LILAQ Real-time air quality monitoring: from collection to dissemination

This work is a collaboration between Professor Lelia Hawkins (Chemistry/Climate) and Professor Julie Medero (Computer Science). Air pollution research can be described as requiring “three legs of a stool” – measurement, modeling, and laboratory studies – which work together to advance our understanding of the sources and processes which shape our air quality from local to global scales. This specific project serves to advance the quality and utility of air pollution measurements here in Claremont through better integration with open source computational tools than has traditionally been used by air pollution scholars.  

Instrumentation and facilities available in Professor Hawkins’ lab include both research-quality specialized chemical instrumentation (e.g. aerosol mass spectrometer) and citizen-science friendly low cost PM sensors (e.g. PurpleAir). Common to both categories is the production of large volumes of time-dependent observations, with varying data formats, temporal frequencies, and ease of communication. Making sense and use of these observations requires a considerable amount of effort in simply “wrangling” these unwieldy datasets. Once the data are in a unified format, more advanced time-series analysis can take place.

In addition to getting this equipment recalibrated for new observations, the primary goal of this semester is to facilitate and streamline the process of source apportionment, or attribution, for particulate air pollution by automating the data streams from instruments with unique temporal resolution and data formats into a single coherent package. Specifically, we aim to stream observations from a meteorological sensor in preparation for the public-facing data portal.

A second goal is to leverage the real-time nature of these instruments and the above-mentioned streamlined analysis to provide real-time “level 1” (basic QA/QC’d) data on a public-facing web page, in a digestible format. This page would include the meteorological data, basic gas phase pollutants (ozone and nitrogen oxides) as well as particulate matter amount and chemical composition. 

Longer term, we hope to display this webpage on one or several monitors at the college, providing real time air quality information to the HMC community while we conduct the more advanced analysis to understand the sources of PM. These monitors would integrate spatially-distributed PurpleAir monitors located on campus, which provide a reading of either indoor or outdoor particulate levels. 

The timing of this proposed work aligns with a new air toxics measurement campaign taking place in the Southern California Air Basin through SCAQMD, MATES VI. The chemical specificity of the measurements in Claremont exceeds that typically available to air quality management staff, and therefore serves as a valuable addition to their observations.

Name of research group, project, or lab
LILAQ - The Lab for Investigations of Local Air Quality
Why join this research group or lab?

We are bridging atmospheric science and computer science. Traditionally, graduate students in atmospheric chemistry are responsible for both the operation and analysis required to obtain complex air quality measurements. Those students often lack the computational skills to do this in an efficient and reproducible manner coming from chemistry programs that are computationally very thin. There is often no training available to graduate students to develop these skills formally, either. This means that observations are not leveraged to their full potential to advance atmospheric chemistry. You will get interdisciplinary training and be part of a team that includes both chemistry and computer science majors.  

Expanding air quality measurements will be critical as we move into the next quarter century, because rising populations and temperatures will work in opposition to stricter regulations for pollutants. The new federal administration is likely to wreak havoc in the areas of environmental protection, and has threatened to revoke waivers for California to enforce stricter standards beyond the federal rules. This means that what we thought we knew about air quality is about to change. The public facing web page will support basic literacy in air quality, and hopefully stimulate interest among our community in increasing knowledge of air pollution causes and impacts.

Logistics Information:
Project categories
Chemistry
Computer Science
Climate Change
Data Science
Earth Science
Environmental Engineering
Environmental Science
Signal Processing
Student ranks applicable
First-year
Sophomore
Junior
Senior
Student qualifications

We are looking for students to work toward automating and unifying the data streams from these instruments to facilitate real time data visualization as well as more advanced analyses. Students who have completed the first year core at HMC can be successful in this work. Students with additional coursework in computer science will be better prepared for this work. 

Depending on student interest, work can be designed to accommodate all physical abilities. 

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

Students will gain familiarity in the topic of air pollution, including sources and processes that govern the amount and type of air pollution. Students will learn to work with varied formats of data in python, to automate the synchronization of data from multiple sources, and to generate simple web-based visualizations of data that has both temporal (time-varying) and spatial (location-varying) attributes. 

Project start
Fall 2025
Contact Information:
Mentors
lhawkins@hmc.edu
Principal Investigator
jmedero@hmc.edu
Faculty
Name of project director or principal investigator
Prof. Lelia Hawkins and Prof. Julie Medero
Email address of project director or principal investigator
lhawkins@g.hmc.edu
2 sp. | 15 appl.
Hours per week
Fall - Part Time
Project categories
Chemistry (+7)
ChemistryComputer ScienceClimate ChangeData ScienceEarth ScienceEnvironmental EngineeringEnvironmental ScienceSignal Processing