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

Abstract

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.

Improved daily PM2.5 estimates in India reveal inequalities in recent enhancement of air quality

“…For the cost function vector, all data is a binary of 0 or 1, with 0 representing wealthy locations and 1 representing poor locations. More details of the method can be found in (55 ).…”

PM2.5 Potential Indoor Exposures and Outdoor Concentrations Related to Smoking Prevalence by Zip Code in Three West Coast States

“…An important review is Mohai et al, 2009 [3]. More recent studies have found such SES variables as income, education, housing, and ethnic groups to result in increased outdoor PM concentrations [4][5][6][7][8][9][10][11]. However, even with the advent of low-cost monitors, none of these studies were able to show an effect on indoor concentrations [12][13][14][15][16].…”