Comparison and assessment of air temperature observations using All-In-One and Low-Cost sensors in an urban environment

Air temperature variability in an urban environment is considered a vital element for understanding urban climate. The advancement in sensor design and data collection methods have made it possible to install and collect cost-effective urban climate data. The user-friendly applicability, sustainability, expandability and acceptance in the scientific community of the All-In-One (AIO) and Low-Cost (LC) sensors have ensured their popularity in an urban environment. The selection of air temperature sensors based on sensor accuracy, cost and various selection methods are being applied in scientific research. In this study we present a comparative assessment of air temperature sensors through a two-stage selection method. First, we selected the sensors (METSENS 600, ClimaVUE50, WXT536 from AIO and nMETOS180, Netatmo, SenseBox Home, iButton, HOBO-U23 from LC sensors) based on commercial availability, cost, accuracy, sensor weight, power supply and consumption, literature review and user-friendly data management system. Second, we tested the sensors applying climate chamber (CC) and in-situ testing. We found that HOBO-U23 had the lowest Mean Absolute Deviation as compared to the reference sensor in the daily cycle test, nMETOS180 in the extreme range test and in stable temperature testing during CC testing. The time series and statistical results for AIO sensors indicated that ClimaVUE50 had the lowest Mean Absolute Deviation (MAD), Root mean square deviation (RMSD), Mean Square Deviation (MSD), and highest coefficient of determination (R 2 ) values among the AIO sensors. Among the LC sensors, nMETOS180 showed lowest MAD, MSD, RMSD and highest R 2 values while Netatmo had the highest MAD, MSD, RMSD and lowest R 2 values. Deviations were mainly higher during the day and lower during the night due to radiation shield type variabilities. AIO sensors responded more slowly to changing temperatures due to thermal inertia and compactness as compared to most LC sensors. The study highlights clear deviations between AIO and LC sensors. Such deviations between sensors need in-depth investigation when comparing results between different sensors and different urban meteorological networks.