AI Air Quality Station based on ESP32-S3

Client

An environmental monitoring company deploying hyperlocal air quality sensor networks for urban areas, industrial parks, and schools.

Challenge

Building a low-cost air quality sensor node that can distinguish pollution sources (traffic vs. construction vs. cooking) using only consumer-grade gas sensors.

Product Concept & Architecture

The ESP32-S3 with its vector extensions runs both sensor management and on-device ML inference. Sensors include SPS30 (PM1/2.5/4/10), SGP40 (VOC index), SCD41 (CO2), and BME280 (T/H/P). Nodes communicate via ESP-WiFi-Mesh.

Engineering Solution

Sensor fusion on the ESP32-S3's dual cores: Core 0 reads all sensors at 1Hz and applies temperature/humidity compensation curves. Core 1 runs a TinyML random forest classifier (100KB) that identifies pollution type every 5 minutes. Nodes synchronize via Wi-Fi mesh and elect a gateway node for cloud uplink.

Result

87% pollution source classification accuracy. Per-node BOM under $60. 500-node mesh network deployed.