Final Report: Democratization of Measurement and Modeling Tools for Community Actionon Air Quality, and Improved Spatial Resolution of Air Pollutant Concentrations

EPA Grant Number: R836286
Title: Democratization of Measurement and Modeling Tools for Community Actionon Air Quality, and Improved Spatial Resolution of Air Pollutant Concentrations
Investigators: Presto, Albert , Pandis, Spyros N. , Ramachandran, Subramanian
Institution: Carnegie Mellon University
EPA Project Officer: Callan, Richard
Project Period: May 1, 2016 through April 30, 2019 (Extended to April 30, 2021)
Project Amount: $749,945
RFA: Air Pollution Monitoring for Communities (2014) RFA Text |  Recipients Lists
Research Category: Environmental Justice , Air Quality and Air Toxics , Air , Particulate Matter


The goals of this project are to develop and test low-cost air pollutant sensors, and to use those sensors in public communication about air pollution and attendant health risks. The specific objectives of the project, as stated in the proposal, are: (1) Develop portable bipollutant PM2.5/gas monitors and stationary multipollutant (PM + 4 criteria gases + VOCs) monitors; characterize their response to different primary and secondary pollutant mixtures; and test the reliability of these monitors under a variety of environmental conditions. (2) Enable environmental justice and biking communities to monitor their pollutant exposure using portable monitors and a new Pittsburgh Air Quality Map (PAQmap), the interfaces of which will be designed with input from a Community Advisory Board. (3) Study the interactions between community members and risk information in the form of personal monitoring and interaction with a pollution map. (4) Examine the spatial representativeness of the existing air pollution monitoring network in Allegheny County using distributed monitoring and both statistical and chemical transport (PMCAMx) modeling. (5) Examine the effect of community-inspired solutions to reduce air pollution exposure. The Community Advisory Board can suggest “what-if” scenarios for PMCAMx runs such as widespread retrofits of public transport buses with diesel particulate filters and emissions reductions at large industrial sources in Allegheny County.

Summary/Accomplishments (Outputs/Outcomes):

The primary sensor package used in this project is the RAMP (Real Time Affordable Multi Pollutant sensor package; Figure 1). The RAMP combines electrochemical gas sensors (CO, NO2, O3, SO2) with non-dispersive infrared CO2 measurements. The RAMP can accommodate external optical PM2.5 monitors, including the PurpleAir and the MetOne Neighborhood PM monitor.

Figure 1. Photographs of the RAMP monitors and the sampling setup. (a) Front view of the RAMP unit in the NEMA-rated enclosure. (b) Bottom view of the RAMP monitors with sensor layout labeled in yellow. (c) Example of collocation setup using tripod mounting.

In previous reports, we detailed our efforts to develop calibration methods and algorithms for the RAMPs. Two papers discuss calibration for electrochemical gas sensors. Zimmerman et al (2018) describes that calibrations using non-parametric machine learning algorithms often outperform linear calibrations (Figure 2). This manuscript has been cited more than 100 times, according to Web of Science. Malings et al (2019) showed that generalized RAMP calibration models that are based on a subset of RAMP monitors collocated with reference monitors, which can be applied to all RAMP monitors with only a small penalty in terms of performance. This is particularly useful when deploying a large network with dozens of nodes, as only a subset of the monitors needs to be collocated annually, thus reducing maintenance efforts significantly. We also developed methods for correcting data from low-cost particle sensors such as Purple Airs (Malings et al, 2020a)

Figure 2. Performance of different calibration models against reference monitor testing data (data not included in model fitting). (a) Pearson r correlation coefficient (higher is better, maximum of 1) of different calibration models (“LAB”, green; “MLR”, blue; “RF”, pink) versus reference monitor. (b) The CvMAE (coefficient of variation of the MAE; MAE normalized by average reference concentration; lower is better) for the three calibration methods. The box plots show the range across the 10–16 RAMP monitors (whiskers: 10th and 90th percentile; box edges: 25th and 75th percentile).

In the aftermath of Hurricane Maria, the electricity grid in Puerto Rico was devastated, with over 90% of the island without electricity. The hurricane also damaged the island’s existing air monitoring network and the University of Puerto Rico’s observing facilities. We deployed four RAMPs and a black carbon (BC) monitor in San Juan beginning in November 2017. The RAMPs showed high concentrations of SO2, often in exceedance of the 1-hr EPA threshold (75 ppb). The most likely source is the use of high-sulfur fuel in diesel backup generators. These results were published in 2018 (Subramanian et al, 2018).

Pollution Data Visualization

We developed the “Pittsburgh air quality map” (Figure 1) as part of this project. The map provides communicates pollutant concentrations in multiple ways. Color saturation indicates concentrations over the Pittsburgh area. Clicking on the map brings up the current concentration in that location (shown with a yellow box) and activates the dial at the top of the map. The dials use the same color scale as the map, and translate the concentrations to plain English (e.g., “unhealthy” for high concentrations). This empowers individuals to understand pollutant levels for specific locations.


  • Zimmerman, N., Presto, A. A., Kumar, S. P. N., Gu, J., Hauryliuk, A., Robinson, E. S., Robinson, A. L., and R. Subramanian (2018). “A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring.” Atmos. Meas. Tech., 11, 291-313,
  • Malings, C., R. Tanzer, A. Hauryliuk, S.P.N. Kumar, N. Zimmerman, L.B. Kara, A.A. Presto, and R. Subramanian (2019) “Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring.” Atmos. Meas. Tech., 12:903-920,
  • Malings, C.; Westervelt, D.M.; Hauryliuk, A.; Presto, A.A.; Grieshop, A.; Bittner, A.; Beekmann, M.; Subramanian, R. Application of low-cost fine particulate mass monitors to convert satellite aerosol optical depth to surface concentrations in North America and Africa. Atmos. Meas. Tech., 2020, 13, 2873-3892, DOI: 10.5194/amt-13-3873-2020
  • Subramanian, R., A. Ellis, E. Torres-Delgado, C. Malings, R. Tanzer, F. Rivera, M. Morales, D. Baumgardner, A. Presto, O.L. Mayol-Bracero (2018). “Air quality in Puerto Rico in the aftermath of Hurricane Maria: A case study on the use of lower-cost air quality monitors.” ACS Earth & Space Science. DOI: 10.1021/acsearthspacechem.8b00079

Journal Articles on this Report : 4 Displayed | Download in RIS Format

Other project views: All 23 publications 14 publications in selected types All 14 journal articles
Type Citation Project Document Sources
Journal Article Bittner A, Cross E, Hagan D, Malings C, Lipsky E, Grieshop A. Performance characterization of low-cost air quality sensors for off-grid deployment in rural Malawi. ATMOSPHERIC MESUREMENT TECHNIQUES 2022;15(11):3353-3376. R836286 (Final)
  • Full-text: EGU - Full Text HTML
  • Other: EGU - Full Text PDF
  • Journal Article Giordano M, Mailings C, Pandis S, Presto A, McNiell V, Wetervelt D, Beekman M, Subrgamanian R. From low-cost sensors to high-quality data:A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors. JOURNAL OF AEROSOL SCIENCE 2021;158. R836286 (Final)
    R835873 (2020)
  • Full-text: ScienceDirect - Full Text HTML
  • Other: ScienceDirect - Full Text PDF
  • Journal Article Jain S, Presto A, Zimmerman N. Spatial Modeling of Daily PM2.5, NO2, and CO Concentrations Measured by a Low-Cost Sensor Network:Comparison of Linear, Machine Learning, and Hybrid Land Use Models. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021;55(13):8631-8641. R836286 (Final)
    R835873 (2020)
  • Abstract from PubMed
  • Full-text: ACS Publications - Full Text HTML
  • Other: ACS Publications - Full Text PDF
  • Journal Article Eilenberg SR, Subramanian R, Malings C, Hauryliuk A, Presto AA, Robinson AL. Using a network of lower-cost monitors to identify the influence of modifiable factors driving spatial patterns in fine particulate matter concentrations in an urban environment. Journal of Exposure Science & Environmental Epidemiology 2020;30(6):949-61. R836286 (Final)
    R835873 (2020)
  • Abstract from PubMed
  • Full-text: Nature - Full-text HTML
  • Other: Nature - Full-text PDF
  • Progress and Final Reports:

    Original Abstract
  • 2016 Progress Report
  • 2017 Progress Report
  • 2018 Progress Report
  • 2019 Progress Report