There is substantial interest in using networks of lower-cost air quality sensors to characterize urban population exposure to fine particulate matter mass (PM2.5). However, sensor uncertainty is a concern with these monitors.
(1) Quantify the uncertainty of lower-cost PM2.5 sensors; (2) Use the high spatiotemporal resolution of a lower-cost sensor network to quantify the contribution of different modifiable and non-modifiable factors to urban PM2.5.
A network of 64 lower-cost monitors was deployed across Pittsburgh, PA, USA. Measurement and sampling uncertainties were quantified by comparison to local reference monitors. Data were sorted by land-use characteristics, time of day, and wind direction.
Careful calibration, temporal averaging, and reference site corrections reduced sensor uncertainty to 1 μg/m3, ~10% of typical long-term average PM2.5 concentrations in Pittsburgh. Episodic and long-term enhancements to urban PM2.5 due to a nearby large metallurgical coke manufacturing facility were 1.6 ± 0.36 μg/m3 and 0.3 ± 0.2 μg/m3, respectively. Daytime land-use regression models identified restaurants as an important local contributor to urban PM2.5. PM2.5 above EPA and WHO daily health standards was observed at several sites across the city.
With proper management, a large network of lower-cost sensors can identify statistically significant trends and factors in urban exposure.
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The authors thank Eric Lipsky, Naomi Zimmerman, Aja Ellis, and Rebecca Tanzer for assistance with instrument setup and operation.
This was developed as part of the Center for Air, Climate and Energy Solution (CACES). Funding was provided by the United States Environmental Protection Agency (assistance agreement nos. RD83587301 and 83628601) and the Heinz Endowments Fund (grants E2375 and E3145). It has not been formally reviewed by the EPA.The views expressed in this document are solely those of authors and do not necessarily reflect those of the funders. The funders do not endorse any products or commercial services mentioned in this publication.
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Rose Eilenberg, S., Subramanian, R., Malings, C. et al. Using a network of lower-cost monitors to identify the influence of modifiable factors driving spatial patterns in fine particulate matter concentrations in an urban environment. J Expo Sci Environ Epidemiol 30, 949–961 (2020). https://doi.org/10.1038/s41370-020-0255-x
- Air pollution
- Particulate matter
- Spatial variation
- Urban air pollution
- low-cost monitors
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