Public transit is a critical component of urban mobility and equity, yet mobility and air-quality linkages are rarely operationalized in reproducible smart-city analytics workflows. This study develops a transparent, multi-source monitoring dataset that integrates agency-reported transit ridership with ambient fine particulate matter PM2.5 from the U.S. EPA Air Quality System (AQS) for four U.S. metropolitan areas - New York City, Chicago, Las Vegas, and Phoenix, using two seasonal snapshots (March and October 2024). We harmonize heterogeneous ridership feeds (daily and stop-level) to monthly system totals and pair them with monthly mean PM2.5 , reporting both absolute and per-capita metrics to enable cross-city comparability. Results show pronounced structural differences in transit scale and intensity, with consistent seasonal shifts in both ridership and PM2.5 that vary by urban context. A set of lightweight regression specifications is used as a descriptive sensitivity analysis, indicating that apparent mobility-PM2.5 relationships are not uniform across cities or seasons and are strongly shaped by baseline city effects. Overall, the paper positions integrated mobility and environment monitoring as a practical smart-city capability, offering a scalable framework for tracking infrastructure utilization alongside exposure-relevant air-quality indicators to support sustainable communities and public-health-aware urban resilience.

翻译：公共交通是城市出行公平性的关键组成部分，然而在可复现的智能城市分析工作流中，出行与空气质量的关联性极少被实际应用。本研究构建了一个透明的多源监测数据集，整合了美国环保署空气质量系统（AQS）报告的四个美国大都市区（纽约、芝加哥、拉斯维加斯和凤凰城）的公交客流量与环境细颗粒物PM2.5数据，并选取2024年3月和10月两个季节快照进行分析。我们通过将异构客流量数据（日度及站点级）统一为月度系统总量，并与月度平均PM2.5浓度配对，报告了绝对指标和人均指标以实现跨城市可比性。结果显示，不同城市在公交规模和强度上存在显著结构性差异，客流量与PM2.5均呈现一致的季节性变化，但其具体模式因城市环境而异。本研究采用一组轻量级回归模型进行描述性敏感性分析，表明出行行为与PM2.5的表观关联性并非在所有城市和季节中保持一致，且受基线城市效应的显著影响。总体而言，本文将综合出行与环境监测定位为智能城市的实用能力，提出了一种可扩展的框架，用于跟踪基础设施利用率及暴露相关空气质量指标，以支持可持续社区建设和注重公共卫生的城市韧性发展。