Laplace domain dynamic calibration in city-wide sensor networks

Abstract Urban sensor networks can provide spatially and temporally resolved environmental information, but their measurement trustworthiness depends on maintaining an unbroken chain of traceability to the International System of Units (SI) and on quantifying the associated uncertainties under dynamic field conditions. This paper applies a Laplace-domain method for in situ calibration of city-wide sensor networks. The method estimates pairwise dynamic relationships between sensor signals, separates a medium-related spatial effect, and solves an overdetermined calibration problem with a known network reference. Measurement uncertainty is propagated using a Monte Carlo approach. The method is evaluated using two urban datasets: a Helsinki network linked to the SMEAR III station and an Antwerp network from the SensEURCity project. Calibration coefficients were obtained for temperature, relative humidity (RH), and pressure in both networks. Particulate matter (PM) calibration was feasible only under restricted sensor-subset conditions because of the localized and weakly correlated nature of PM signals. A transfer function-based malfunction indicator was also tested by introducing artificial shifts, showing that shifted measurement signals can be identified when the network geometry and signal correlations are sufficient. The results demonstrate the potential of Laplace-domain modeling as an uncertainty-aware route for dynamic calibration transfer in deployed sensor networks, while highlighting limitations related to interpolation, spatial heterogeneity, network geometry, and measurand-specific correlation.