A Comprehensive Analysis of the Air Quality in the NYC Subway System


Exposure to ambient particulate matter (PM) air pollution is one of the leading risk factors for global disease burden, including respiratory, cardiovascular, metabolic, and mental health disorders. These include asthma, heart disease, diabetes, as well as bipolar disorder, and anxiety. The mechanism for the development of these adverse outcomes is that the particulates (often as byproducts of fossil fuel combustion) have a very small size (less than 3 microns in diameter) and they can penetrate the lung tissue and into the blood stream, where the toxicity of the metal ions within the particles leads to oxidative damage, inflammation, and neurotoxicity. In the US, the Environmental Protection Agency regulates the standards for clean air. They also monitor these standards working with local/state agencies. For example, in the early 2000s, NYC was deemed out of compliance in the Sulphur Dioxide (SO2) concentrations. This resulted in the city initiating a major program to identify the sources and to plan for remedies. Buildings’ fuel oil was subsequently identified as the source, and local laws were set in place to convert from heavy fuel oil to alternatives. At the moment the SO2 concentrations in NYC are very low, well below clean air standards. This has been considered as one of the success stories where timely information and good governance have rapidly led to positive climate action and public health impact. Elimination of heavy fuel oils in NYC also resulted in reductions in the concentrations of particulate matter, namely PM2.5 (particles with a size smaller than 2.5 microns). However, there are still neighborhoods in the city which are out of compliance, in terms of short-term ambient concentrations which are set by US EPA at 35 mg/m3. These measurements pertain to outdoor street-level environments.

Preliminary measurements carried out by the PI and colleagues have identified that concentrations of PM2.5 in subway cars frequently reach 100 mg/m2, and concentrations in stations reach 200 mg/m3, almost 6 times EPA standards. To this date, there is no comprehensive survey of temporal and spatial variability of these pollutants in the underground city, an environment where people spend hours each day.

Research Objectives & Deliverables

The proposed work will enable statistically robust conclusions on the nature of exposure to particulate matter in the NYC subway system through a comprehensive temporal-spatial analysis of PM2.5 concentration and composition across the NYC subway system stations and cars. This includes the development of a data product incorporating realtime measurements, to be used by owners and agencies.

Measurements for the spatial model will be carried out with high-quality portable detectors for time-resolved determination of PM2.5 concentrations at 5-second cadence. This data registration speed will allow sampling at stations while continuously sampling the same train/car. The detectors are also equipped with flow-through filters, which will be used for the assessment of particle composition, aggregated over each trip. Measurements will be carried out on all subway lines from start to end, in the morning rush and midday (during off rush having lower train frequency). These measurements will be done on subway cars and at all stations along the route. The multivariant analysis will be carried out in order to determine the association of underground concentrations (cars and stations) with ambient outdoor levels of PM2.5, and a variety of cofactors including infrastructure characteristics and train frequency. 

A sensor network will be subsequently developed and deployed at selected sites for time-series measurement of particulate matter. The time series analysis and at the above-mentioned full-field spatial model will result in a spatial-temporal measure of the subway system air quality. This model would generate the critical statistics needed by agencies to plan for system maintenance and upgrades.

Deliverables associated with these objectives include:

  • A model of the spatial distribution of PM2.5 during peak and off-peak throughout the subway system
  • A web-based portal for mapping the spatial distribution of particulate matter
  • Sensor network prototype (4 units) for continuous telemetry of PM2.5 concentrations
  • Spatial-Temporal model and web-based portal (and training) for real-time mapping the distribution of PM2.5 in the subway system


Masoud Ghandehari

Masoud Ghandehari


Principal InvestigatorMasoud Ghandehari
Funding SourcePhase 1: $99,910 (C2SMART) + $59,820 (Cost Share)

Phase 2: $88,143 (C2SMART) + $47,097 (Cost Share)

Total Project Cost$294,970
USDOT Award #69A3551747124 
Implementation of Research OutcomesInstrumentation for the spatial model: Measurements will be carried out on all subway lines from start to end, in the morning peak and midday off-peak. These measurements will be done on subway cars and at all stations along the route. The multivariant analysis will be carried out in order to determine the association of underground concentrations (cars and stations) with ambient outdoor levels of PM2.5 pollution, train frequency, elevation below ground, and other rail and tunnel infrastructure characteristics.

Product Development for the temporal model: a prototype field-deployable air quality sensor network with telemetry will be developed for the measurement of the concentration of particulate matter. These monitors will be strategically located in selected stations using the results of the spatial model. 

Spatial-temporal model and data derivatives: Using the monitor data and results of the full-field spatial campaign, a kriging algorithm will be developed to estimate the air quality in stations and in subway cars across the system.  A dashboard will allow owners to monitor the system air quality on a real-time basis and provide statistically robust data products which may be used for decision support.

Impacts/Benefits of ImplementationAcademic: data derivatives will help advance public health research where analyses can be carried out, incorporating ridership and exposure during the commute. The higher spatial granularity of exposure data will enable deeper research in previously identified health risks (e.g. cardiovascular and respiratory diseases), as well as emerging concerns such as risks attributed to Covid-19.

Agencies: The proposed study and the resulting products can serve as a tool for agencies and owners to prioritize systems upgrades and capital improvements. These may include isolation of selected subway stations from rails (as has been done in certain stations of the London Underground), where here the prioritization will be based on the level of exposure determined through the proposed study statistics.

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