Forecasting Air Quality Using Deep Learning Techniques

Air pollution severely affects human health, ecosystems, and urban sustainability. Major cities face critical challenges maintaining safe air quality levels due to vehicular emissions, industrial activities, and climate factors. Traditional air pollution monitoring is reactive, based on real-time sensors. Deep learning models enable proactive forecasting, helping authorities and citizens prepare for pollution spikes, implement restrictions, and protect public health more efficiently.

Using historical air quality data (PM2.5, PM10, NO₂, SO₂, CO, O₃ concentrations), meteorological data (wind speed, humidity, temperature), and deep learning architectures like LSTM or GRU networks, pollution levels can be forecasted ahead of time. These models capture temporal dependencies and seasonal variations, enabling smart city systems, environmental organizations, and health departments to take timely actions to reduce exposure and emissions.

Historical AQI datasets along with meteorological parameters are collected from monitoring stations and environmental agencies. After preprocessing and feature scaling, deep learning models like LSTM, GRU, or 1D-CNNs are trained to capture temporal air pollution patterns. Forecasts predict hourly or daily pollution levels, supporting early warnings and smarter policy-making. Performance is evaluated using RMSE, MAE, and R² metrics.

• Collect historical AQI data (PM2.5, PM10, gases) along with temperature, wind speed, humidity, and other climate variables.
• Preprocess time-series data: resample to uniform intervals, fill missing entries, normalize feature scales, and structure input sequences.
• Train deep learning models like LSTM, GRU, or CNN-based time-series models to predict AQI or pollutant concentrations ahead of time.
• Evaluate forecasting models using RMSE, MAE, and R² score for predicting pollutant concentration trends accurately.
• Deploy dashboards displaying predicted air quality levels, enabling citizens, policymakers, and businesses to take timely actions.

• OpenAQ – Free Global Air Quality Data
• Air Pollution Prediction Using LSTM
• UCI Machine Learning Repository – Air Quality Dataset