Surface Water Antibiotic Resistance Monitoring
ISEF Category: Microbiology
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Subcategory: Environmental Microbiology · Difficulty: Advanced · Setup: University Lab · Time: Full Year
The Hook
Antibiotic resistance does not stay in hospitals. It can move through rivers, ditches, and storm drains, then collect where runoff hits wastewater, farms, or dense neighborhoods. You can turn that hidden spread into a map, a culture count, and a strong science fair project.
What Is It?
This project asks a simple question with a big public health angle, where do antibiotic-resistant bacteria show up in local surface water, and what nearby land uses seem to predict them? You start by collecting water samples from different sites, then grow coliforms on MacConkey agar. MacConkey helps you spot a group of bacteria often used as a pollution indicator. You then compare colony counts on plates with and without low-dose antibiotic discs, which gives you a rough proxy for resistance.
Think of it like a field survey plus a filter test. The field survey comes from your sites, like streams, drainage canals, ponds, and outfalls. The filter test comes from the plates and discs. You are not trying to identify every gene in the water yourself. Instead, you use student-friendly culture data, then compare your pattern to public long-read datasets and local land-use maps from OpenStreetMap. That helps you ask whether places downstream of roads, farms, or wastewater inputs show higher resistance signals.
Why This Is a Good Topic
This is a strong science fair topic because you can test a real environmental problem with clear numbers. You are not guessing, you are comparing resistant colony counts, site features, and land-use patterns. The project connects microbiology, public health, and environmental science, so your results can matter outside the classroom. You also get room to learn field sampling, culture methods, mapping, and basic statistics.
Research Questions
- How does upstream land use affect the number of antibiotic-resistant coliform colonies in local surface water?
- What is the effect of sampling site type, such as creek, storm drain, pond, or outfall, on resistant colony counts?
- Does proximity to wastewater infrastructure predict higher growth on low-dose tetracycline or ampicillin discs?
- To what extent do resistant colony counts differ after rain compared with dry weather sampling?
- Which land-use features from OpenStreetMap best match sites with the highest resistant-CFU counts?
- How does the culture-based resistance proxy compare with resistance patterns reported in public long-read surface-water datasets?
Basic Materials
- Sterile sample bottles, one per site plus extras.
- MacConkey agar plates.
- Low-dose tetracycline discs.
- Low-dose ampicillin discs.
- Sterile swabs or spreaders.
- Incubator or warm school lab space with temperature control.
- Permanent marker and waterproof labels.
- Digital kitchen scale or balance for sample prep, if needed.
- Measuring cylinder or sterile pipettes.
- Nitrile gloves.
- Notebook or sample log sheet.
- Smartphone camera for plate images.
- Laptop with spreadsheet software.
- Local map printouts or a mapping app.
Advanced Materials
- Membrane filtration setup.
- Vacuum pump and sterile filters.
- Colony counter or image analysis setup.
- Autoclave or access to sterile media prep.
- Incubator with tighter temperature control.
- DNA extraction kit, if you decide to validate isolates later.
- Gel electrophoresis equipment, if you add a follow-up marker check.
- Access to long-read public datasets and bioinformatics tools.
- Water-quality meter for conductivity, pH, and dissolved oxygen.
- GPS device or phone with accurate location tracking.
Software & Tools
- Google Earth: Helps you trace sampling points and compare them with nearby roads, buildings, and drainage paths.
- OpenStreetMap: Lets you classify land use around each site and measure upstream features.
- Google Sheets: Organizes colony counts, site metadata, and comparison tables.
- ImageJ: Measures colony area or plate signal from photos in a more consistent way.
- R: Lets you test whether land-use variables and resistant counts move together.
Experiment Steps
- Define your site list and decide which water bodies best represent different upstream land uses.
- Choose one culture-based proxy for resistance and make sure your control plates separate background growth from antibiotic-tolerant growth.
- Plan how you will record site metadata, including weather, visible pollution, and nearby land use.
- Build a map-based comparison plan before you collect anything, so you know which land-use variables you will test.
- Set up a counting method that turns plate growth into a consistent numeric measure.
- Decide how you will compare your student-collected results with public long-read datasets without mixing incompatible methods.
Common Pitfalls
- Using sites that are too similar, which makes land-use differences too small to detect.
- Counting crowded MacConkey plates without a preset rule, which makes resistant-CFU values inconsistent.
- Treating all antibiotic growth as the same, which can blur the difference between background tolerance and stronger resistance signals.
- Ignoring rain, which can wash new bacteria into the water and distort the site pattern.
- Comparing your culture proxy directly to gene data without stating that the methods measure related but not identical things.
What Makes This Competitive
A strong version of this project uses careful site selection, clean controls, and a clear plan for data analysis before sampling starts. You can raise the level by testing several land-use variables, not just one, and by checking whether the same pattern appears across multiple sampling rounds. If you connect your culture results to public long-read datasets, you add a broader context that most school projects miss. Strong statistics, good maps, and honest limits about what your proxy can and cannot prove make the project much stronger.
Project Variations
- Compare urban storm drains, suburban creeks, and agricultural runoff sites to see whether resistant coliform counts change by setting.
- Add water-quality measurements such as turbidity, conductivity, and pH to test which features track resistance best.
- Replace plate-image counting with isolate-level follow-up tests to compare species mix and tolerance patterns among sites.
Learn More
- NIH PubMed: Search review articles on environmental antibiotic resistance, surface waters, and coliform indicators.
- NOAA National Water Quality Portal: Find background on water monitoring concepts and site data used in environmental studies.
- USGS Water Data for the Nation: Explore stream conditions, discharge, and watershed context for your sampling sites.
- OpenStreetMap: Use map layers and land-use features to classify what sits upstream of each site.
- MIT OpenCourseWare Biology and Microbiology materials: Review free lecture notes on bacterial growth, selection, and basic experimental design.