Grantee Research Project Results

Final Report: Mapping air pollution disparities using low-cost particulate sensors

EPA Grant Number: SU840572
Title: Mapping air pollution disparities using low-cost particulate sensors
Investigators: Couzo, Evan , Coley, Jackson , Sonney, Jacob , Shuster, Piper
Institution: University of North Carolina at Asheville
EPA Project Officer: Spatz, Kyle
Phase: I
Project Period: August 1, 2023 through July 31, 2024
Project Amount: $24,932
RFA: 19th Annual P3 Awards: A National Student Design Competition Focusing on People, Prosperity and the Planet Request for Applications (RFA) (2022) RFA Text |  Recipients Lists
Research Category: P3 Awards , P3 Challenge Area - Air Quality

Objective:

It is well documented that environmental stressors such as air pollution disproportionately affect low-income and minority neighborhoods. Routine ambient air quality monitoring is mandated by federal law, but the small number of monitoring stations fails to capture fine-scale differences between neighborhoods within a city. This project used low-cost PM_2.5 sensors to provide in-situ measurements of air pollution in Asheville, North Carolina, at 12 separate locations, which span geographic and demographic ranges.  Specifically, we learned how PM_2.5 concentrations vary across the city and which communities are most impacted. This project directly relates to the EPA’s P3 approach.  First, undergraduate students were involved in every aspect of the research, which engaged and educated the next generation of scientists.  Second, we developed a cost-effective solution to neighborhood-scale ambient PM_2.5 monitoring, and our project identified which communities are most impacted by particulate pollution.  Additionally, all equipment can be reused or repurposed by science teachers in K-12 or university settings.  

 

This project had two objectives:  design, build, and test low-cost (~$100) fine particulate matter (PM_2.5) sensors, and measure ambient PM_2.5 disparities across Asheville, North Carolina.  The market for low-cost ambient air pollution sensors is growing, and consumers have several options.  Despite widespread availability, though, many of the sensors have not been independently tested in realistic operating conditions.  Field tests conducted by federal, state, and local environmental agencies indicate the accuracy of many of these sensors is questionable.  This project’s first objective sought to improve the low-cost sensor market in three ways.  First, the sensors are truly low cost at about $100 each.  Second, the sensors were rigorously field-tested and co-located in partnership with the Asheville-Buncombe Air Quality Agency (ABAQA).  Third, unlike the “black box” sensors available to consumers, the sensors that we designed and built during this project are programmed with open-source software and double as valuable STEM teaching tools at the K-12 and university level.  The project’s second objective addressed longstanding environmental disparities resulting from housing discrimination in Asheville for much of the 20^th Century.  After field-testing, the sensors were placed on public K-12 Asheville City Schools (ACS) campuses in nine different neighborhoods, a public charter school, ABAQA’s regulatory monitoring site, and UNC Asheville’s ECONet weather tower, thus providing a dense network of ambient measurements.  Each ACS campus is readily identifiable on maps that were used by the Home Owners’ Loan Corporation (HOLC) in the 1930s to rate residential neighborhoods.  This rating system, commonly known as redlining, discouraged investment in predominantly minority communities.  The effects of this disinvestment are understood to be widespread, and this project extends that understanding to include ambient PM_2.5 concentrations.  Specifically, this research sought to determine the extent to which communities historically subjected to racial housing discrimination are exposed to higher ambient concentrations of fine particulate matter.  This information can help improve public health by providing neighborhood-scale PM_2.5 levels used to inform local pollution control strategies.

Summary/Accomplishments (Outputs/Outcomes):

• The Raspberry Pi-based low-cost sensor platform is resilient to a range of ambient conditions.  Nearly all sensors ran continuously for several weeks without reporting errors.
• All low-cost sensors were strongly correlated with each other.  During the co-location fieldtests, the minimum r-value between any two sensors was 0.81 and the maximum r-value was 0.95.
• Correlation between the low-cost sensors and ABAQA’s regulatory monitor was worse.  The minimum and maximum r-values between any one sensor and the ABAQA monitor was 0.56 and 0.65.  The aggregate r-value for all low-cost sensors compared to the ABAQA monitor was 0.58.
• The low-cost sensors had a persistent low-bias as relative to the ABAQA monitor.  Aggregate normalized mean bias was -0.64, and root mean square error was 4.6 μg/m³ 
• A simple linear regression correction factor was calculated for and applied to the low-cost sensor measurements.  Slope and y-intercept values were 0.15 and 0.89.
• Measurement deployment sites were categorized based on the HOLC color-coded rating scale.  Mean concentration differences across both fall and winter deployments between categories were measured; Green (one sensor) = 1.64 μg/m³, Blue (two sensors) = 7.01 μg/m³, Yellow (four sensors) = 7.79 μg/m³, and Red (two sensors in fall, one sensor in winter) = 6.67 μg/m³.  Note, one of the Red sensors disappeared during the winter deployment.

Conclusions:

The Raspberry Pi platform is a resilient and low-cost option for continuous measurements of PM_2.5.

• The Raspberry Pi-based low-cost sensor is a valuable educational tool that teaches students how to code, collect and analyze data, and troubleshoot instrumentation, in addition to teaching about ambient PM_2.5 pollution.
• The SDS-011 particle sensor, while meeting the criteria of low-cost, had persistent low bias when compared to a regulatory reference monitor.  The sensor should be replaced with a better performing one. 
• Despite the particle sensor’s relatively poor performance, heterogeneities were measured across Asheville.  This indicates a need for further study of ambient PM_2.5 concentration fields in the region.

Journal Articles:

No journal articles submitted with this report: View all 1 publications for this project

Supplemental Keywords:

low-cost particulate sensor, ambient measurement, environmental justice