Using Mobile Air Quality Sensors to Detect Differences in Particulate Matter Around Penn’s Campus

example of mobile air quality sensor

Students

2020
College

Faculty

Associate Professor of Informatics

Project Summary

This summer, PURM gave me the opportunity to work in the Himes Lab in the Department of Biostatistics, Epidemiology, and Informatics at the Perelman School of Medicine. The lab studies the genetic, social, and environmental causes of lung diseases like asthma using statistics, which aligned well with my career goal of being an epidemiologist. I and another PURM student worked on a project testing mobile air quality monitors (Airbeams) and investigating their potential for research and medical use. Government air monitors are sparsely distributed and the data is infrequently updated online, so giving patients a way to personally measure their exposure to pollutants in real time could provide them useful information for medical decision-making as well as uncover disparities in air quality. Philadelphia’s asthma rates are relatively high and unequally distributed, making it a good place to focus this research.

We began by testing the monitors in an enclosed indoor room with burning incense, plotting the correlation of their data, and picking out the most functional monitors. We then took these monitors on mile-long routes in University City, Center City, West Philadelphia, and Kingsessing throughout June and July. We mapped the data from each dataset to observe the day’s overall air quality and find irregular areas, and also made heatmaps to compare our measurements to government data. All the analysis was done in the R programming language. We found that the sensors are very effective in detecting small point sources of pollution, such as construction sites and trains, that aren’t detected by government monitoring.

This project helped me learn many skills that I will need for my education and career. In terms of hard skills, I learned how to program statistical methods and manipulate and visualize data using R, as well as how to structure a research presentation and poster. In addition, I learned more about the city of Philadelphia both through reading background literature and physically visiting different neighborhoods, which was a great opportunity to get out of the “Penn bubble” and see the city’s diversity and disparities. I still have more ideas for how to analyze our data, and I’ll be continuing this investigation during the school year!