Environmental Sensor Data Logger

A practical STEM project by James Godswill
Environmental monitoring and IoT learning, with Enviro Education Tools as project context.

This starter project demonstrates how environmental readings can be loaded from a CSV file, checked for basic data-quality issues, summarized, and plotted. It is designed as a beginner-friendly foundation for later sensor integration.

Data notice: The included CSV contains simulated demonstration values. They are not field measurements, calibrated readings, or evidence of a physical deployment.

Features

• Load timestamped CSV records for CO₂, PM2.5, temperature, and relative humidity.
• Validate required columns and numeric values.
• Calculate minimum, average, and maximum values.
• Plot a selected metric over time.
• Run locally with Python's standard library; no third-party packages required.

Current scope

Implemented: CSV parsing, basic validation, summary statistics, a simple chart, and sample simulated data.

Not yet implemented: live sensor acquisition, Arduino/ESP32 firmware, cloud storage, calibration, alerts, or regulatory/health interpretation. These should be added only after design and testing.

Requirements

• Python 3.9+
• A modern web browser

Run

1. Extract the project folder.
2. Open a terminal in that folder.
3. Run python app.py.
4. Open http://127.0.0.1:8000 in your browser.
5. Click Load sample data or choose your own CSV file.

The server binds to localhost only. Set the PORT environment variable to use another port.

CSV format

Required columns:

Column	Unit	Example
timestamp	ISO-like date/time text	2026-10-01T09:00:00
co2_ppm	parts per million	640
pm25_ug_m3	micrograms per cubic metre	8.4
temperature_c	degrees Celsius	24.1
humidity_pct	percent relative humidity	48

Example row:

timestamp,co2_ppm,pm25_ug_m3,temperature_c,humidity_pct
2026-10-01T09:00:00,640,8.4,24.1,48

The CSV parser expects simple comma-separated values; fields containing embedded commas or quoted multiline text are outside this starter's current scope.

Interpretation limits

• CO₂ is not a direct measure of every indoor pollutant and should not be treated as a stand-alone health or safety verdict.
• Low-cost PM2.5 sensors may be affected by humidity, aerosol composition, placement, and device limitations.
• This dashboard does not calibrate or correct readings.
• For real measurements, document the sensor model, configuration, collection time and location (without exposing private information), and known limitations.

Project structure

environmental-sensor-data-logger/
├── app.py
├── sample_data/simulated_readings.csv
├── tests/test_data.py
├── LICENSE
├── CHANGELOG.md
└── CONTRIBUTING.md

Roadmap

• [ ] Add an ESP32 serial-import example after the hardware and serial format are specified.
• [ ] Add tests for malformed timestamps and edge cases.
• [ ] Add CSV export for summary reports.
• [ ] Document sensor-specific uncertainty and calibration practices.
• [ ] Add a reproducible hardware test only after it has been performed.

Author and project context

James Godswill — IoT Engineer / Technical Contributor.

Enviro Education Tools provides the environmental-monitoring and STEM-learning context for this educational project.

• Enviro Education Tools: https://enviroeducationtools.com/
• GitHub: https://github.com/semajlliwsdog

This is an independent educational starter project. Do not infer that hardware has been tested or deployed unless a future release explicitly documents that evidence.