Applying machine learning for large scale field calibration of low-cost PM2.5 and PM10 air pollution sensors
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Applied AI Letters
Abstract
Low-cost air quality monitoring networks can potentially increase the
availability of high-resolution monitoring to inform analytic and evidence-informed
approaches to better manage air quality. This is particularly relevant in
low and middle-income settings where access to traditional reference-grade monitoring
networks remains a challenge. However, low-cost air quality sensors are
impacted by ambient conditions which could lead to over- or underestimation
of pollution concentrations and thus require field calibration to improve their
accuracy and reliability. In this paper, we demonstrate the feasibility of using
machine learning methods for large-scale calibration of AirQo sensors, lowcost
PM sensors custom-designed for and deployed in Sub-Saharan urban settings.
The performance of various machine learning methods is assessed by
comparing model corrected PM using k-nearest neighbours, support vector
regression, multivariate linear regression, ridge regression, lasso regression,
elastic net regression, XGBoost, multilayer perceptron, random forest and gradient
boosting with collocated reference PM concentrations from a Beta Attenuation
Monitor (BAM). To this end, random forest and lasso regression models
were superior for PM2.5 and PM10 calibration, respectively. Employing the random
forest model decreased RMSE of raw data from 18.6 μg/m3 to 7.2 μg/m3
with an average BAM PM2.5 concentration of 37.8 μg/m3 while the lasso
regression model decreased RMSE from 13.4 μg/m3 to 7.9 μg/m3 with an average
BAM PM10 concentration of 51.1 μg/m3. We validate our models through
cross-unit and cross-site validation, allowing analysis of AirQo devices' consistency.
The resulting calibration models were deployed to the entire large-scale
air quality monitoring network consisting of over 120 AirQo devices, which
demonstrates the use of machine learning systems to address practical challenges
in a developing world setting.
Description
Keywords
Air pollution, Field calibration, Low-cost sensors, Machine learning applications, PM10, PM2.5
Citation
Adong, P., Bainomugisha, E., Okure, D., & Sserunjogi, R. (2022). Applying machine learning for large scale field calibration of low‐cost PM2. 5 and PM10 air pollution sensors. Applied AI Letters, 3(3), e76. DOI: 10.1002/ail2.76