The AQ-drone project is an open source air quality measuring system utilizing drone technology to conduct air quality metrics. This project is inspired by the JPL Open Source Rover Project.
The goals of this project are two-fold:
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To develop a pedagogical curriculum from which students can apply engineering and science skills to a real problem
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To aggregate data from a distributed network of air quality sensors with the ultimate goal of understanding and improving environment impact of pollution on the community
According to the EPA, air quality differs from region to region due to geographic location, proximity to pollution sources, and weather. Public health and environmental justice issues affect vulnerable communities, such as East Los Angeles, due to their proximity to freeways and interchanges, rail yards, industrial complexes and population density. Poor air quality is associated with detrimental effects on health and an increase in mortality (Dockery, et al., 1993). Establishing a cost effective and sustainable community driven air quality program is not only beneficial but necessary.
As community air quality investigators, we need to educate various stakeholders in the community about the air they experience in their surroundings. These units of concern are the residents, municipalities, elected officials, industrial/commercial businesses.
A key component of successful STEM instruction involves the application of concepts learned to real-world problems. Not only does this increase the retention of information and scientific inquiry, but, in the process, can lead to discoveries and contributions, thereby expanding the knowledge base as well as increasing the probability of pursuing a STEM-related career.
To test ambient air quality and pollution levels using aerial data collection rather than stationary, ground-level measuring locations. The data collection should include metrics on carbon monoxide, ozone, nitrogen oxide, volatile organic compounds, particulate matter, hydrogen sulfide, methane and airborne lead.
The DJI Matrice 600 Pro Hexacopter drone is determined to be the most appropriate vehicle for carrying out this mission. Additionally, we will use on commercially available sensors for data collection. The sensors used and needed hardware can be found in the hardware section. Post flight data analysis will be accomplished using open source software, and for campaign planning, we rely on Google Earth.
The software used can be found in the software directory. Documentation and instructions can be found in the software guide.
John J. Tran
Kevin Benavente, Steven Conaway, Michael Murray, and Jacqueline Curiel.
