Functional Spatiotemporal Profile Monitoring: A Case Study from Beijing on Air Quality

Monitoring urban air quality requires statistical tools that can account for strong temporal dynamics, spatial dependence, and within-day variation, while providing timely signals for operational decision-making. In this case study, we consider the problem of monitoring hourly ozone (O3) concentration profiles observed across a network of monitoring stations in Beijing, China. Daily O3 evolution is represented through spatiotemporal functional profiles, whose relationship with meteorological covariates is modeled using a geostatistical functional mixed-effects framework. The monitoring objective is to ensure that this relationship remains stable over time and to detect deviations as they occur within the course of a day. To this end, a residual-based multivariate exponentially weighted moving average (MEWMA) control chart is employed within a Phase I-Phase II statistical process monitoring setting. The procedure is calibrated using a dependence-aware block bootstrap and its finite-sample behavior is assessed through Monte Carlo simulation scenarios tailored to the Beijing application. The proposed monitoring framework is demonstrated on hourly O3 concentrations collected from a ground-level monitoring network, illustrating how functional and spatial modeling can be combined with SPM tools to support real-time environmental monitoring.