Low-cost sensors (LCSs) for measuring air pollution are
increasingly
being deployed in mobile applications, but questions concerning the
quality of the measurements remain unanswered. For example, what is
the best way to correct LCS data in a mobile setting? Which factors
most significantly contribute to differences between mobile LCS data
and those of higher-quality instruments? Can data from LCSs be used
to identify hotspots and generate generalizable pollutant concentration
maps? To help address these questions, we deployed low-cost PM2.5 sensors (Alphasense OPC-N3) and a research-grade instrument
(TSI DustTrak) in a mobile laboratory in Boston, MA, USA. We first
collocated these instruments with stationary PM2.5 reference
monitors (Teledyne T640) at nearby regulatory sites. Next, using the
reference measurements, we developed different models to correct the
OPC-N3 and DustTrak measurements and then transferred the corrections
to the mobile setting. We observed that more complex correction models
appeared to perform better than simpler models in the stationary setting;
however, when transferred to the mobile setting, corrected OPC-N3
measurements agreed less well with the corrected DustTrak data. In
general, corrections developed by using minute-level collocation measurements
transferred better to the mobile setting than corrections developed
using hourly-averaged data. Mobile laboratory speed, OPC-N3 orientation
relative to the direction of travel, date, hour-of-the-day, and
road class together explain a small but significant amount of variation
between corrected OPC-N3 and DustTrak measurements during the mobile
deployment. Persistent hotspots identified by the OPC-N3s agreed with
those identified by the DustTrak. Similarly, maps of PM2.5 distribution produced from the mobile corrected OPC-N3 and DustTrak
measurements agreed well. These results suggest that identifying hotspots
and developing generalizable maps of PM2.5 are appropriate
use-cases for mobile LCS data.