Data-driven placement of PM2.5 air quality sensors in the United States: An approach to target urban environmental injustice

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: This is version 2 of this Preprint.


Download Preprint

Supplementary Files

Makoto Michael Kelp, Timothy Fargiano, Samuel Lin, Tianjia Liu, Jay Turner, Nathan Kutz, Loretta Mickley


In the United States, citizens and policymakers heavily rely upon Environmental Protection Agency (EPA) mandated regulatory networks to monitor air pollution; increasingly they also depend on low-cost sensor networks to supplement spatial gaps in regulatory monitor networks coverage. Although these regulatory and low-cost networks in tandem provide enhanced spatiotemporal coverage in urban areas, sensors are located often in higher income, predominantly White areas. Such disparity in coverage may exacerbate existing inequalities and impact the ability of different communities to respond to the threat of air pollution. Here we present a study using cost-constrained multiresolution dynamic mode decomposition (mrDMDcc) to identify the optimal and equitable placement of fine particulate matter (PM2.5) sensors in four U.S. cities with histories of racial or income segregation: St. Louis, Houston, Boston, and Buffalo. This novel approach incorporates the variation of PM2.5 on timescales ranging from one day to over a decade to capture air pollution variability. We also introduce a cost function into the sensor placement optimization that represents the balance between our objectives of capturing PM2.5 extremes and increasing pollution monitoring in low-income and nonwhite areas. We find that the mrDMDcc algorithm places a greater number of sensors in historically low-income and nonwhite neighborhoods with known environmental pollution problems compared to networks using PM2.5 information alone. Our work provides a roadmap for the creation of equitable sensor networks in U.S. cities and offers a guide for democratizing air pollution data through increasing spatial coverage of low-cost sensors in less privileged communities.



Engineering, Physical Sciences and Mathematics


fine particulate matter (PM2.5), sensor placement, sensor networks, environmental justice, citizen science, sensor placement, sensor networks, environmental justice, citizen science


Published: 2023-03-03 09:00

Last Updated: 2023-07-22 08:21

Older Versions

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:

Add a Comment

You must log in to post a comment.


There are no comments or no comments have been made public for this article.