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Revista hábitat sustentable

versión On-line ISSN 0719-0700

Rev. hábitat sustentable vol.11 no.1 Concepción jun. 2021

http://dx.doi.org/10.22320/07190700.2021.11.01.04 

Articles

OFFICE USER-WORK PERFORMANCE INDICATOR IN WARM TEMPERATE SUMMER PERIOD

Yesica Alamino Naranjo* 
http://orcid.org/0000-0002-2325-2206

Alcion Alonso Frank** 
http://orcid.org/0000-0002-9227-3449

*Investigadora adjunta, Universidad Nacional de San Juan, Instituto Regional de Planeamiento y Hábitat, Facultad de Arquitectura, Urbanismo y Diseño, San Juan, Argentina, alaminoyesica@gmail.com.

**Profesora Adjunta, Universidad Nacional de San Juan, Instituto Regional de Planeamiento y Hábitat - Facultad de Arquitectura, Urbanismo y Diseño, San Juan, Argentina, arqalcionfrank@gmail.com.

ABSTRACT:

The purpose of this work was to develop a methodological tool to evaluate office space work performance during the summer period. The proposed tool is an optimal work performance indicator called IRLO, which combines environmental variables on thermal, air quality, visual and acoustic influence. Integrated measurements were run for its development alongside surveys to users-workers of an office building in the city of San Juan - Argentina. The results reveal the preference ranges of each variable, recognizing that in open plan offices, there is a greater environmental adaptive capacity than in closed plan offices. It is concluded, that the indicator stands out by providing a basis to identify work performance considering environmental variables that should, in the future, be considered in the design phase.

Keywords: Environmental quality; office building; typology

INTRODUCTION

In the world, a fifth of the population inhabits their work spaces more than 48 hours a week (International Labor Organization [ILO], 2020). These spaces are diverse, depending on the type of activity taking place. In Argentina, 60% of them are from the office sector (National Institute of Statistics and Censuses [INDEC], 2010). These work sites are conceived in terms of elements containing the roles that users-workers (UW) perform, underestimating the important of indoor environmental quality (IEQ) (Marín Galeano, 2013), which is a priority, given that the spatial setup modifies environmental factors, and a result, has an influence on the sensation of comfort and work performance (WP) of the UW (Nag, 2019).

From the scientific world, progress has emerged on the topic, indicating the indoor environmental variables that have the greatest impact on health and performance (WEI et al., 2020) and that, at the same time allow understanding issues related to spatial design. Among these the temperature (Wargocki & Wyon, 2017; Lamb & Kwok, 2016; Maula, Hongisto, Koskela & Haapakangas, 2016), CO2 concentration (Candanedo & Feldheim, 2016; Shriram, Ramamurthy & Ramakrishnan, 2019), lighting level (Liu, Lin, Huang & Chen, 2017, Yang & Moon, 2019; H. Wu, Y. Wu, Sun & Liu, 2020), and the indoor noise level (Liebl & Jahncke, 2017; Kari, Makkonen & Frank, 2017) stand out. There is also research that addresses these variables holistically, seeking to find relations between them, as well as to identify those that have the greatest effect on people’s wellbeing (Haegerstrand & Knutsson, 2019; Lou & Ou, 2019; Shin, Jeong & Park, 2018, Wei et al., 2020). However, studies that address WP in offices and how this is holistically affected by the aforementioned variables, are not known, particularly in a warm template climate. For this reason, it is necessary to broaden knowledge focused on these latitudes, especially in a critical period, like summer.

This research has the purpose of getting to know the relationship between IEQ in offices and the WP of UW, for the purposes of determining optimal WP ranges, and making their numerical valuations. For this, an Optimal Work Performance Indicator (OWPI) is designed. In this sense, it is worth underlining that, from architectural spatiality, two clearly defined typologies are recognized, open (OO) and closed (CO). These are studied independently, seeking to find possible similarities and differences.

METHODOLOGY

The research begins with an experimental approach, using field work in offices in a warm template region. Integrated measurements are made on environmental variables, enquiring about the self-reported WP evaluation, through surveys made for this research.

The ranges of highest and lowest influence on WP are obtained from the results, for each environmental variable analyzed, where these are quantitatively evaluated and graphically expressed. Finally, each one of the performance variability ranges leads to the construction of the OWPI, the target of this study.

CHARACTERIZATION OF THE SITE

The city of San Juan (Argentina) is located 630 meters above sea level, at 31.6° south and 68.5 west. The climate, according to the IRAM 11603 standard (1996), is warm template with large temperature variations (Figure 1), atmospheric transparence (Figure 2), and low humidity (Figure 3). The rainfall is continental, with a medium low frequency (Figure 4). According to the Köppen classification (Minetti, Carletto & Sierra, 1986), it is cold desert type (BWh), where winters are very cold, and summers template or warm. It has a regular moderate southeasterly wind, a characteristic dry-warm zonda wind, considered as a severe westerly event because of its intense gusts (Puliafito, Allende, Mulena, Cremades & Lakkis, 2015). It is most common in August and September (Perucca & Martos, 2012).

Source: Prepared by the authors based on data from Weather Atlas.

Figure 1: Mean maximum and minimum annual temperatures (Cº)-San Juan, Argentina. 

Source: Prepared by the authors based on data from Weather Atlas.

Figure 2: Daylight / Sunlight hours (annual) -San Juan, Argentina. 

Source: Prepared by the authors based on data from Weather Atlas.

Figure 3: Annual humidity percentage (%)-San Juan, Argentina. 

Source: Prepared by the authors based on data from Weather Atlas.

Figure 4: Mean annual rainfall (mm)-San Juan, Argentina. 

OBJECT OF STUDY

The choice of the case study is based on the environmental impact analysis arising from its level of consumption in the city of San Juan. For this reason, the energy consumption of buildings is analyzed and their relationship per meter squared of useful surface (with climate control), destined for work spaces (offices), considering those that exceed 3 (three) floors (Figure 5).

Source: Provincial Energy Regulating Entity.

Figure 5: Electricity consumption per meter squared of office buildings located in the city of San Juan, Argentina. 

The Civic Center building (CCV) (Figure 6 and Figure 7) has the highest electricity consumption, with values of over 340 kWh/m2.year, which is why it was chosen as the case study. Table 1 summarizes its most relevant characteristics.

Source: Urban Development and Planning Direction.

Figure 6: Civic Center Building-Ground Floor. 

Source: Preparation by the Authors.

Figure 7: East facade Civic Center Building. Source: Preparation by the Authors. 

Table 1: Characterization of the Civic Center building. 

Total surface area 80,873m2
Orientation East-West
Office Surface area [%] 59%
Hold-Cold climate control system HVAC (Heating-Ventilation-Air-Conditioning)
Consumption [kWh/m2.year] 342
Structure type Reinforced Concrete
Enclosure masonry Light mdf sheet
Glazed surface for open office 0%
Glazed surface for closed office 50%
Lighting system Circuits differentiated by floor (led system)
Number of UW 4046
UW surveyed 636

Source: Preparation by the Authors.

CLASSIFICATION OF OFFICE SPACES

The variability of IEQ requires distinguishing elements and grouping them by their characteristics. It is for this reason that in this work, office spaces are distinguished as OO (Figure 8 and Figure 9) and CO (Figure 10 and Figure 11). Both have differences that stand out, which a priori leads to thinking about the advantages of the CO over the OO (Pan et al., 2018). Table 2 shows the characteristics that allow establishing the main comparisons.

Source: Preparation by the Authors.

Figure 8: Standard floor plan of two open architectural typology offices (OO). Measured in meters. 

Source: Preparation by the Authors.

Figure 9: Cross-section of the two open architectural typology offices (OO). Measured in meters. 

Source: Preparation by the Authors.

Figure 10: Standard floor plan of two closed architectural typology offices (CO). Measured in meter. 

Source: Preparation by the Authors.

Figure 11: Cross-section of the two closed architectural typology offices (CO). Measured in meters. 

Table 2: Typological characterization of Offices. 

Characterization Closed office (CO) Open Office (OO)
Presence of windows Yes No
Possibility to open Yes No
Daylight control Yes No
Height of enclosure-panel 3.60 m. (100%) 0.80 m - 2.10 m. (25 %)
Average occupation factor 5.10 m2/people 4.50 m2/people
Capacity 2 to 6 people 3 to 11 people
Activity Internal Work Internal work-attention to the public

Source: Preparation by the Authors.

MEASUREMENT SYSTEMATIC

To collect data, the “Spot” type systematic (focused) was used, based on the techniques of De Dear (2004) and Kuchen and Fisch (2009), and adapted to the collection of the four environmental variables. In this framework, a mobile measurement unit (MMU) is designed (Figure 12), which allows examining 164 spaces, with 636 surveys made during the summer period.

Source: Preparation by the Authors.

Figure 12: Mobile measurement unit.  

The MMU comprises sensors (Figure 13) that are capable of identifying the following factors:

Thermal comfort: HOMO U12-006 sensor. This allows measuring the air temperature (°C) in a range of +40 to + 100°C, with a precision of ±0.5°C to 20°C, in humidity conditions of 5 to 95% H.r without condensing. A stabilization time of between 4 to 5 minutes (in static air) is needed for the measurement.

Thermal comfort: Ajavision WH380 laser infrared thermometer. This allows measuring the mean radiant temperature (°C) in a range of +50°C to +380°C. It has a precision of ±3ºC.

Air quality: TELAIRE 7001 sensor. This allows measuring CO2(ppm) levels in a range of 0 to 2500 in real time. It has a reading sensitivity of ±1ppm and accuracy of ±50ppm.

Visual comfort: YK-2005LX light meter sensor. This allows measuring illuminance levels (lux) on the work plane, in a range of 000/100, 000Lux in real time, with a spectral sensitivity that follows the requirements of the CIE (International Commission on Illumination) curve with an accuracy of ± 4%+2 digits).

Acoustic comfort: SL-4023SD decibel-meter sensor. This allows measuring noise levels (dB) in an automatic range of 30 to 130 dB and in a manual range (3 ranges) of 30 to 80 dB, 50 to 100 dB and 80 to 130 dB. Time weight: quick/slow. Frequency weight of A (dBA) / C(dBC).

The measurement made in this work was done in a range of 50 to 100 dB, with a slow time weight and A frequency weight.

The measurement begins by positioning the MMU alongside a work space (desk) used by a sat UW, at a distance of 0.50 meters from one another, and at a height of 0.90 m above the floor level.

Source: Preparation by the users.

Figure 13: Comfort/performance sensors. 

SURVEY

The survey helps to make a diagnostic of UW, that summarizes the effect of the influence variables. Among the questions asked, those that inquire about the Performance Vote (PV) of the UW become relevant. These are based on studies made by Humphreys and Nicol (2007), where they ask to what extent (0-100%) do they feel that IEQ negatively affected their WP. Figure 14 shows the survey questions made about the perception of IEQ by the UW, which allows obtaining the subjective data.

Source: Preparation by the Authors.

Figure 14: Survey made to UW. 

IMPLEMENTATION AND RESULTS

WP ranges are built as a means to get to know the degrees of “vulnerability” of the UW, depending on the influence variable by office typology. The steps for its construction are detailed in this section.

The PV values of each environmental variable in which the UW self-reports zero influence (0%) on their performance, are recorded.

The maximum and minimum Thermal/Performance Vote (PVt), Air Quality/Performance Vote (PVa), Illuminance Level/Performance Vote (PVi) and Noise/Performance Vote (PVn) are defined, which determine the maximum possible variability of each environmental influence parameter.

The intermediate ranges are defined considering the division between the optimal value (PV=0%) and the maximum value, and the division between the optimal value (PV=0%) and the minimum value.

Finally, to obtain the ranges, numerical equivalents are defined (EqN) and scoring intervals to establish the qualitative evaluation of each range, from “excellent” with an EqN equal to 5, to “bad”, with an EqN equal to 1, for PVt, PVa, PVi and PVn, as indicated in Table 3.

Table 3: Numerical equivalents of the performance ranges. 

Qualitative evaluation Numerical evaluation (EqN) Scoring Interval
Excellent 5 4.2< to ≤ 5
Very Good 4 3.4 < to ≤ 4.2
Good 3 2.6 < to ≤ 3.4
Regular 2 1.8 < to ≤ 2.6
Bad 1 1 ≤ to ≤ 1.8

Source: Preparation by the Authors.

ANALYSIS OF THE RESULTS

The relationship between each range by study variable and the WP variability valued qualitatively and quantitatively by means of EqN is shown in Tables 4 to 7, making a distinction between office typologies. In addition, each table is summarized in graphs comprising an X-axis for the measurement values of each environmental variable, and a Y-axis, for the EqN of the analysis variables.

The highest or lowest amplitude of the ranges in the graphs is associated to the UW’s capacity to adapt regarding the variable in question. It is seen that these are represented with one or two poles of disconformity, depending on the environmental variable analyzed. Each one of these is described below.

OPERATING TEMPERATURE

The operating temperature values are taken to evaluate the WP affected by thermal variability, since this represents the temperature perceived by a person in an indoor environment. This constitutes the average between the air temperature and the mean radiant temperature, measured in degrees Celsius (°C).

Table 4 shows the WP variability considering the operating temperature ranges by office typology, while Figure 15 and Figure 16 graphically represent the results obtained.

From the analysis made, it can be highlighted that the WP ranges found in the OO typology, have a greater amplitude compared to CO. This is seen to a greater extent on analyzing the “excellent” range. The variability for this level is of 0.8°C in OO, while in CO it is 0.3°C. This situation allows confirming that the UW of OO have a greater capacity to thermal adaptation compared to the CO. After this, it is noticed that there is a preference to work with higher temperatures in UW of CO, this is distinguished more on comparing the “excellent” range of both typologies, with the variability for OO being between 24.7 and 23.9°C, while for the CO, this variability increases on being 25.1 to 24.9°C.

Table 4: Valuation of WP ranges (of thermal impact) during the summer period for OO and CO. 

Open Office (OO)
Qualitative evaluation Bad Regular Good Very Good Excellent Very Good Good Regular Bad
EqN 1 2 3 4 5 4 3 2 1
Maximum [C°] <21.5 ≤22.3 <23.1 <23.9 <24.7 <25.5 <26.3 <27.1 -
Minimum [C°] - 21.5 22.3 23.1 23.9 24.7 25.5 26.3 ≥27.1
Closed Office (CO)
Qualitative evaluation Bad Regular Good Very Good Excellent Very Good Good Regular Bad
EqN 1 2 3 4 5 4 3 2 1
Maximum [C°] <22.8 <23.5 <24.2 <24.9 <25.1 <25.8 <26.5 <27.2 -
Minimum [C°] - 22.8 23.5 24.2 24.9 25.1 25.8 26.5 ≥27.2

Source: Preparation by the Authors.

Source: Preparation by the Authors.

Figure 15: Variability ranges of WP affected by operating temperature during the summer in OO. 

Source: Preparation by the Authors.

Figure 16: Variability ranges of WP affected by operating temperature during the summer in CO. 

AIR QUALITY

The air quality is measured in the carbon dioxide (CO2) concentration levels present. Said levels, dependent on the presence of people and the renewed air percentage, could affect the comfort of the UW, and with this, their WP. The CO2 levels are measured in ppm (parts per million) in each analyzed space.

Table 5 presents the variability of the WP considering the ranges of CO2 levels by office typology, while Figure 17 and Figure 18 graphically represent the results achieved.

In the study, a higher WP range amplitude is seen in OO compared to CO for an EqN equal to 5. The amplitude of this range allows identifying UW of OO with a greater adaptation capacity to values of up to 840 ppm (Figure 17), without their performance being affected. This range is lower for CO, admitting CO2 levels that do not exceed 627 ppm (Figure 18).

Table 5: WP ranges valuation (air quality impact) during summer in OO and CO. 

Open Office (OO)
Qualitative evaluation Excellent Very Good Good Regular Bad
EqN 5 4 3 2 1
Maximum [ppm] <842 <953 <1064 <1175 -
Minimum [ppm] - 842 953 1064 ≥1175
Closed Office (CO)
Qualitative evaluation Excellent Very Good Good Regular Bad
EqN 5 4 3 2 1
Maximum [ppm] <627 <700 <771 <843
Minimum [ppm] 627 700 771 ≥843

Source: Preparation by the Authors.

Source: Preparation by the Authors.

Figure 17: WP variability ranges, affected by air quality during summer in OO. 

Source: Preparation by the Authors.

Figure 18: WP variability ranges, affected by air quality during summer in CO. 

LIGHTING LEVEL

The light comfort is measured in terms of illuminance levels on the work plane, without considering the source of lighting (natural or artificial). These are measured in Lux.

Table 6 presents the WP variability considering the lighting level ranges by office typology on the work plane, and Figure 19 and Figure 20 graphically present the results achieved.

From the observation of the ranges, it stands out that the excellent level (EqN =5) has a different luminance with higher values in CO compared to OO. This characteristic has an average difference of 100lux (Figure 19 and Figure 20).

The behavior of the data allows determining that the UW of OO can work optimally at lower lux levels, without their performance being affected, i.e. they have a higher capacity to adapt to darker work planes.

Table 6: Valuation of WP ranges (light impact) during summer in OO and CO. 

Open Office (OO)
Qualitative evaluation Bad Regular Good Very Good Excellent Very Good Good Regular Bad
EqN 1 2 3 4 5 4 3 2 1
Maximum [Lux] <210 <238 <325 <413 >500 >588 >675 >763 -
Minimum [Lux] - 210 238 325 413 500 588 675 ≥763
Closed Office (CO)
Qualitative evaluation Bad Regular Good Very Good Excellent Very Good Good Regular Bad
EqN 1 2 3 4 5 4 3 2 1
Maximum [Lux] <243 <331 <419 <508 <596 <684 <773 <861 .
Minimum [Lux] 243 331 419 508 596 684 773 ≥861

Source: Preparation by the Authors.

Source: Preparation by the Authors.

Figure 19: WP variability ranges, affected by light level during summer in OO. 

Source: Preparation by the Authors.

Figure 20: WP variability ranges, affected by light level during summer in CO. 

NOISE LEVEL

Sound comfort is affected by the noise level, when this is a sound that causes bother. It is measured in sound power (dBA, weighted decibel).

Table 7 shows the WP variability considering the noise level ranges by office typology, while Figure 21 and Figure 22 graphically represent the results.

From the values found, it is detected that the ranges in OO have a higher amplitude compared to CO, with a difference of almost 5 dBA between both office typologies. As such, it is acknowledged that the UW of OO have a higher capacity to accept higher noise levels, without seeing their work performance affected.

Table 7: WP range valuation (of acoustic impact) during summer in OO and CO. 

Open Office (OO)
Qualitative evaluation Excellent Very Good Good Regular Bad
EqN 5 4 3 2 1
Maximum [dBA] <62 <67 <71 <75 -
Minimum [dBA] - 62 67 71 ≥75
Closed Office (CO)
Qualitative evaluation Excellent Very Good Good Regular Bad
EqN 5 4 3 2 1
Maximum [dBA] <57 <61 <65 <68 -
Minimum [dBA] - 57 61 65 ≥68

Source: Preparation by the Authors.

Source: Preparation by the Authors.

Figure 21: WP variability ranges, affected by sound level (dBA) during PVn in OO. 

Source: Preparation by the Authors.

Figure 22: WP variability ranges, affected by sound level (dBA) during PVn in CO. 

OPTIMAL WORK PERFORMANCE INDICATOR

From the response to the question “Do you think that this variable negatively affects your performance?” in this study’s survey, the total percentage of those that answer YES [%] and NO [%] are considered. This allows knowing the level of influence of each variable on the individual WP.

Considering the percentages obtained, proportionality constants are built, to compare the total of the variables as a whole and each one, with their weight in importance.

Source: Preparation by the Authors.

Figure 23: Level of impact of each variable on the individual WP and the resulting proportionality constants in OO typology. 

Source: Preparation by the Authors.

Figure 24: Level of impact of each variable on the individual WP and the resulting proportionality constants in CO typology. 

What is presented in Figure 23 leads to the construction of the OWPI for OO (see Equation 1).

What is presented in Figure 24 leads to the construction of the OWPI for CO (see Equation 2).

As can be seen, the order of influence of the variables changes for both typologies. However, in both cases the CO2 concentration appears as the one with the greatest influence.

The value obtained in Equation 1 and Equation 2 is qualitatively translated, following Table 3.

OWPI VALIDATION-APPLICATION

The OWPI tool is applied in this section, on two real OO and CO typology office cases, to validate the results (Table 8 and Table 9).

Case A - OO:

Table 8 shows the data obtained from measurements for each environmental variable and their valuation (EqN), following Figure 15, Figure 17, Figure 19 and Figure 21.

Table 8: Environmental values measured in case A and their numerical evaluation by ranges. 

Eq PVt =2 Eq PVa =1 Eq PVi =1 Eq PVn =2
Type People CO2 Lux Dba
OO 4 27 1190 810 74
Evaluation

Source: Preparation by the Authors

As a result, the following OWPI value is obtained:

OWPI = 0.23 .2+0.31 .1+0.19 .1+0.27 .2 =1.50→Bad

Case B - CO:

Table 9 presents the data obtained from measurements for each environmental variable and its evaluation (EqN) as per Figure 16, Figure 18, Figure 20 and Figure 22.

Table 9: Environmental values measured in case B and their numerical evaluation by ranges. 

Eq PVt =4 Eq PVa =5 Eq PVi =5 Eq PVn =4
Type People CO2 Lux Dba
CO 1 24.5 550 495 53
Evaluation

Source: Preparation by the Authors

As a result, the following OWPI value is obtained:

OWPI = 0.33 .4+0.36 .5+0.08 .5+0.23 .4 =4.44→Excellent

CONCLUSION

Connecting the self-reported work performance vote with the levels of each environmental variable studied, allows getting to know optimal values and the most vulnerable values of operating temperature, air quality, light level, and noise level, to achieve a good WP in UW in a warm template area during the summer period.

The construction of ranges evaluated through the EqN, reports the WP level of users by open and closed office typology, varying from 1 (bad WP) to 5 (excellent WP). Thus, the valuation of an OWPI equal or close to 5, as well as indicating the best environmental conditions for the optimal performance of the UW considering the health, assumes a “beneficial” contribution to comfort conditions (thermal, visual, acoustic and air quality) of the UW. On the contrary, an OWPI equal or close to 1 indicates to the Building Manager about the need to address comfort related environmental solutions, and as a result, of the WP in the work setting.

Regarding the comparison between office typologies, it is confirmed that the UW develops a higher level of environmental adaptation in OO, so that said offices are a less advantage space on having a lower occupation factor, lack of windows, lack of total enclosure, and higher noise levels.

Finally, it highlights that the development of the OWPI tool characterizes WP conditions in offices for warm template climate regions during the summer. In future research, the idea is to extrapolate this progress for winter and transitory periods, as well as how to apply them in other local case studies.

LIST OF ABBREVIATIONS

CAI: Indoor Environmental Quality.

CO2: Carbon Dioxide.

EqN: Numerical Equivalent.

IRLO: Optimal Return on Labour Indicator.

OA: Open Typology Office.

OC: Closed Typology Office.

RL: Labour Performance.

MWU: Mobile Measurement Unit.

UT: User-Workers.

VR: Performance Vote.

VRa: Air Quality Performance Vote.

VRi: Lighting Level Performance Vote.

VRr: Noise Level Performance Vote.

VRt: Thermal Performance Vote.

EqVRt: Equivalent Thermal Performance Vote.

EqVRa: Equivalent Air Quality Performance Vote.

EqVRi: Equivalent Lighting Performance Vote.

EqVRr: Equivalent Noise Performance Vote.

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Received: February 25, 2021; Accepted: June 24, 2021

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