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A group of classmates and I recently completed a project for our Indoor Air Quality class. We wanted to explore the impact that particulate matter exposure has on university students. Particulate matter (PM) is an air pollutant. It is the sum of all solid and liquid particles suspended in air (Pražnikar et al., 2011). It can be produced from many different seemingly, harmless activities. For example, cooking produces high concentrations of particulate matter (Anderson et al., 2012). Even if someone is not directly by the cook stove and is just near a commercial kitchen, they can still be exposed to significant levels of PM.

When people breathe in PM, the particles can travel into our respiratory region, down into our lungs, and can even get into our bloodstream (Pražnikar et al., 2011). When we inhale these particles, they can disrupt our body and cause a lot of health issues, such as asthma, decreased lung function, and even cognitive function imparities (Pražnikar et al., 2011).

There are different sizes of particulate matter. We group PM based on the size of their diameter. Most of the particles we care about are 10 micrometers in diameter or less. Yes, I said MICROMETERS. To give some context, the diameter of human hair is about 50 micrometers, so these are very small particles. The infographic below describes the impact that particle size has on our body.

On most university campuses, fast food can be found in all areas. For example, on my university’s campus, around ten buildings are home to food vendors, such as Panda Express, Chick-fil-a and typical dining cafeterias. In all of these buildings, students can be found studying for hours upon hours in tables near the food vendors. It makes sense why students would want to study in these areas. Should they get hungry while studying, they can easily grab a quick bite and immediately get back to work. However, is there a downside to studying in these areas? Could there be high levels of PM that could impact students’ health and their ability to study? That’s what we wanted to find out.

For our project, we tested the particulate matter concentrations in 4 different buildings on campus. We wanted to see if there would be a significant difference in PM levels in study areas near food sources and study areas away from food sources. We used two air monitors called PurpleAir sensors ( They measure particulate matter concentration along with a few other things. They can distinguish between the different size range of particles that are mentioned in the infographic, so we were able to see the levels of PM10, PM2.5, and so on.

To start our experiment, we did something a co-location test. This test just makes sure that the sensors are recording the same PM levels when they are placed in the same location. We put the sensors right next to each other and had them collect data for about 10 minutes. The sensors should be reading the same PM concentrations since they are right next to each other. We found that the sensors did read fairly similar values, within about 15% difference range.

After doing the co-location test, we placed one sensor at a study table near a restaurant and the other sensor in a study area that was nowhere near the restaurant, usually on a completely different building level. The sensors collected the PM concentration in each area simultaneously for about 30 minutes. Then we looked at the results.

As I previously mentioned, cooking is known to produce high levels of PM, so we expected to see higher levels of PM in food areas than in non-food areas. For 3 out of the 4 buildings that we tested, the PM1 concentrations were noticeably higher in the food areas than in the non-food areas. In 1 of the buildings, the PM concentrations were somewhat equal. We’re not sure why this was. Many things other than cooking can impact PM concentration, such as the size of the building and the ventilation. Some of these factors could have influenced the data that we collected in this particular building. When we compared the average PM concentrations in food areas versus non-food areas, we observed that PM10 and PM2.5 levels varied quite a bit. When determining the significance of the data, it was concluded that PM10 and PM2.5 in each building were relatively the same. However, PM1 levels were higher in food areas than in non-food areas.

As this was the main difference we saw in each area, we concluded that PM1 is most associated with cooking. The levels of PM1 in each area were not specifically dangerous at the time of collection; however, students can study in these areas for hours a day and multiple days a week. This long-term exposure can cause health issues, such as respiratory diseases and even premature death (Apte et al., 2018). More importantly, a growing number of studies is suggesting that long-term exposures to PM can impair cognitive function. One study found that reducing China’s yearly average PM concentration down to the EPA standard could increase people’s math and verbal test scores as much as 13% (Zhang et al., 2018). Another study found that high level exposure to PM physically changes certain cells in the brain of mice. These physical changes disrupt short-term memory (Fonken et al., 2011). The researchers of this study feel that these memory deficiencies could occur in human brains. They are actually currently testing this hypothesis.

In summary, study areas near commercial kitchens tend to have high concentrations of PM. Although the concentrations that we measured were not of great concern, inhaling even low levels of particulate matter over a long period of time can leave students susceptible to health risks, and could also affect their cognitive performance. The results from this mini project suggest that these areas may not be optimal for studying, and students should choose study areas away from food areas.

Written by Paloma Villareal Villarreal for the Indoor Air Quality class (CE369R). Edited by Dr. Maestre.


Apte, J.S., Brauer, M., Cohen, A.J., Ezzati, M., Pope, C.A. (2018) Ambient PM2.5 reduces global and regional life expectancy, Env. Sci. and Tech., 5, 546-551

Fonken, L. K., Xu, X., Weil, Z. M., Chen, G., Sun, Q., Rajagopalan, S., Nelson, R. J. (2011) Air pollution impairs cognition, provokes depressive-like behaviors and alters hippocampal cytokine expression and morphology, Molecular Psychiatry, 16, 987–995

Anderson, J. O., Thundiyil, J. G., Stolbach, A. (2012) Clearing the Air: A Review of the Effects of Particulate Matter Air Pollution on Human Health, Jour. Med. Toxicol., 8, 166-175

Pražnikar, Z. J., Pražnikar, J. (2011) The Effects of Particulate Matter Air Pollution on Respiratory Health and on the Cardiovascular System, Slovenian Jour. of Pub. Health, 51, 190-199

Zhang, X., Chen, X., Zhang, X. (2018) The impact of exposure to air pollution on cognitive performance, Proceedings of the National Academy of Sciences, 115, 9193-9197