Build an ESP32 Weather Station with Forecasts, Sensors and Air Quality

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An ESP32 weather station can combine local temperature, humidity and pressure readings with an internet forecast and particulate measurements. Build it as a modular system: sensors report conditions where the device sits, a weather service supplies the forecast, and an air-quality sensor measures a specific pollutant. Those are different data sources, so label them separately. For a dependable first build, use an ESP32, a BME280, a PMS5003-class particle sensor, and a display or local dashboard. An outdoor installation also needs a ventilated radiation shield and a power plan—not just a weatherproof box.

What the station measures—and what it does not

The ESP32 is the controller and Wi-Fi connection, not a weather sensor. A BME280 measures local temperature, relative humidity and barometric pressure. A particulate sensor measures particles in its airflow. An internet API supplies model-based forecast data for a selected location. Keep those readings distinct on the display: a forecast is not a measurement made at the station, and a pressure trend is not a multi-day forecast.

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A BME280 is a practical baseline for local conditions. Bosch specifies I²C and SPI interfaces, a pressure range of 300–1100 hPa, an operating temperature range of −40 to 85 °C, and typical relative-humidity accuracy of ±3% under specified conditions. Those are sensor specifications, not a guarantee for a breakout board mounted beside a warm ESP32 in a sunlit enclosure. See Bosch’s BME280 product information and data sheet.

A BME280 pressure trend can be shown as a rough local indicator, but the ESP32 should retrieve a forecast from a weather service rather than claim to predict tomorrow from its own readings. OpenWeather offers weather, forecast, geocoding, air-pollution and station-upload products; Open-Meteo is another option. Check the provider’s current endpoint, terms and limits before building around it.

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Choose a build for its location and purpose

Optional additions include a tipping-bucket rain gauge, wind speed and direction sensors, UV or light sensing, microSD logging, MQTT, or a home-automation dashboard. Add them to meet a specific need; each adds wiring, power draw, calibration or software failure modes.

Pick an air sensor that answers the right question

Particles: PMS5003-class sensor

A PMS5003-class optical sensor is a straightforward way to add PM1.0-, PM2.5- and PM10-related readings. It uses UART and needs air to pass through its sensing chamber. These are particulate measurements, not automatically a regulatory-grade result or an official AQI. Verify the exact sensor or carrier-board documentation for its supply voltage, logic levels, connector pinout, UART framing, warm-up and sleep behavior; modules and breakout boards are not necessarily interchangeable. A manual reference is available from the South Coast Air Quality Management District.

Use a hardware UART if possible. Parse complete frames, check the checksum, reject invalid data, allow warm-up before publishing readings, and keep the inlet and outlet clear. The AirGradient ESP32 firmware is a useful example of particulate-sensor integration; its documentation also illustrates why board and library versions matter.

VOC trends and CO₂ are different measurements

BME680/BME688 and SGP-family devices provide gas-resistance or VOC-related information. Label such output “VOC index” or “gas-resistance trend,” not PM2.5 or a universal pollution concentration, unless a validated calibration and model support that interpretation. Bosch provides environmental-sensor software and drivers at its software tools page.

If the real goal is ventilation monitoring, choose an NDIR CO₂ sensor. CO₂ is not a substitute for particulate monitoring, just as a PM sensor is not a CO₂ sensor. Use separate sensors when both questions matter.

Wire the sensors safely

The following connections describe interfaces, not universal GPIO assignments. ESP32 boards differ, and a pin available on one model may be occupied, unavailable or a boot-strapping pin on another. Choose pins for the specific board and define them once in the firmware configuration.

BME280 over I²C

BME280 VIN/VDD  -> ESP32 3.3 V (subject to breakout-board specifications)
BME280 GND      -> ESP32 GND
BME280 SDA      -> configured ESP32 I²C SDA GPIO
BME280 SCL      -> configured ESP32 I²C SCL GPIO

Check the module’s voltage requirements and I²C address. Modules may use different addresses depending on the SDO/ADR connection, so document the expected address or scan for the common alternatives. A BMP280-shaped module does not measure humidity; do not treat it as a BME280 just because its board is labeled similarly.

Particle sensor over UART

PMS5003 VCC     -> supply specified for the exact module
PMS5003 GND     -> common ground
PMS5003 TX      -> ESP32 hardware-UART RX
PMS5003 RX      -> ESP32 hardware-UART TX, if commands are needed

ESP32 GPIO is generally a 3.3-V logic environment. Verify peripheral output levels before connecting signals, and do not assume the sensor’s power input or logic requirements from a generic product photo. Avoid powering a high-current particle sensor from a regulator that cannot support it. Use a common ground unless the design intentionally provides isolation, and add appropriate local decoupling. For exposed outdoor runs, keep UART wiring short or provide suitable protection, and protect connectors against moisture.

Before finalizing the pin map, check for conflicts with the display, microSD, flash and board boot configuration. A wiring diagram for one ESP32 board is not a universal ESP32 pinout.

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Separate sensing, networking and presentation in firmware

Organize the project into modules for configuration, local sensors, Wi-Fi and time, forecast retrieval, air-quality validation, display, and storage. Keep provider-specific API code behind an interface such as bool fetchForecast(ForecastData& forecast);. This makes it easier to change forecast providers without rewriting sensor, display or logging code.

Read sensors, fetch forecasts and refresh the screen on independent timers. Do not make an API request on every pass through loop(), and avoid long blocking waits that prevent local sensing or display updates.

if (millis() - lastSensorRead >= SENSOR_INTERVAL_MS) {
    readLocalSensors();
    lastSensorRead = millis();
}

if (millis() - lastForecastFetch >= FORECAST_INTERVAL_MS) {
    if (WiFi.isConnected()) fetchForecastIfAvailable();
    lastForecastFetch = millis();
}

if (millis() - lastDisplayUpdate >= DISPLAY_INTERVAL_MS) {
    updateDisplay();
    lastDisplayUpdate = millis();
}

Use explicit states instead of silently presenting stale or missing data: “Sensor warming up,” “Forecast unavailable,” “Using cached forecast,” “Time not synchronized,” or an appropriate sensor/API error. A network failure should not erase valid local measurements.

For recovery, give Wi-Fi connection attempts a timeout, continue local sensing offline, retry with increasing delays, and retain the last successful forecast with its age. Distinguish authentication, quota, DNS, TLS and timeout errors. Rebooting should be a last-resort recovery mechanism, not the ordinary response to a failed request. The Arduino-ESP32 Wi-Fi documentation covers station and access-point modes, security and Wi-Fi events.

Retrieve forecast data without confusing it with local readings

A forecast request normally uses a configured latitude and longitude or a location identifier, an HTTPS client, and JSON parsing. Select only fields the display needs, such as forecast high and low, precipitation probability, condition and wind. Location lookup can be separate from forecast retrieval. Configure units and the location’s time zone deliberately.

OpenWeather’s API overview describes its weather products, and Open-Meteo’s documentation describes its available weather data. Account or API-key requirements, endpoint eligibility, quotas, attribution and commercial terms can change; consult the live provider documentation and OpenWeather pricing page for the plan and endpoint you intend to use. Do not assume an API is unlimited or that every product is available on every plan.

Synchronize time using NTP or another time source before labeling observations or forecast periods. Handle daylight-saving time and local time-zone display explicitly. For pressure, label whether the device reports station pressure or sea-level-adjusted pressure: a raw reading at elevation is not directly comparable with an app’s sea-level pressure.

HTTPS and JSON can use substantial memory on a small device. Limit response size where the provider allows it, extract only needed fields, avoid unnecessary dynamic-string concatenation, and free request buffers. Cache the last valid forecast and show when it was retrieved. A forecast that is present but old should not look current.

Display particulate readings and AQI accurately

Show pollutant concentration before any index, for example, “PM2.5: 12 µg/m³” and “PM10: 19 µg/m³,” provided those values are valid for the sensor and its reporting mode. If you also show AQI, identify the pollutant, units, averaging period, named standard and whether the number is instantaneous, rolling or forecast. National AQI methods differ; an unqualified 0–500 scale is not universal.

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For a U.S. display, use the current U.S. EPA method and its pollutant-specific breakpoints, concentration truncation and averaging requirements. Do not use a generic breakpoint table or label a sensor’s proprietary score as regulatory AQI. Interpolation follows this general pattern once the correct standard-specific inputs are selected:

float calculateAQI(float concentration,
                   float concentrationLow, float concentrationHigh,
                   int indexLow, int indexHigh) {
    return ((indexHigh - indexLow) /
            (concentrationHigh - concentrationLow)) *
           (concentration - concentrationLow) + indexLow;
}

The formula alone does not establish a valid AQI: the implementation still needs the applicable authority’s current breakpoints, pollutant, averaging period and rounding rules. If the sensor is warming up or its data is invalid, show that state rather than assigning an AQI category.

Place sensors for useful readings

Protect the BME280 from heat and sun

The BME280’s temperature can be biased by the ESP32, voltage regulator, battery charging, display, enclosure heat or direct sunlight. Mount it away from heat-producing electronics in a shaded, ventilated area. For outdoor readings, use a radiation shield rather than a sealed electronics box. Ventilation must admit ambient air without exposing the sensor to direct sun or rain.

Give the particle sensor a protected airflow path

A particle sensor in a sealed weatherproof box cannot sample outdoor air properly. Keep its inlet and outlet unobstructed while shielding them from rain, condensation, insects and direct exposure to exhaust heat. Cooking aerosols, smoke, dust, cleaning products, humidity and contamination can affect readings; make the air path accessible for inspection and cleaning.

Treat outdoor construction as a separate design problem

An outdoor station needs a radiation shield, condensation management, protected cable entries, drainage, suitable materials and a maintenance plan. Weatherproofing and ventilation work against each other: protect electronics and connectors while preserving a shielded path for the sensing elements to sample air. Sensor separation matters too; a warm power supply should not sit beside the temperature sensor.

Choose a display, dashboard and logging path

Keep the screen legible about provenance and age. A useful layout separates “Local,” “Forecast” and “Air quality,” includes units, and marks offline or cached data. A cloud dashboard or local display should not imply that API forecast values were measured by the station.

Plan power, privacy and security

USB power is the simplest choice for an indoor build. Battery or solar operation needs a measured power budget for the completed station, including Wi-Fi activity, particle-sensor operation, display updates and sleep intervals. A PMS5003-class sensor can dominate consumption, so consider scheduled measurements, supported sleep modes, ESP32 deep sleep, e-paper or no display, and less frequent forecast requests. Do not promise a battery life based only on the ESP32’s bare-chip specifications.

Keep Wi-Fi credentials and API keys out of public repositories; store secrets in an excluded configuration file and provide a reset or Wi-Fi reprovisioning path. Prefer local-network access or a properly secured VPN over exposing the ESP32 web server directly to the internet. Use HTTPS where supported and test memory behavior on the chosen board. Arduino-ESP32 documents security modes including WPA2 and WPA3, but compatibility depends on the specific board, software and router.

Bring up the project in stages

1. Confirm the board and toolchain. Select the exact ESP32 board in Arduino IDE or PlatformIO and record the tested Arduino-ESP32 core and library versions. At the time represented by Espressif’s documentation, its site identifies Arduino-ESP32 3.3.11 based on ESP-IDF 5.5, while the repository lists a 3.3.8 release entry based on ESP-IDF 5.5.4. Pin the package actually tested by your build rather than assuming those pages update together. See the Arduino-ESP32 documentation and repository.
2. Test the BME280 alone. Confirm the I²C address and verify that temperature, humidity and pressure return plausible values. A missing humidity reading may indicate a BMP280 or incorrectly identified module.
3. Add the display. Establish the pin map and check for I²C or SPI conflicts before connecting other modules.
4. Add the particle sensor. Confirm the module’s supply and logic specifications, UART connection, valid frames and checksum handling. Wait through warm-up before treating measurements as available.
5. Add Wi-Fi and time. Test connection timeout, offline sensing, retry behavior and the display’s status indicators.
6. Add forecast retrieval last. Request only the fields needed, check provider terms and limits, test error paths, and display the timestamp or age of cached data.
7. Test the assembled enclosure and power source. Check thermal bias, airflow, rain protection, sensor access and measured current in active and sleep states before deploying it outdoors or relying on a battery.

Troubleshoot common failures

A balanced first build

For a capable first station, use an ESP32 development board, a verified BME280 breakout, a PMS5003-class sensor on a suitable supply and UART, and a USB-powered display. Start indoors, prove each sensor independently, then add a forecast provider with cached-data and offline handling. Move outdoors only after designing a shielded, ventilated sensor path and checking temperature bias and moisture protection. Add CO₂, logging, wind or rain measurement only when those measurements serve a defined purpose.

For component documentation and implementation references, consult Adafruit’s BME280 breakout, SparkFun’s BME280 breakout, Adafruit’s PMSA003I page, the Adafruit display catalog, and Espressif development boards. These are examples, not a guarantee that every breakout shares the same voltage, pinout or environmental suitability.

Quick Recap

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