Project Title: Air Quality Index (AQI) Analysis

Objective:

To monitor, analyze, and predict air quality levels using AQI data, with the goal of understanding pollution trends, identifying sources, and supporting public health and environmental policies.

Key Components:

Data Collection:

Gathers AQI and pollutant concentration data from:

Government monitoring stations

IoT air sensors

Open APIs (e.g., OpenAQ, EPA, CPCB)

Satellite data (for large-scale regional analysis)

Pollutants include:

PM2.5, PM10

NO₂, SO₂, CO, O₃

Data Preprocessing:

Handles missing values, sensor errors, and time-zone alignment.

Aggregates AQI values by location and time (hourly, daily, monthly).

Exploratory Data Analysis (EDA):

Identifies high-pollution areas and seasonal trends.

Visualizes AQI fluctuations over time and space.

Compares AQI levels to health thresholds (e.g., WHO standards).

Trend & Correlation Analysis:

Examines how AQI is influenced by:

Weather variables (temperature, wind speed, humidity)

Traffic patterns and industrial activity

Public events (lockdowns, holidays)

Predictive Modeling:

Builds models to forecast AQI levels using:

Regression (Linear, Random Forest, XGBoost)

Time series models (ARIMA, LSTM)

Inputs include pollutant levels, meteorological features, and historical AQI data.

Geospatial Mapping & Visualization:

Creates real-time heatmaps of AQI by region.

Highlights pollution hotspots and safe zones.

Integrates with interactive dashboards (e.g., using Plotly, GIS tools).

Outcomes:

Provides early warning for air pollution spikes.

Helps cities and governments take preventive measures.

Informs public health advisories and citizen awareness.

Enables data-driven decisions for urban planning and emission control.