Spatiotemporal Learning with Dynamic Coordination for Simultaneous Classification and Concentration Regression of Ozone and Coexisting Pollutant Gases Via MOS Sensor Arrays

Accurate monitoring of ground-level ozone and coexisting air pollutants remains challenging for low-cost metal oxide semiconductor (MOS) sensor arrays because their cross-sensitive responses are jointly governed by inter-sensor spatial correlations, gas-surface temporal dynamics, and task conflicts between gas identification and concentration regression. Here, we propose Spatiotemporal feature Decoupling with Dynamic task Coordination for Multi-Task Learning (STDC-MTL), an uncertainty-coordinated learning framework for simultaneous gas classification and concentration regression using MOS sensor-array signals. STDC-MTL explicitly integrates a hybrid spatiotemporal encoder for array-temporal correlation modeling, a dual-branch local-global feature learning module that separately captures neighborhood temporal variations and long-range response dependencies, and a Bayesian uncertainty-driven coordination mechanism for adaptive task balancing. Experimental results on a custom dataset of ozone, acetone, NO, NO2, and NH3 show that STDC-MTL achieves 100% classification accuracy and a concentration regression R2 of 0.998. More importantly, it reduces the mean absolute error (MAE) by 42.6% compared with the state-of-the-art Gaussian-TCN baseline, with an average normalized root mean square error (NRMSE) of 0.0138 and only 0.37 million parameters. These results demonstrate that the proposed framework improves quantitative prediction under cross-sensitive MOS responses while maintaining a lightweight architecture, providing an effective algorithmic strategy for intelligent air-pollutant sensing.