Introduction

For this project, I analyzed the NYC Air Quality Surveillance Data to explore air pollution trends across different boroughs. The dataset consists of 18,025 records, and I discovered that 79% of the data (14,247 records) is related to air pollutants.

Through my initial exploration, I found that the ‘Name’ column contains a mix of different data types. With the help of ChatGPT, I identified that the entries could be grouped into four main pollutant categories:

• Ozone (O3)
• Nitrogen dioxide (NO2)
• Fine particles (PM 2.5)
• Boiler Emissions (SO₂, NOₓ, PM2.5)

I used Tableau to build bar charts and line graphs that compared pollutant levels across boroughs and tracked their trends over time. My objective was to identify borough-level pollution patterns and long-term trends in NYC’s air quality.

Data Exploration & Challenges

One major challenge in using this dataset was that each pollutant is measured using different units, making it difficult to combine them into a single comparison:

• Boiler Emissions (SO₂, NOₓ, PM2.5) → Measured per km²
• Ozone (O3) and Nitrogen dioxide (NO2) → Measured in ppb (parts per billion)
• Fine particles (PM 2.5) → Measured in mcg/m³ (micrograms per cubic meter)

Because of this, I couldn’t create one single visualization for all pollutants. Instead, I analyzed them individually to see their distribution across NYC.

I tried to display all four pollutants in a single chart like below, but I realized it could be hard to read and might convey misleading information. Therefore, I decided not to include it in my final dashboard.

Based on these challenges, I chose to visualize each pollutant separately and focus on those that appeared most frequently in the data.

Key Analytical Decisions

As Ozone (O3) and Nitrogen dioxide (NO2)  appeared in nearly 8,000 records out of the 14,200 pollutant-related records, I decided to focus my first analysis on these two pollutants. Next, I analyzed Fine Particles (PM2.5) by borough, followed by Boiler Emissions by borough.

Findings & Visualizations

Ozone (O3) and Nitrogen dioxide (NO2) Levels by Borough

This visualization revealed that Manhattan consistently has the highest levels of both pollutants, which aligns with Manhattan’s urban profile.

Fine Particles (PM2.5) by Borough

Again, Manhattan showed the highest concentration, followed by the Bronx.

Boiler Emissions by Borough

While not as extreme, Manhattan and Brooklyn had the highest emissions per km². Unlike other pollutants, boiler emissions are measured per km², which means boroughs with higher building density tend to show higher emissions.

Pollution Trends Over Time (Annual Averages for Ozone and Nitrogen dioxide)

Overall decline in Ozone and Nitrogen dioxide levels across all boroughs. While all boroughs show improvement, Manhattan remains the most polluted throughout the period, with Ozone and Nitrogen dioxide levels consistently higher than in other boroughs. In 2010, Manhattan had a significantly higher pollution level than other boroughs. By 2022, the gap has narrowed.

Placing NYC’s Air Quality in Context

The findings from this project align broader research on city air pollution. Various studies show that traffic congestion, industrial activity, and high population density are major causes of bad air quality in cities. The dataset confirms that Manhattan always has the highest levels of Ozone, Nitrogen Dioxide, and Fine Particles. This supports earlier research that connects air pollution to areas with many vehicles and buildings that trap pollution.

Also, the decline in Ozone and Nitrogen Dioxide levels from 2010 to 2022 lines up with efforts to make NYC’s air cleaner, such as stronger air pollution laws (Clean Air Act), reducing car emissions, and using cleaner energy. However, even though the pollution gap between boroughs has become smaller, Manhattan is still the most polluted, showing how big city environments continue to affect air quality.

Limitations

While this dataset is useful, it has a few limitations:

• Borough-Level Aggregation: Without neighborhood-level data, we may miss local hotspots.
• Lack of Real-Time Data: These are annual averages, so temporary pollution spikes aren’t visible.
• Different Units of Measurement: Pollutants are measured differently, making direct comparisons difficult.

Reflections & Next Steps

Areas for Improvement

• Neighborhood-Level Analysis
  • As I aggregated the visualization at the borough level, I would like to try more granule level of analysis and incorporate more localized air quality data and this will be helpful to understand which neighborhoods experience the worst or best pollution within corresponding borough and this can be more engaging to the audience like New Yorkers.
• Correlation with External Factors
  • Air quality is influenced by many outside factors, like weather, how much people use public transportation, and industrial activities. Adding traffic data, energy use reports, and weather records could help us understand why pollution levels go up or down in different areas.
• Comparing Different Time Frames
  • The dataset focuses on annual averages,because there is no consecutive  but analyzing seasonal or daily trends could reveal fluctuations in air quality, such as higher Ozone levels in summer and increased Nitrogen Dioxide levels in winter due to heating emissions.

Next Steps

As a next step, I would like to incorporate a map-based data visualization into the project. Using this visualization, I aim to explore more granular data, such as neighborhood-level information, to provide a clearer picture of pollution hotspots within boroughs.

Additionally, I plan to analyze the relationship between air pollution levels and hospitalization rates to determine if there is a link between respiratory diseases and cardiovascular conditions.

Lastly, it would be interesting to compare New York City with other major cities like Los Angeles or Chicago and examine how city policies affect air quality.

Conclusion

Through this project, I was able to identify borough-level patterns in NYC’s air pollution and observe how pollutant levels have changed over time. The visualizations clearly show that Manhattan consistently records the highest levels of Nitrogen Dioxide, Ozone, and Fine Particles, even as overall levels have declined citywide. While the data revealed long-term improvements, it also highlighted persistent disparities that reflect broader patterns found in urban air quality research. This analysis helped me better understand how data visualization can communicate complex environmental issues, and it provided a strong foundation for asking new questions and exploring deeper relationships in future work.

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Link & Citation

1. Aggravated air pollution and health burden due to traffic congestion in urban China
   • Wang, P., Zhang, R., Sun, S., Gao, M., Zheng, B., Zhang, D., Zhang, Y., Carmichael, G. R., and Zhang, H.: Aggravated air pollution and health burden due to traffic congestion in urban China, Atmos. Chem. Phys., 23, 2983–2996, https://doi.org/10.5194/acp-23-2983-2023, 2023.
2. Evaluating the impact of urban traffic patterns on air pollution emissions in Dublin: a regression model using google project air view data and traffic data
   • Tafidis, P., Gholamnia, M., Sajadi, P. et al. Evaluating the impact of urban traffic patterns on air pollution emissions in Dublin: a regression model using google project air view data and traffic data. Eur. Transp. Res. Rev. 16, 47 (2024). https://doi.org/10.1186/s12544-024-00671-z
3. The New York City Community Air Survey | Neighborhood Air Quality 2008 – 2014
   • Iyad Kheirbek, Sarah Johnson, Kazuhiko Ito, Kazue Anan, Chris Huskey, Thomas Matte, Daniel Kass, Holger Eisl, John Gorczynski, & Steven Markowitz. (n.d.). The New York City Community Air Survey. New York City; New York City. 
4. The contribution of motor vehicle emissions to ambient fine particulate matter public health impacts in New York City: a health burden assessment
   • Kheirbek, I., Haney, J., Douglas, S. et al. The contribution of motor vehicle emissions to ambient fine particulate matter public health impacts in New York City: a health burden assessment. Environ Health 15, 89 (2016). https://doi.org/10.1186/s12940-016-0172-6