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Applying Low-Cost Air Sensors for Spatiotemporal Variability of Particulate Matter in a Local Community Adjacent to Interstate Highway

  • Author(s): Chen, Yu-Han
  • Advisor(s): Zhu, Yifang
  • et al.
Abstract

A key contributor to urban ambient air pollutants is road traffic. Vehicle emissions are known to be associated with various adverse health effects. Particulate matter (PM), as one of the traffic-related air pollutants (TRAPs), is particularly crucial due to its various chemical composition, morphology, size and numerous adverse health impacts. In this study, we deployed twelve low-cost air sensors to explore the temporal characteristic and the spatial variability of PM in a community adjacent to an interstate highway. In addition, the long-term field performance of the low-cost air sensors and its potential for identifying the traffic-related PM were also examined. At every sampling site, ambient PM was continuously measured with a 120s resolution from December 1, 2017- November 30, 2018. These data were later converted into hourly data in order to match with the hourly PM2.5 and NO2 data acquired from the EPA stations. The highest PM2.5 was observed in the winter (15.7 � 16.7 g/m3) and the lowest was observed in spring (8.8 � 7.21 g/m3). Pairwise sensors were found to be temporally correlated (r > 0.98) to each other and spatially (CoD < 0.02) homogeneous within the community. A good correlation (r > 0.79) and small CoD (value = 0.17) were also found between the PM2.5 at the community and the PM2.5 at the EPA monitor site. This suggests that the PM2.5 across the community and the EPA monitor site are temporally correlated and spatially homogenous to each other. The temporal patterns of PM2.5, PN0.3 and NO2 clearly demonstrated that the traffic is one of the major contributors to the air pollutants at the University Village. In particular, by using pair-wise sensors (downwind sensor and upwind sensor), the highway adjacent to the community, was found to be the main cause of higher concentration of PM.

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