Air Quality Modeling and Meteorological Analysis
Summary
Air quality modelling and meteorological analysis form an integrated framework for understanding and forecasting the dispersion, chemical transformation and removal of atmospheric pollutants. By coupling detailed emission inventories with meteorological fields such as wind speed and direction, temperature profiles and boundary layer dynamics, models can simulate spatial and temporal variations in concentrations of fine particulate matter (PM2.5), ozone, nitrogen oxides and other key species. Traditional deterministic approaches, notably chemical transport models, represent physical and chemical processes explicitly, while statistical techniques and emerging machine-learning methods offer data-driven alternatives to disentangle the effects of emissions and weather. Meteorological normalisation—also known as deweathering—controls for variability in weather conditions, enabling more robust trend attribution to policy measures and emission controls. Such combined analyses underpin air quality forecasting, health impact assessments and the design of effective interventions, from urban traffic management to regional clean-air plans. Globally, this research supports international assessments of pollution burdens, informs climate-air quality co-benefit strategies and guides public advisories in the face of extreme events such as wildfires and inversion episodes.
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Air Quality Modeling and Meteorological Analysis publication trend
The graph below shows the total number of articles in air quality modeling and meteorological analysis across all publications each year (not limited to Nature Index journals).
Technical terms
PM2.5: Fine particulate matter with aerodynamic diameter less than 2.5 µm, associated with adverse health effects.
Chemical transport model: A deterministic numerical model that simulates the emission, transport, chemical transformation and deposition of atmospheric pollutants.
Meteorological normalisation: A statistical or machine-learning process to remove the influence of weather variability from air quality time series, isolating emission-driven trends.
Random forest: A supervised machine-learning algorithm comprising an ensemble of decision trees, used for regression or classification of complex data sets.
References
- Attribution of Air Quality Benefits to Clean Winter Heating Policies in China: Combining Machine Learning with Causal Inference. Environmental Science and Technology (2023).
- An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models. npj Climate and Atmospheric Science (2023).
- Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns. Science Advances (2021).
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