Environmental data science: Opportunities for teaching, research, and service
Poster Number
3A
Introduction/Abstract
Data science, a burgeoning field arising from the disciplines of math and computer science, has great potential in environmental engineering and science. Data science methods offer opportunities to streamline and improve data acquisition, processing, modeling, and visualization. Increased environmental monitoring has resulted in extensive data sets that are publicly available and have the potential to improve our understanding of environmental health. Integration of data science into environmental engineering and science education is important for building proficiency.
Purpose
The purpose of this project was to improve my data science skills to use data science approaches and tools in teaching, research projects, and service activities.
Method
The programming language R was used with the RStudio interface. Various packages were used although the work relied heavily on packages found in the “tidyverse” that are used for cleaning, wrangling, and visualizing data. Maps were created using the “sf” and “ggmap” packages. Data acquisition was accomplished using the “cder”, “dataRetrieval”, “rnoaa”, and “RAQSAPI” packages. Machine learning models were developed using the “caret” package. Case studies were completed using data from agencies such as the US EPA and California Geologic Energy Management Division.
Results
To apply what was learned in teaching, a previously taught course, ENGR 293 – Environmental Data Analysis, was redesigned to have a data science focus instead of an applied statistics structure. A pedagogy project was designed and conducted during the Fall of 2022 to evaluate the efficacy of the new course content (IRB2022-179). The project results were summarized in a paper that will be presented at the 2023 Association for Engineering Education conference in Baltimore, MD.
Data science techniques were integrated into a research project to study the environmental impacts of increased geothermal power production in the Salton Sea to facilitate lithium extraction from geothermal brines. Data science tools were used to aggregate publicly available data sets for analysis and visualization.
To integrate data science into service activities, information learned in the project was presented to the campus chapter of Theta Tau, a professional engineering fraternity. The presentation was titled: “Top 10 Ways to use Data Science in Engineering.”
Significance
Application of data science in environmental fields can assist engineers and scientists in analyzing data sets for quantifying pollution and evaluating risks to humans and ecosystems. Integrating data science into the curriculum is important to build data skills in our graduates.
Location
Library and Learning Center, 3601 Pacific Ave., Stockton, CA 95211
Format
Poster Presentation
Environmental data science: Opportunities for teaching, research, and service
Library and Learning Center, 3601 Pacific Ave., Stockton, CA 95211
Data science, a burgeoning field arising from the disciplines of math and computer science, has great potential in environmental engineering and science. Data science methods offer opportunities to streamline and improve data acquisition, processing, modeling, and visualization. Increased environmental monitoring has resulted in extensive data sets that are publicly available and have the potential to improve our understanding of environmental health. Integration of data science into environmental engineering and science education is important for building proficiency.
