Design and Validation of a Multimodal Environmental Monitoring System Based on Sensors and Artificial Intelligence

Reliable and real-time environmental monitoring is essential for controlling pollution and protecting public health. However, conventional station-based measurements are expensive and often lack spatial and temporal resolution. This paper proposes a low-cost multimodal environmental monitoring system. Experiments verified that thin-film thermocouples exhibit near-linear voltage–temperature characteristics (R2>0.99). Integration of the AI data pipeline substantially enhances monitoring accuracy: the proposed fusion strategy reduces relative error to approximately 2.3% under typical noise conditions, with a correlation coefficient of 0.79 between predicted and observed PM2.5 values. This research provides a scalable blueprint for edge-deployable environmental monitoring. A thin-film thermocouple with a fast response time is used as a temperature sensor and is statically calibrated against a K-type reference. To improve dynamic tracking and reduce measurement noise, a Kalman filter-based fusion strategy is employed, which is then compared with weighted averaging and Bayesian fusion. Simulation-driven validation is performed for thermocouple linearity, PID-based temperature control, micro-signal filtering and system-level latency and robustness. The results demonstrate that thin-film thermocouples exhibit near-linear voltage–temperature characteristics (R2 > 0.99) with Seebeck coefficients ranging from 40.92 to 42.08 μV/°C, close to the theoretical K-type value of 42.87 μV/°C. The proposed fusion strategy reduces relative error to ~2.3% under typical noise conditions, enabling stable, real-time processing with near-second latency for 10,000-point batches. This study summarizes the design considerations for selecting and calibrating sensors and for achieving AI robustness in the presence of drift and faults. It provides a scalable blueprint for edge-deployable environmental monitoring.