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Efficacy of Low-Cost Sensor Networks at Detecting Fine-Scale Variations in Particulate Matter in Urban Environments

Author

Listed:
  • Asrah Heintzelman

    (Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA
    Environmental Resilience Institute, Indiana University, Bloomington, IN 47408, USA)

  • Gabriel M. Filippelli

    (Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA
    Environmental Resilience Institute, Indiana University, Bloomington, IN 47408, USA)

  • Max J. Moreno-Madriñan

    (Department of Global Health, DePauw University, Greencastle, IN 46135, USA)

  • Jeffrey S. Wilson

    (Department of Geography, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA)

  • Lixin Wang

    (Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA)

  • Gregory K. Druschel

    (Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA)

  • Vijay O. Lulla

    (Independent Researcher, Indianapolis, IN 46214, USA)

Abstract

The negative health impacts of air pollution are well documented. Not as well-documented, however, is how particulate matter varies at the hyper-local scale, and the role that proximal sources play in influencing neighborhood-scale patterns. We examined PM 2.5 variations in one airshed within Indianapolis (Indianapolis, IN, USA) by utilizing data from 25 active PurpleAir (PA) sensors involving citizen scientists who hosted all but one unit (the control), as well as one EPA monitor. PA sensors report live measurements of PM 2.5 on a crowd sourced map. After calibrating the data utilizing relative humidity and testing it against a mobile air-quality unit and an EPA monitor, we analyzed PM 2.5 with meteorological data, tree canopy coverage, land use, and various census variables. Greater proximal tree canopy coverage was related to lower PM 2.5 concentrations, which translates to greater health benefits. A 1% increase in tree canopy at the census tract level, a boundary delineated by the US Census Bureau, results in a ~0.12 µg/m 3 decrease in PM 2.5 , and a 1% increase in “heavy industry” results in a 0.07 µg/m 3 increase in PM 2.5 concentrations. Although the overall results from these 25 sites are within the annual ranges established by the EPA, they reveal substantial variations that reinforce the value of hyper-local sensing technologies as a powerful surveillance tool.

Suggested Citation

  • Asrah Heintzelman & Gabriel M. Filippelli & Max J. Moreno-Madriñan & Jeffrey S. Wilson & Lixin Wang & Gregory K. Druschel & Vijay O. Lulla, 2023. "Efficacy of Low-Cost Sensor Networks at Detecting Fine-Scale Variations in Particulate Matter in Urban Environments," IJERPH, MDPI, vol. 20(3), pages 1-18, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:3:p:1934-:d:1042256
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    References listed on IDEAS

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    1. Ronan Hart & Lu Liang & Pinliang Dong, 2020. "Monitoring, Mapping, and Modeling Spatial–Temporal Patterns of PM 2.5 for Improved Understanding of Air Pollution Dynamics Using Portable Sensing Technologies," IJERPH, MDPI, vol. 17(14), pages 1-18, July.
    2. Ji, Xi & Yao, Yixin & Long, Xianling, 2018. "What causes PM2.5 pollution? Cross-economy empirical analysis from socioeconomic perspective," Energy Policy, Elsevier, vol. 119(C), pages 458-472.
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