OFFICE USER WORK PERFORMANCE INDICATOR IN WARM TEMPERATE SUMMER PERIOD INDICADOR DE RENDIMIENTO LABORAL DEL USUARIO-TRABAJADOR DE OFICINA EN PERÍODO DE VERANO DE CLIMA TEMPLADO CÁLIDO

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.


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), CO 2 concentration (Candanedo & Feldheim, 2016;Shriram, Ramamurthy & Ramakrishnan, 2019), lighting level (Liu, Lin, Huang & Chen, 2017, Yang & Moon, 2019H. 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 selfreported 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 1163IRAM 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).

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.
The Civic Center building (CCV) (Figures 6 and 7) has the highest electricity consumption, with values of over 340 kWh/m 2 .year, which is why it was chosen as the case study. Table 1 summarizes its most relevant characteristics.

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 (Figures 8 and 9) and CO ( Figures 10 and 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.

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.
The MMU comprises sensors ( Figure 13) that are capable of identifying the following factors: a. 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.
b. 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. c. Air quality: TELAIRE 7001 sensor. This allows measuring CO 2 (ppm) levels in a range of 0 to 2500 in real time.
It has a reading sensitivity of ±1ppm and accuracy of ±50ppm.
d. 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). e. 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.

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.

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. division between the optimal value (PV=0%) and the maximum value, and the division between the optimal value (PV=0%) and the minimum value. 4. 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.

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).

Air Quality
The air quality is measured in the carbon dioxide (CO 2 ) 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 CO 2 levels are measured in ppm (parts per million) in each analyzed space. 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 CO 2 levels that do not exceed 627 ppm (Figure 18).

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.  (Figures 19 and 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.
Indicador de rendimiento laboral del usuario-trabajador de oficina en período de verano de clima templado cálido Yesica Alamino Naranjo, Alcion Alonso Frank Revista Hábitat Sustentable Vol. 11, N°. 1. ISSN 0719 -0700 / Págs. 44 -57 https://doi.org/10.22320/07190700.2021.11.01.04 Table 5. WP ranges valuation (air quality impact) during summer in OO and CO. Source: Preparation by the Authors.     Figure 19. WP variability ranges, affected by light level during summer in OO. Source: Preparation by the Authors.       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). 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.

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.
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 CO 2 concentration appears as the one with the greatest influence.
The value obtained in Equation 1 and 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 9).
Case A -OO: Table 8 shows the data obtained from measurements for each environmental variable and their valuation (EqN), following Figures 15, 17, 19 and 21.
As a result, the following OWPI value is obtained: Case B -CO: Table 9 presents the data obtained from measurements for each environmental variable and its evaluation (EqN) as per Figures 16, 18, 20

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.