Title: Sensor Network Optimization, Source Mapping with Inverse Modelling and Real-Time Air Quality Forecasting Using Advanced CFD based Tools
Abstract
Effective air quality management largely depends on representative monitoring, accurate source detection, and robust forecasting. An integrated
approach using Fluidyn tools to optimize sensor placement, identify/quantify emission sources, and enable real-time air quality forecasting is
presented here. Sensor Location Optimization: CFD models are capable of accurately capturing the influence of local micro scale terrain conditions, weather, and pollution patterns in determining optimal sensor locations, ensuring no sources go un-attended. Source Detection: Using inverse modelling technique to track, identify and quantify the emission sources from sensor feedback – for reliable and effective pollution management. Real-Time Forecasting: A predictive framework integrating live data and advanced modelling methods to forecasts pollutant levels and potential
hotspots, in days advance, under dynamic conditions. Case studies validated such framework’s effectiveness in industrial and urban settings, demonstrating improved monitoring, source attribution, and forecasting. This highlights the role of advanced computational techniques in modelling air quality and effectively mitigating pollutant level.
About the Speaker
Serves as Senior Manager in Fluidyn, a global leader in air quality and dispersion modelling using advanced numerical tools. Post graduation from NITC with specialization in energy and environmental management. 10+ years of academic, research and corporate experience. Hold 4 patents and several publications. Currently focusing on integrating simulation, data analytics, and machine learning tools to develop sustainable, practical solutions for air quality management and broader environmental challenges.
