Grantee Research Project Results
MINTS: Multi-scale intelligent sensing
EPA Grant Number: SU840570Title: MINTS: Multi-scale intelligent sensing
Investigators: Lary, David J , Schermbeck, Jim , Mayo, Evelyn
Current Investigators: Lary, David J , Patra, Rittik , Sooriyaarachchi, Vinu , Dewage, Prabuddha , Balagopal, Gokul , Waczak, John
Institution: The University of Texas at Dallas , Paul Quinn College , Downwinders at Risk Education Fund
EPA Project Officer: Page, Angela
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
Description:
To provide low-cost calibrated air quality sensors that can be distributed at scale and used sustainably across predominantly Black and Brown DFW neighborhoods and related to real-time remote sensing observations from the geostationary GOES-R satellite providing scientifically valid information for data-driven decisions by communities and residents. The air quality sensors are intended for promoting human health, education, transparent participation of all the stakeholders, and environmental justice.
Objective:
Carefully and individually calibrate low-cost sensors against a reference monitor using machine learning. Reduce recurring infrastructure and installation costs by having the sensors solar powered and communicating using a Long Range Wireless Area Network (LoRaWAN). All data is made publicly available. The purpose of this proposal is to extend the deployment from a previous P3 award and to also relate the in-situ data collected to remotely sensed data. The collaborating graduate (Physics and Electrical Engineering) and undergraduate students (Computer Science) will learn new skills and experience the benefit of teamwork for societal benefit. The previous P3 award also had broad community engagement and was featured on a national BET network documentary on Environmental Justice hosted by Soledad O’Brien.
Approach:
Use machine learning for individualized and validated calibration of carefully chosen long-lifetime sensors against an EPA reference instrument.
Apply technology developed for NASA satellite bias detection for real-time comparison of each sensor to its neighbors to detect drift and sensor degradation.
Relate high time resolution in-situ PM2.5 to a high time-resolution data product using remotely sensed observations from GOES-R, real-time meteorology and machine learning.
Collaboration of STEM students from local universities with nearby neighborhoods in need.
Expected Results:
Providing accessible data from an open-source platform giving the West Dallas community the ability to weave new, never-before-seen data into everyday public awareness and public policy, with quantifiable results, which can be replicated elsewhere. Data from the sensors will also be available for integration into STEM curricula by the participating educational institutions.
Publications and Presentations:
Publications have been submitted on this project: View all 8 publications for this projectJournal Articles:
Journal Articles have been submitted on this project: View all 8 journal articles for this projectSupplemental Keywords:
Low-cost sensors, calibration, machine learning, solar powered, LoRaWAN, Environmental Justice.
Relevant Websites:
https://stage-www-bet-com.webplex.vmn.io/episodes/8198nh/disrupt-dismantle-shinglemountain-season-1-ep-1
Progress and Final Reports:
The 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.