LSTM with time-lagged features and harmonic encodings calibrates low-cost sensors to higher R2 and regulatory-compliant uncertainties of 9.1-22.11% for three pollutants.
Sensor based ambient air concen- tration data for nitrogen dioxide and particles in oxford, measured by the oxaria project 2020 to 2021,
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A temporal deep learning framework for calibration of low-cost air quality sensors
LSTM with time-lagged features and harmonic encodings calibrates low-cost sensors to higher R2 and regulatory-compliant uncertainties of 9.1-22.11% for three pollutants.