Abstract. Assessing the intracity spatial distribution and temporal variability in air
quality can be facilitated by a dense network of monitoring stations.
However, the cost of implementing such a network can be prohibitive if
traditional high-quality, expensive monitoring systems are used. To this end,
the Real-time Affordable Multi-Pollutant (RAMP) monitor has been developed,
which can measure up to five gases including the criteria pollutant gases
carbon monoxide (CO), nitrogen dioxide (NO2), and ozone
(O3), along with temperature and relative humidity. This study
compares various algorithms to calibrate the RAMP measurements including
linear and quadratic regression, clustering, neural networks, Gaussian
processes, and hybrid random forest–linear regression
models. Using data collected by almost 70 RAMP monitors over periods ranging
up to 18 months, we recommend the use of limited quadratic regression
calibration models for CO, neural network models for NO, and hybrid models
for NO2 and O3 for any low-cost monitor using
electrochemical sensors similar to those of the RAMP. Furthermore,
generalized calibration models may be used instead of individual models with
only a small reduction in overall performance. Generalized models also
transfer better when the RAMP is deployed to other locations. For long-term
deployments, it is recommended that model performance be re-evaluated and new
models developed periodically, due to the noticeable change in performance
over periods of a year or more. This makes generalized calibration models
even more useful since only a subset of deployed monitors are needed to build
these new models. These results will help guide future efforts in the
calibration and use of low-cost sensor systems worldwide.
Supplementary material to "Long-term behavior and stability of calibration models for NO and NO<sub>2</sub> low cost sensors"