Critical Transit Infrastructure in Smart Cities and Urban Air Quality: A Multi-City Seasonal Comparison of Ridership and PM2.5

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.

💡 Research Summary

This paper presents a reproducible, multi‑city smart‑city analytics workflow that links public‑transit ridership with ambient fine particulate matter (PM2.5) concentrations. The authors focus on four major U.S. metropolitan areas—New York City, Chicago, Las Vegas, and Phoenix—and examine two seasonal snapshots, March and October 2024. Data sources are publicly available: EPA’s Air Quality System (AQS) provides daily PM2.5 measurements, which are aggregated to monthly city‑level means, while transit agencies supply ridership information in heterogeneous formats (daily subway and bus counts for NYC, monthly totals for Chicago and Phoenix, and stop‑level daily boardings for Las Vegas). All ridership series are summed to obtain total monthly boardings, then normalized by 2024 population estimates to create per‑capita ridership and per‑capita PM2.5 metrics.

The methodological design proceeds in three analytical stages. First, descriptive statistics and visualizations illustrate baseline differences across cities and the seasonal shift from March (generally lower ridership, higher PM2.5) to October (higher ridership, variable PM2.5). Second, bivariate scatterplots—using log‑transformed ridership where appropriate—expose divergent patterns: large‑scale systems such as NYC and Chicago show high ridership but relatively low PM2.5, whereas smaller metros like Las Vegas and Phoenix exhibit higher PM2.5 relative to their ridership levels.

Third, a series of linear regressions assess the robustness of the observed association. A pooled OLS model relates log‑ridership directly to monthly mean PM2.5. Adding city fixed effects dramatically attenuates the coefficient, indicating that structural, city‑specific factors dominate the simple correlation. A two‑way fixed‑effects model (city + month) further controls for seasonal shocks, rendering the ridership‑PM2.5 link statistically insignificant in most specifications. The authors emphasize that these models are descriptive tools, not causal estimators; the goal is to detect co‑movement rather than to attribute PM2.5 changes to transit activity per se.

Key contributions include: (1) a transparent data‑integration pipeline that can be replicated for any metropolitan area using only open datasets; (2) the simultaneous presentation of absolute and per‑capita metrics, enabling fair cross‑city comparisons despite order‑of‑magnitude differences in population and system size; (3) a simple fixed‑effects regression framework that isolates the influence of structural city characteristics on the mobility‑air‑quality relationship.

Limitations are acknowledged. EPA monitors may not capture intra‑urban spatial variability, ridership counts are proxies rather than direct emission measures, and the analysis is confined to two months, precluding long‑term trend assessment. Future work should incorporate high‑resolution atmospheric modeling, extend the temporal window, and disaggregate by transit mode (e.g., electric buses, light rail) to refine causal inference.

In conclusion, the study demonstrates that while public‑transit usage and ambient PM2.5 often move together, the strength and direction of this relationship are highly context‑dependent. Policymakers aiming to improve urban air quality should therefore tailor interventions to local structural factors—such as baseline industrial emissions, meteorological conditions, and population density—rather than relying solely on ridership reduction strategies. The presented workflow offers a scalable foundation for ongoing smart‑city monitoring of mobility and environmental health indicators.