Air Pollution Modelling via Mobile Sensor Networks

Air pollution modeling is a complex problem involving high dimensional data and sparsity of high quality sensor data. Due to its high cost, high quality sensors are deployed primarily by the government in very few locations, often only a few per city. This has opened up the space for companies like PurpleAir to deploy low cost air quality sensors to monitor air pollution throughout cities and rural areas. These low cost air sensors, however, are unable to effectively measure ultra fine particles (UFP) (smaller than 0.1 micron), which are currently unregulated but crucial to monitoring air quality since these small particles are more likely to make it into the human circulatory system and cause health problems. This raises the question, how can we combine intermittent high quality sensors with pervasive low quality sensor to estimate the air quality at any place? And can we use this to inform us where to deploy the next sensor to reduce uncertainty?

Students will build upon work from summer work using the Kalman Filter and Fixed Rank Filter (FRF) to create a data-driven statistics-based model to estimate air pollution. In the fall, we will learn and practice the algorithms and data collection techniques and in the spring, we will begin data analysis. A stretch goal in the spring is to develop algorithms to optimally select the best location to deploy a sensor to reduce overall uncertainty in the air pollution estimate and deploy the sensor.

Name of research group, project, or lab
Shia lab
Why join this research group or lab?

You are interested in applying techniques in engineering and robotics to study air pollution and take a research project from the beginning to the end.

Logistics Information:
Project categories
Engineering
Data Science
Optimization
Robotics
Student ranks applicable
Junior
Senior
Student qualifications

CS60/70, E80

Time commitment
Fall - Part Time
Spring - Part Time
Compensation
Academic Credit
Number of openings
2
Techniques learned

State estimation, optimization,

Contact Information:
Mentor
Victor Shia
vshia@hmc.edu
Visiting Assistant Professor
Name of project director or principal investigator
Victor Shia
Email address of project director or principal investigator
vshia@g.hmc.edu
2 sp. | 4 appl.
Hours per week
Fall - Part Time (+1)
Fall - Part TimeSpring - Part Time
Project categories
Engineering (+3)
EngineeringData ScienceOptimizationRobotics