Abstract

            The aim of this experiment was to compare the concentration of carbon dioxide in heavily trafficked study spaces at the California State University, Sacramento Campus, namely the Library and the Academic Information Resource Center (AIRC). Data were collected between the hours of 9:00 am and 11:00 am. The AIRC had significantly higher CO2 concentrations (p=<0.0001) compared to the library. Floor levels had a significant difference in CO2 concentration (p=0.0019) and also the interaction between buildings and floors was significant (p=0.0014). The significant difference in CO2 concentration may be due to a variety of reasons such as area of the buildings, student density, time of day, and ventilation systems. Future studies need to account for these parameters to truly understand the reason for these elevated carbon dioxide levels.

 

Introduction

Carbon dioxide is a pollutant that reduces the cognitive abilities of those affected by high levels.  While indoor carbon dioxide levels are not regulated by the state or federal government, the American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE) provides guidelines of what indoor air quality standards should be.  ASHRAE and other air quality groups recommend not allowing carbon dioxide levels to exceed 800-1200 parts per million (ppm) as it is also an “indoor air quality indicator”(Cummins, 2005).  However, ASHRAE claims that not until the accumulation of 5000 ppm is CO2 a serious health risk posed for building occupants exposed to these high levels (Ventilation for Acceptable Indoor Air Quality, 2007).  Indoor carbon dioxide can originate from a number of sources, including human respiration, which is what we are primarily concerned with in our study.  

Our team was interested in looking at the carbon dioxide rates in heavily trafficked study spaces on the California State University, Sacramento Campus.  The campus has two main buildings used primarily for study areas, both with very different design and layout.  The buildings were constructed at different periods of time, and we were curious to see if any of these factors could influence the levels of indoor carbon dioxide found in them.  We also looked to see if there was any effect of floor level on the accumulation of carbon dioxide particles.  

Indoor carbon dioxide has been reviewed in many other studies to assess its effects and ways to reduce its presence in the air.  Tsai et al. (2012) found that carbon dioxide levels exceeding 800 ppm resulted in an increase in Sick Building Symptom in office workers, who displayed symptoms of tired or strained eyes, difficulty remembering things or in concentrating, and dry, itching or irritated eyes.  Shendell et al. (2004) found an association between an increase in carbon dioxide levels and an increase in absences amongst students at elementary schools.  

Indoor carbon dioxide can also be an indicator of ventilation rates in buildings.  ASHRAE recommends a ventilation rate of 7.5 cfm/person for a lecture hall (Ventilation for Acceptable Indoor Air Quality, 2007).  Rashidi et al compared naturally ventilated classrooms, with open doors and windows, with air-conditioned ventilation and found lower carbon dioxide content in the naturally regulated rooms.  Study spaces on campuses typically have closed windows and only have entrances on the first of many levels of the building.  

Most studies found were observing elementary schools or offices, with none looking at college study spaces.  The difference is that university study spaces can be occupied at some level for twenty four hours of the day, or at least longer than the average school and work hours.  Effects on students can cause reduced efficiency, making it harder to study in the places designated for such a purpose.  The purpose of this study was to evaluate the effects of building type and floor level on the amount of carbon dioxide found in the air.  

 

Methods

            Carbon dioxide measurements were taken indoors on four floors at both the Academic Information Resource Center (AIRC) and the University Library using Aeroqual Series 500 portable air quality monitors.  Study spaces were the areas of focus in each building, so measurements were only taken in areas that were known study spaces and designed for such purposes.

We began sampling at the Library, working our way up from level one to level four.  After the Library was completed, we sampled the AIRC in the same way.  Measurements were taken in the morning, between 9 and 11 am, with four replicates on each floor. The replicates were obtained by determining a starting location on each floor and taking a random number of steps into the study space, ensuring samples were representative of the entire floor.  We adjusted the range of steps possible in our random number generator to account for the differing size of area in the Library and the AIRC.  In the Library, a counterclockwise direction was taken around the study spaces, while in the AIRC measurements was taken in a relatively straight line due to the limited amount of space.  At each “stop”, we allowed a one minute collection time for the air quality meter to adjust and collect data at the location.

We used StatCrunch to run a two factor ANOVA comparing the effect of floor level and building on carbon dioxide levels.  We also ran a Tukey HSD post-hoc test to determine which floors were statistically significant from each other.

 

Results

            The mean carbon dioxide concentration in the AIRC was 1182.88 ppm (Figure 1), with the highest level found on the third floor at an average of 1465.25 ppm. The lowest concentration found in the AIRC was on the second floor at 1021.25 ppm. The library had a mean CO2 concentration of 909.06 with the second floor having the highest mean at 1164.5 ppm and the first floor having the lowest concentration at 638 ppm. The AIRC had a higher concentration (270 ppm) average than the library.

 

 

 

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Our ANOVA demonstrated that the AIRC had a significantly higher CO2 concentration than the library (p =<.0001); only the second floor had a lower CO2 concentration than the library. There was also a significant effect of floor levels between the two buildings  (p=0.0019).  The interaction between floor levels and building demonstrated a significant interaction (p=0.0014).   Our Tukey HSD post-hoc test revealed the significant differences between floor levels (Figure 1).   

 

Discussion

            We originally hypothesized that the library would have a higher CO2 concentration due to its older age (built in 1990) compared to the recently constructed AIRC (built in the 2000s). We expected the newer building to have improvements in its air ventilation system and energy efficiency that could reduce CO2. However, we did not consider how the time of day, total area of the buildings, overall floor plans, and positioning of the two buildings.  After completing the survey, we suspect these variables all had an effect on the CO2 levels in the two buildings.  

While both are study buildings, the floorplans and total area of the AIRC and the Library differ dramatically. The AIRC has an area of 67,807 square feet while the library has a square footage of 267,636, making it nearly four times as big as the AIRC.  The AIRC, although newer, is more densely packed, with tight corridors, while the library has open floor plans with book shelves separating study areas. We assume?expect, because of its younger age, the AIRC was built for better efficiency in holding a constant temperature with energy efficient windows and other building components.  However, these efficient techniques may also restrict the movement of gases between the building and its environment. This might be the reason why the 3rd floor for the AIRC has higher CO2 concentrations. The first and second floors of the AIRC are both located on ground levels, which means that doors are regularly opened, allowing for better air circulation and a constant supply of fresh air. Naturally ventilated rooms have been found to have lower concentrations of carbon dioxide than air-conditioned ventilated rooms (Al-Rashidi, 2011). Neither the third or fourth floors have any openings to the outdoors. This might also be the case for the library, as the second floor has doors opening that would allow for better ventilation. It is probable that the AIRC and the library have similar ventilation systems with occupant density, time of day, square footage, and natural ventilation playing a major factor in the CO2 concentrations.

The time of day is an important factor when observing study behaviors amongst college students because there are certain times of the day when the study areas are busier than others.  Unfortunately, our observation window was between the hours of 9 and 11am, which are not typically peak study hours.  The CO2 concentrations may have shifted if we had sampled a peak study period and may have given us a more representative sample of study conditions.

Another factor to consider is the positioning of the AIRC. The main study areas in the AIRC have big panel windows that are directly facing east (Figure 2). During our sampling time (9am-11am), the sun was directly hitting the building, increasing indoor temperatures. The library’s study areas on the other hand, are not exposed to the sun at this time. Although this does not affect the concentration of CO2, it does further exacerbate the effects of high CO2 concentrations. High temperatures (>80 degrees fahrenheit) alone increase distractions to work performance compared to cooler temperatures and indoor air stuffiness (Varjo et al 2015). The optimum temperature for office workers has been found to be at 70 degrees fahrenheit; anything lower or higher has been shown to decrease performance (Vimalanathan and Babu 2014).  High temperature plus low ventilation rates decrease performance, cognitive functions, and environmental satisfaction (Varjo et al. 2015).

 

 

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The effect of raised levels of CO2 are varied and depending on how high they are, can have a marked decrease in attention and efficiency. Xu et al (2011) found that just a 5% increase in ambient CO2 levels caused a suppression in resting state neural activities in humans. This is due to a mild hypercapnia, or excessive carbon dioxide in the bloodstream, caused by breathing in CO2. Researchers went even further in saying that the raised CO2 levels caused an effect similar to a mild sedative or anaesthetic and saw the subjects into a lowered state of arousal during the inhalation of CO2 (Fukuda et al, 2006). Students attempting to review or learn new material could also find themselves with strained eyes, reduced cognitive abilities, and fatigue if CO2 levels were to exceed 800 ppm (Tsai et al.2012). In terms of our team’s experiment, this could mean that students studying in the AIRC or library could possibly be affected by raised levels of CO2 and, as a result, not operating at their maximum efficiency. Raised carbon dioxide levels can also have implications in regards to student attendance as Shendell et al. (2004) discussed and can affect a student’s ability to focus in the classroom.

Our study resulted in finding significant differences between the library and the AIRC and future studies could possibly help broaden our understanding as to why this may occur.  Because our study took place between 9am and 11am in the morning, the data does not reflect the levels of CO2 accumulated throughout the course of a day. The AIRC is a 24-hour building that is  frequented heavily by students studying as they wait for their classes, so it would be interesting to see how the levels changed as the day progressed. Further studies could possibly include measurements of CO2 throughout the day as student flow in the buildings changed.

Another factor to consider for future research would be to include some measurement of student density. In each area where a measurement was taken there were varied amounts of students nearby that the CO2 emissions could be attributed to. A future study could include some sort of student population count within a certain radius to help researchers see if the number of students nearby affected the CO2 levels. Because our measurements took place in the mornings, there were less students populating these study spaces; so by having a measurement of student density, the data collected would be more representative of the CO2 levels in both buildings.

 

Works Cited

 

Al-Rashidi, K., Loveday, D. Mutawa, N. (2011). Impact of ventilation modes on carbon dioxide concentration levels in Kuwait classrooms.  Elsevier, 47, 540-549.

Cummins, J., Bolander, M., Delaney, J., Byers, E., & Choi, H. (2005). Indoor Air Pollution in California. California Environmental Protection Agency Air Resources Board. 138.

Fukuda, T., Hisano, S., & Toyooka, H. (2006). Moderate hypercapnia-induced anesthetic effects and endogenous opioids. Neuroscience letters, 403(1), 20-23.

Shendell, D. G., Fisk, W. J., Apte, M. G., & Faulkner, D. (2004). Associations Between classroom CO2 Concentrations and Student Attendance in Washington and Idaho.  Indoor air, Vol.14(5), 333-41.

Tsai, D., Lin, J., & Chan, C. (2012). Office Workers’ Sick Building Syndrome and Indoor Carbon Dioxide Concentrations. Journal of Occupational and Environmental Hygiene, Vol. 9, 345-351. DOI: 10.1080/15459624.2012.675291

Varjo, J., Hongisto, V., Haapakangas, A., Maula, H., Koskela, H., & Hyönä, J. (2015). Simultaneous effects of irrelevant speech, temperature and ventilation rate on performance and satisfaction in open-plan offices. Journal of Environmental Psychology, 44, 16-33.

Ventilation for Acceptable Indoor Air Quality. (2007) American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. 62.1-2007, 13.

Vimalanathan, K., and Ramesh Babu, T. (2014). The effect of indoor office environment on the work performance, health and well-being of office workers. Journal of Environmental Health Science and Engineering, 12, 113. http://doi.org/10.1186/s40201-014-0113-7

Xu, F., Uh, J., Brier, M. R., Hart Jr, J., Yezhuvath, U. S., Gu, H., ... & Lu, H. (2011). The influence of carbon dioxide on brain activity and metabolism in conscious humans. Journal of Cerebral Blood Flow & Metabolism, 31(1), 58-67