Air quality has become a rising issue in the last decade due to high pollution concentrations and the adverse health effects of climate change. Air quality may seem like a trivial part of the climate crisis, but it is far more important than you might expect. Air quality impacts human health most directly through particulate matter (PM). Particulate matter varies by size, with the smallest materials less than 0.1mm in diameter, and the largest materials anything greater than 2.0mm in diameter. The largest particles cause upper respiratory irritation, whereas the smallest particles can enter your bloodstream, potentially causing heart attacks once inhaled. The World Health Organization (WHO) provides annual reports on air quality guidelines, specifically threshold limits for pollutants.  WHO report

The main goal of this experiment was to determine if there is a relationship between indoor air quality and the age of buildings. We hypothesized that air quality would worsen with increasing building ages, particularly near ventilation systems. Additionally, we measured a multitude of factors within buildings to better assess potential sources that deplete indoor air quality. This experiment was conducted at the University of San Diego at seven buildings located on campus. This data can provide the student body with a better understanding of pollution sources on campus, and potential solutions to the uncovered issues. By assessing indoor air quality across buildings on campus, students will better understand their surrounding air quality in accordance with national guidelines. 

In order to test our hypothesis, we needed to determine three variables to measure air quality inside buildings. Based on current scientific research, we decided to analyze measurements within each building varying by proximity to the entrance of the building, which floor level we were on, and if we were near a ventilation system. Additionally, two measurements were recorded outside each building, either in the presence of cars or not. The actual measurements were recorded using a Flow Sensor which records Air Quality Index, Particulate Matter (both PM10 and PM2.5), Nitrogen Dioxide (NO2), and Volatile Organic Carbons. See Figure 1. For our experiment, we assessed PM10 and NO2. To test our hypothesis, we completed six measurements per building, four inside and two outside. The indoor measurements varied by multiple factors, whereas outdoor measurements only had one variable, the presence of cars. A summary of recorded variables can be seen in Table 1 which reflects a fractional factorial experimental design. Measurements were taken for approximately five minutes, and the air quality values were recorded at the end of the five minute duration once readings had stabilized. 

Once data collection was completed and the data had been processed, we were able to begin summarizing our findings. The most prominent trend we found was higher NO2 concentrations outside versus indoor measurements. Additionally, the presence of cars indicates the direct effect cars have on air quality. As for PM10, the data was more complicated because an explicit trend was not found. There are many sources of PM10, and because of this it is difficult to track direct sources that impact air quality. One potential cause of fluctuating PM10 data is due to the resuspension of particles through foot traffic. This is a difficult variable to track, especially on the scale at which we completed our experiment. However, we did find that PM10 was generally more concentrated on the ground level of buildings, likely due to more direct exposure to outdoor particulate sources. The variance found across buildings indicates the multitude of factors by which PM10 and NO2 are influenced by, not only indoors but outdoors as well. 

Though an explicit correlation between air quality and building age was not found, the data collected still offers important insights for the USD community. Though our hypothesis was ultimately refuted by our data, the multitude of factors that influence air quality now provide us with better tools for future experimentation. For example, given the higher concentrations of PM10 on ground floor levels, we can explicitly assess ground floor versus not ground floor levels moving forward. Additionally, assessing the meteorological conditions on sampling days will provide a better understanding of our results. Finally, specifically analyzing the resuspension of dust and particulate matter through foot traffic and other movements will offer further insight. Moving forward, it is necessary to understand the sources of these pollutants and ways they can be reduced. To better understand indoor air quality we must address unanswered questions like these to better understand our results, and improvements that can be made moving forward.