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This paper introduces a deep learning framework using LSTM networks to calibrate low-cost air quality sensors for PM$_{2.5}$, PM$_{10}$, and NO$_2$, addressing limitations like sensor drift and environmental cross-sensitivity. The LSTM model leverages temporal dependencies and delayed environmental effects, outperforming a Random Forest baseline by achieving higher $R^2$ values across training, validation, and test sets. Validation against regulatory standards demonstrates compliance with acceptable uncertainty levels, making the calibrated LCS data reliable for urban air quality monitoring.
LSTMs can bring low-cost air quality sensors up to regulatory compliance, unlocking dense urban monitoring networks previously limited by calibration challenges.
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$_{2.5}$.
