Urban air quality forecasting is challenging because pollutant concentrations are nonlinear, nonstationary, spatiotemporally dependent, and often affected by anomalous observations caused by traffic congestion, industrial emissions, and seasonal meteorological variability. This study proposes a Graph Convolutional Support Vector Regression (GCSVR) framework for robust spatiotemporal forecasting of urban air pollution. The model combines graph convolutional learning to capture inter-station spatial dependence with support vector regression to model nonlinear temporal dynamics while reducing sensitivity to outlier observations. The proposed framework is evaluated using air quality records from 37 monitoring stations in Delhi and 18 stations in Mumbai, representing inland and coastal metropolitan environments in India. Forecasting performance is assessed across multiple horizons and compared with established temporal and spatiotemporal benchmarks. The results show that GCSVR consistently improves predictive accuracy and maintains stable performance across seasons and outlier-prone pollution episodes. Statistical test further confirms the reliability of the proposed approach across the two cities. Finally, conformal prediction is integrated with GCSVR to generate calibrated prediction intervals, enhancing its practical value for uncertainty-aware air quality monitoring and public health decision-making.

翻译：城市空气质量预测具有挑战性，因为污染物浓度呈现非线性、非平稳、时空依赖特性，且常受交通拥堵、工业排放及季节性气象变化导致的异常观测值影响。本研究提出一种图卷积支持向量回归（GCSVR）框架，用于城市空气污染的鲁棒时空预测。该模型结合图卷积学习捕捉站点间空间依赖关系，并通过支持向量回归建模非线性时间动态特性，同时降低对异常观测值的敏感性。利用印度德里37个监测站和孟买18个监测站的空气质量记录（分别代表内陆与沿海大都市环境）对所提框架进行评估。预测性能在多个时间跨度上进行了评估，并与已建立的时域及时空基准模型进行了比较。结果表明，GCSVR能持续提升预测精度，并在不同季节及易受异常值影响的污染事件中保持稳定性能。统计检验进一步证实了该方法在两座城市中的可靠性。最后，将保形预测与GCSVR相结合以生成校准后的预测区间，从而增强其在不确知性感知的空气质量监测与公共卫生决策中的实用价值。