Abstract

Low-cost PM sensors have garnered interest for their ability to reduce the cost of investigating PM concentrations in both indoor and outdoor spaces. They perform well in high concentration lab testing with correlation coefficients greater than 0.9. In real-world applications, the correlation coefficients drop significantly because of sensing floors and adverse ambient conditions. There are plenty of supervised machine learning techniques that aim to correct the measurements ranging from linear regression to more advanced neural networks and random forests. This work aims to use those more complicated techniques to adjust the measurements using other data sets gathered by a sensor suite. The Minnesota Pollution Control Agency (MPCA) has deployed a network of 47 AQ-Mesh sensors around the Minneapolis-St. Paul Metro Area. The network was active for two years, with mass colocations at a regional federal sensing site before and after deployment. The sensor suite includes electrochemical sensors for nitric oxide (NO), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and sulfur dioxide (SO2). The suite also has an Alphasense OPC-N2 particle counter for PM measurements, along with temperature, pressure, and relative humidity sensors. Using most of these sensors in combination with basic supervised machine learning regression and a large dataset spanning over two years predictors are trained, applied, and examined for stability.

Advisor

Jacob Swanson

Committee Member

Rebecca Bates

Committee Member

Robert Sleezer

Date of Degree

2022

Language

english

Document Type

Thesis

Program

Integrated Engineering

Degree Program/Certificate

Engineering with an Electrical Focus

Degree

Bachelor of Science in Engineering (BSE)

Department

Integrated Engineering

College

Science, Engineering and Technology

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