Demonstration of a Low-Cost Multi-Pollutant Network to Quantify Intra-Urban Spatial Variations in Air Pollutant Source Impacts and to Evaluate Environmental Justice

Rebecca Tanzer, Carl Malings, Aliaksei Hauryliuk, R. Subramanian and Albert A. Presto
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Rebecca Tanzer: Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Carl Malings: Center for Atmospheric and Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Aliaksei Hauryliuk: Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
R. Subramanian: Center for Atmospheric and Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Albert A. Presto: Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA

IJERPH, 2019, vol. 16, issue 14, 1-15

Abstract: Air quality monitoring has traditionally been conducted using sparsely distributed, expensive reference monitors. To understand variations in PM 2.5 on a finely resolved spatiotemporal scale a dense network of over 40 low-cost monitors was deployed throughout and around Pittsburgh, Pennsylvania, USA. Monitor locations covered a wide range of site types with varying traffic and restaurant density, varying influences from local sources, and varying socioeconomic (environmental justice, EJ) characteristics. Variability between and within site groupings was observed. Concentrations were higher near the source-influenced sites than the Urban or Suburban Residential sites. Gaseous pollutants (NO 2 and SO 2 ) were used to differentiate between traffic (higher NO 2 concentrations) and industrial (higher SO 2 concentrations) sources of PM 2.5 . Statistical analysis proved these differences to be significant (coefficient of divergence > 0.2). The highest mean PM 2.5 concentrations were measured downwind (east) of the two industrial facilities while background level PM 2.5 concentrations were measured at similar distances upwind (west) of the point sources. Socioeconomic factors, including the fraction of non-white population and fraction of population living under the poverty line, were not correlated with increases in PM 2.5 or NO 2 concentration. The analysis conducted here highlights differences in PM 2.5 concentration within site groupings that have similar land use thus demonstrating the utility of a dense sensor network. Our network captures temporospatial pollutant patterns that sparse regulatory networks cannot.

Keywords: lower-cost sensor network; PM 2.5; near-source (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
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