Crowdsourced Storm-Drain Pollution Mapping

ISEF Category: Chemistry

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

The Hook

A storm drain can carry yesterday’s chemicals straight into a river. That means one sidewalk can affect water quality blocks away. You can turn that hidden path into a map. Your phone, a paper test strip, and good data handling can reveal patterns no single grab sample would catch.

What Is It?

This project measures water chemistry in places that are usually hard to monitor, like storm drains, gutters, and runoff points. You use paper-based test strips to estimate pH and nitrate, then upload photos from a phone so the color change can be read more consistently. Think of each strip as a tiny chemical report card.

The real power comes from combining many small measurements. One sample tells you very little. A city-wide set of samples, collected by different people and placed on a map, can show where pollution clusters happen and how they change after rain. A Bayesian spatial model is a math tool that helps you combine noisy data from many locations and estimate the most likely pollution pattern across space.

Why This Is a Good Topic

This is a strong science fair topic because you can ask real, testable questions with simple chemistry and public data collection. It connects to runoff, water quality, and urban pollution, which are real problems that cities track. You can learn sampling design, color-based measurement, uncertainty, and spatial analysis without needing a full research lab.

Research Questions

• How does distance from a storm drain outlet affect measured nitrate concentration in runoff water?
• What is the effect of rainfall timing on pH and nitrate levels at the same sampling site?
• Does smartphone image analysis reduce variation in paper-strip readings compared with visual scoring alone?
• To what extent do neighborhoods with more pavement show higher nitrate readings after rain?
• Which sampling schedule gives the clearest city-wide pollution map, weekly sampling or post-storm sampling?
• How does pH vary between storm-water samples from residential, commercial, and park areas?

Basic Materials

• Paper-based pH test strips or universal indicator paper.
• Paper-based nitrate test strips.
• Smartphone with a camera.
• Free image analysis app or software.
• Clean sample cups or small containers.
• Disposable gloves.
• Waterproof labels and a permanent marker.
• Notebook or spreadsheet for field notes.
• City map or printed neighborhood map.
• Distilled water for blanks and rinsing.

Advanced Materials

• UV-Vis spectrophotometer for validation measurements.
• Laboratory-grade pH meter with calibration buffers.
• Nitrate assay reagents for comparison testing.
• Analytical balance for preparing standards.
• Filtration setup for removing debris from runoff samples.
• GPS-enabled data logger or phone app with location export.
• Compressed reference color chart for calibration.
• Statistical software for Bayesian spatial modeling.
• Temperature probe for recording field conditions.

Software & Tools

• ImageJ: Measures color intensity in each strip photo and helps you compare samples from different sites.
• Python: Organizes data, fits calibration curves, and supports spatial analysis.
• R: Runs statistical models, including Bayesian spatial workflows, and makes map plots.
• QGIS: Places sample points on a city map and helps you visualize pollution patterns.
• NOAA rainfall data tools: Provide storm timing and precipitation records that help explain runoff changes.

Experiment Steps

1. Define the pollutant signals you want to track and decide whether pH, nitrate, or both will be your main outcome.
2. Choose a sampling map that covers different land uses, such as residential, commercial, and green spaces.
3. Build a photo-based readout plan so every strip is captured under similar lighting and framing.
4. Create calibration rules that turn color changes into comparable numbers across many uploads.
5. Plan a spatial model that can combine all samples with location and rainfall data.
6. Set controls that help separate true pollution patterns from lighting, handling, and site-to-site noise.

Common Pitfalls

• Taking photos in changing sunlight, which shifts strip color and breaks comparison across sites.
• Collecting samples after different amounts of rain, which makes one location look cleaner or dirtier for the wrong reason.
• Mixing up labels or GPS points, which puts the chemistry result on the wrong street.
• Using dirty containers or tap water residue, which can change pH and nitrate readings.
• Treating every strip reading as exact, which hides uncertainty and weakens the spatial model.

What Makes This Competitive

A stronger project goes beyond simple before-and-after comparisons. You can test whether your photo method agrees with another measurement method, then quantify uncertainty in each sample. You can also compare land use, rainfall timing, and sampling density to see which factor best predicts runoff chemistry. That kind of careful design and analysis makes the project feel like real environmental research.

Project Variations

• Focus only on nitrate and compare storm-water from different neighborhood types.
• Add a second pollutant marker, such as phosphate, and test whether it changes the spatial pattern.
• Compare manual strip scoring with app-based image analysis to see which method is more consistent.

Learn More

• PubMed: Search for review articles on nitrate runoff, storm-water pollution, and paper-based colorimetric sensors.
• NIH PubChem: Look up nitrate, pH indicators, and related chemical properties for background chemistry.
• NOAA Climate Data Online: Find rainfall records that help you link sampling results to storm events.
• USGS Water Resources: Read about runoff, watershed chemistry, and water-quality sampling methods.
• MIT OpenCourseWare: Search for courses on statistics, data analysis, or environmental chemistry to strengthen your model design.
• Water Research: Search this journal for peer-reviewed studies on urban runoff and water-quality mapping.