Diego Ripley
4.0 KiB
title, summary, date, authors, tags, excludeSearch
| title | summary | date | authors | tags | excludeSearch | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Deployed an Open Sensor Environmental Station Using Raspberry Pi and Enviro+ | I deployed a static environmental monitoring station that contributes readings to the Open Sensor network, and made the raw sensor and health data openly available on Source Cooperative. | 2026-04-22T10:00:00-04:00 |
|
|
false |
I recently deployed a static environmental monitoring station that contributes readings to the Open Sensor network. This is a quick post describing what the station is, how it is built, and where you can access the data it produces.
1. What is Open Sensor?
Open Sensor is a community-run network of low-cost environmental sensors. Each station publishes its readings in an open and efficient file format so that anyone can use them. You can read more about how the network is designed on the architecture page.
2. Hardware
The station follows Open Sensor's reference build, which is documented under hardware requirements. At a high level, it consists of:
- A Raspberry Pi Zero 2 W as the host computer.
- A Pimoroni Enviro+ HAT, which provides sensors for temperature, humidity, pressure, light, noise, and air quality (gas and particulate matter when paired with a PMS5003).
Once the hardware was assembled, the Pi was flashed with the Raspberry Pi OS Lite image, and the OpenSensor Enviroplus was installed and configured to upload data to Source Cooperative.
3. Data
The station is registered on the network with the identifier 019d97ff-3220-74fc-8923-f9fb69e2273d. Two datasets are archived on Source Cooperative under the d4c-datapkg-environment-climate-health data package:
- Sensor readings (temperature, humidity, pressure, gas, particulates, etc.):
archive/opensensor.space/enviroplus/station=019d97ff-3220-74fc-8923-f9fb69e2273d - Station health metrics (CPU temperature, uptime, memory, etc.):
archive/opensensor.space/enviroplus-health/station=019d97ff-3220-74fc-8923-f9fb69e2273d
Both datasets are partitioned by station, which makes it straightforward to query only this station with tools such as DuckDB, or to combine it with other stations in the network.
4. The Future
The real value lies in scaling this into a high-density network for smarter urban governance. High-resolution air quality data can transition cities from static policies to intelligent decision-making, for example: driving real-time traffic signal adjustments, dynamic speed limits, pollution-aware routing for vehicles, cross-border air pollution, etc.
I’ve already purchased a second station to take into the field. By syncing both the Pi and my phone to the same time server (NTP), I can join the air quality readings with my phone's GPS track using timestamps. This turns future hikes into mobile environmental surveys, mapping the environment one trail at a time.
5. Join the Network
Join the network, it is super easy, it took me less than 2 hours to get everything working.
