BEACO2N: Calibration and analyses of sensor networks

Partner: Ronald Cohen, UC Berkeley, Department of Chemistry, Academic

Overview

Project Description

Observations from electrochemical sensors require assessment of data quality, calibration when the data is good and cross checks among different chemicals and different locations in order to establish the data set is accurate enough for further analysis.

Expected Deliverable

Code for calibrating sensors, evaluation of that code, possibly expanding to analyses of the observations if calibration is completed.

What would a successful semester look like to you?

1. Student understanding of BEACO2N goals
2. Stable efficient code that automates calibration
3. Use of BEACO2N data to analyze Bay Area or Galsgow, Scotland emissions

Additional Skills from ideal candidates

Good writing skills a plus. Completeion of DATA 100 is essential.

Data

Models

Conclusion