AI-Powered Air Quality Monitoring
AirQo AI provides advanced tools for monitoring, analyzing, and optimizing air quality across African cities using artificial intelligence.
Our AI Technologies
At AirQo, we've developed specialized AI solutions to address the unique challenges of air quality monitoring in Africa. Our cutting-edge technologies power all aspects of our platform.
Forecasting AI
Our forecasting AI predicts air quality conditions up to 7 days in advance with high accuracy. Using recurrent neural networks and ensemble methods, we analyze historical air quality data, weather patterns, and human activity to generate reliable forecasts.
- •Time-series prediction models
- •Weather data integration
- •Seasonal pattern recognition
Calibration AI
Our calibration AI transforms data from low-cost sensors into reference-grade measurements. Using advanced machine learning algorithms, we account for environmental factors, sensor drift, and cross-sensitivities to ensure accurate readings.
- •Adaptive calibration models
- •Environmental compensation
- •Sensor drift correction
Location AI
Our location AI optimizes the placement of air quality monitors to maximize coverage and data value. Using spatial analysis, population density, and pollution source modeling, we identify the most strategic locations for monitoring networks.
- •Spatial optimization algorithms
- •Population exposure modeling
- •Geographic constraint handling
Satellite PM2.5 AI
Our satellite-based AI models predict PM2.5 concentrations in areas without ground-based monitors. By analyzing satellite imagery, meteorological data, and land use information, we provide air quality estimates for remote and underserved regions.
- •Remote sensing data integration
- •Wide geographic coverage
- •Historical trend analysis
Source Prediction AI
Our source prediction AI identifies and characterizes stationary pollution sources. Using advanced pattern recognition and dispersion modeling, we can pinpoint industrial emissions, waste burning sites, and other significant pollution contributors.
- •Emission source fingerprinting
- •Pollution dispersion modeling
- •Temporal pattern analysis
Our Technical Approach
Data Collection & Processing
Our AI systems process data from multiple sources, including:
- •Low-cost sensor networks deployed across Africa
- •Satellite imagery and remote sensing data
- •Weather and meteorological information
- •Traffic patterns and urban activity data
Model Development & Deployment
We employ a rigorous approach to AI model development:
- •Continuous training with expanding datasets
- •Regular validation against reference instruments
- •Adaptation to local environmental conditions
- •Edge deployment for low-connectivity areas
Additional AI Applications
Anomaly Detection
Our AI systems automatically identify unusual patterns in air quality data, flagging potential pollution events, sensor malfunctions, or data quality issues for investigation.
Health Impact Modeling
We use AI to model the relationship between air pollution exposure and health outcomes, helping to quantify the impact of air quality interventions on public health.
Source Attribution
Our AI algorithms help identify the likely sources of pollution by analyzing the composition of pollutants, weather conditions, and other environmental factors.
Intervention Analysis
We use AI to evaluate the effectiveness of air quality interventions by comparing actual measurements with counterfactual scenarios.
Powered by Artificial Intelligence
Our platform leverages cutting-edge AI to provide accurate, real-time air quality data and insights for researchers, policymakers, and citizens.
Optimal Site Location
Use AI algorithms to determine the best locations for air quality monitors based on population density, pollution sources, and geographic factors.
Air Quality Categorization
Automatically categorize monitoring sites based on surrounding land use, traffic patterns, and environmental factors.
Data Analytics
Generate comprehensive reports with trends, forecasts, and actionable insights from air quality data.
Machine Learning Models
Continuously improving prediction models that account for seasonal variations, weather patterns, and human activities.
Health Impact Assessment
Evaluate potential health impacts of air pollution on different population groups and geographic areas.
Interactive Mapping
Visualize air quality data across regions with interactive maps showing real-time pollution levels.
How AirQo AI Works
Our platform combines low-cost sensors, advanced algorithms, and user-friendly interfaces to democratize air quality monitoring.
Data Collection
Our network of sensors continuously collects air quality data across multiple locations.
AI Processing
Advanced algorithms clean, analyze, and interpret the data to generate insights.
Actionable Insights
Users access visualizations, reports, and recommendations through our platform.
Ready to Improve Air Quality?
Start using our AI-powered tools to make data-driven decisions for cleaner air.