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 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, low-cost 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 (PM_2.5) 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 PM_2.5 on timescales ranging from 1 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 PM_2.5 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 PM_2.5 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.

Keywords: citizen science; environmental justice; fine particulate matter (PM2.5); sensor networks; sensor placement.

Figure 1

Spatial extent of St. Louis, Houston, Buffalo, and Boston metropolitan areas. Image source: Google Earth Engine software with data from SIO, NOAA, U.S. Navy, NGA, GEBCO, and images from Landsat/Copernicus, U.S. Geological Survey.

Figure 2

Maps of decadal PM_2.5 concentrations, socioeconomic inequality metrics, and EPA and PurpleAir sensor locations for St. Louis, Houston, Buffalo, and Boston metropolitan areas. The 11‐year mean of annual averages of PM_2.5 over 2006–2016 are from estimates from Di et al. (2021). The proportion nonwhite and median annual household income are from the 2020 American Community Survey interpolated onto the centroids of the Di et al. (2021) PM_2.5 data set. EPA (locations used in Di et al. (2021)) and PurpleAir (downloaded on 19 July 2021) sensor locations are gridded onto the same 1 km × 1 km Di et al. (2021) grid. White areas of the sensor location maps represent the built environment, while the shades of green represent the natural vegetation colors of the area.

Figure 3

PM_2.5 sensor locations for St. Louis, Houston, Buffalo, and Boston. Distribution of sensor locations identified as optimal by the mrDMD algorithm, and those identified as optimal and equitable by the mrDMDcc using race and income metrics. All sensor locations are gridded onto the same 1 km × 1 km Di et al. (2021) grid. Dots represent sensor locations with the shading representing the proportion of nonwhite (left and center columns) or low‐income households (right column) in that grid box. Dots outlined in red indicate areas with historic environmental justice issues mentioned in the text—for example, Granite City, IL, and East St. Louis, IL, for the race optimized mrDMDcc case.

Figure 4

Cumulative frequency distributions for proportion of nonwhite locations and median annual income for the three different sensor network optimizations for St. Louis. Each point represents one sensor location out of the 250 designed for St. Louis. The mrDMD network is designed with only air pollution modal information, mrDMD‐race includes race information from the United States Census in the sensor network optimization, and mrDMD‐income includes annual income information from the Census in the sensor network optimization. An additional set of points in each plot represents the distribution across racial composition (light blue) and income (orange) for a high‐density, uniformly distributed sensor network across all 1 km² grid cells within the city bounds. The y‐axis for median annual income has been reversed to make this panel consistent with the other panels, with the neighborhoods of greatest interest in this study plotted at the high end of the distributions.