🌬️ AirAware: Air Quality Monitoring & Alert System

Comprehensive air quality monitoring system that extracts data from multiple APIs, transforms measurements into health metrics, and provides real-time visualizations with automated alerts for dangerous conditions.

🎯 Features

• 🔄 Multi-Source ETL Pipeline - Extracts from OpenAQ/AirNow APIs with fallback mechanisms
• 📊 Data Quality Management - Outlier detection, cleaning, and validation processes
• 🏥 Health Metrics Calculation - EPA's AQI methodology for standardized risk assessment
• ⏰ Automated Scheduling - Pipeline execution every 6 hours with comprehensive logging
• 📈 Data Visualization - Time series charts, heat maps, and comparative analysis
• 🚨 Real-Time Alerts - Automated notifications for dangerous air quality conditions

🛠️ Tech Stack

Core: Python 3.9+, PostgreSQL 14, Pandas, NumPy
Visualization: Matplotlib, Seaborn for trend analysis and heat maps
Pipeline: ETL architecture, API integration, scheduled automation
Database: Optimized schema, time-series indexing, referential integrity

🚀 Quick Start

Installation

# Clone and setup
git clone https://github.com/AjayMaan13/AirAware.git
cd AirAware
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt

# Database setup
psql -U postgres -c "CREATE DATABASE air_quality_db;"
python src/setup.py

Usage

# Run ETL pipeline once
python src/pipeline.py --run-once

# Scheduled execution (every 6 hours)
python src/pipeline.py

# Generate visualizations
python src/visualize.py

📁 Structure

AirAware/
├── src/
│   ├── extract.py      # API data extraction
│   ├── transform.py    # Data cleaning & AQI calculation
│   ├── load.py         # PostgreSQL loading
│   ├── pipeline.py     # ETL orchestration
│   ├── visualize.py    # Chart generation
│   └── alert.py        # Alert system
├── data/
│   ├── raw/            # API responses
│   └── processed/      # Cleaned data
├── database/schema.sql # Database schema
└── visualizations/     # Generated charts

🏗️ Architecture

ETL Pipeline Flow

1. Extract - Multi-API data collection (OpenAQ, AirNow) with error handling
2. Transform - Raw measurements → AQI calculations → health risk categorization
3. Load - PostgreSQL storage with optimized time-series indexing
4. Visualize - Automated chart generation and pollution heat maps
5. Alert - Real-time monitoring with dangerous condition notifications

Data Processing

# AQI Calculation Example
def calculate_aqi(pollutant_concentration, pollutant_type):
    # EPA methodology implementation
    breakpoints = get_aqi_breakpoints(pollutant_type)
    return linear_interpolation(concentration, breakpoints)

# Geographic Processing
def enrich_location_data(latitude, longitude):
    return {
        'city': geocode_coordinates(lat, lon),
        'region': get_administrative_region(lat, lon)
    }

📊 Sample Visualizations

AQI Heat Map - Geographic Distribution

AQI Distribution Analysis

Time Series Analysis - Pollutant Trends

Multi-City Comparison

🧪 Testing & Deployment

# Environment validation
python -c "import psycopg2, pandas, requests; print('Dependencies OK')"

# Pipeline testing
python src/pipeline.py --test-mode

📈 Performance Metrics

• 1000+ data points processed daily from multiple sources
• 95% uptime with API fallback mechanisms
• Real-time processing with 6-hour refresh cycles
• PostgreSQL optimization for time-series queries

👨‍💻 Author

Ajaypartap Singh Maan
GitHub • LinkedIn • ajayapsmaanm13@gmail.com

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