Design and implement an interactive air quality monitoring dashboard that visualizes pollution levels, Air Quality Index (AQI), and pollutant concentration trends across regions and time periods. The system should support environmental monitoring, public awareness, and policy decision-making through real-time or historical data analytics.
Collect air quality datasets from public sources (e.g., CPCB, WHO datasets, or simulated IoT sensor data) including AQI, PM2.5, PM10, CO, NO₂, SO₂, O₃, temperature, and humidity.
Clean and preprocess data to handle missing values, outliers, and inconsistent measurement units.
Design a relational database schema to store location-wise and time-series pollution data.
Create ER diagrams and document system architecture.
Implement ETL pipelines to transform raw sensor or CSV data into structured analytical format.
Develop backend APIs using Python (Flask/Django) or Node.js for data aggregation and filtering.
Define KPIs such as average AQI, peak pollution hours, pollutant concentration trends, and safe vs hazardous day counts.
Build interactive dashboards using Tableau, Power BI, or Plotly/D3.js.
Implement filters by city, date range, pollutant type, and AQI category.
Add alert visualization indicators for hazardous air quality levels.
Integrate basic forecasting models to predict short-term AQI trends.
Ensure responsive UI design and performance optimization.
Conduct system testing using simulated real-time data streams.
Prepare detailed project documentation including methodology, visualization techniques, and environmental insights derived from the dashboard.
