Seasonally optimized calibrations improve low-cost sensor performance: Long-term field evaluation of PurpleAir sensors in urban and rural India

Lower-cost air pollution sensors can fill critical air quality data gaps
in India, which experiences very high fine particulate matter (PM2.5) air
pollution but has sparse regulatory air monitoring. Challenges for
low-cost PM2.5 sensors in India include high aerosol mass concentrations
and pronounced regional and seasonal gradients in aerosol composition.
Here, we report on a detailed long-time performance evaluation of a
popular sensor, the Purple Air PA-II, at multiple sites in India. We
established 3 distinct sites in India across land-use categories and
population density extremes (North India: Delhi [urban], Hamirpur [rural];
South India: Bangalore [urban]), where we collocated the PA-II with
reference beta-attenuation monitors. We evaluated the performance of
uncalibrated sensor data, and then developed, optimized, and evaluated
calibration models using a comprehensive feature selection process with a
view to reproducibility in the Indian context. We assessed the seasonal
and spatial transferability of sensor calibration schemes, which is
especially important in India because of the paucity of reference
instrumentation. Without calibration, the PA-II was moderately correlated
with the reference signal (R2: 0.55–0.74) but was inaccurate (NRMSE ≥
40%). Relative to uncalibrated data, parsimonious annual calibration
models improved PA performance at all sites (cross-validated NRMSE 20–30%,
R2: 0.82–0.95), and greatly reduced seasonal and diurnal biases. Because
aerosol properties and meteorology vary regionally, the form of these
long-term models differed among our sites, suggesting that local
calibrations are desirable when possible. Using a moving-window
calibration, we found that using seasonally-specific information improves
performance relative to a static annual calibration model, while a
short-term calibration model generally does not transfer reliably to other
seasons. Overall, we find that the PA-II can provide reliable PM2.5 data
with better than ± 25% precision and accuracy when paired with a rigorous
calibration scheme that accounts for seasonality and local aerosol
composition.