Optimal Sensor Placement for Predicting Extreme Urban Pollution Events: A Unified Empirical and Optimization Approach

Urban air quality monitoring networks are traditionally designed to capture mean concentration fields. However, public health and environmental regulations are increasingly focused on extreme pollution events (e.g., the 99th percentile, p99), which exhibit high spatial variability driven by turbulence. Placing sensors at locations of maximum mean concentration (Cmean) often fails to capture these rare, extreme peaks. This study proposes a unified empirical and optimization framework for the active design of sensor networks specifically tailored for extreme value prediction. Using the COST ES1006 Michelstadt wind tunnel dataset, we combine the empirical relation p99 = Cmean + Kcσ with spatial interpolation and two optimization algorithms: Greedy forward selection and a Genetic Algorithm (GA). The objective is to identify the minimal set of sensor locations that minimizes the Mean Absolute Error (MAE) of p99 predictions across all non-sensor locations. The results demonstrate that classical placement strategies (sensors at maximum Cmean) yield an MAE of 96.98 ppmV, highlighting their inability to capture spatial extremes. In contrast, the proposed optimization framework drastically reduces the prediction error. With only 10 sensors, the Greedy algorithm achieves an MAE of 18.16 ppmV, while the GA achieves 19.21 ppmV. Furthermore, a sensitivity analysis reveals a clear "elbow" in the performance curve: a network of 8 to 10 optimally placed sensors is sufficient to reconstruct the p99 field with an MAE of approximately 20 ppmV, beyond which diminishing returns are observed. The optimal sensor distribution reveals a balanced strategy, combining source-proximal, high-altitude, and peripheral locations. This methodology provides a robust, cost-effective tool for engineers and policymakers to design efficient urban air quality monitoring networks capable of accurately predicting worst-case pollution scenarios.