Real-Time Air Quality

NYC’s air quality is generally good and has been improving over time. But hour to hour, there’s large variation in our air quality - even in neighborhoods with the cleanest air. That means that for short periods, some New Yorkers are exposed to high levels of pollutants.

Fine particles (PM2.5) are among the most harmful pollutants. Long-term exposure to PM2.5 contributes to an estimated 2,300 excess deaths from lung and heart disease each year in NYC (1 out of every 20 deaths in NYC), and short-term exposure contributes to asthma incidents severe enough to require a trip to the emergency department, as well as other health threats.

For PM2.5, NYC meets the national air quality standard of an annual average under 12 μg/m3, and a 24-hour average under 35 μg/m3. But hour-to-hour and location-by-location, PM2.5 levels vary dramatically and can spike to levels that could harm health, especially for sensitive populations. These higher levels are driven by daily changes in traffic volume, weather patterns that can trap emissions, and other short-term events.

Explore near real-time PM2.5 measurements from sensors around NYC below.

About the Data

Data are hourly measurements of PM2.5, in micrograms per cubic meter of air (µg/m3).

Data come from: NYCCAS near-real-time street-level monitors, and from roof-top air quality sensors operated by the NYS Department of Environmental Conservation as required by the Federal Clean Air Act. External factors can sometimes affect monitor functioning; these data are preliminary and subject to change.

Hourly PM2.5 measurements (in µg/m3)

Note: "DEC Monitor Average" is the average value of 11 roof-top monitors throughout the city, designed and placed for regulatory reporting. DOHMH street-level monitors are designed to measure ground-level exposures.

Air quality varies because sources vary

In New York City, about 45% of PM2.5 comes from far-away sources, like coal-burning power plants in the Midwest. But more than half comes from local sources.


Building density affects a neighborhood's air quality because like vehicles, buildings burn fuel and emit pollutants: their boilers burn oil and gas to produce heat and hot water. This is one reason we often see more air pollution in the winter. Because of new heating oil regulations, PM2.5 has gone down dramatically, and SO2 levels are now indetectable. Read more at the NYCCAS annual report.

Industrial area

Industrial areas affect a neighborhood's air quality because of diesel exhaust from trucks idling and traveling through industrial areas, and from industrial combustion equipment.


Traffic density affects a neighborhood's air quality because engines produce PM2.5, black carbon, and NOx. While electric vehicles help reduce emissions, all vehicles also contribute to PM2.5 through tire wear and braking. Traffic volume is one reason we often see daily spikes in PM2.5 concentration in the mornings and evenings.


Truck traffic density affects a neighborhood's air quality because diesel combustion produces additional pollutants.

Common patterns in the data

There are several patterns that commonly show up in the data from our air quality monitors. Look at the recent data and see if you can spot:

Spatial differences

The monitors are in neighborhoods with different emissions sources, so have different PM2.5 levels. Midtown, which has the highest traffic density, usually has the most PM2.5.

Daily spikes

PM2.5 levels usually rise in the morning as traffic volume increases. These temporal differences (time spikes) are usually greater than spatial differences (the differences between neighborhoods).

Weather patterns

Weather can trap emissions and cause PM2.5 to build up. Sometimes we see a clear west-to-east pattern in rising PM2.5, likely related to a weather pattern moving into New York City and causing pollution levels to rise as emissions are trapped.

Other spikes

Sometimes there are dramatic, short-term spikes at unexpected times, and without having a camera on each monitor, we don't know what causes them. However, they can be explained by something as simple as a truck idling for a few minutes underneath the monitor.