males. This example shows that more research in this field is needed.
3. Materials and Method
3.1. Method for Determination of CO2 Gains from People
Our method is based on the evolution of CO2 concentration in indoor air. Previous studies have been described in [33,34] and presented as a useful e-tool [35]: ! T Pin(t=0) R P = in [ + + out ] CCO2in CCO2in(t=0) gN CCO2out ACH t (3) Pin(1 + ACH t) Tin(t=0) µCO2 V Tout
1 where ACH is the air change rate in h− , t is time in h, P is pressure in Pa, CCO2in and CCO2out are carbon dioxide concentrations in indoor and outdoor air, respectively, in ppm, V is the room volume in m3, Sustainability 2020, 11, x FOR PEER REVIEW 4 of 12
According this equation, CO2 concentration in a building is continuously for low ventilation. Higher ventilation produces a reduction of this concentration and stabilization at a given value (FigureSustainability 1a). 2019As ,a11 result, 7128 of multiplying the CO2 concentration by a factor (1 + ACH t) a linear4 of 12 dependence on time is obtained (Equation 4) when indoor temperature and pressure remain approximately constant (large rooms) (Figure 1b): g are CO2 human gains in g/(h person), N is the number of occupants, T is the temperature in K, while R and µCO2 are constants, the gas constant and CO𝑇 2 molar𝑅 mass respectively.𝑃 𝐶 (1 + 𝐴𝐶𝐻 𝑡) =𝐶 ( ) + 𝑔𝑁 +𝐶 𝐴𝐶𝐻 𝑡 (4) According this equation, CO2 concentration𝑃 in a building𝜇 𝑉 is continuously𝑇 for low ventilation. Higher ventilation produces a reduction of this concentration and stabilization at a given value The slope of this linear dependence on time, m, is given by Equation (5): (Figure1a). As a result of multiplying the CO 2 concentration by a factor (1 + ACH t) a linear dependence on time is obtained (Equation (4))𝑇 when indoor𝑅 temperature𝑃 and pressure remain approximately 𝑚= 𝑔𝑁 +𝐶 𝐴𝐶𝐻 (5) constant (large rooms) (Figure1b): 𝑃 𝜇 𝑉 𝑇 ! T R P ( + ) = + in + out CCO2in 1 ACH t CCO2in(t=0) gN CCO2out ACH t (4) Pin µCO2 V Tout
4000 12,000 -1 ACH = 0.1 h-1 ACH = 5 h -1 3600 0.5 h-1 4 h 10,000 -1 3200 3 h 2 h-1 8,000 -1 2800 1 h 1 h-1 0.5 h-1 2400 -1 6,000 0.1 h (ppm)
2 2000 2 h-1
CO 4,000 1600
-1 (ppm) (1+ACH*t) 3 h 2 1200 -1 2,000
4 h CO 5 h-1 800 0 0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0 2.5 t (h) t(h)
(a) (b)
FigureFigure 1. Theoretical 1. Theoretical time time variation variation of ofCO CO2 concentration2 concentration (a) ( aand) and CO CO2 concentration2 concentration multiplied multiplied byby the the factorfactor (1 (1+ ACH+ ACH t), ( t),b)( bfor) for different different ACH ACH values. values.
3.2. ExperimentalThe slope Measurement of this linear Method dependence on time, m, is given by Equation (5): ! To verify the method, we conductedT measurementsR in severalP rooms located in different parts m = in gN + C out ACH (5) of a building that belongs to the Faculty of EnvironmentalCO2out and Civil Engineering (Bialystok Pin µCO2 V Tout University of Technology, Poland). The experiment was carried out in the meeting rooms, computer classrooms,3.2. Experimental a project Measurement room and a Method workshop classroom (Figure 2). Physical activity was sedentary in every case, from passive participation in lectures to a slight locomotion during workshops. In the To verify the method, we conducted measurements in several rooms located in different parts of a experiment as many men as women took part. building that belongs to the Faculty of Environmental and Civil Engineering (Bialystok University of There was a natural ventilation in the meeting, computer and project rooms, thus air inflow was Technology, Poland). The experiment was carried out in the meeting rooms, computer classrooms, a in the area of external windows (in all cases windows occupied one external wall) and air exhaust project room and a workshop classroom (Figure2). Physical activity was sedentary in every case, from was provided by outlets in the opposite walls, near ceilings. In order to determine the effect of passive participation in lectures to a slight locomotion during workshops. In the experiment as many ventilation on the results of the methods, rooms had variation in the quality and tightness of men as women took part. windows, in the location in terms of cardinal directions and in the wind power, however the range of ACH was in a range from 0.3 h−1 to 0.55 h−1. Thus, an additionally workshop room with mechanical ventilation (ceilings diffusers and extractors) was selected. It allowed a higher ACH (1.15 h−1), constant during both test series, to be obtained. During the tests the ACH was estimated based on airflow (measurements of exhaust air velocity and area of the stack ventilation grids) and room volume. Also, measurements with a different density of occupants (from 0.066 to 0.191 people/m3) and volume (from 88.9 to 308.8 m3) are presented and analyzed. Finally, the method was repeated twice Sustainability 2020, 11, x FOR PEER REVIEW 5 of 12
Sustainabilityunder similar2019 ,experimental11, 7128 conditions, to determinate its repeatability. Characteristics of the tested5 of 12 rooms are shown in Table 2.
(a) (b)
(c) (d)
(e)
Figure 2. Photos of tested rooms: ( (aa)) Computer Computer classroom classroom 1, 1, ( (b)) Computer classroom 2, ( c) Project Room, ( d) Meeting Room, (e) Workshop classroom.classroom.
There was a natural ventilation in the meeting, computer and project rooms, thus air inflow was in the area of external windows (in all cases windows occupied one external wall) and air exhaust was provided by outlets in the opposite walls, near ceilings. In order to determine the effect of ventilation on the results of the methods, rooms had variation in the quality and tightness of windows, in the location in terms of cardinal directions and in the wind power, however the range of ACH was in a range 1 1 from 0.3 h− to 0.55 h− . Thus, an additionally workshop room with mechanical ventilation (ceilings 1 diffusers and extractors) was selected. It allowed a higher ACH (1.15 h− ), constant during both test series, to be obtained. During the tests the ACH was estimated based on airflow (measurements of exhaust air velocity and area of the stack ventilation grids) and room volume. Also, measurements with a different density of occupants (from 0.066 to 0.191 people/m3) and volume (from 88.9 to 308.8 m3) are presented and analyzed. Finally, the method was repeated twice Sustainability 2020, 11, x FOR PEER REVIEW 6 of 12
Sustainability 2019, 11, 7128 Table 2. Parameters of tested rooms. 6 of 12 Density of Percentage Age of Volume Number of ACH (h−1) Occupants Male/Female Occupants (m3) People under similar experimental conditions, to determinate its repeatability.(m−3) Characteristics(%) of(Year the Old) tested computer classroom 1 0.3 215 15 0.070 48 18–25 roomscomputer are shown classroom in Table 2 2. 0.33 212.6 14 0.066 53 18–25 project room 0.4 217 17 0.078 55 18–25 meeting room 0.55 Table 308.82. Parameters 55 of tested rooms. 0.178 60 30–65 workshop classroom—two tests 1.15 88.9 17 0.191 47 18–25 Density of Percentage Age of ACH Volume Number of 1 3 Occupants Male/Female Occupants Measurements were conducted(h− ) using(m a )multi-funcPeopletion meter TSI3 9565 (Figure 3 and Table 3). (m− ) (%) (Year Old) The equipment location was fixed after taking into account recommendations of Brulinska et al., computer classroom 1 0.3 215 15 0.070 48 18–25 [36] thatcomputer claimed classroom the most 2 favourable 0.33 positioning 212.6 of the 14 carbon dioxide 0.066 sensor 53to be in the 18–25centre of the room, projectfar from room heat sources like 0.4 radiators, 217 walls etc. 17 Also, Oliveira 0.078 et al., [37] 55 demonstrated 18–25 that meeting room 0.55 308.8 55 0.178 60 30–65 CO2 concentration measurements in different sensor positions revealed low variability, that is always workshop classroom—two tests 1.15 88.9 17 0.191 47 18–25 lower that the sensibility of the sensor. This fact confirms that this central position is representative of CO2 concentration inside the room, thus, the multi-function meter was placed on a stand in the roomMeasurements center, about 1.0 were m above conducted the floor. using a multi-function meter TSI 9565 (Figure3 and Table3).
Figure 3. Multi-function meter TSI 9565.9565.
Table 3. Basic parameters of the multi-function meter.
ParameterParameter RangeRange Precision Precision Temperature between −10 and +60 °C ±0.1 °C Temperature between 10 and +60 ◦C 0.1 ◦C Carbon dioxide between− +0 and +5000 ppm ±50 ppm± (±3%) Carbon dioxide between +0 and +5000 ppm 50 ppm ( 3%) Humidity between 5% and 95% ± ±3% ± Humidity between 5% and 95% 3% Pressure between −3757 and +3757 Pa ±1 hPa± Pressure between 3757 and +3757 Pa 1 hPa Velocity between− 0.25 do 30 m/s ±0.1 m/s± Velocity between 0.25 do 30 m/s 0.1 m/s ± The multifunction meter was also used to determined atmospheric conditions during measurements:The equipment indoor, location and outdoor was fixed pr afteressure taking and intotemperature, account recommendationsoutdoor CO2 concentration, of Brulinska etc., et al.,which [36 ]were that claimednecessary the to most determine favourable the CO positioning2 gains by ofpeople the carbon according dioxide to Equations sensor to be5. Values in the centre of these of theparameters room, far are from shown heat in sources Table 4. like radiators, walls etc. Also, Oliveira et al., [37] demonstrated that CO2 concentration measurements in different sensor positions revealed low variability, that is always lower that the sensibilityTable of the 4. sensor. Atmospheric This fact conditions confirms during that thismeasurements central position. is representative of CO concentration inside the room, thus, the multi-function meter was placed on a stand in the room 2 Indoor Outdoor Outdoor Outdoor CO2 Indoor center, about 1.0 m above the floor. Presure Pressure (hPa) Temperature (K) Concentration (ppm) Temperture (K) The multifunction meter was also used to determined atmospheric(hPa) conditions during computer measurements: indoor,1017 and outdoor pressure270 and temperature, 390 outdoor 1017.5 CO2 concentration,298 etc., classroom 1 which were necessary to determine the CO gains by people according to Equations 5. Values of these computer 2 975 266 400 977 297 parametersclassroom are 2 shown in Table4. project room 976 264 400 976 297 meeting room 1020 269 390 1020 295 Sustainability 2019, 11, 7128 7 of 12
Table 4. Atmospheric conditions during measurements. Sustainability 2020, 11, x FOR PEER REVIEW 7 of 12 Outdoor Outdoor CO Indoor Outdoor 2 Indoor Presure Temperature Concentration Temperture Pressure (hPa) (hPa) Sustainabilityworkshop 2020 , 11, x FOR PEER REVIEW (K) (ppm) (K) 7 of 12 classroom—two 976 271 404 976 298 computer classroom 1 1017 270 390 1017.5 298 tests workshopcomputer classroom 2 975 266 400 977 297 classroom—twoproject room 976 976271 264 404 400 976 976 297298 4. Resultstestsmeeting and Discussion room 1020 269 390 1020 295 workshop classroom—two tests 976 271 404 976 298 Temporal variations of CO2 concentrations and the variation corrected by multiplication of the 4. Results and Discussion 4.factor Results (1 + andACH Discussion t) for five rooms are presented in Figures 4–8 together with linear fitting. In the case of theTemporal workshop variations classroom, of COmeasurements2 concentrations were and repeated the variation twice with corrected similar by conditions multiplication in order of the to Temporal variations of CO concentrations and the variation corrected by multiplication of the factordetermine (1 + ACHthe method t) for five of reproducibility. rooms2 are presented in Figures 4–8 together with linear fitting. In the case factor (1 + ACH t) for five rooms are presented in Figures4–8 together with linear fitting. In the case of theIt workshop is apparent classroom, that the time measurements variation of were CO2 reconcentrationpeated twice has with a linearsimilar behaviour conditions in inthe order case toof of the workshop classroom, measurements were repeated twice with similar conditions in order to determinelow ventilation, the method but when of reproducibility. ACH is increased this growing behaviour tends to stabilize with the effect determine the method of reproducibility. of airIt change. is apparent In spite that of the this, time when variation corrections of CO of2 concentration ACH are made, has the a linear linear behaviour behaviour in predicted the case byof It is apparent that the time variation of CO concentration has a linear behaviour in the case of lowEquation ventilation, (3) is fulfilled but when for ACH all cases. is increased The correlati this2 growingon factor behaviour is higher tends at 0.99 to instabilize six measurements. with the effect A low ventilation, but when ACH is increased this growing behaviour tends to stabilize with the effect ofslope air change.of the linear In spite fitting, of this, m, allows when correctionsus to obtain of the ACH CO 2are gains made, g from the Equationlinear behaviour (5). predicted by ofEquation air change. (3) is Infulfilled spite of for this, all whencases. correctionsThe correlati ofon ACH factor are is made,higher theat 0.99 linear in behavioursix measurements. predicted A byslope Equation of the linear (3) is fulfilledfitting, m, for allows all cases. us to The obtain correlation the CO2 factor gains isg higherfrom Equation at 0.99 in (5). six measurements. A slope of the linear fitting, m, allows us to obtain the CO2 gains g from Equation (5).
Figure 4. Temporal variations of CO2 concentrations and variation corrected by the factor (1+ ACH t)
Figurefor the4. computerTemporal classroom variations 1. of CO2 concentrations and variation corrected by the factor (1+ ACH t) forFigure the computer4. Temporal classroom variations 1. of CO2 concentrations and variation corrected by the factor (1+ ACH t) for the computer classroom 1.
Figure 5. Temporal variation of CO2 concentrations and variation corrected by the factor (1+ ACH t) forFigure the computer 5. Temporal classroom variation 2. of CO2 concentrations and variation corrected by the factor (1+ ACH t) for the computer classroom 2. Figure 5. Temporal variation of CO2 concentrations and variation corrected by the factor (1+ ACH t)
for the computer classroom 2.
1400 1800
Experimental data 1300 1600 Linear fitting 1200 1400 1800 1400 1100 Experimental data 1300 1600 r = 0.99527 1200 Linear fitting
1000 1200 (ppm) 2 900 1400
CO 1100 1000 (1+ACH t) (ppm) t) (1+ACH 2 r = 0.99527 800
CO 1200
1000 800 (ppm) 2 700 900 m = 1646 ± 28 ppm/h
CO 1000 (1+ACH t) (ppm) t) (1+ACH 600 2 600 800 0.00.10.20.30.40.50.6 0.0 0.1 0.2 0.3 0.4 0.5 0.6 CO 800 t(h) t (h) 700 m = 1646 ± 28 ppm/h 600 600 0.00.10.20.30.40.50.6 0.0 0.1 0.2 0.3 0.4 0.5 0.6 t(h) t (h) Figure 6. Temporal variation of CO2 concentrations and this variation corrected the factor (1+ ACH t) for the project room. Figure 6. Temporal variation of CO2 concentrations and this variation corrected the factor (1+ ACH t) for the project room. Sustainability 2020, 11, x FOR PEER REVIEW 7 of 12
workshop classroom—two 976 271 404 976 298 tests
4. Results and Discussion
Temporal variations of CO2 concentrations and the variation corrected by multiplication of the factor (1 + ACH t) for five rooms are presented in Figures 4–8 together with linear fitting. In the case of the workshop classroom, measurements were repeated twice with similar conditions in order to determine the method of reproducibility. It is apparent that the time variation of CO2 concentration has a linear behaviour in the case of low ventilation, but when ACH is increased this growing behaviour tends to stabilize with the effect of air change. In spite of this, when corrections of ACH are made, the linear behaviour predicted by Equation (3) is fulfilled for all cases. The correlation factor is higher at 0.99 in six measurements. A slope of the linear fitting, m, allows us to obtain the CO2 gains g from Equation (5).
Sustainability 2020, 11, x FOR PEER REVIEW 8 of 12 Figure 4. Temporal variations of CO2 concentrations and variation corrected by the factor (1+ ACH t) for the computer classroom 1.
3500 4000
3500 Experimental data 3000 Linear fitting 3000 2500 r = 0.99927 2500
2000 (ppm) 2 2000 CO
1500 (ppm) t) (1+ACH 2 1500 CO 1000 1000 m = 3393 ± 18 ppm/h 500 500 0.0 0.5 1.0 1.5 2.0 0.0 0.2 0.4 0.6 0.8 1.0 t(h) t(h)
Figure 7. Temporal variation of CO2 concentrations and this variation corrected the factor (1+ ACH t) Figure 5. Temporal variation of CO2 concentrations and variation corrected by the factor (1+ ACH t) for the meeting room (Source: Rodero and Krawczyk [34]). Sustainabilityfor the2019 computer, 11, 7128 classroom 2. 8 of 12 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 1400 1800
Experimental data are calculated from1300 this slope similar values are obtained1600 in all cases from 0.0043 to 0.0048 L/s, and Linear fitting agree with the theoretical1200 value of Table 1 for young university students (18–25 year old) with 1400 1100 sedentary activity (M = 1.3 MET) with similar male and female contribution.r = 0.99527 1200
1000
(ppm) 2 900
CO 1000 (1+ACH t) (ppm) t) (1+ACH 2 2 800 Table 5. Results of CO gains from a person (g) calculations. CO 800 700 Slope m (ppm/h) g (g/h) g ACHm = (L/s)1646 ± 28 ppm/h 600 600 computer0.00.10.20.30.40.50.6 classroom 1 1286 ± 11 29.980.0 ± 0.28 0.1 0.2 0.00425 0.3 0.4± 0.00004 0.5 0.6 computer classroomt(h) 2 1365 ± 11 32.16 ± 0.29 0.00455t (h) ± 0.00004 Sustainability 2020, 11, x FORproject PEER REVIEWroom 1646 ± 28 31.84 ± 0.61 0.00450 ± 0.00009 8 of 12 Figure 6. Temporal variation of CO2 concentrations and this variation corrected the factor (1+ ACH t) meeting room 3393 ± 18 32.45 ± 0.18 0.00459 ± 0.00003 for the project room. Figure 6. Temporalworkshop variation room of test CO 1 2 concentrations 4178 ± 36 and 33.69this variatio ± 0.33 n 0.00476 corrected ± 0.00005 the factor (1+ ACH t)
for the project room.workshop room test 2 3865 ± 32 30.79 ± 0.30 0.00435 ± 0.00004 3500 4000
3500 Experimental data The study of3000 the method reproducibility in the workshop Linear classroom, fitting shows a high discrepancy 3000 2500 r = 0.99927 between the results of both measurements (0.044 vs. 0.0482500 L/s). The cause of this discrepancy could
2000 (ppm) be the different2 activity of room occupants before2000 beginning of measurements. According to CO
1500 (ppm) t) (1+ACH Batterman et al., [24], type of previous activity can have2 1500 a significant effect on cumulative methods CO 1000 1000 as s prior activity has a high effect in accumulative methods, as the purposem = 3393 ± 18 ppm/h of this paper. Both 500 500 repeated measurements0.0 were 0.5 not 1.0 made 1.5 at 2.0 the same instant.0.0 0.2They 0.4 were 0.6 performed 0.8 1.0 during two consecutive one hour classes witht(h) a break between them. A comparisont(h) of the measured data of
FigureFigure 8 shows 7. Temporal that it is variation at the beginning of CO2 concentrations where both and curves this variation have different corrected behaviors. the factor (1+ ACH t) forFigure the meeting7. Temporal room variation (Source: of Rodero CO2 concentrations and Krawczyk and [34 ]).this variation corrected the factor (1+ ACH t)
for the meeting room (Source: Rodero and Krawczyk [34]). 6000 2200 2000 Test 1 5000 Test 1 Results for six measurements Test 2 are shown in Table 5. The Test slope2 of linear dependence of the 1800 corrected CO2 concentration1600 increases with higher occupant4000 density and ACH, but when CO2 gains 1400 3000 are calculated from1200 this slope similar values are obtained in all cases from 0.0043 to 0.0048 L/s, and (ppm) 2 1000 agree with theCO theoretical value of Table 1 for young2000 university students Linear fitting(18–25 Test 1 year old) with (1+ACH*t) (ppm)
800 2 m = 4178 ± 36 ppm/h
sedentary activity600 (M = 1.3 MET) with similar male andCO 1000 female contribution. Linear Fitting Test 2 400 m = 3865 ± 32 ppm/h 0 200 0.0Table 0.2 0.4 5. 0.6Results 0.8 1.0of CO 1.22 gains 1.4 from a person0.0 0.2 (g) 0.4 calculations. 0.6 0.8 1.0 1.2 1.4 t(h) t(h) Slope m (ppm/h) g (g/h) g ACH (L/s) Figure 8. Temporal variation of CO concentrations and this variation corrected by the factor computer classroom 1 2 1286 ± 11 29.98 ± 0.28 0.00425 ± 0.00004 Figure 8. Temporal variation of CO2 concentrations and this variation corrected by the factor (1+ACHt) for thecomputer workshop classroom classroom. 2 Test 1365 1 and± 11 2 represent 32.16 ± repetition0.29 0.00455 of measurements± 0.00004 to study method(1+ACHt) reproducibility. for the workshopproject room classroom. Test 1646 1 and± 28 2 represent 31.84 ± 0.61repetition 0.00450 of ±measurements 0.00009 to study method reproducibility.meeting room 3393 ± 18 32.45 ± 0.18 0.00459 ± 0.00003 Results for six measurementsworkshop room test are 1 shown 4178 in Table ± 36 5. The 33.69 slope ± 0.33 of linear 0.00476 dependence ± 0.00005 of the corrected workshop room test 2 3865 ± 32 30.79 ± 0.30 0.00435 ± 0.00004 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 theoreticalThe study value of of the Table method1 for young reproducibility university in students the workshop (18–25 year classroom, old) with shows sedentary a high activity discrepancy (M = 1.3between MET) withthe results similar of male both and measurements female contribution. (0.044 vs. 0.048 L/s). The cause of this discrepancy could be the different activity of room occupants before beginning of measurements. According to
Batterman et al., [24], typeTable of 5. previousResults of activity CO2 gains can from have a person a significant (g) calculations. effect on cumulative methods as s prior activity has a high effect in accumulative methods, as the purpose of this paper. Both repeated measurements were notSlope made m at (ppm the/h) same instant. g (g/h) They were gperformed ACH (L/s) during two computer classroom 1 1286 11 29.98 0.28 0.00425 0.00004 consecutive one hour classes with a break± between them. A± comparison of the± measured data of computer classroom 2 1365 11 32.16 0.29 0.00455 0.00004 Figure 8 shows that it is at the beginning where± both curves have± different behaviors.± project room 1646 28 31.84 0.61 0.00450 0.00009 ± ± ± meeting room 3393 18 32.45 0.18 0.00459 0.00003
± ± ± workshop room test 1 4178 366000 33.69 0.33 0.00476 0.00005 2200 ± ± ± workshop room test 2 3865 32 30.79 0.30 0.00435 0.00004 2000 Test 1 5000 Test 1 Test 2 ± ± Test 2 ± 1800 1600 4000 1400 3000
1200 (ppm) 2 1000 CO 2000 Linear fitting Test 1 (1+ACH*t) (ppm)
800 2 m = 4178 ± 36 ppm/h
600 CO 1000 Linear Fitting Test 2 400 m = 3865 ± 32 ppm/h 0 200 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 t(h) t(h)
Figure 8. Temporal variation of CO2 concentrations and this variation corrected by the factor (1+ACHt) for the workshop classroom. Test 1 and 2 represent repetition of measurements to study method reproducibility. Sustainability 2019, 11, 7128 9 of 12
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). The cause of this discrepancy could be the different activity of room occupants before beginning of measurements. According to Batterman et al., [24], type of previous activity can have a significant effect on cumulative methods as s prior activity has a high effect in accumulative methods, as the purpose of this paper. Both repeated measurements were not made at the same instant. They were performed during two consecutive one hour classes with a break between them. A comparison of the measured data of Figure8 shows that it is at the beginning where both curves have different behaviors. A way to solve this problem is to consider only the times after the first 15 min, where the situation has stabilized. Results of the repetition of the method with this modification are shown in Table6.
Table 6. Results of CO2 gains from a person (g) calculations considering times above 15 min.
Slope m (ppm/h) g (g/h) g ACH (L/s) computer classroom 1 1338 12 34.66 0.31 0.00443 0.00004 ± ± ± computer classroom 2 1371 11 36.26 0.29 0.00457 0.00003 ± ± ± project room 1617 28 31.20 0.61 0.00442 0.00009 ± ± ± meeting room 3393 18 34.44 0.31 0.00453 0.00007 ± ± ± workshop room test 1 3967 46 32.14 0.43 0.00454 0.00003 ± ± ± workshop room test 2 3841 32 30.9 0.30 0.00435 0.00004 ± ± ±
In this case, the discrepancy in the results is reduced drastically, which confirms that prior physical activity could be the main reason for the discrepancy in this method Representative values of g gains by person in conditions of sedentary activity in an educational building can be obtained from the average value of the values obtained in Table5. This values is
5. Conclusions
In the paper an experimental method to determine CO2 gains from people in buildings based on measurements of the temporal evolution of CO2 concentrations was developed. This method was verified based on data from five classrooms with occupants with low physical activity. Various ACH conditions were selected for this study. Despite a different temporal evolution under each condition, the results of CO2 gains obtained are similar in all cases. Results of g values agree with the theoretical estimation given in the literature for young students with similar male and female contribution, g = 0.0045 L/s. Results obtained from the method did not demonstrate differences between the g value for young students with ages between 18 and 25 years old (classrooms) and adults between 30–65 years old (meeting room). A study of reproducibility was done. High discrepancy in the results was found, due to the big influence of the prior physical activity before each measurement. Results of the analysis showed that the developed method can be useful for determination of CO2 gains from people with sedentary activity and low-medium ACH of the rooms. Therefore it can be applied for the analysis of the microclimate in such cases and would be helpful in maintaining optimal conditions for humans. Further research will be focused on the usefulness of this method in the estimations for rooms with other conditions of activity, metabolic rate and ventilation. Sustainability 2019, 11, 7128 10 of 12
Author Contributions: Conceptualization, A.R. and D.A.K.; Methodology and Validation, A.R.; Formal Analysis, A.R. and D.A.K.; Investigation and Data Curation, D.A.K.; Writing—Original Draft Preparation, A.R. and D.A.K.; Visualization, A.R. and D.A.K. Funding: Research was founded by “BUT InterAcademic Partnerships” (PPI/APM/2018/1/00033/DEC/1) project and WZ/WBiIS/9/2019 scientific grant at Bialystok University of Technology. Acknowledgments: The study was carried out under the scientific project realized by Bialystok University of Technology and School of Engineering Sciences of Belmez, University of Córdoba “BUT InterAcademic Partnerships (PPI/APM/2018/1/00033/DEC/1) and the project “The possibility of the renewable energy sources usage in the context of improving energy efficiency and air quality in buildings and civil constructions”. It was implemented from the resources of the Ministry of Science and Higher Education of Poland References WZ/WBiIS/9/2019. Conflicts of Interest: The authors declare no conflict of interest.
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