A Comparative Analysis of Conventional Methods for Sensor-Driven Spatial Interpolation for Air Quality Monitoring

Accurate air quality mapping in regions with sparse sensor deployment remains a challenge due to high infrastructure costs. While modern literature increasingly favors heavy, cloud-based Artificial Intelligence frameworks that assume dense input networks, the operational boundaries and mathematical fidelity of lean, edge-computed spatial interpolation models in ultra-sparse (e.g., 5-node) live frameworks remain poorly defined. This work presents a case-study comparative evaluation of conventional spatial interpolation techniques for estimating pollutant concentrations in sensor-limited environments. Eight interpolation methods, namely Inverse Distance Weighting (IDW), Kriging, Empirical Bayesian Kriging (EBK), Radial Basis Function (RBF), Nearest Neighbor (NN), Akima, Piecewise Cubic Hermite Interpolation (PCHIP), and Cubic Spline (CS), were analyzed using real-time environmental data collected from five locations in Kalady, Kerala, India. The interpolation performance was evaluated using RMSE and MAE metrics through leave-one-out validation. Results indicate that, within this deployment, Kriging and IDW achieved the best estimation accuracy for PM2.5, PM10, humidity, and temperature compared to the other methods evaluated. A LoRa-based sensing framework was also integrated to support low-power real-time environmental monitoring. As a single-region case study based on five monitoring nodes, the findings are specific to this deployment, and broader validation across additional stations and regions is identified as future work. The proposed approach demonstrates the feasibility of resource-efficient air quality estimation in regions with limited monitoring infrastructure.