Museum and archive storage facilities worldwide face persistent difficulties in maintaining stable microclimatic conditions. This study presents a Smart Archival Box, a modular hardware–software system for preventive conservation, together with a model-based evaluation of its microclimate control. The numerical domain has a nominal volume of 0.125 m³ (0.5 m × 0.5 m × 0.5 m) and combines a 1 kg ArtSorb cassette, four TEC1-12706 thermoelectric modules, particulate/activated-carbon filtration, distributed monitoring and RFID-based access control. Temperature is regulated by a single proportional–integral–derivative (PID) controller with ambient-load feedforward, back-calculation anti-windup, hot-side derating and a dew-point safety limiter. Three scenarios—hybrid (H), active (A) and passive (P)—were evaluated over 10,173 one-second intervals (10,174 records; 2.8258 h) while the prescribed ambient temperature rose from 16 to 32 °C, and ambient relative humidity fell from 67% to 27%. H and A remained within the broader 16–20 °C preservation band for 100% of the run and within the tighter 17.5–18.5 °C control band for 79.7% and 80.5%, respectively, finishing at 18.53 °C and 18.56 °C. P reached 21.20 °C and remained within the tighter band for 47.7% of the run. Calculated electrical consumption was 0.05177 kWh for H, 0.05156 kWh for A and 0.00565 kWh for P. Relative humidity remained within 45–55% in all scenarios. The filtered H and A scenarios reduced mean PM2.5 and total volatile organic compound (TVOC) exposure by 98.4% and 94.4%, respectively, relative to the prescribed ambient profiles. The graphs for document prototype construction and functional bench observations and model results that have not yet been validated by enclosure tests are presented in Results Section. The close H/A performance over this short disturbance does not establish an advantage of ArtSorb and requires repeated humidity-disturbance experiments.

The rapid growth of sensor networks, satellite platforms, Internet-of-Things (IoT) devices, and urban monitoring systems has produced vast volumes of spatiotemporal data. Modeling such data is central to applications ranging from air-quality forecasting and climate monitoring to traffic prediction and urban planning, yet it poses challenges, including high dimensionality, spatial autocorrelation, and complex temporal dependencies that classical statistical models struggle to capture jointly. This review provides a comprehensive survey of spatiotemporal machine learning (STML), synthesizing the literature published between 2020 and 2025. We present a taxonomy of STML methods spanning traditional machine learning, recurrent neural networks (LSTM and GRU), convolutional architectures (3D CNN and ConvLSTM), graph neural networks, transformer-based models, and hybrid or ensemble designs. We organize representative studies by application domain, climate and environmental monitoring, transportation and traffic forecasting, and urban planning within smart cities, and provide a comparative analysis of models, datasets, evaluation metrics, and open-source tools. Finally, we identify emerging research trends and open challenges, including real-time processing, interpretability and explainability, scalability and edge deployment, multimodal fusion, federated learning for privacy, self-supervised learning, and ethical concerns such as fairness. The review is intended to serve as a structured reference for researchers and practitioners developing STML systems.