Closing the gap on lower cost air quality monitoring: machine learning calibration models to improve low-cost sensor performance

In this study, we investigated different calibration models for the Real-time Affordable Multi-Pollutant (RAMP) sensor package, which measures CO, NO2, O3, and CO2. We explored three methods: 1) laboratory univariate linear regression, 2) empirical multivariate linear regression and 3) machine-learning based calibration models using random forests (RF).
Zimmerman et al (2018)

Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly
improve our understanding of personal air pollution exposure. Additionally, low-cost air quality sensors could be deployed to
areas where limited monitoring exists. However, low-cost sensors are frequently sensitive to environmental conditions and
pollutant cross-sensitivities, which have historically been poorly addressed by laboratory calibrations, limiting their utility for
monitoring. In this study, we investigated different calibration models for the Real-time Affordable Multi-Pollutant (RAMP)
sensor package, which measures CO, NO2, O3, and CO2. We explored three methods: 1) laboratory univariate linear regression,
2) empirical multivariate linear regression and 3) machine-learning based calibration models using random forests (RF).
Calibration models were developed for 19 RAMP monitors using training and testing windows spanning August 2016 through
February 2017 in Pittsburgh, PA. The random forest models matched (CO) or significantly outperformed (NO2, CO2, O3) the
other calibration models, and their accuracy and precision was robust over time for testing windows of up to 16 weeks.
Following calibration, average mean absolute error on the testing dataset from the random forest models was 38 ppb for CO
(14% relative error), 10 ppm for CO2 (2% relative error), 3.5 ppb for NO2 (29% relative error) and 3.4 ppb for O3 (15% relative
error), and Pearson r versus the reference monitors exceeded 0.8 for most units. Model performance is explored in detail,
including a quantification of model variable importance, accuracy across different concentration ranges, and performance in a
range of monitoring contexts including the National Ambient Air Quality Standards (NAAQS), and the US EPA Air Sensors
Guidebook recommendations of minimum data quality for personal exposure measurement. A key strength of the RF approach
is that it accounts for pollutant cross sensitivities. This highlights the importance of developing multipollutant sensor packages
(as opposed to single pollutant monitors); we determined this is especially critical for NO2 and CO2. The evaluation reveals
that only the RF-calibrated sensors meet the US EPA Air Sensors Guidebook recommendations of minimum data quality for
personal exposure measurement. We also demonstrate that the RF model calibrated sensors could detect differences in NO2
concentrations between a near-road site and a suburban site less than 1.5 km away. From this study, we conclude that
combining RF models with the RAMP monitors appears to be a very promising approach to address the poor performance that
has plagued low cost air quality sensors.

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