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

Asrah Heintzelman, Gabriel M. Filippelli (), Max J. Moreno-Madriñan, Jeffrey S. Wilson, Lixin Wang, Gregory K. Druschel and Vijay O. Lulla
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Asrah Heintzelman: Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA
Gabriel M. Filippelli: Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, 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

IJERPH, 2023, vol. 20, issue 3, 1-18

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.

Keywords: PM 2.5; citizen science; low-cost sensor; PA; tree canopy coverage (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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