A temporal deep learning framework for calibration of low-cost air quality sensors

TL;DR

This paper introduces a deep learning LSTM-based framework for calibrating low-cost air quality sensors, improving accuracy and regulatory compliance by capturing temporal dependencies and environmental effects.

Contribution

It presents a novel LSTM-based calibration method that outperforms traditional models by modeling temporal dependencies in sensor data.

Findings

LSTM calibration achieves higher R^2 than Random Forest baseline.

The method meets regulatory compliance with expanded uncertainties.

Incorporating temporal features improves calibration accuracy.

Abstract

Low-cost air quality sensors (LCS) provide a practical alternative to expensive regulatory-grade instruments, making dense urban monitoring networks possible. Yet their adoption is limited by calibration challenges, including sensor drift, environmental cross-sensitivity, and variability in performance from device to device. This work presents a deep learning framework for calibrating LCS measurements of PM$_{2.5}$, PM$_{10}$, and NO$_2$ using a Long Short-Term Memory (LSTM) network, trained on co-located reference data from the OxAria network in Oxford, UK. Unlike the Random Forest (RF) baseline, which treats each observation independently, the proposed approach captures temporal dependencies and delayed environmental effects through sequence-based learning, achieving higher $R^2$ values across training, validation, and test sets for all three pollutants. A feature set is constructed combining time-lagged parameters, harmonic encodings, and interaction terms to improve generalization on unseen temporal windows. Validation of unseen calibrated values against the Equivalence Spreadsheet Tool 3.1 demonstrates regulatory compliance with expanded uncertainties of 22.11% for NO$_2$, 12.42% for PM$_{10}$, and 9.1% for PM$_{2.5}$.