Final Report: Developing a Wearable Air Quality Sensor for Understanding Community Air Quality

EPA Grant Number: SU836123
Title: Developing a Wearable Air Quality Sensor for Understanding Community Air Quality
Investigators: Henriques, Justin
Institution: James Madison University
EPA Project Officer: Page, Angela
Phase: I
Project Period: September 1, 2015 through August 31, 2016
Project Amount: $14,961
RFA: P3 Awards: A National Student Design Competition for Sustainability Focusing on People, Prosperity and the Planet (2015) RFA Text |  Recipients Lists
Research Category: P3 Awards , Pollution Prevention/Sustainable Development , Sustainability , P3 Challenge Area - Built Environment

Objective:

This project is for the design and development of a wearable air quality sensor for understanding community air quality. According to the World Health Organization, exposure to air pollution is now the largest single environmental health risk globally, leading to approximately 7 million deaths in 2012 alone. Understanding the local spatiotemporal patterns of air pollution is an important step in helping to inform communities on decisions related to addressing the challenges of air quality. However, it can be difficult to analyze these pollution patterns using traditional stationary and spatially disperse air quality monitoring stations, particularly in urban environments with multiple pollutant sources and complex transport dynamics. Understanding these dynamics with greater spatial and temporal resolution may help inform smart growth policies and contribute to preventing air pollution in built environments by identifying pollution sources, resulting in more sustainable communities

Summary/Accomplishments (Outputs/Outcomes):

The EPA P3 Phase I project team first focused on finalizing the design and building of portable air quality monitor that both used open source hardware and was small enough to be carried by individuals. The team chose to limit pollutant measurements to ozone and particulate matter sensor for the wearable monitor because high levels of these pollutants have the most significant negative health effects. The final main components were edited from the original proof of concept to include the Arduino microcontroller, GPS shield, memory card reader, a dust sensor, and an ozone sensor. Down selecting the monitor components helped to reduce the power consumption and overall device footprint. The team then designed an innovative chassis for the sensor assembly that can be interchanged with different enclosures depending on application. To date, a total of eight prototype air quality monitors have been built. To evaluate the quality of the data obtained from the air quality sensors, the team conducted laboratory tests that compared the results of the dust sensor in the wearable air quality monitor to a high accuracy airborne particle counter (Climet CI-150t). The team created a simple environmental chamber that could hold both the dust sensor and particle counter. An oil smoke generator was used to produce the particulate matter, which was measured by the reference sensor and the prototype air quality monitors. Measurements from the prototype air quality monitors were compared the data from the Climet CI-150t results. The output of the prototype air quality monitors was shown to be well correlated with that of the high accuracy particle counter. The team also developed a data dashboard to display and analyze the spatial air quality distribution. The dashboard was created in Tableau, a powerful data analytics and visualization desktop software package. Tableau was selected because of its ability to interface with cloud based databases that are spatially explicit (important for collecting data from the sensors) and the ability to publish maps created on Tableau’s server. Using this software, the data from multiple sensors was analyzed. A series of tests were conducted to evaluate the performance of multiple sensors during simultaneous operation in urban and semi-urban environments. This data was integrated into the data dashboard to evaluate the combined sensor and dashboard approach to analyzing the spatial pollutant distributions in the environments tested. Additionally, the team created an informational website to explain the project and serve as a hub for managing and uploading data.

Conclusions:

This project developed and tested a wearable air quality monitor to measure the spatiotemporal distribution of particulate matter and ozone and an accompanying data dashboard to analyze the resulting air quality data. Portable air quality sensors have the potential to fill in the gap left by traditional air pollution monitoring by providing increased spatial and temporal resolution. Air pollution sensor technology is decreasing in cost and size, meaning it is now tenable to develop low-cost portable air pollution sensors that could be worn by individuals.

Supplemental Keywords:

Air quality, sensors, open source

Relevant Websites:

Project EnvirSensor Exit