The Atmospheric Composition Modelling group focuses on enhancing the current understanding of atmospheric composition and its evolution. We synthesise weather prediction, dispersion, chemistry transport, and reduced complexity models, which can be employed to comprehend the intricate atmospheric processes influencing air quality over a region. The group utilises a variety of ground- and space-based atmospheric measurements to validate model outcomes. By harmonising diverse models and observations, we aim to provide insights for formulating effective air pollution control strategies within a specified geographical area.
Multiple PM Low-Cost Sensors, Multiple Seasons’ Data, and Multiple Calibration Models
In this study, we combined state-of-the-art data modelling techniques (machine learning [ML] methods) and data from state-of-the-art low-cost particulate matter (PM) sensors (LCSs) to improve the accuracy of LCS-measured PM2.5 (PM with aerodynamic diameter less than 2.5 microns) mass concentrations. We collocated nine LCSs and a reference PM2.5 instrument for 9 months, covering all local seasons, in Bengaluru, India. Using the collocation data, we evaluated the performance of the LCSs and trained around 170 ML models to reduce the observed bias in the LCS-measured PM2.5.