Using crowd-sourced low-cost sensors in a land use regression of PM2.5 in 6 US cities

Tianjun Lu, Matthew J. Bechle, Yanyu Wan, Albert A. Presto, Steve Hankey

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Assessing exposure to ambient fine particulate matter (PM2.5) is important for improving human health. With rapidly expanding low-cost sensor networks globally, it is possible for monitoring networks to be located by a variety of users (i.e., crowd sourcing) to increase measurement density and coverage for use in exposure assessment, e.g., national land use regression (LUR) models. Few studies have integrated low-cost sensors into LUR models across multiple cities, limiting the ability of modelers to fully utilize growing low-cost sensor networks worldwide. We developed five LUR models to predict annual average PM2.5 concentrations using combinations of regulatory (six cities: n = 68; national: n = 757) and low-cost monitors (n = 149) from six US cities. We found that developing Hybrid LURs that include the low-cost (i.e., PurpleAir) network may better capture within-city variation. LURs with the PurpleAir data only (tenfold CV R2 = 0.66, MAE = 2.01 µg/m3) performed slightly worse than a conventional LUR based on regulatory data only (tenfold CV R2 = 0.67, MAE = 0.99 µg/m3). Hybrid models that included both low-cost and regulatory data performed similarly to existing national models that rely on regulatory data (hybrid models: tenfold CV R2 = 0.85, MAE = 1.02 µg/m3; regulatory monitor models: R2 = 0.83, MAE = 0.72 µg/m3). Integrating crowd-sourced low-cost sensor networks in LUR models has promising applications to help identify intra-city exposure patterns especially for regions with limited regulatory networks internationally.

Original languageEnglish
Pages (from-to)667-678
Number of pages12
JournalAir Quality, Atmosphere and Health
Issue number4
StatePublished - Apr 2022

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature B.V.


  • Empirical model
  • Hybrid model
  • Low-cost monitoring
  • Open data
  • Within-city variability

ASJC Scopus subject areas

  • Pollution
  • Atmospheric Science
  • Management, Monitoring, Policy and Law
  • Health, Toxicology and Mutagenesis


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