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Daylighting design for healthy environments: Analysis of educational spaces for optimal circadian stimulus
Solar Energy 193 (2019) 584–596
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Solar Energy journal homepage: www.elsevier.com/locate/solener
Daylighting design for healthy environments: Analysis of educational spaces for optimal circadian stimulus
T
⁎
Ignacio Acostaa, , Miguel Ángel Campanoa, Russell Leslieb, Leora Radetskyb a b
Instituto Universitario de Arquitectura y Ciencias de la Construcción, Universidad de Sevilla, Spain Lighting Research Center, Rensselaer Polytechnic Institute, Troy, NY, United States
A R T I C LE I N FO
A B S T R A C T
Keywords: Circadian stimulus Circadian stimulus autonomy Lighting Window Lighting simulation Classroom
The human biological clock, also known as circadian rhythm, is mainly synchronized by the light perceived, specifically short-wavelength. Insufficient access to daylight or equivalent electric lighting can compromise human health and well-being. The effect of light on the circadian rhythm is greater in children and adolescents than in adults, making lighting design for classrooms important for good circadian entrainment for students. This research shows the results of circadian stimulus autonomy, that is to say, the percentage of days during the year when circadian stimulus is above a minimum threshold in typical classroom designs. Circadian stimulus, promoted by either natural or electric lighting, is quantified. The venue studied has a window of variable size, position and orientation, as well as different reflectance values of the inner surfaces for a classroom under three typical sky conditions. As deduced from the results, the reflectance of the environment has a noticeable effect on circadian entrainment, as well as on the spectral distribution of the light source. The results also serve to compare the impact of architectural design parameters on promoting good circadian rhythm for students.
1. Introduction 1.1. Background At present, architectural design is clearly influenced by the sustainable principles of energy saving and occupants’ well-being, and as one of its most relevant elements, window design can have a significant impact on electric lighting energy consumption and human comfort, as shown by Leslie et al. (2005), Acosta et al. (2016, 2018) and Ricciardi and Buratti (2018). The importance of this architectural element today can be seen in its impact in modern facade configurations (Bedon et al., 2019). Lighting for educational spaces requires special care. Children and adolescents need visual comfort to carry out their tasks (Korsavi et al., 2016) as well as suitable thermal conditions (Ricciardi and Buratti, 2018; Campano et al., 2011, 2017). Daylight in particular plays an important role in the perceptive ability of the students (Al-Khatatbeh and Ma’Bdeh, 2017), which mainly depends on a suitable window design (Zomorodian and Tahsildoost, Jan. 2017). However, lighting does not just have an impact on human visual and thermal comfort, given that the regulation of melatonin, a hormone produced by the pineal gland, is greatly dependent on the light perceived (Rea et al., 2005). Melatonin suppression, also known as circadian stimulus (CS), ⁎
synchronizes the circadian rhythm and therefore affects sleep, alertness, and other biological functions (Leslie et al., 2012). Light spectrum, duration, intensity, and timing affect the circadian system differently from the way they affect the visual system. Melatonin suppression is more sensitive to short-wavelength light, with a peak sensitivity near 460 nm, while visual acuity is most sensitive to the middle-wavelength fraction of the visible spectrum, at around 555 nm (Brainard, 2001; Thapan et al., 2001). The circadian response is dependent on the duration of light exposure. The visual system responds in milliseconds to light stimulus while it may take minutes for this to affect melatonin suppression. Daylight is an ideal light source to promote a circadian entrainment, providing the suitable amount, spectrum and duration for entrainment to local time. In fact, for centuries daylight was the sole light source used by humans for circadian entrainment. In modern society, where people spend most of their time indoors, electric lighting blurs the distinction between day and night, compromising the circadian rhythm. Without access to daylight, human health may be compromised. The effect of the disruption of the CS was initially tested in mice and later in humans (Bullough et al., 2006), confirming its importance for human health. Circadian disruption can also promote morbidity (Radetsky et al., 2013), depression (Figueiro, 2017) and multiple sclerosis (López-González, 2015). In particular, the impact of circadian
Corresponding author at: Instituto Universitario de Arquitectura y Ciencias de la Construcción, Universidad de Sevilla, 41012 Seville, Spain. E-mail address: [email protected] (I. Acosta).
https://doi.org/10.1016/j.solener.2019.10.004 Received 2 August 2019; Received in revised form 28 September 2019; Accepted 1 October 2019 0038-092X/ © 2019 International Solar Energy Society. Published by Elsevier Ltd. All rights reserved.
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Fig. 1. (A) The modeled spectral sensitivity of the human circadian system for narrowband and for polychromatic lights (Rea et al., 2005). (B) The relationship between the spectrally weighted levels of circadian light (CLA) and the measured levels of nocturnal melatonin suppression (Figueiro et al., 2011).
The definition of CSA is based on the calculation of DA, knowing the illuminance required to achieve an appropriate value of CS. The illuminance required mainly depends on the spectral power distribution (SPD) perceived by the eye of the observer, determined by the sky conditions and the reflectance values of the environment of the subject. This illuminance corresponds to a suitable CS, usually defined as 0.3, following previous studies (Figueiro, 2017). That CS value is enough to promote a sufficient circadian entrainment for students. The calculation period must be during the morning, preferably from 11 am to 12 pm “noon”, as the luminance of the sky is higher during that period. This new metric helps determine a healthy architectural design for educational spaces.
disruption is higher in adolescents than in adults (Bel, 2013). Therefore, lighting design in educational spaces is crucial to promoting good circadian entrainment for students (Figueiro et al., 2011). In 2005, Rea et al. proposed an empirical model of human circadian response based on the neuroanatomy and neurophysiology of the retina and on results from published psychophysical studies (Rea et al., 2005). Although the photosensitive retinal ganglion cell (ipRGC) is the key element in the empirical model, subsequent studies have shown that signals from rods and cones also provide photic information to the biological clock. The method proposed by Rea et al. studies these multichannel inputs and quantifies the CS, also defined as melatonin suppression, considering 1 h exposure time with a fixed 2.3 mm diameter pupil. Fig. 1 shows the spectral and absolute sensitivities of the human model. As seen in Fig. 1B, the saturation of the CS produced by daylight is observed at 0.7, while other variables also affect melatonin suppression. According to the latest studies by Figueiro et al. (2017) a CS value of 0.3 during the morning is suitable for the promotion of good circadian entrainment. There are several metrics to measure the daylight incidence through windows. Daylight factor is the most common definition to determine the daylight in a venue. It shows the relative illuminance inside a space as a fraction of the illuminance outside, using the less favorable scenario, overcast sky conditions (CIE, 2011), avoiding sunlight. Many of the studies on daylight design are based on this concept; Love established the calculations for determining the daylight factors under real overcast skies (Love, 1993), Acosta et al. developed a predictive method for quantifying this metric according to daylighting allowed by windows (Acosta et al., 2015) and subsequently by courtyards (Acosta et al., 2014, 2017) and Chel et al. (2010) carried out a study about the measurement of this metric related to skylights. However, the new research trend in this field follows daylight dynamic metrics, based on climate data, occupancy hours and blind control (Munoz et al., 2014). The most commonly used dynamic metric is daylight autonomy (DA), which determines the percentage of the occupancy time during which an illuminance threshold is met by daylight alone (Reinhart et al., 2006). In accordance with this trend, the new concept of circadian stimulus autonomy (CSA) appears, based on the CS requirements described above and the potential of the daylight dynamic metrics, and this determines the percentage of days throughout the year when a suitable threshold of circadian stimulus is met by daylight (Acosta et al., 2017) during the morning.
1.2. Aim and objectives This study aims a first approach for determining the suitable window size for multipurpose classrooms of educational buildings, in order to promote a proper CS value for students and provide an entrained circadian system, as well as assessing the impact of electric lighting on their biological clock. The calculations were carried out for three locations, considering the different daylight SPDs and a variable luminance of the sky vault. The classroom studied had a variable window size with two main orientations and a variable reflectance of inner surfaces. This research provides the following innovations:
• The analysis of the CS according to the geometry and external • •
conditions of the classroom is using the CSA metric, concept described for the first time in a previous investigation on circadian entrainment in hospital rooms (Acosta et al., 2017). Unlike previous research, the architectural design is assessed using three different sky conditions, with different SPDs and luminance values so that the impact of climate conditions on the CS can be evaluated. In contrast to previous studies (Leslie et al., 2012; Acosta et al., 2017), three different reflectance values were considered for the environment of the subject, so that the effect of the context of the student is also assessed to determine the CS value.
2. Methods The methodology of this research is based on a calculation model of a typical classroom, which serves to assess the impact of natural and 585
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Fig. 2. Methodology flowchart.
final location was Madrid, Spain, with mainly clear skies. Weather data for the three locations were defined using the EnergyPlus reference (LBNL, 2012), from the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) (ASHRAE, 2001) and the Spanish National Institute of Meteorology (AEMET) (de la Flor et al., 2008). The average SPD for each location is presented in Fig. 5, which shows the deduced spectral distribution of each sky according to statistical data on the percentage of overcast skies throughout the year. As seen from Fig. 5, the SPD in London is closer to a typical overcast sky, while in the case of Paris the result is near the usual CIE Standard Illuminant D65 distribution for northern Europe. In the case of Madrid, located in southern Europe with mainly clear skies, the SPD is slightly higher for short wavelength than the CIE Standard Illuminant D65 distribution. In accordance with the previous sky conditions and the environment of the classroom, Fig. 6 shows the SPDs produced by the reflection of the daylight spectrums on the three spectral reflectance values considered for the student’s desk. The spectrums shown above, in Fig. 6, serve for determining the CS value in accordance with an illuminance value. The impact of the variation of the SPD in the CS calculation is quantified in a complementary test, included in this study.
electric lighting in the circadian stimulus. The deduction of the resulting SPD perceived by the eyes of the observer—defined by the light source spectra and the reflectance of the environment—serves to quantify the CS produced and therefore the suitable architectural variables, such as the window size or the characteristics of the electric light source. Fig. 2 shows the flowchart which represents the methodology described below. 2.1. Characteristics of the room model According to the most common classroom design and considering large dimensions in order to assess the CSA over a sufficient surface, a virtual venue 3.0 m high by 8.0 m deep by 8.0 m wide was defined. The virtual classroom and the quantification of its calculation variables are shown in Fig. 3. The room had a single 7.0 m long window of variable height located on one facade. The window had two different locations: centered in the facade or in an upper position with a height of sill of 1.5 m. The CS and CSA were analyzed according to two different reflectance values of the inner surfaces of the classroom, considering a diffuse reflection. The analysis of the calculation models was carried out for two window orientations—north and south—and three locations—London, Paris and Madrid. The light received by the eye of the student depends on the spectral distribution of the average sky conditions and its reflection on the environment of the subject. For this research, three spectral reflectance values were considered for the student’s desk, in order to determine the resulting SPD perceived by the subject. Fig. 4 shows the reflectance values per wavelength for three different desks: white, light wood and light blue.
2.2.2. Orientation of the window Given the importance of the window orientation for the use of natural light (Munoz et al., 2014), two orientations were proposed to assess the CSA promoted by lighting: north and south. Both situations show the best and worst case scenarios for determining daylight illuminance. West and east orientations provide an intermediate scenario in most of the cases, due to lower elevation of the Sun. Particularly, windows facing east could provide similar lighting within the classrooms than that observed for South orientation, considering occupancy hours just during the morning. Further studies may be convenient to properly quantify the influence of other orientations on the circadian stimulus.
2.2. Selecting the daylight conditions 2.2.1. Location of the room In order to determine the CSA for a wide range of climate conditions, the classroom was analyzed in three different locations in Europe. The first location was London, UK, with mainly overcast skies. The second was Paris, France, with predominantly intermediate skies. The 586
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Fig. 3. Calculation model of the classroom.
in Fig. 8, modified by the reflectance of the proposed work planes previously defined in Fig. 4. These spectrums define the electric light perceived by students and therefore the circadian rhythm. Regardless of the average illuminance of the classroom, the different spectral distributions promote a variable CS for students, so that it is important to analyze the results according to the color temperature of the proposed luminaires.
2.3. Selecting electric light conditions A suitable circadian rhythm can also be promoted by means of electric lighting, although its capacity for enabling proper synchronization of the biological clock is less than that of daylighting. Two scenarios were used to determine the effect of electric lighting in CS: one with warm LED lamps—with a Correlated Color Temperature (CCT) of 2700 K—and another with cool luminaires—with a CCT of 6500 K. The spectral distributions of warm and cool fixtures are shown in Fig. 7, according to the spectrometry measurements. In accordance with the variables required by the CS calculation, the resulting spectral distributions of warm and cool luminaires are shown
2.4. Selecting the calculation metrics 2.4.1. Calculation of CS As deduced from the above, the CS promoted by daylight depends
Fig. 4. Reflectance of the work plane according to different spectral reflectance values. 587
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Fig. 5. Average spectral power distribution of the locations studied.
75% to provide the same circadian rhythm as that of the warm fixtures. The suitable CS value, established by Figueiro (2017) is usually defined as 0.3, so the lowest illuminance thresholds, in accordance with the sky conditions, are 186 lx for Madrid, 237 lx for Paris and 263 lx for London. These values serve to determine the CSA calculation, as explained previously. For the case of electric lighting, the lowest illuminance required corresponds to 400 lx for warm LED lamps and 295 lx for cool luminaires.
mainly on the SPD and the illuminance perceived by the study subject. The influence of both variables serves to determine the circadian light (CLA), which is used in the model of phototransduction developed by Rea et al. (2005) to calculate the CS, as seen in Eq. (1):
⎛ CS = 0.7·⎜1 − ⎜ 1+ ⎝
1
⎞
⎟ CLA 1.1026 ⎟ 355.7 ⎠
( )
(1)
CS is directly proportional to the predicted levels of light-induced nocturnal melatonin suppression from threshold to saturation, assuming a pupil size of 2.3 mm and a 1 h duration of exposure. According to the SPDs shown in Fig. 6 and Eq. (1), the illuminance thresholds can be determined depending on the average sky conditions perceived by the observer. Table 1 shows the illuminance values for the work plane, depending on different CS values and the resulting SPDs for Madrid, Paris and London. As can be deduced from Table 1, the optimal environment for the student to promote a good CS corresponds to a white work plane. In accordance with the perceived spectrums shown in Fig. 8 and Eq. (1), the illuminance level can be also defined for a specific CS value depending on the CCT of the electric light source. Table 2 shows the illuminance values required in the work plane, according to different CS values and the resulting SPDs of warm and cool LED luminaires. As deduced from Table 2, cool LED lamps require a lower illuminance level than warm luminaires in order to promote the same CS value. Quantifying the previous statement for the study case with a white work plane, cool LED lamps need a luminous flux between 70 and
2.4.2. Calculation of CSA As mentioned above, CSA is defined as the percentage of days throughout the year when a suitable threshold of CS is met by daylight alone (Acosta et al., 2017) during the morning. This definition is similar to the approach used in the calculation of DA, in accordance with the illuminance threshold determined by the CS calculation. The illuminance required depends on the SPD perceived by the eye of the observer, determined by the sky conditions and the reflectance values of the environment of the subject. As can be deduced, CSA cannot be applied for electric lighting, since it is only useful for a light source with a variable luminous flux, such as in the case of daylighting. 2.5. Selecting the calculation program The illuminance threshold for each location and the calculation period determine the DA values, which correspond to the CSA, according to the values established previously. This dynamic metric is calculated using DaySim 3.1. The accuracy of this lighting program has
Fig. 6. Resulting spectral power distribution of the locations studied. 588
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Fig. 7. Spectral power distribution of the electric light sources studied.
Fig. 8. Resulting spectral power distribution for electric lighting.
Table 1 Illuminance required in the work plane to promote a specific CS value (0.1, 0.3 and 0.5) according to the resulting SPD produced by different sky conditions and the reflectance values of the environment.
Table 2 Illuminance required in the work plane to promote a specific CS value (0.1, 0.3 and 0.5) according to the resulting SPD produced by warm and cool LED luminaires and the reflectance values of the environment.
Location
Circadian Stimulus
White work plane
Light Wood work plane
Light Blue work plane
Luminaire
Circadian Stimulus
White work plane
Light Wood work plane
Light Blue work plane
Madrid
0.1 0.3 0.5
48 lx 186 lx 543 lx
255 lx 996 lx 2961 lx
88 lx 343 lx 994 lx
Warm LED
0.1 0.3 0.5
102 lx 400 lx 1195 lx
613 lx 2387 lx 7097 lx
147 lx 574 lx 1705 lx
Paris
0.1 0.3 0.5
61 lx 237 lx 684 lx
286 lx 1118 lx 3344 lx
108 lx 422 lx 1215 lx
Cool LED
0.1 0.3 0.5
77 lx 295 lx 847 lx
327 lx 1276 lx 3804 lx
138 lx 531 lx 1513 lx
London
0.1 0.3 0.5
68 lx 263 lx 757 lx
299 lx 1174 lx 3498 lx
120 lx 461 lx 1321 lx
means of real measurements in two classrooms of the High School of Architecture of Seville (Spain), one oriented to North and the other facing South, as seen in Fig. 9. The relative difference of the CS results according to the SPDs studied varies between 2.6 and 7.3%, considering an illuminance value between 500 and 1500 lx measured on the work plane. Accordingly, it can be concluded that the SPD of the sky barely affects to the CS promoted, except in extreme circumstances, such as very low illuminance values or high variations of the sky SPD.
been tested in several studies (Reinhart and Walkenhorst, 2001; Reinhart and Breton, 2009; Bellia et al., 2015).
2.6. Quantification of the spectrum variation in the CS 2.6.1. Impact of the spectrum of the sky In order to validate the methodology described above, two trials are carried out with the aim to determine the impact of the SPD variation on the CS calculation, keeping a constant value of illuminance. The first trial analyzes the impact of the variation of the sky SPD by
2.6.2. Impact of the observation conditions In the methodology proposed, it was assumed that the student was looking directly at the desk and perceiving the light reflected from it. 589
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Fig. 9. Classrooms tested for determining the impact of the sky SPD on the CS calculation. A: Classroom with windows facing north. B: Classroom with windows facing south.
Fig. 10. Observer locations tested for determining the impact of the perceived SPD. (A) Observer looking directly at the table (angle 0°). (B) Observer looking at the back of the chair in front (angle 30°). C: Observer looking at the end of the classroom (angle 60°).
double glazing, which has a solar factor of 0.75. The reflectance considered for the inner envelope is 0.72 in the case of walls and ceiling, and 0.22 for the floor. Illuminance measurements were taken throughout 2017, from January 1st to December 31st, using 8 illuminance-meters with a range of 20–2000 lx and accuracy of ± 3.0%, placed above the floor of the cell, on the axis of symmetry with a spacing of 0.40 m between sensors, as Fig. 11A and B shows. The simulation model has a superimposed calculation grid, representing the location of these sensors. The weather conditions for computations correspond to Seville (Spain), at Latitude 37.42° and Longitude 5.40°, with mainly clear skies.
However, the observer’s point of view can also affect to the resulting SPD perceived by the eyes, modifying the CS promoted by daylighting. Accordingly, a second trial is carried out for determining the impact of the point of view of the observer on the CS calculation. As in the previous trial, real measurements were taken in a classroom of the High School of Architecture of Seville (Spain) with windows facing South. Three angles of vision are considered for the observer: angle 0° (looking at the table), angle 30° and angle 60° (looking at the end of the classroom). Fig. 10 shows the procedure for the measures taken. The variation between different point of views hardly affects to the CS calculation, determining a maximum divergence of 0.6% in the worst case, for an illuminance value of 100 lx. Accordingly, it can be stated that the point of view of the observer barely affects to the CS value, except in the case of different conditions to those analyzed in this trial, such as the case of an observer looking directly to the window.
2.7.2. Boundary conditions of the validation process The occupancy schedule for the calculation of DA and Continuous Daylight Autonomy (DAC), both for measurements and computer simulation, was considered from 8:00 to 17:00. Three illuminance thresholds were established for DA and DAC calculations, 100, 250 and 500 lx.
2.7. Validation of the calculation program The use of dynamic metrics or a computational tool is not reliable without a previous validation process. Consequently, a validation study was developed through experimental trials with a test cell (Campano et al., 2018).
2.7.3. Results of the validation process Fig. 11C shows the DA and DAC values calculated for the study, through a graph for each of the three illuminance thresholds (100, 250 and 500 lx), comparing results from measurements and computer simulations. These graphs also show their different percentages. DA values obtained in simulations are close to those observed in measurements, with the highest maximum deviation for the 500 lx threshold case (8.4%). In addition, although the divergences between measurements and simulations are greater in terms of depth for the three illuminance thresholds, as low values of under 10% they are therefore acceptable for the validation of this simulation program.
2.7.1. Definition of the test cell and the simulation model The south-facing test cell used for this study by TEP-130 research group (León-Rodríguez et al., 2017) is located in the city of Seville (Spain). This room is 2.40 m wide, 3.20 m deep and 2.70 m high. Its enclosure, including the roof and the floor, is built using high-density white polyurethane sandwich panels fixed to a steel frame. The southfacing facade has a window 108 cm high by 116 cm wide, with 4.8.4 590
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Fig. 11. Test cell for validation of metrics used. (A) Test cell inner view with illuminance-meter distribution. (B) Simulation model with array of study points. (C) Daylight Autonomy (DA) and Continuous Daylight Autonomy (DAC) results obtained from illuminance measurements and simulation calculations.
DA and 1.0% for DAC, showing a standard deviation (95% reliability) of 6.8% for DA and 4.9% for DAC. As in the previous analysis, these deviations are acceptable, given that they are below 10%. From these results it can be concluded that DaySim 3.1 accurately calculates DA
Divergences in DAC values are smaller than those of the case of DA, showing the highest maximum deviation (4.2%) for 100 lx and obtaining better approaches for higher illuminance thresholds. The bias error obtained for these two metrics is 1.9% in the case of 591
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Fig. 12. Average circadian stimulus for the Paris location according to north orientation.
3. Results
and DAC dynamic metrics in rooms with similar boundary conditions and can therefore provide an accurate calculation for CSA.
3.1. Average circadian stimulus promoted by daylighting Following the methodology described above, the average CS values measured at the study points can be calculated from the established variables. Fig. 12 for example shows these values for the Paris location 592
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metrics, the CSA determines the percentage of days when a minimum CS value is met by daylight alone (Acosta et al., 2017) during the morning. This concept, consistent with the method for DA calculation, is used to assess the suitability of a space to promote a good circadian entrainment with various design parameters. Fig. 13 shows the CSA values for the Paris location, depending on the distance from the facade, in accordance with a CS threshold of 0.3. Each row in the figure shows the measurements for a particular window size. Left columns show the results for windows facing north, while right columns represent openings facing south. Following this figure description, odd columns describe the CSA values for centered windows while even columns show the results for openings in upper position. The functions shown in the graphs represent the CSA measurement for two main reflectance values of the classroom—bright and dark—considering the three desks described in the methodology—white, light wood and light blue. As observed in Fig. 13A, a small window with an opening equivalent to 30% of the facade allows a CSA value higher than 50% in the entire room, considering a white work plane and high reflectance values of the inner surfaces of the classroom. Taking into account a light blue desk, the window to facade ratio must increase up to 45% to ensure a good circadian entrainment for students, as can be deduced from the results shown in Fig. 13B. In dark classrooms, with a low reflectance of the inner surfaces, only a white work plane can achieve a proper CSA value considering a window to facade ratio equal or higher than 60%, as deduced from Fig. 13C. The light wood work plane only allows a sufficient CSA value—equal or higher than 50%—in the zone near the facade. It is worth noting that there is a clear divergence of CSA values between bright and dark classroom models, which increases according to the distance from the facade. In some cases, such as venues with blue or wood work planes, the CSA in the back of the room is null, hence a suitable CS value—higher than 0.3—cannot be achieved for any day of the year. Therefore, both the reflectance of the inner surfaces and the work plane are decisive in allowing a minimum circadian entrainment. In the case of London location, a small window size, close to a window to facade ratio of 30%, barely promotes a good CSA value—higher than 50%—for a white work plane, except in the case of classrooms with a high reflectance value and a south-facing window. Considering worse boundary conditions, with a window facing north, a window to facade ratio of 45% should be selected to achieve a CSA value above 50%. Regardless of window size, the light wood work plane never promotes a good CSA value, except in the area close to the facade for south-facing openings. Considering the climate conditions of Madrid, a bright classroom with a white work plane achieves an almost perfect CSA value —higher than 90%—considering a minimum window size and regardless of the
considering a window facing north. As can be seen in Fig. 12, the classroom sections show the average CS, measured from 0 to 70%, according to the distance from the facade. The window size varies from 30 to 60%, while its position is considered centered in the facade or in an upper position, considering a sill 1.5 m high. Bright rooms, with a high reflectance value of the inner surfaces, are located on the left side of the graph, while dark rooms, with a low reflectance value, are shown on the right. As expected, the average CS values are higher in the area near the window, gradually decreasing toward the back of the room, following a similar tendency—although not proportional—to the average illuminance values (Acosta et al., 2016). As deduced from the results observed in Fig. 12, the resulting CS and the window area are not directly proportional. However, a linear tendency can be observed comparing the CS values from the three window sizes: The window to facade ratio of 60% shows an average increase of CS near 15% compared to the medium size window, while the window to facade ratio of 45% promotes an increase of 14% compared to the smaller window. The SPD generated by the color spectrum of the environment notably affects the resulting CS value, as can be observed from the room sections shown in Fig. 12. The white work plane promotes an average CS value between 20 and 50% higher than that of the light blue desk, depending on the window size and the inner reflectance values. Compared to the light wood desk, the white work plane produces an increase of the average CS between 100 and 255%. According to these statements, the spectral reflectance of the environment is probably the most important variable in determining CS values. In order to define a suitable window design in classrooms to promote a sufficient CS over the entire work plane—that is to say, a CS value equal or higher than 0.3—the minimum window size can be determined for each location and orientation. Table 3 summarizes the minimum window to facade ratio for each scenario according to the results shown in Fig. 12 and those observed for London and Madrid locations. As can be deduced from Table 3, a classroom with inner surfaces with a low reflectance barely provided a suitable CS value, regardless of the window size. Following the previous statement, a light wood environment, with an SPD predominantly close to a long wavelength, notably hinders a good circadian entrainment. It is therefore clear that the environment to promote a minimum value of CS should be white or pale blue. Moreover, the location and orientation of the window are relevant variables for determining the opening size, regardless of the relevance of the color of the environment. 3.2. Circadian stimulus autonomy promoted by daylighting As explained in the section on background and the calculation
Table 3 Minimum window to facade ratio to promote an average CS value higher than 0.3 during the morning. Location
London
Orientation
North South
Paris
North South
Madrid
North South
Window Position
Bright inner surfaces
Dark inner surfaces
White work plane
Light wood work plane
Light blue work plane
White work plane
Light wood work plane
Light blue work plane
Centered Upper Centered Upper
45% 45% 30% 30%
– – – –
60% 60% 45% 30%
– – 60% 60%
– – – –
– – – –
Centered Upper Centered Upper
30% 30% 30% 30%
– – – –
45% 60% 30% 30%
– 60% 60% 60%
– – – –
– – – –
Centered Upper Centered Upper
30% 30% 30% 30%
– – 60% 60%
45% 45% 30% 30%
60% 60% 45% 45%
– – – –
– – – 60%
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Fig. 13. Circadian stimulus autonomy for a CS value of 0.3 in the Paris location. (A) Rooms with a window to facade ratio of 30%. (B) Rooms with a window to facade ratio of 45%. (C) Rooms with a window to facade ratio of 60%.
luminaires was also analyzed. As described in the methodology, nine fixtures with a luminous flux of 3700 lm 2.4 m apart are located in the ceiling of the classroom. According to the results of the Dialux 4.13 lighting simulation program, an average illuminance higher than 500 lx was achieved in the work plane of the venue. As shown in Fig. 8, warm LED lamps—with a CCT of 2700 K—and cool LED luminaires—with a CCT of 6500 K—generate different spectral distributions modified by the reflectance of the work planes. These spectrums determine the CS promoted by observers, depending on the illuminance value perceived by eye. In accordance with the illuminance values perceived by the
orientation or position of the opening. This statement is true for the predominantly clear skies typical of Madrid, which promote a good circadian entrainment irrespective of the architectural design. However, a classroom with dark inner surfaces or a light blue work plane requires a larger window, close to 45% of the facade. As in the previous cases, the light wood plane barely achieves a suitable CSA value, except in the case of medium or large south-facing windows.
3.3. Average circadian stimulus promoted by electric lighting In addition to the previous test, the circadian rhythm allowed by 594
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Fig. 14. Circadian stimulus for warm and cool electric lighting.
a CS value equal or higher than 0.3— regardless of window size, orientation or location. Moreover, the environment to promote a minimum value of CS should be white or close to pale blue. It is worth noting that a white work plane allows an increase of CS up to 30% compared to a pale blue desk for a large window—a 60% ratio—and up to 50% in the case of a small opening—a 30% ratio. For the case of the locations studied and with a white work plane, a window to facade ratio of 45% should be selected for the case of London and a minimum ratio of 30% is needed for Paris and Madrid. In accordance with the previous statement, it can be concluded that environment reflectance is one of the most influential variables, just as the typical weather conditions are more decisive than latitude in promoting a good CS. The above methodology was applied to electric lighting, which also provided a suitable CS value. As deduced, cool LED lamps with a CCT of 6500 K allowed up to 5% more CS than warm luminaires in the case of a light blue work plane, 19% more compared to the results for a white work desk and 81% more than the scenario with a light wood environment, taking into account the same luminous flux for both types of luminaire. As in the study of the influence of daylight illuminance in CS values, the spectral reflectance of the observed environment is crucial in defining a suitable circadian entrainment. According to the scenario with electric light sources, a white work plane provided an increase of CS up to 40% compared to a light blue desk and was three times higher than that of a light wood environment. CSA has proven to be an effective metric to assess the performance of an architectural space in the well-being of occupants. As explained in the methodology, this definition determines the percentage of days throughout the year when a suitable threshold of CS is met by daylight alone during the morning. As deduced from Fig. 13, CSA varies noticeably depending on the sky conditions. On one hand, for the London location, with a mainly cloudy sky, a small opening size with a window to facade ratio of 30% barely promotes a good CSA value above 50%. On the other hand, for the Paris and Madrid locations, with predominantly intermediate or clear skies, a small window equivalent to 30% of the facade surface, promotes a good CSA value. Thus, a spectral distribution of the sky with a dominant short wavelength range is relevant in producing a sufficient circadian rhythm. Moreover, analyzing the results observed for CSA, window orientation also affects the measurements of this metric, although its influence and lintel height are not as decisive when the reflectance of the inner surfaces of the classroom and of the observed environment are high enough. From the above, the spectral reflectance of the observed
observers and the resulting spectral distribution, Fig. 14 shows the CS values for warm and cool LED luminaires, depending on the reflectance of the inner surfaces of the room and on the color of the work plane. Analyzing the effect of color temperature of luminaires in Fig. 14, it is shown that cool LED lamps, with a CCT of 6500 K, produce an increase in CS values compared to warm light sources, with a CCT of 2700 K. In fact, cool lamps promote 5% more CS than warm luminaires in the case of a light blue desk, 19% more than that of a white work plane and 81% more than that of the scenario of a light wood environment. As seen for the case of daylight conditions, the spectral reflectance of the observed work plane is key for determining a proper CS value. In fact, considering a scenario with electric lighting, a white work plane promotes an increase of CS of up to 40% in comparison with a light blue desk and three times higher than that of a light wood environment. The reflectance of the inner surfaces of the classroom also affects the CS provided by electric light sources. As deduced from Fig. 14, a room model with bright surfaces promotes between 20 and 60% more CS than that defined with dark surfaces. Therefore, the reflectance of the surfaces of the room is decisive in allowing a suitable CS value under electric lighting conditions. Finally, comparing the results from Figs. 12 and 14, and deduced from Table 3, it can be concluded that a classroom with a sufficient window to facade ratio does not require electric lighting to promote a good CS value. This is particularly true of classrooms with high reflectance surfaces in the zone near the facade.
4. Conclusions Circadian rhythm is an essential variable which decisively affects the well-being of occupants and should be taken into account in architectural design. The effect of light on the CS is greater on children and adolescents than on adults, hence natural and electric lighting design in classrooms is important for good circadian entrainment for students. As seen from the results, the CS can be quantified for different scenarios and variables, such as window size, reflectance of the inner surfaces and the work plane observed, as well as the color temperature of the luminaires located in the ceiling. In accordance with the window design of classrooms for a proper CS by daylighting, Table 3 above summarizes the minimum window to facade ratio for each location, window position and environment reflectance. As shown, a room with a low reflectance of the inner surfaces or of the work plane barely provided a suitable CS value—that is to say, 595
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environment is one of the key variables in providing sufficient circadian entrainment for students. As deduced from the CSA quantification, a white work plane provides a good CS for students up to 54% more time during the year than light blue desks, considering the optimal architectural design; with the largest window facing south and high reflectance of the inner surfaces of the classroom. In the case of a worse scenario, with a north-facing opening or low reflectance surfaces, this increase is noticeably higher. Obviously, a light wood work plane is rejected, since it never produces a proper circadian rhythm, regardless of window dimensions or orientation. In conclusion, the circadian rhythm is a decisive variable in the well-being of students, and hence both the architectural and electric lighting design are extremely important for providing a sufficient amount of light and suitable spectrum in order to promote a good circadian entrainment. Window size in particular should be selected according to typical sky conditions, always considering a high reflectance of the inner surfaces of the classroom and paying special attention to the color of the work plane.
Eng. 25. Bel, S., et al., 2013. Association between self-reported sleep duration and dietary quality in European adolescents. Br. J. Nutr. 110 (5), 949–959. Bellia, L., Pedace, A., Fragliasso, F., 2015. The impact of the software’s choice on dynamic daylight simulations’ results: a comparison between Daysim and 3ds Max Design®. Sol. Energy 122, 249–263. Brainard, G.C., et al., 2001. 4 regulation in humans: evidence for a novel circadian photoreceptor. J. Neurosci. 21 (16), 6405–6412. Bullough, J.D., Rea, M.S., Figueiro, M.G., 2006. Of mice and women: light as a circadian stimulus in breast cancer research. Cancer Causes Control 17 (4), 375–383. Campano, M.A., Sendra, J.J., Domínguez, S., 2011. Analysis of thermal emissions from radiators in classrooms in Mediterranean climates. Procedia Eng. 21, 106–113. Campano, M.A., Pinto, A., Acosta, I., Muñoz-González, C., 2017. Analysis of the perceived thermal comfort in a Portuguese Secondary School: methodology and results. Int. J. Eng. Technol. 9 (6), 448–455. Campano, M.A., Acosta, I., León, A.L., Calama, C., 2018. Validation study for daylight dynamic metrics by using test cells in Mediterranean area. Int. J. Eng. Technol. 10 (6), 487–491. Chel, A., Tiwari, G.N., Singh, H.N., 2010. A modified model for estimation of daylight factor for skylight integrated with dome roof structure of mud-house in New Delhi (India). Appl. Energy 87 (10), 3037–3050. CIE, 2011. CIE S 017:2011 – International Lighting Vocabulary. Commission Internationale de l’Éclairage, Vienna (Austria). de la Flor, F.J.S., Domínguez, S.Á., Félix, J.L.M., Falcón, R.G., 2008. Climatic zoning and its application to Spanish building energy performance regulations. Energy Build. Figueiro, M.G., et al., 2017. The impact of daytime light exposures on sleep and mood in office workers. Sleep Heal. 3 (3), 204–215. Figueiro, M.G., Brons, J.A., Plitnick, B., Donlan, B., Leslie, R.P., Rea, M.S., 2011. Measuring circadian light and its impact on adolescents. Light. Res. Technol. 43 (2), 201–215. Korsavi, S.S., Zomorodian, Z.S., Tahsildoost, M., 2016. Visual comfort assessment of daylit and sunlit areas: a longitudinal field survey in classrooms in Kashan, Iran. Energy Build. 128, 305–318. LBNL, 2012. Lawrence Berkeley National Laboratory Technical Report (2012) 1278 – EnergyPlus Engineering Reference. The Reference to EnergyPlus Calculations. León-Rodríguez, A.L., Suárez, R., Bustamante, P., Campano, M.A., Moreno-Rangel, D., 2017. Design and performance of test cells as an energy evaluation model of facades in a mediterranean building area. Energies 10 (11), 1816. Leslie, R.P., Raghavan, R., Howlett, O., Eaton, C., 2005. The potential of simplified concepts for daylight harvesting. Light. Res. Technol. 37 (1), 21–40. Leslie, R.P., Radetsky, L.C., Smith, A.M., 2012. Conceptual design metrics for daylighting. Light. Res. Technol. 44 (3), 277–290. López-González, A., et al., 2015. Melatonin treatment improves primary progressive multiple sclerosis: a case report. J. Pineal Res. 58 (2), 173–177. Love, J.A., 1993. Determination of the daylight factor under real overcast skies. J. Illum. Eng. Soc. 22 (2), 176–182. Munoz, C.M., Esquivias, P.M., Moreno, D., Acosta, I., Navarro, J., 2014. Climate-based daylighting analysis for the effects of location, orientation and obstruction. Light. Res. Technol. 46 (3), 268–280. Radetsky, L.C., Rea, M.S., Bierman, A., Figueiro, M.G., 2013. Circadian disruption: comparing humans with mice. Chronobiol. Int. 30 (8), 1066–1071. Rea, M.S., Figueiro, M.G., Bullough, J.D., Bierman, A., 2005. A model of phototransduction by the human circadian system. Brain Res. Rev. 50 (2), 213–228. Reinhart, C.F., Breton, P.-F., 2009. Experimental validation of Autodesk® 3ds Max® design 2009 and Daysim 3.0. LEUKOS - J. Illum. Eng. Soc. North Am. 6 (1), 7–35. Reinhart, C.F., Walkenhorst, O., 2001. Validation of dynamic RADIANCE-based daylight simulations for a test office with external blinds. Energy Build. 33 (7), 683–697. Reinhart, C.F., Mardaljevic, J., Rogers, Z., 2006. Dynamic daylight performance metrics for sustainable building design. LEUKOS – J. Illum. Eng. Soc. North Am. 3 (1), 7–31. Ricciardi, P., Buratti, C., 2018. Environmental quality of university classrooms: Subjective and objective evaluation of the thermal, acoustic, and lighting comfort conditions. Build. Environ. 127, 23–36. Thapan, K., Arendt, J., Skene, D.J., 2001. An action spectrum for melatonin suppression: evidence for a novel non-rod, non-cone photoreceptor system in humans. J. Physiol. 535 (1), 261–267. Zomorodian, Z.S., Tahsildoost, M., 2017. Assessment of window performance in classrooms by long term spatial comfort metrics. Energy Build. 134, 80–93.
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The authors wish to thank the editor and reviewers for their labor improving this research piece. The results presented were funded by the Government of Spain through the research project: Efficient design for biodynamic lighting to promote the circadian rhythm in shift work centers (Ref BIA201786997-R). The authors wish to express their gratitude for all the technical and financial support provided. The authors are especially grateful to all those who collaborated in this research project, specially to Blas de Lezo for the encouragement of this article. We would also like to express a special acknowledgment to the Lighting Research Center at Rensselaer Polytechnic Institute (LRC), USA. References Acosta, I., Navarro, J., Sendra, J.J., 2014. Lighting design in courtyards: predictive method of daylight factors under overcast sky conditions. Renew. Energy 71, 243–254. Acosta, I., Munoz, C., Campano, M.A., Navarro, J., 2015. Analysis of daylight factors and energy saving allowed by windows under overcast sky conditions. Renew. Energy 77 (1), 194–207. Acosta, I., Campano, M.A., Molina, J.F., 2016. Window design in architecture: Analysis of energy savings for lighting and visual comfort in residential spaces. Appl. Energy 168, 493–506. Acosta, I., Leslie, R.P., Figueiro, M.G., 2017. Analysis of circadian stimulus allowed by daylighting in hospital rooms. Light. Res. Technol. 49 (1), 49–61. Acosta, I., Varela, C., Molina, J.F., Navarro, J., Sendra, J.J., 2017. Energy efficiency and lighting design in courtyards and atriums: a predictive method for daylight factors. Appl. Energy 211 (November), 1216–1228. Acosta, I., Campano, M.A., Dominguez-Amarillo, S., Muñoz, C., 2018. Dynamic daylight metrics for electricity savings in offices: Window size and climate smart lighting management. Energies 11 (11). Al-Khatatbeh, B.J., Ma’Bdeh, S.N., 2017. Improving visual comfort and energy efficiency in existing classrooms using passive daylighting techniques. Energy Procedia 136, 102–108. ASHRAE, 2001. International Weather for Energy Calculations. Bedon, C., et al., 2019. Structural characterisation of adaptive facades in Europe – Part I: Insight on classification rules, performance metrics and design methods. J. Build.
Glossary CS: Circadian Stimulus DA: Daylight Autonomy DAC: Continuous Daylight Autonomy CSA: Circadian Stimulus Autonomy SPD: Spectral Power Distribution CLA: Circadian Light
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Daylighting design for healthy environments: Analysis of educational spaces for optimal circadian stimulus
T
⁎
Ignacio Acostaa, , Miguel Ángel Campanoa, Russell Leslieb, Leora Radetskyb a b
Instituto Universitario de Arquitectura y Ciencias de la Construcción, Universidad de Sevilla, Spain Lighting Research Center, Rensselaer Polytechnic Institute, Troy, NY, United States
A R T I C LE I N FO
A B S T R A C T
Keywords: Circadian stimulus Circadian stimulus autonomy Lighting Window Lighting simulation Classroom
The human biological clock, also known as circadian rhythm, is mainly synchronized by the light perceived, specifically short-wavelength. Insufficient access to daylight or equivalent electric lighting can compromise human health and well-being. The effect of light on the circadian rhythm is greater in children and adolescents than in adults, making lighting design for classrooms important for good circadian entrainment for students. This research shows the results of circadian stimulus autonomy, that is to say, the percentage of days during the year when circadian stimulus is above a minimum threshold in typical classroom designs. Circadian stimulus, promoted by either natural or electric lighting, is quantified. The venue studied has a window of variable size, position and orientation, as well as different reflectance values of the inner surfaces for a classroom under three typical sky conditions. As deduced from the results, the reflectance of the environment has a noticeable effect on circadian entrainment, as well as on the spectral distribution of the light source. The results also serve to compare the impact of architectural design parameters on promoting good circadian rhythm for students.
1. Introduction 1.1. Background At present, architectural design is clearly influenced by the sustainable principles of energy saving and occupants’ well-being, and as one of its most relevant elements, window design can have a significant impact on electric lighting energy consumption and human comfort, as shown by Leslie et al. (2005), Acosta et al. (2016, 2018) and Ricciardi and Buratti (2018). The importance of this architectural element today can be seen in its impact in modern facade configurations (Bedon et al., 2019). Lighting for educational spaces requires special care. Children and adolescents need visual comfort to carry out their tasks (Korsavi et al., 2016) as well as suitable thermal conditions (Ricciardi and Buratti, 2018; Campano et al., 2011, 2017). Daylight in particular plays an important role in the perceptive ability of the students (Al-Khatatbeh and Ma’Bdeh, 2017), which mainly depends on a suitable window design (Zomorodian and Tahsildoost, Jan. 2017). However, lighting does not just have an impact on human visual and thermal comfort, given that the regulation of melatonin, a hormone produced by the pineal gland, is greatly dependent on the light perceived (Rea et al., 2005). Melatonin suppression, also known as circadian stimulus (CS), ⁎
synchronizes the circadian rhythm and therefore affects sleep, alertness, and other biological functions (Leslie et al., 2012). Light spectrum, duration, intensity, and timing affect the circadian system differently from the way they affect the visual system. Melatonin suppression is more sensitive to short-wavelength light, with a peak sensitivity near 460 nm, while visual acuity is most sensitive to the middle-wavelength fraction of the visible spectrum, at around 555 nm (Brainard, 2001; Thapan et al., 2001). The circadian response is dependent on the duration of light exposure. The visual system responds in milliseconds to light stimulus while it may take minutes for this to affect melatonin suppression. Daylight is an ideal light source to promote a circadian entrainment, providing the suitable amount, spectrum and duration for entrainment to local time. In fact, for centuries daylight was the sole light source used by humans for circadian entrainment. In modern society, where people spend most of their time indoors, electric lighting blurs the distinction between day and night, compromising the circadian rhythm. Without access to daylight, human health may be compromised. The effect of the disruption of the CS was initially tested in mice and later in humans (Bullough et al., 2006), confirming its importance for human health. Circadian disruption can also promote morbidity (Radetsky et al., 2013), depression (Figueiro, 2017) and multiple sclerosis (López-González, 2015). In particular, the impact of circadian
Corresponding author at: Instituto Universitario de Arquitectura y Ciencias de la Construcción, Universidad de Sevilla, 41012 Seville, Spain. E-mail address: [email protected] (I. Acosta).
https://doi.org/10.1016/j.solener.2019.10.004 Received 2 August 2019; Received in revised form 28 September 2019; Accepted 1 October 2019 0038-092X/ © 2019 International Solar Energy Society. Published by Elsevier Ltd. All rights reserved.
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Fig. 1. (A) The modeled spectral sensitivity of the human circadian system for narrowband and for polychromatic lights (Rea et al., 2005). (B) The relationship between the spectrally weighted levels of circadian light (CLA) and the measured levels of nocturnal melatonin suppression (Figueiro et al., 2011).
The definition of CSA is based on the calculation of DA, knowing the illuminance required to achieve an appropriate value of CS. The illuminance required mainly depends on the spectral power distribution (SPD) perceived by the eye of the observer, determined by the sky conditions and the reflectance values of the environment of the subject. This illuminance corresponds to a suitable CS, usually defined as 0.3, following previous studies (Figueiro, 2017). That CS value is enough to promote a sufficient circadian entrainment for students. The calculation period must be during the morning, preferably from 11 am to 12 pm “noon”, as the luminance of the sky is higher during that period. This new metric helps determine a healthy architectural design for educational spaces.
disruption is higher in adolescents than in adults (Bel, 2013). Therefore, lighting design in educational spaces is crucial to promoting good circadian entrainment for students (Figueiro et al., 2011). In 2005, Rea et al. proposed an empirical model of human circadian response based on the neuroanatomy and neurophysiology of the retina and on results from published psychophysical studies (Rea et al., 2005). Although the photosensitive retinal ganglion cell (ipRGC) is the key element in the empirical model, subsequent studies have shown that signals from rods and cones also provide photic information to the biological clock. The method proposed by Rea et al. studies these multichannel inputs and quantifies the CS, also defined as melatonin suppression, considering 1 h exposure time with a fixed 2.3 mm diameter pupil. Fig. 1 shows the spectral and absolute sensitivities of the human model. As seen in Fig. 1B, the saturation of the CS produced by daylight is observed at 0.7, while other variables also affect melatonin suppression. According to the latest studies by Figueiro et al. (2017) a CS value of 0.3 during the morning is suitable for the promotion of good circadian entrainment. There are several metrics to measure the daylight incidence through windows. Daylight factor is the most common definition to determine the daylight in a venue. It shows the relative illuminance inside a space as a fraction of the illuminance outside, using the less favorable scenario, overcast sky conditions (CIE, 2011), avoiding sunlight. Many of the studies on daylight design are based on this concept; Love established the calculations for determining the daylight factors under real overcast skies (Love, 1993), Acosta et al. developed a predictive method for quantifying this metric according to daylighting allowed by windows (Acosta et al., 2015) and subsequently by courtyards (Acosta et al., 2014, 2017) and Chel et al. (2010) carried out a study about the measurement of this metric related to skylights. However, the new research trend in this field follows daylight dynamic metrics, based on climate data, occupancy hours and blind control (Munoz et al., 2014). The most commonly used dynamic metric is daylight autonomy (DA), which determines the percentage of the occupancy time during which an illuminance threshold is met by daylight alone (Reinhart et al., 2006). In accordance with this trend, the new concept of circadian stimulus autonomy (CSA) appears, based on the CS requirements described above and the potential of the daylight dynamic metrics, and this determines the percentage of days throughout the year when a suitable threshold of circadian stimulus is met by daylight (Acosta et al., 2017) during the morning.
1.2. Aim and objectives This study aims a first approach for determining the suitable window size for multipurpose classrooms of educational buildings, in order to promote a proper CS value for students and provide an entrained circadian system, as well as assessing the impact of electric lighting on their biological clock. The calculations were carried out for three locations, considering the different daylight SPDs and a variable luminance of the sky vault. The classroom studied had a variable window size with two main orientations and a variable reflectance of inner surfaces. This research provides the following innovations:
• The analysis of the CS according to the geometry and external • •
conditions of the classroom is using the CSA metric, concept described for the first time in a previous investigation on circadian entrainment in hospital rooms (Acosta et al., 2017). Unlike previous research, the architectural design is assessed using three different sky conditions, with different SPDs and luminance values so that the impact of climate conditions on the CS can be evaluated. In contrast to previous studies (Leslie et al., 2012; Acosta et al., 2017), three different reflectance values were considered for the environment of the subject, so that the effect of the context of the student is also assessed to determine the CS value.
2. Methods The methodology of this research is based on a calculation model of a typical classroom, which serves to assess the impact of natural and 585
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Fig. 2. Methodology flowchart.
final location was Madrid, Spain, with mainly clear skies. Weather data for the three locations were defined using the EnergyPlus reference (LBNL, 2012), from the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) (ASHRAE, 2001) and the Spanish National Institute of Meteorology (AEMET) (de la Flor et al., 2008). The average SPD for each location is presented in Fig. 5, which shows the deduced spectral distribution of each sky according to statistical data on the percentage of overcast skies throughout the year. As seen from Fig. 5, the SPD in London is closer to a typical overcast sky, while in the case of Paris the result is near the usual CIE Standard Illuminant D65 distribution for northern Europe. In the case of Madrid, located in southern Europe with mainly clear skies, the SPD is slightly higher for short wavelength than the CIE Standard Illuminant D65 distribution. In accordance with the previous sky conditions and the environment of the classroom, Fig. 6 shows the SPDs produced by the reflection of the daylight spectrums on the three spectral reflectance values considered for the student’s desk. The spectrums shown above, in Fig. 6, serve for determining the CS value in accordance with an illuminance value. The impact of the variation of the SPD in the CS calculation is quantified in a complementary test, included in this study.
electric lighting in the circadian stimulus. The deduction of the resulting SPD perceived by the eyes of the observer—defined by the light source spectra and the reflectance of the environment—serves to quantify the CS produced and therefore the suitable architectural variables, such as the window size or the characteristics of the electric light source. Fig. 2 shows the flowchart which represents the methodology described below. 2.1. Characteristics of the room model According to the most common classroom design and considering large dimensions in order to assess the CSA over a sufficient surface, a virtual venue 3.0 m high by 8.0 m deep by 8.0 m wide was defined. The virtual classroom and the quantification of its calculation variables are shown in Fig. 3. The room had a single 7.0 m long window of variable height located on one facade. The window had two different locations: centered in the facade or in an upper position with a height of sill of 1.5 m. The CS and CSA were analyzed according to two different reflectance values of the inner surfaces of the classroom, considering a diffuse reflection. The analysis of the calculation models was carried out for two window orientations—north and south—and three locations—London, Paris and Madrid. The light received by the eye of the student depends on the spectral distribution of the average sky conditions and its reflection on the environment of the subject. For this research, three spectral reflectance values were considered for the student’s desk, in order to determine the resulting SPD perceived by the subject. Fig. 4 shows the reflectance values per wavelength for three different desks: white, light wood and light blue.
2.2.2. Orientation of the window Given the importance of the window orientation for the use of natural light (Munoz et al., 2014), two orientations were proposed to assess the CSA promoted by lighting: north and south. Both situations show the best and worst case scenarios for determining daylight illuminance. West and east orientations provide an intermediate scenario in most of the cases, due to lower elevation of the Sun. Particularly, windows facing east could provide similar lighting within the classrooms than that observed for South orientation, considering occupancy hours just during the morning. Further studies may be convenient to properly quantify the influence of other orientations on the circadian stimulus.
2.2. Selecting the daylight conditions 2.2.1. Location of the room In order to determine the CSA for a wide range of climate conditions, the classroom was analyzed in three different locations in Europe. The first location was London, UK, with mainly overcast skies. The second was Paris, France, with predominantly intermediate skies. The 586
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Fig. 3. Calculation model of the classroom.
in Fig. 8, modified by the reflectance of the proposed work planes previously defined in Fig. 4. These spectrums define the electric light perceived by students and therefore the circadian rhythm. Regardless of the average illuminance of the classroom, the different spectral distributions promote a variable CS for students, so that it is important to analyze the results according to the color temperature of the proposed luminaires.
2.3. Selecting electric light conditions A suitable circadian rhythm can also be promoted by means of electric lighting, although its capacity for enabling proper synchronization of the biological clock is less than that of daylighting. Two scenarios were used to determine the effect of electric lighting in CS: one with warm LED lamps—with a Correlated Color Temperature (CCT) of 2700 K—and another with cool luminaires—with a CCT of 6500 K. The spectral distributions of warm and cool fixtures are shown in Fig. 7, according to the spectrometry measurements. In accordance with the variables required by the CS calculation, the resulting spectral distributions of warm and cool luminaires are shown
2.4. Selecting the calculation metrics 2.4.1. Calculation of CS As deduced from the above, the CS promoted by daylight depends
Fig. 4. Reflectance of the work plane according to different spectral reflectance values. 587
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Fig. 5. Average spectral power distribution of the locations studied.
75% to provide the same circadian rhythm as that of the warm fixtures. The suitable CS value, established by Figueiro (2017) is usually defined as 0.3, so the lowest illuminance thresholds, in accordance with the sky conditions, are 186 lx for Madrid, 237 lx for Paris and 263 lx for London. These values serve to determine the CSA calculation, as explained previously. For the case of electric lighting, the lowest illuminance required corresponds to 400 lx for warm LED lamps and 295 lx for cool luminaires.
mainly on the SPD and the illuminance perceived by the study subject. The influence of both variables serves to determine the circadian light (CLA), which is used in the model of phototransduction developed by Rea et al. (2005) to calculate the CS, as seen in Eq. (1):
⎛ CS = 0.7·⎜1 − ⎜ 1+ ⎝
1
⎞
⎟ CLA 1.1026 ⎟ 355.7 ⎠
( )
(1)
CS is directly proportional to the predicted levels of light-induced nocturnal melatonin suppression from threshold to saturation, assuming a pupil size of 2.3 mm and a 1 h duration of exposure. According to the SPDs shown in Fig. 6 and Eq. (1), the illuminance thresholds can be determined depending on the average sky conditions perceived by the observer. Table 1 shows the illuminance values for the work plane, depending on different CS values and the resulting SPDs for Madrid, Paris and London. As can be deduced from Table 1, the optimal environment for the student to promote a good CS corresponds to a white work plane. In accordance with the perceived spectrums shown in Fig. 8 and Eq. (1), the illuminance level can be also defined for a specific CS value depending on the CCT of the electric light source. Table 2 shows the illuminance values required in the work plane, according to different CS values and the resulting SPDs of warm and cool LED luminaires. As deduced from Table 2, cool LED lamps require a lower illuminance level than warm luminaires in order to promote the same CS value. Quantifying the previous statement for the study case with a white work plane, cool LED lamps need a luminous flux between 70 and
2.4.2. Calculation of CSA As mentioned above, CSA is defined as the percentage of days throughout the year when a suitable threshold of CS is met by daylight alone (Acosta et al., 2017) during the morning. This definition is similar to the approach used in the calculation of DA, in accordance with the illuminance threshold determined by the CS calculation. The illuminance required depends on the SPD perceived by the eye of the observer, determined by the sky conditions and the reflectance values of the environment of the subject. As can be deduced, CSA cannot be applied for electric lighting, since it is only useful for a light source with a variable luminous flux, such as in the case of daylighting. 2.5. Selecting the calculation program The illuminance threshold for each location and the calculation period determine the DA values, which correspond to the CSA, according to the values established previously. This dynamic metric is calculated using DaySim 3.1. The accuracy of this lighting program has
Fig. 6. Resulting spectral power distribution of the locations studied. 588
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Fig. 7. Spectral power distribution of the electric light sources studied.
Fig. 8. Resulting spectral power distribution for electric lighting.
Table 1 Illuminance required in the work plane to promote a specific CS value (0.1, 0.3 and 0.5) according to the resulting SPD produced by different sky conditions and the reflectance values of the environment.
Table 2 Illuminance required in the work plane to promote a specific CS value (0.1, 0.3 and 0.5) according to the resulting SPD produced by warm and cool LED luminaires and the reflectance values of the environment.
Location
Circadian Stimulus
White work plane
Light Wood work plane
Light Blue work plane
Luminaire
Circadian Stimulus
White work plane
Light Wood work plane
Light Blue work plane
Madrid
0.1 0.3 0.5
48 lx 186 lx 543 lx
255 lx 996 lx 2961 lx
88 lx 343 lx 994 lx
Warm LED
0.1 0.3 0.5
102 lx 400 lx 1195 lx
613 lx 2387 lx 7097 lx
147 lx 574 lx 1705 lx
Paris
0.1 0.3 0.5
61 lx 237 lx 684 lx
286 lx 1118 lx 3344 lx
108 lx 422 lx 1215 lx
Cool LED
0.1 0.3 0.5
77 lx 295 lx 847 lx
327 lx 1276 lx 3804 lx
138 lx 531 lx 1513 lx
London
0.1 0.3 0.5
68 lx 263 lx 757 lx
299 lx 1174 lx 3498 lx
120 lx 461 lx 1321 lx
means of real measurements in two classrooms of the High School of Architecture of Seville (Spain), one oriented to North and the other facing South, as seen in Fig. 9. The relative difference of the CS results according to the SPDs studied varies between 2.6 and 7.3%, considering an illuminance value between 500 and 1500 lx measured on the work plane. Accordingly, it can be concluded that the SPD of the sky barely affects to the CS promoted, except in extreme circumstances, such as very low illuminance values or high variations of the sky SPD.
been tested in several studies (Reinhart and Walkenhorst, 2001; Reinhart and Breton, 2009; Bellia et al., 2015).
2.6. Quantification of the spectrum variation in the CS 2.6.1. Impact of the spectrum of the sky In order to validate the methodology described above, two trials are carried out with the aim to determine the impact of the SPD variation on the CS calculation, keeping a constant value of illuminance. The first trial analyzes the impact of the variation of the sky SPD by
2.6.2. Impact of the observation conditions In the methodology proposed, it was assumed that the student was looking directly at the desk and perceiving the light reflected from it. 589
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Fig. 9. Classrooms tested for determining the impact of the sky SPD on the CS calculation. A: Classroom with windows facing north. B: Classroom with windows facing south.
Fig. 10. Observer locations tested for determining the impact of the perceived SPD. (A) Observer looking directly at the table (angle 0°). (B) Observer looking at the back of the chair in front (angle 30°). C: Observer looking at the end of the classroom (angle 60°).
double glazing, which has a solar factor of 0.75. The reflectance considered for the inner envelope is 0.72 in the case of walls and ceiling, and 0.22 for the floor. Illuminance measurements were taken throughout 2017, from January 1st to December 31st, using 8 illuminance-meters with a range of 20–2000 lx and accuracy of ± 3.0%, placed above the floor of the cell, on the axis of symmetry with a spacing of 0.40 m between sensors, as Fig. 11A and B shows. The simulation model has a superimposed calculation grid, representing the location of these sensors. The weather conditions for computations correspond to Seville (Spain), at Latitude 37.42° and Longitude 5.40°, with mainly clear skies.
However, the observer’s point of view can also affect to the resulting SPD perceived by the eyes, modifying the CS promoted by daylighting. Accordingly, a second trial is carried out for determining the impact of the point of view of the observer on the CS calculation. As in the previous trial, real measurements were taken in a classroom of the High School of Architecture of Seville (Spain) with windows facing South. Three angles of vision are considered for the observer: angle 0° (looking at the table), angle 30° and angle 60° (looking at the end of the classroom). Fig. 10 shows the procedure for the measures taken. The variation between different point of views hardly affects to the CS calculation, determining a maximum divergence of 0.6% in the worst case, for an illuminance value of 100 lx. Accordingly, it can be stated that the point of view of the observer barely affects to the CS value, except in the case of different conditions to those analyzed in this trial, such as the case of an observer looking directly to the window.
2.7.2. Boundary conditions of the validation process The occupancy schedule for the calculation of DA and Continuous Daylight Autonomy (DAC), both for measurements and computer simulation, was considered from 8:00 to 17:00. Three illuminance thresholds were established for DA and DAC calculations, 100, 250 and 500 lx.
2.7. Validation of the calculation program The use of dynamic metrics or a computational tool is not reliable without a previous validation process. Consequently, a validation study was developed through experimental trials with a test cell (Campano et al., 2018).
2.7.3. Results of the validation process Fig. 11C shows the DA and DAC values calculated for the study, through a graph for each of the three illuminance thresholds (100, 250 and 500 lx), comparing results from measurements and computer simulations. These graphs also show their different percentages. DA values obtained in simulations are close to those observed in measurements, with the highest maximum deviation for the 500 lx threshold case (8.4%). In addition, although the divergences between measurements and simulations are greater in terms of depth for the three illuminance thresholds, as low values of under 10% they are therefore acceptable for the validation of this simulation program.
2.7.1. Definition of the test cell and the simulation model The south-facing test cell used for this study by TEP-130 research group (León-Rodríguez et al., 2017) is located in the city of Seville (Spain). This room is 2.40 m wide, 3.20 m deep and 2.70 m high. Its enclosure, including the roof and the floor, is built using high-density white polyurethane sandwich panels fixed to a steel frame. The southfacing facade has a window 108 cm high by 116 cm wide, with 4.8.4 590
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Fig. 11. Test cell for validation of metrics used. (A) Test cell inner view with illuminance-meter distribution. (B) Simulation model with array of study points. (C) Daylight Autonomy (DA) and Continuous Daylight Autonomy (DAC) results obtained from illuminance measurements and simulation calculations.
DA and 1.0% for DAC, showing a standard deviation (95% reliability) of 6.8% for DA and 4.9% for DAC. As in the previous analysis, these deviations are acceptable, given that they are below 10%. From these results it can be concluded that DaySim 3.1 accurately calculates DA
Divergences in DAC values are smaller than those of the case of DA, showing the highest maximum deviation (4.2%) for 100 lx and obtaining better approaches for higher illuminance thresholds. The bias error obtained for these two metrics is 1.9% in the case of 591
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Fig. 12. Average circadian stimulus for the Paris location according to north orientation.
3. Results
and DAC dynamic metrics in rooms with similar boundary conditions and can therefore provide an accurate calculation for CSA.
3.1. Average circadian stimulus promoted by daylighting Following the methodology described above, the average CS values measured at the study points can be calculated from the established variables. Fig. 12 for example shows these values for the Paris location 592
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metrics, the CSA determines the percentage of days when a minimum CS value is met by daylight alone (Acosta et al., 2017) during the morning. This concept, consistent with the method for DA calculation, is used to assess the suitability of a space to promote a good circadian entrainment with various design parameters. Fig. 13 shows the CSA values for the Paris location, depending on the distance from the facade, in accordance with a CS threshold of 0.3. Each row in the figure shows the measurements for a particular window size. Left columns show the results for windows facing north, while right columns represent openings facing south. Following this figure description, odd columns describe the CSA values for centered windows while even columns show the results for openings in upper position. The functions shown in the graphs represent the CSA measurement for two main reflectance values of the classroom—bright and dark—considering the three desks described in the methodology—white, light wood and light blue. As observed in Fig. 13A, a small window with an opening equivalent to 30% of the facade allows a CSA value higher than 50% in the entire room, considering a white work plane and high reflectance values of the inner surfaces of the classroom. Taking into account a light blue desk, the window to facade ratio must increase up to 45% to ensure a good circadian entrainment for students, as can be deduced from the results shown in Fig. 13B. In dark classrooms, with a low reflectance of the inner surfaces, only a white work plane can achieve a proper CSA value considering a window to facade ratio equal or higher than 60%, as deduced from Fig. 13C. The light wood work plane only allows a sufficient CSA value—equal or higher than 50%—in the zone near the facade. It is worth noting that there is a clear divergence of CSA values between bright and dark classroom models, which increases according to the distance from the facade. In some cases, such as venues with blue or wood work planes, the CSA in the back of the room is null, hence a suitable CS value—higher than 0.3—cannot be achieved for any day of the year. Therefore, both the reflectance of the inner surfaces and the work plane are decisive in allowing a minimum circadian entrainment. In the case of London location, a small window size, close to a window to facade ratio of 30%, barely promotes a good CSA value—higher than 50%—for a white work plane, except in the case of classrooms with a high reflectance value and a south-facing window. Considering worse boundary conditions, with a window facing north, a window to facade ratio of 45% should be selected to achieve a CSA value above 50%. Regardless of window size, the light wood work plane never promotes a good CSA value, except in the area close to the facade for south-facing openings. Considering the climate conditions of Madrid, a bright classroom with a white work plane achieves an almost perfect CSA value —higher than 90%—considering a minimum window size and regardless of the
considering a window facing north. As can be seen in Fig. 12, the classroom sections show the average CS, measured from 0 to 70%, according to the distance from the facade. The window size varies from 30 to 60%, while its position is considered centered in the facade or in an upper position, considering a sill 1.5 m high. Bright rooms, with a high reflectance value of the inner surfaces, are located on the left side of the graph, while dark rooms, with a low reflectance value, are shown on the right. As expected, the average CS values are higher in the area near the window, gradually decreasing toward the back of the room, following a similar tendency—although not proportional—to the average illuminance values (Acosta et al., 2016). As deduced from the results observed in Fig. 12, the resulting CS and the window area are not directly proportional. However, a linear tendency can be observed comparing the CS values from the three window sizes: The window to facade ratio of 60% shows an average increase of CS near 15% compared to the medium size window, while the window to facade ratio of 45% promotes an increase of 14% compared to the smaller window. The SPD generated by the color spectrum of the environment notably affects the resulting CS value, as can be observed from the room sections shown in Fig. 12. The white work plane promotes an average CS value between 20 and 50% higher than that of the light blue desk, depending on the window size and the inner reflectance values. Compared to the light wood desk, the white work plane produces an increase of the average CS between 100 and 255%. According to these statements, the spectral reflectance of the environment is probably the most important variable in determining CS values. In order to define a suitable window design in classrooms to promote a sufficient CS over the entire work plane—that is to say, a CS value equal or higher than 0.3—the minimum window size can be determined for each location and orientation. Table 3 summarizes the minimum window to facade ratio for each scenario according to the results shown in Fig. 12 and those observed for London and Madrid locations. As can be deduced from Table 3, a classroom with inner surfaces with a low reflectance barely provided a suitable CS value, regardless of the window size. Following the previous statement, a light wood environment, with an SPD predominantly close to a long wavelength, notably hinders a good circadian entrainment. It is therefore clear that the environment to promote a minimum value of CS should be white or pale blue. Moreover, the location and orientation of the window are relevant variables for determining the opening size, regardless of the relevance of the color of the environment. 3.2. Circadian stimulus autonomy promoted by daylighting As explained in the section on background and the calculation
Table 3 Minimum window to facade ratio to promote an average CS value higher than 0.3 during the morning. Location
London
Orientation
North South
Paris
North South
Madrid
North South
Window Position
Bright inner surfaces
Dark inner surfaces
White work plane
Light wood work plane
Light blue work plane
White work plane
Light wood work plane
Light blue work plane
Centered Upper Centered Upper
45% 45% 30% 30%
– – – –
60% 60% 45% 30%
– – 60% 60%
– – – –
– – – –
Centered Upper Centered Upper
30% 30% 30% 30%
– – – –
45% 60% 30% 30%
– 60% 60% 60%
– – – –
– – – –
Centered Upper Centered Upper
30% 30% 30% 30%
– – 60% 60%
45% 45% 30% 30%
60% 60% 45% 45%
– – – –
– – – 60%
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Fig. 13. Circadian stimulus autonomy for a CS value of 0.3 in the Paris location. (A) Rooms with a window to facade ratio of 30%. (B) Rooms with a window to facade ratio of 45%. (C) Rooms with a window to facade ratio of 60%.
luminaires was also analyzed. As described in the methodology, nine fixtures with a luminous flux of 3700 lm 2.4 m apart are located in the ceiling of the classroom. According to the results of the Dialux 4.13 lighting simulation program, an average illuminance higher than 500 lx was achieved in the work plane of the venue. As shown in Fig. 8, warm LED lamps—with a CCT of 2700 K—and cool LED luminaires—with a CCT of 6500 K—generate different spectral distributions modified by the reflectance of the work planes. These spectrums determine the CS promoted by observers, depending on the illuminance value perceived by eye. In accordance with the illuminance values perceived by the
orientation or position of the opening. This statement is true for the predominantly clear skies typical of Madrid, which promote a good circadian entrainment irrespective of the architectural design. However, a classroom with dark inner surfaces or a light blue work plane requires a larger window, close to 45% of the facade. As in the previous cases, the light wood plane barely achieves a suitable CSA value, except in the case of medium or large south-facing windows.
3.3. Average circadian stimulus promoted by electric lighting In addition to the previous test, the circadian rhythm allowed by 594
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Fig. 14. Circadian stimulus for warm and cool electric lighting.
a CS value equal or higher than 0.3— regardless of window size, orientation or location. Moreover, the environment to promote a minimum value of CS should be white or close to pale blue. It is worth noting that a white work plane allows an increase of CS up to 30% compared to a pale blue desk for a large window—a 60% ratio—and up to 50% in the case of a small opening—a 30% ratio. For the case of the locations studied and with a white work plane, a window to facade ratio of 45% should be selected for the case of London and a minimum ratio of 30% is needed for Paris and Madrid. In accordance with the previous statement, it can be concluded that environment reflectance is one of the most influential variables, just as the typical weather conditions are more decisive than latitude in promoting a good CS. The above methodology was applied to electric lighting, which also provided a suitable CS value. As deduced, cool LED lamps with a CCT of 6500 K allowed up to 5% more CS than warm luminaires in the case of a light blue work plane, 19% more compared to the results for a white work desk and 81% more than the scenario with a light wood environment, taking into account the same luminous flux for both types of luminaire. As in the study of the influence of daylight illuminance in CS values, the spectral reflectance of the observed environment is crucial in defining a suitable circadian entrainment. According to the scenario with electric light sources, a white work plane provided an increase of CS up to 40% compared to a light blue desk and was three times higher than that of a light wood environment. CSA has proven to be an effective metric to assess the performance of an architectural space in the well-being of occupants. As explained in the methodology, this definition determines the percentage of days throughout the year when a suitable threshold of CS is met by daylight alone during the morning. As deduced from Fig. 13, CSA varies noticeably depending on the sky conditions. On one hand, for the London location, with a mainly cloudy sky, a small opening size with a window to facade ratio of 30% barely promotes a good CSA value above 50%. On the other hand, for the Paris and Madrid locations, with predominantly intermediate or clear skies, a small window equivalent to 30% of the facade surface, promotes a good CSA value. Thus, a spectral distribution of the sky with a dominant short wavelength range is relevant in producing a sufficient circadian rhythm. Moreover, analyzing the results observed for CSA, window orientation also affects the measurements of this metric, although its influence and lintel height are not as decisive when the reflectance of the inner surfaces of the classroom and of the observed environment are high enough. From the above, the spectral reflectance of the observed
observers and the resulting spectral distribution, Fig. 14 shows the CS values for warm and cool LED luminaires, depending on the reflectance of the inner surfaces of the room and on the color of the work plane. Analyzing the effect of color temperature of luminaires in Fig. 14, it is shown that cool LED lamps, with a CCT of 6500 K, produce an increase in CS values compared to warm light sources, with a CCT of 2700 K. In fact, cool lamps promote 5% more CS than warm luminaires in the case of a light blue desk, 19% more than that of a white work plane and 81% more than that of the scenario of a light wood environment. As seen for the case of daylight conditions, the spectral reflectance of the observed work plane is key for determining a proper CS value. In fact, considering a scenario with electric lighting, a white work plane promotes an increase of CS of up to 40% in comparison with a light blue desk and three times higher than that of a light wood environment. The reflectance of the inner surfaces of the classroom also affects the CS provided by electric light sources. As deduced from Fig. 14, a room model with bright surfaces promotes between 20 and 60% more CS than that defined with dark surfaces. Therefore, the reflectance of the surfaces of the room is decisive in allowing a suitable CS value under electric lighting conditions. Finally, comparing the results from Figs. 12 and 14, and deduced from Table 3, it can be concluded that a classroom with a sufficient window to facade ratio does not require electric lighting to promote a good CS value. This is particularly true of classrooms with high reflectance surfaces in the zone near the facade.
4. Conclusions Circadian rhythm is an essential variable which decisively affects the well-being of occupants and should be taken into account in architectural design. The effect of light on the CS is greater on children and adolescents than on adults, hence natural and electric lighting design in classrooms is important for good circadian entrainment for students. As seen from the results, the CS can be quantified for different scenarios and variables, such as window size, reflectance of the inner surfaces and the work plane observed, as well as the color temperature of the luminaires located in the ceiling. In accordance with the window design of classrooms for a proper CS by daylighting, Table 3 above summarizes the minimum window to facade ratio for each location, window position and environment reflectance. As shown, a room with a low reflectance of the inner surfaces or of the work plane barely provided a suitable CS value—that is to say, 595
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environment is one of the key variables in providing sufficient circadian entrainment for students. As deduced from the CSA quantification, a white work plane provides a good CS for students up to 54% more time during the year than light blue desks, considering the optimal architectural design; with the largest window facing south and high reflectance of the inner surfaces of the classroom. In the case of a worse scenario, with a north-facing opening or low reflectance surfaces, this increase is noticeably higher. Obviously, a light wood work plane is rejected, since it never produces a proper circadian rhythm, regardless of window dimensions or orientation. In conclusion, the circadian rhythm is a decisive variable in the well-being of students, and hence both the architectural and electric lighting design are extremely important for providing a sufficient amount of light and suitable spectrum in order to promote a good circadian entrainment. Window size in particular should be selected according to typical sky conditions, always considering a high reflectance of the inner surfaces of the classroom and paying special attention to the color of the work plane.
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Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The authors wish to thank the editor and reviewers for their labor improving this research piece. The results presented were funded by the Government of Spain through the research project: Efficient design for biodynamic lighting to promote the circadian rhythm in shift work centers (Ref BIA201786997-R). The authors wish to express their gratitude for all the technical and financial support provided. The authors are especially grateful to all those who collaborated in this research project, specially to Blas de Lezo for the encouragement of this article. We would also like to express a special acknowledgment to the Lighting Research Center at Rensselaer Polytechnic Institute (LRC), USA. References Acosta, I., Navarro, J., Sendra, J.J., 2014. Lighting design in courtyards: predictive method of daylight factors under overcast sky conditions. Renew. Energy 71, 243–254. Acosta, I., Munoz, C., Campano, M.A., Navarro, J., 2015. Analysis of daylight factors and energy saving allowed by windows under overcast sky conditions. Renew. Energy 77 (1), 194–207. Acosta, I., Campano, M.A., Molina, J.F., 2016. Window design in architecture: Analysis of energy savings for lighting and visual comfort in residential spaces. Appl. Energy 168, 493–506. Acosta, I., Leslie, R.P., Figueiro, M.G., 2017. Analysis of circadian stimulus allowed by daylighting in hospital rooms. Light. Res. Technol. 49 (1), 49–61. Acosta, I., Varela, C., Molina, J.F., Navarro, J., Sendra, J.J., 2017. Energy efficiency and lighting design in courtyards and atriums: a predictive method for daylight factors. Appl. Energy 211 (November), 1216–1228. Acosta, I., Campano, M.A., Dominguez-Amarillo, S., Muñoz, C., 2018. Dynamic daylight metrics for electricity savings in offices: Window size and climate smart lighting management. Energies 11 (11). Al-Khatatbeh, B.J., Ma’Bdeh, S.N., 2017. Improving visual comfort and energy efficiency in existing classrooms using passive daylighting techniques. Energy Procedia 136, 102–108. ASHRAE, 2001. International Weather for Energy Calculations. Bedon, C., et al., 2019. Structural characterisation of adaptive facades in Europe – Part I: Insight on classification rules, performance metrics and design methods. J. Build.
Glossary CS: Circadian Stimulus DA: Daylight Autonomy DAC: Continuous Daylight Autonomy CSA: Circadian Stimulus Autonomy SPD: Spectral Power Distribution CLA: Circadian Light
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