Integrating AI With Iot Sensors For Real-Time Air Quality Monitoring And Prediction

Abstract

Objective: The goal of this study is to develop and deploy a system that integrates AI algorithms with low-cost IoT air quality sensors for continuous, real-time monitoring and short-term prediction of urban air quality. Specifically, we targeted PM2.5, PM10, NO2, and CO levels in a densely populated area of Delhi, India.

Method: We installed a network of 50 IoT-based air quality sensor nodes across North Delhi, each capable of measuring PM2.5, PM10, NO2, and CO every 2 minutes. Sensor data streams were transmitted to a central server via LTE. We used a Long Short-Term Memory (LSTM) neural network model trained on three months of historical sensor data and meteorological inputs (temperature, humidity, wind speed) to predict air quality indices (AQI) one hour ahead.

Methodology:

• Data Collection: Deployed sensors recorded real-time air quality and weather data from Jan to Mar 2024.
• Data Preprocessing: Cleaned data, removed outliers, and synchronized time stamps.
• Model Training: Used 70% of the data for training and 30% for testing the LSTM model.
• Real-Time Prediction: The system generated hourly AQI forecasts and live dashboards for public use.
• Validation: Compared model predictions with official Delhi Pollution Control Committee (DPCC) station data.

Results: The LSTM-based system achieved a mean absolute error (MAE) of 8.2 on the AQI scale, significantly outperforming classical ARIMA models (MAE: 14.7). The real-time dashboard enabled early warnings for pollution spikes, with 87% of high AQI events predicted at least 45 minutes in advance. The solution provided granular, street-level air quality data that closely matched the government’s reference stations, but with higher spatial and temporal resolution.

Conclusion: Integrating AI models with IoT sensor networks can deliver accurate, real-time air quality monitoring and forecasting in urban environments. Our Delhi case study demonstrates that this approach is both technically feasible and cost-effective, offering a scalable template for smart city air quality management and timely public health advisories.