ABSTRACT
BACKGROUND: 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. OBJECTIVES: (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. METHODS: 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. RESULTS: 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. SIGNIFICANCE: With proper management, a large network of lower-cost sensors can identify statistically significant trends and factors in urban exposure.