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Article

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

1
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
2
Center for Atmospheric and Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA
3
OSU-EFLUVE, CNRS, Université Paris-Est Créteil, 61 Avenue du Général de Gaulle, 94000 Créteil, France
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(14), 2523; https://doi.org/10.3390/ijerph16142523
Received: 8 May 2019 / Revised: 22 June 2019 / Accepted: 28 June 2019 / Published: 15 July 2019
(This article belongs to the Special Issue Near-Source Air Pollution)
Air quality monitoring has traditionally been conducted using sparsely distributed, expensive reference monitors. To understand variations in PM2.5 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 (NO2 and SO2) were used to differentiate between traffic (higher NO2 concentrations) and industrial (higher SO2 concentrations) sources of PM2.5. Statistical analysis proved these differences to be significant (coefficient of divergence > 0.2). The highest mean PM2.5 concentrations were measured downwind (east) of the two industrial facilities while background level PM2.5 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 PM2.5 or NO2 concentration. The analysis conducted here highlights differences in PM2.5 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. View Full-Text
Keywords: lower-cost sensor network; PM2.5; near-source lower-cost sensor network; PM2.5; near-source
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MDPI and ACS Style

Tanzer, R.; Malings, C.; Hauryliuk, A.; Subramanian, R.; Presto, A.A. Demonstration of a Low-Cost Multi-Pollutant Network to Quantify Intra-Urban Spatial Variations in Air Pollutant Source Impacts and to Evaluate Environmental Justice. Int. J. Environ. Res. Public Health 2019, 16, 2523. https://doi.org/10.3390/ijerph16142523

AMA Style

Tanzer R, Malings C, Hauryliuk A, Subramanian R, Presto AA. Demonstration of a Low-Cost Multi-Pollutant Network to Quantify Intra-Urban Spatial Variations in Air Pollutant Source Impacts and to Evaluate Environmental Justice. International Journal of Environmental Research and Public Health. 2019; 16(14):2523. https://doi.org/10.3390/ijerph16142523

Chicago/Turabian Style

Tanzer, Rebecca, Carl Malings, Aliaksei Hauryliuk, R. Subramanian, and Albert A. Presto 2019. "Demonstration of a Low-Cost Multi-Pollutant Network to Quantify Intra-Urban Spatial Variations in Air Pollutant Source Impacts and to Evaluate Environmental Justice" International Journal of Environmental Research and Public Health 16, no. 14: 2523. https://doi.org/10.3390/ijerph16142523

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