AirQuality Modeling
Empowering cleaner air through advanced modeling. We provide cutting-edge air quality simulation and forecasting solutions to help cities, industries, and researchers understand, predict, and improve environmental health. Explore our tools and insights for a sustainable future.
What we do:
We build and run air-quality models to estimate pollution (PM 2.5, PM 10, NO₂, O₃) across locations and time, turning emissions + weather + monitoring data into actionable insights.
What you’ll find here: Project overview, methods, interactive maps/plots, validation results, and downloadable reports/datasets for research and decision-making.
Integrate Multisource Environmental Data
We fuse emissions inventories, satellite retrievals, land-use variables, and reanalysis meteorology with surface monitoring networks exceeding 5,000 stations using rigorous quality control and harmonized spatiotemporal resolutions.
Build, Run, And Validate Models
We apply chemical transport and machine-learning models at 1–10 km resolution, reporting cross-validated R² up to 0.80 for PM2.5 and mean bias typically below 5% against independent monitoring datasets.
Deliver Interactive Analytics And Data
The site provides project documentation, methodological protocols, interactive pollution maps, uncertainty plots, validation dashboards, and downloadable model outputs, reports, and code to support transparent research and regulatory decision-making.
Dispersion Modeling Of Local Plumes
Gaussian and Lagrangian dispersion models simulate source-to-receptor transport using hourly emissions, stack parameters, and meteorology, resolving street-to-neighborhood scales for regulatory screening and hotspot diagnostics.
Chemical Transport And Atmospheric Chemistry
3D Eulerian chemical transport models (e.g., CMAQ, WRF‑Chem) couple dynamics, gas–particle chemistry, and deposition, driven by gridded emissions inventories and meteorology to quantify regional secondary PM and ozone formation.
Machine-Learning And Hybrid Fusion
Gradient boosting, random forests, and deep networks fuse satellite AOD, land-use, CTM outputs, and monitoring data, achieving typical cross‑validated R² ≈ 0.70–0.90 for daily PM2.5 at 1–10 km resolution.
Methods: Dispersion, Chemical Transport, ML/Hybrid
We integrate dispersion, chemical transport, and machine-learning models to estimate PM2.5, PM10, NO₂, and O₃ from emissions, meteorology, and monitoring networks for policy assessment and research-grade analysis.
Applications of Air Quality Models
Air Quality Models are indispensable in:
Conducting health impact assessments in urban areas.
Forecasting air pollution levels to inform the public and authorities.
Evaluating the effectiveness of emission reduction initiatives.
Key Components of Our Models
Our air quality modeling framework includes:
- Emission Inventories: Comprehensive databases of pollutants released into the atmosphere.
- Meteorological Data: Weather patterns that influence the dispersion and transformation of pollutants.
- Chemical Transport Models: Simulations of the transport and chemical transformations of pollutants in the atmosphere.
Tools and Technologies Used
To ensure precise and reliable results, we leverage the following technologies:
Statistical Approaches: Data assimilation and uncertainty analysis to enhance model accuracy.
Advanced Computational Techniques: High-performance computing for model simulations.
CONTACT US
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