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Improving Correlations between Land Use and Air Pollutant Concentrations Using Wavelet Analysis: Insights from a Low-cost Sensor Network

Category: Urban Air Quality

Accepted Manuscripts
DOI: 10.4209/aaqr.2019.03.0124
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Naomi Zimmerman 1, Hugh Z. Li1, Aja Ellis1, Aliaksei Hauryliuk1, Ellis S. Robinson1, Peishi Gu1, Rishabh U. Shah1, Qing Ye1, Luke Snell2, R. Subramanian1, Allen L. Robinson1, Joshua S. Apte2, Albert A. Presto1

  • 1 Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA
  • 2 Department of Civil, Architectural and Environmental Engineering University of Texas at Austin, Austin, TX 78712, USA


  • A network of 15 real-time air quality stations was deployed in Pittsburgh, PA.
  • Data was separated into short-lived (< 2 h) events and persistent enhancements.
  • Short-lived and persistent enhancements are better correlated to land use covariates.


City-wide air pollution assessments have typically relied on a small number of widely separated regulatory monitoring sites or land use regression (LUR) models built using time-integrated samples to assess annual average population-scale exposure. However, air pollutant concentrations often exhibit significant spatial and temporal variability depending on local sources and features of the built environment. In 2016, the Center for Air, Climate, and Energy Solutions (CACES) Air Quality Observatory was launched at Carnegie Mellon University to better understand urban spatial and temporal pollution gradients on the 8 h) above the regional background. Compared to the non-decomposed total pollutant signal, the short-lived or persistent enhancement pollutant signals, which should come from local sources, were better correlated with covariates used in LUR model construction. For example, Pearson r between total vehicle counts in a 100 m buffer and NO2 increased from 0.57 using the total pollutant signal to 0.83 using the persistent enhancement only. The findings from this study support building more accurate and higher time resolution (e.g., daily, hourly) LURs using low-cost sensors.


Air quality Exposure Low-cost sensors Land use regression Urban emissions

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