GIS Analysis of Pollution and Asthma Burden

ISEF Category: Environmental Engineering

Subcategory: Other  ·  Difficulty: Advanced  ·  Setup: Home Setup  ·  Time: 1 to 2 Months

The Hook

Some neighborhoods breathe dirtier air and pay the health price for it. You can see that pattern on a map before you ever step into a lab. This project lets you connect pollution, demographics, and asthma data in one analysis. That gives you a real way to study environmental justice, not just talk about it.

What Is It?

This project asks one big question: where do pollution and health burdens stack up in the same places? You use public datasets to compare industrial emissions from EPA TRI, demographic and environmental burden data from EJScreen, and asthma hospitalization data from public health sources. Think of it like layering transparencies on a map. Each layer tells part of the story, and the overlap points to the tracts that may need the most attention.

GIS means geographic information system. That is just a digital mapping tool that lets you study patterns by place. Instead of looking at one number at a time, you compare locations. You can ask whether tracts with more emissions also have more asthma hospitalizations and higher social vulnerability, which means greater exposure to health and economic stress.

Why This Is a Good Topic

This is a strong science fair topic because it starts with public data, so you can do real research without a private lab. The question is measurable, spatial, and tied to a live problem that cities, health departments, and community groups care about. You can learn mapping, data cleaning, correlation analysis, and how to think about environmental justice. You also get a project that can lead to a clear recommendation, not just a map.

Research Questions

• How does TRI emissions density relate to asthma hospitalization rates across census tracts?
• What is the effect of EJScreen demographic vulnerability on the link between emissions and asthma burden?
• Does the distance to major emission sources predict higher asthma hospitalization rates in nearby tracts?
• To what extent do tracts with higher minority population shares also show higher combined pollution and asthma burden?
• Which variables best predict asthma burden when emissions, poverty, and population density are analyzed together?
• How does the ranking of highest-burden tracts change when you compare emissions alone versus a combined justice index?

Basic Materials

• Laptop with internet access.
• Free GIS software such as QGIS.
• EPA TRI dataset.
• EPA EJScreen data.
• CDC or state asthma hospitalization data.
• Census tract boundary shapefiles.
• Spreadsheet software such as Google Sheets or Excel.
• Digital notebook for data cleaning notes.

Advanced Materials

• Laptop with internet access.
• QGIS or ArcGIS Pro if available through school access.
• EPA TRI facility-level data.
• EPA EJScreen downloadable datasets.
• CDC PLACES or state health department asthma data.
• Census tract shapefiles from the US Census Bureau.
• Python with pandas, geopandas, and matplotlib.
• R with sf and ggplot2 if you prefer statistical mapping.
• ImageJ or another image tool only if you create custom map exports for analysis figures.

Software & Tools

• QGIS: Builds maps, joins census tract data, and layers pollution and health variables.
• Python: Cleans datasets, computes correlations, and ranks tracts by burden.
• R: Runs spatial statistics and creates publication-style plots.
• Google Sheets: Helps you sort data, inspect missing values, and track source notes.
• CDC PLACES: Provides local health estimates you can compare with pollution patterns.

Experiment Steps

1. Define the exact geographic unit you will analyze, such as census tracts, so every dataset lines up the same way.
2. Choose the one outcome you will treat as your main burden measure, such as asthma hospitalization rate or a combined index.
3. Gather public datasets and match them by geography, then clean missing values and mismatched years.
4. Build a map that layers emissions, demographics, and health data so you can see overlap before testing numbers.
5. Decide which comparison gives the strongest test, such as correlation, grouped comparison, or a multiple regression model.
6. Plan a way to rank the highest-burden tracts so your results can support a mitigation priority list.

Common Pitfalls

• Mixing data from different years, which can make emissions and asthma rates look linked when they actually came from different time windows.
• Using county data when your analysis needs census tracts, which hides block-level inequality.
• Comparing raw emission totals instead of emissions per area or per person, which makes large industrial tracts dominate the map.
• Ignoring missing health data in small-population tracts, which can create false highs or false lows.
• Treating correlation as causation, which can lead you to claim pollution caused asthma without enough evidence from your data.

What Makes This Competitive

A stronger project goes beyond making a map. You can test multiple burden metrics, compare different spatial scales, and show how the ranking of hot spots changes under each method. You can also check whether emissions still matter after you control for poverty, population density, and racial or ethnic composition. That kind of careful analysis shows real judgment, not just data collection.

Project Variations

• Focus on one state or metro area and compare urban versus suburban burden patterns.
• Swap asthma hospitalization data for emergency department visits or another respiratory outcome if your local data source supports it.
• Build a composite environmental justice index and test whether it predicts health burden better than emissions alone.

Learn More

• EPA TRI Program: Search the EPA site for facility emissions data, chemicals, and annual release reports.
• EPA EJScreen: Find demographic and environmental burden screening layers on the EPA website.
• CDC PLACES: Use this CDC data source for model-based local health estimates, including asthma-related measures.
• US Census Bureau Geography Program: Find census tract boundary files and geographic reference data.
• Journal of Exposure Science and Environmental Epidemiology: Search the journal for studies on environmental justice, spatial exposure, and health outcomes.
• NOAA Education and USGS Publications: Search for background on mapping, spatial data, and environmental indicators.