Incorporating Low-Cost Sensor Measurements into High-Resolution PM2.5
Modeling at a Large Spatial Scale
Abstract
Low-cost air quality monitors (LCAQMs) are promising supplements to
regulatory monitors for PM2.5 exposure assessment. However, the
application of LCAQM in spatially extensive exposure modeling is
hindered by the difficulty in performing calibration at large spatial
scales and the adverse influence of LCAQM residual uncertainty after
calibration. We aimed to develop an efficient spatially scalable
calibration method for LCAQM and design a residual uncertainty-derived
down-weighting strategy to optimize the use of LCAQM data with
regulatory monitoring data in PM2.5 modeling. In California, for each
monitor from PurpleAir, a global LCAQM network, we identified a station
within a 500-m radius from the Air Quality System (AQS), a U.S.
regulatory monitoring network. Regional calibration of PurpleAir to AQS
was performed at the hourly level with Geographically Weighted
Regression (GWR). The calibrated PurpleAir measurements were
down-weighted according to their residual uncertainty and then
incorporated into a Random Forest (RF) prediction model as a dependent
variable to generate 1-km daily PM2.5 exposure estimates. The
state-level PurpleAir calibration reduced the systematic bias to
~0 ug/m3 and decreased the random error by 38%. The
considerably large samples also enabled quantitative analyses regarding
potential factors related to the PurpleAir bias. The RF-based model with
both AQS and down-weighted PurpleAir data outperformed the RF model
based solely on AQS with an improved CV R2 of 0.86, an improved spatial
CV R2 of 0.81, and a lower prediction error of 5.40 ug/m3. The
down-weighting allowed the prediction model to show more spatial details
of PM2.5 and to better detect pollution hot-spots. Our spatially
scalable calibration and down-weighting strategies, for the first time,
allowed an effective application of a state-level LCAQM network in
high-resolution PM2.5 exposure modeling. The proposed framework can be
generalized to regions worldwide for advancing the evaluation of heavy
PM2.5 episodes and health-related applications.