Grantee Research Project Results
Final Report: MINTS: Multi-scale intelligent sensing
EPA Grant Number: SU840570Title: MINTS: Multi-scale intelligent sensing
Investigators:
Institution:
EPA Project Officer:
Phase: I
Project Period: August 1, 2023 through April 23, 2025
Project Amount: $24,999
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: Air , P3 Awards
Objective:
Build and calibrate a scalable, low-cost, solar-powered air-quality sensing network— deployed with and for communities in the Dallas–Fort Worth metroplex—linking Long Range Wireless Area Network (LoRaWAN) IoT sensors, machine-learning calibration, and satellite data (e.g., GOES-R) to deliver scientifically valid, real-time information for environmental justice, public health, education, and transparent decision-making.
Summary/Accomplishments (Outputs/Outcomes):
• Deployed and operated an affordable, solar-powered IoT particulate monitoring network (PM0.1–PM10; PM1/2.5/5/10) with live, open data access at https://www.sharedairdfw.com.
• Performed individualized machine-learning calibration against EPA reference instruments; addressed humidity/dew point edge cases via theory-informed and empirical corrections; validated through co-location with FEM monitors.
• Integrated complementary satellite and in situ data to produce hyper-local maps of air quality, increasing temporal (seconds) and spatial (neighborhood) resolution.
• Advanced environmental justice outcomes via sustained community partnerships (e.g., Downwinders at Risk), providing transparent data that communities used in advocacy and local decision-making.
• Extended innovation: incorporated avian biodiversity sensing (every ~3 s) and explored biometric responses to inhaled pollutants (PM, NO₂, CO₂, NO) as a humansensor paradigm.
• Trained and graduated multiple student researchers; produced 10+ peer-reviewed publications and preprints documenting methods, calibration, results, and applications.
• Created a replicable, cost-effective blueprint (solar + LoRaWAN + ML calibration) for municipal and community-scale deployments in underserved neighborhoods.
Journal Articles:
No journal articles submitted with this report: View all 8 publications for this projectSupplemental Keywords:
Air Quality; Particulate Matter; PM2.5; IoT Sensors; LoRaWAN; Machine Learning; Calibration; Remote Sensing; GOES-R; Environmental Justice; Dallas–Fort Worth; Hyper-local Mapping; Avian Biodiversity; Biometric Sensing; Public Health; Open Data; Community Science; Exposome; Source ApportionmentRelevant Websites:
SharedAirDFW Particulate Monitoring Network Exit
Progress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.