Air quality is a measure of the condition of the air around us. The less air pollution there is, the better the air quality.

Like many cities, New York City’s air contains particles, liquid droplets, gases, and other contaminants that could impact health. Two pollutants, particulate matter and ground-level ozone, are of particular health concern.

Although New York City’s air does not currently meet federal air quality standards, it has improved significantly over the past two decades.

Air Pollution and Health

Pollutants may affect human health negatively in a variety of ways. People may experience symptoms and health issues such as:

• Coughing
• Tightness in the chest
• Worsening of asthma
• Reduced lung function
• Increased risk of heart attack and stroke
• Low birthweight in newborns

Air pollution may affect anyone. Certain groups may be more vulnerable to health risks from poor air quality, such as:

• Older adults (above 65 years)
• Children
• People with heart or lung conditions

To Reduce Health Risks

• Keep track of your local Air Quality Index (AQI) if you or your child is sensitive to air pollution. Avoid strenuous activity or going outside on poor air quality days.
• Reduce air pollutants by walking, biking, or using mass transit instead of a car, especially during rush hour.
• Support policies that promote public transportation and energy conservation.

About Neighborhood Air Quality

Air quality can vary from neighborhood to neighborhood. That is why the New York City Community Air Survey (NYCCAS) keeps track of street-level air pollution from about 100 locations throughout New York City every season. NYCCAS is part of OneNYC, New York City’s long-term plan for sustainability.

In NYC, health impacts from fine particulate matter, black carbon, and sulfur dioxide are higher in high-poverty neighborhoods.

There are many schemes of neighborhood boundaries (or geographies) available for different datasets. These include:

• Citywide
• Borough
• United Hospital Fund 42 (UHF42) and United Hospital Fund 34 (UHF34) neighborhoods
• Community Districts (CD), and, related, Community District Tabulation Areas (CDTA)
• Public Use Microdata Areas (PUMA)
• Neighborhood Tabulation Areas (NTA)

We try to make data available at as fine a geographic resolution as we can. Read more about different geographies at this data story.

It would be convenient if each dataset could be made available at the same geography, but this is not always possible. The neighborhood boundaries available depend on the specifics of the underlying dataset.

When we have multiple years of data, we show the data as trend charts.

Trends for individual neighborhoods tend to be very unstable (or "bouncy"), due to small counts for outcomes in individual neighborhoods. To show clearer trends, we usually aggregate up and show trend data by Borough.

Options on some indicators let you compare this indicator to other similar indicators.

We try to make data available as soon as we can.

Many datasets take a long time to reach us — a "lag" of several years, during which data are gathered from different entities to try to create a complete dataset; data are processed (cleaned, deduplicated, etc), and analyzed. It can take several years to prepare a dataset — so a dataset that is several years old is sometimes the most up-to-date information available. Still, this data continues to show us valuable trends and insights.

We also try to display datasets that have been available for many years. Some datasets (like those that are asked on surveys) are not asked every year of the survey, which means that we cannot provide annual data.

For some datasets, we have to aggregate time periods in order to break down data by borough or neighborhood.

Measures are the numeric ways that a dataset (or indicator) are expressed. Examples of different measures might be: number, percent or rate.

A number tells us how much of an outcome is in a certain area. But a rate (usually expressed as, for example, 40 per 100,000) tells us how much of an outcome there is, relative to the population of the area in question. This lets us compare neighborhoods of different sizes.

On this site, we try to show how environmental factors might be connected to health outcomes. One way we do this is with Correlates. We choose potentially-related datasets and display them on a scatterplot so you can see how these two datasets are correlated.

The Disparities view displays neighborhoods by poverty category to show underlying disparities in the data.

Read more about correlation here.