# Leveraging Temporal Information to Improve Machine Learning-Based Calibration Techniques for Low-Cost Air Quality Sensors

## TL;DR

This paper shows that using time-based data improves the accuracy of low-cost air quality sensors when calibrated with machine learning.

## Contribution

The novel use of temporal information, like deployment duration and time of day, in calibrating low-cost air quality sensors.

## Key findings

- Temporal data as a co-variate significantly improves calibration accuracy for low-cost sensors.
- Machine learning models like Random Forest and LSTM benefit from incorporating time-based features.
- Results are validated using three global datasets of CO and NO2 sensor readings.

## Abstract

Low-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambient gas pollutant sensors. We make use of three data sets collected by research groups around the world, who gathered the data from field-deployed low-cost CO and NO2 sensors co-located with accurate reference sensors. Our investigation shows that using the temporal information as a co-variate can significantly improve the accuracy of common machine learning-based calibration techniques, such as Random Forest and Long Short-Term Memory.