Ideaz

Air pollution in cities remains a major challenge due to vehicle emissions, construction dust, and industrial activity. Current solutions—like masks or indoor purifiers—protect individuals but fail to address pollution at a community scale. My proposal is to design IoT & AI-integrated Smart Air-Purification Poles that combine air filtration, data analytics, and smart-city integration.
Each pole would run on solar panels with Li-ion battery storage for 24/7 uptime. The purification unit would include:
Cyclone separator + pre-filter for coarse dust,
HEPA H14 filter for PM2.5/PM10,
Activated carbon layer for SO₂, NOx, and VOCs,
Electrostatic precipitator for fine aerosols.
Pollution data is captured using sensors like SDS011 (PM2.5/PM10), MQ-135 (gases), CCS811 (VOC/CO₂), and DHT22 (temperature/humidity for calibration). These feed into a Raspberry Pi / ESP32-based microcontroller that preprocesses data before pushing it via LoRaWAN or 5G to a central cloud server (AWS IoT Core / Azure IoT Hub).
A real-time dashboard displays pollution heatmaps, while an AI model (LSTM or Prophet) predicts pollution spikes based on historical data + weather APIs. Citizens access this through a mobile/web app, which can issue alerts (e.g., “High PM2.5 tomorrow morning”).
Additional modules can be added: edge computing for local decision-making, public Wi-Fi hotspot, or CCTV integration.
Beneficiaries include residents (especially vulnerable groups), local authorities (better planning), and health agencies (data-driven policies). Unlike existing fragmented solutions, this system offers scalable, self-sustaining, and data-driven pollution control at the community level.
This matters to me because I’ve seen how poor air quality disrupts lives. By fusing IoT hardware, AI forecasting, and clean-tech engineering, we can build cities where clean air isn’t a luxury, but a standard.