快速城市化要求对湍流风场和污染物扩散进行高效监测，然而现有的重构与传感器布置策略在现实稀疏约束下往往失效。本文提出 Diff-SPORT，一种基于扩散模型的框架，结合生成式扩散先验、最大后验推断（MAP）及 Shapley 值归因方法，实现高保真流场重构与最优传感器布置。通过在单一域上训练一次扩散先验模型，Diff-SPORT 能够实现非线性最优传感器布置，并从稀疏测量数据中进行近实时流场重构，其速度比 RANS 或 LES 模拟快数个数量级，且性能持续优于现有最先进方法。该框架无需算法修改即可扩展至实验性被动标量浓度数据集（作为污染物扩散的直接代理指标），该数据源自北京海淀区街区在真实城市流动条件下按 1:2400 比例尺的水槽模型测量结果。在极端稀疏条件下，Shapley 引导的传感器布置相比随机布置可将重构误差降低高达 57%，并识别出紧凑且物理可解释的配置方案。这些结果确立了 Diff-SPORT 作为一个模块化基础框架的地位，为需要大量重训练的下游策略提供了零样本替代方案，支持面向空气质量管理和韧性城市设计的可扩展城市流动监测。

Rapid urbanization demands efficient monitoring of turbulent wind and pollutant dispersion, yet existing reconstruction and sensor placement strategies fail under realistic sparsity constraints. Here, we introduce Diff--SPORT, a diffusion-based framework that combines a generative diffusion prior with maximum a posteriori inference and Shapley-value attribution for high-fidelity flow reconstruction and optimal sensor placement. By training a diffusion prior model once over a domain, Diff--SPORT enables non-linear optimal sensor placement and near-real-time flow reconstruction from sparse measurements orders of magnitude faster than RANS or LES simulations, consistently outperforming state-of-the-art methods. The framework also extends, without algorithmic modification, to experimental passive scalar concentration dataset, a direct proxy for pollutant dispersion, measured in a 1:2400 scale water-flume model of the Beijing Haidian neighbourhood under realistic urban flow conditions. Shapley-guided sensor placement achieves up to 57\% lower reconstruction error than randomly placed sensors at extreme sparsity, identifying compact and physically interpretable configurations. These results establish Diff--SPORT as a modular foundation offering a zero-shot alternative to retraining-intensive downstream strategies, supporting scalable urban flow monitoring for air quality management and resilient city design.