System Overview

The AeroViz atmospheric monitoring system is a comprehensive platform designed for real-time air quality monitoring, data analysis, and environmental research. Our system integrates multiple data sources including ground-based sensors, satellite imagery, and meteorological data to provide accurate and timely air quality information.

Key Features

  • Real-time Monitoring: Continuous air quality data collection and visualization
  • Multi-sensor Integration: Support for various sensor types and data formats
  • Advanced Analytics: Machine learning algorithms for data analysis and prediction
  • Alert System: Automated notifications for air quality events
  • API Access: RESTful API for third-party integrations

System Requirements: The AeroViz system requires Python 3.8+, PostgreSQL 12+, and at least 4GB RAM for optimal performance.

Installation Guide

Prerequisites

Before installing AeroViz, ensure you have the following prerequisites:

  • Python 3.8 or higher
  • PostgreSQL 12 or higher
  • Node.js 16 or higher (for frontend development)
  • Git for version control

Installation Steps

Terminal
# Clone the repository
git clone https://github.com/aeroviz/aero-web-server.git
cd aero-web-server

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your configuration

# Initialize database
python script/init_database.py

# Start the application
python aero_web_app/run.py

Important: Make sure to configure your database connection and API keys in the .env file before starting the application.

Quick Start

Get started with AeroViz in just a few steps:

1. Access the Dashboard

After installation, navigate to http://localhost:5000 to access the main dashboard.

2. Configure Sensors

Add your air quality sensors through the management interface:

  1. Go to Admin → Sensor Management
  2. Click Add New Sensor
  3. Enter sensor details and connection parameters
  4. Test the connection and save

3. Set Up Alerts

Configure alert thresholds for different air quality parameters:

  1. Navigate to Settings → Alert Configuration
  2. Set threshold values for PM2.5, PM10, O₃, etc.
  3. Configure notification methods (email, SMS, webhook)
  4. Test alert functionality

Congratulations! You're now ready to start monitoring air quality with AeroViz.

API Overview

The AeroViz API provides programmatic access to air quality data and system functionality. All API endpoints are RESTful and return JSON responses.

Base URL

API Endpoint
https://api.aeroviz.com/api/v1/

Authentication

Most API endpoints require authentication. Include your API key in the request header:

HTTP Header
Authorization: Bearer YOUR_API_KEY

Common Endpoints

GET
/api/v1/data/current
Get current air quality data
GET
/api/v1/data/history
Get historical air quality data
POST
/api/v1/alerts/configure
Configure alert thresholds

Instruments Overview

Our integrated instrument setup enables comprehensive characterization of atmospheric aerosols, measuring various properties including size distribution, chemical composition, optical properties, and mass concentration.

Note: For detailed information about specific instruments, visit our Instruments page or contact our technical team.

Available Instruments

Aurora Integrating Nephelometer

Real-time measurement of light scattering coefficients across multiple wavelengths (450, 525, 635 nm)

Measurement Range: 0.1-1000 Mm⁻¹

Scanning Mobility Particle Sizer (SMPS)

Measures particle size distributions by electrical mobility separation and counting

Size Range: 10-800 nm | Time Resolution: 5 min

Tapered Element Oscillating Microbalance (TEOM)

Continuous measurement of particulate mass concentration using an oscillating microbalance

Mass Range: 0.1-1000 μg/m³ | Time Resolution: 1 min

Aerodynamic Particle Sizer (APS)

Measures aerodynamic particle size distributions for larger aerosol particles

Size Range: 0.5-20 μm | Time Resolution: 1 min

Sunset OCEC Analyzer

Quantifies organic and elemental carbon using thermal-optical method

Time Resolution: 30 min

AE33 Black Carbon Analyzer

Measures black carbon concentrations with high sensitivity

Concentration Range: 0.1-100 μg/m³ | Time Resolution: 1 min

BC1054 Black Carbon Analyzer

High-sensitivity black carbon concentration measurement

Concentration Range: 0.1-100 μg/m³ | Time Resolution: 1 min

X-Ray Fluorescence (XRF) Analyzer

Identifies elemental composition in aerosol samples using X-ray fluorescence

Elemental Analysis: Na-U | Analysis Time: 10-30 min

Technical Specifications

Detailed technical specifications for our atmospheric monitoring instruments:

Data Collection & Integration

All instruments are integrated into our centralized data collection system, which:

  • Automatically collects data at specified intervals
  • Performs real-time quality control and validation
  • Stores data in our PostgreSQL database
  • Provides API access for data retrieval

Maintenance & Calibration

Regular maintenance schedules ensure optimal instrument performance:

  • Daily: Automated system checks and data validation
  • Weekly: Filter changes and basic cleaning
  • Monthly: Calibration verification
  • Quarterly: Full calibration and maintenance

Important: Proper calibration is essential for accurate measurements. Always follow manufacturer guidelines and maintain calibration records.

Troubleshooting

Common Issues

Database Connection Errors

Problem: Unable to connect to the database

Solution:

  • Check database credentials in .env file
  • Ensure PostgreSQL service is running
  • Verify database exists and user has proper permissions

Sensor Data Not Updating

Problem: Sensor readings are not being received

Solution:

  • Check sensor connection status in admin panel
  • Verify sensor configuration and data format
  • Check network connectivity and firewall settings

Performance Issues

Problem: System running slowly or high memory usage

Solution:

  • Check database query performance
  • Monitor system resources (CPU, RAM, disk)
  • Consider database indexing optimization