About the training
The SERVIR-HKH Initiative of the International Centre for Integrated Mountain Development (ICIMOD) is organising a two-day training on multi-satellite and model air quality data products on air quality (AQ) and their applications to understand the air pollution issues and challenges in the Hindu Kush Himalaya (HKH) region. The training includes a combination of theoretical and practical knowledge, incorporating sessions that cover discussion on advanced technologies and data analytics for AQ data products. Additionally, this training aims to provide a platform for networking and knowledge sharing/learning from different research groups across the HKH region.
Objective
This training aims to create a platform for participants to acquire insights into various data accessibilities and develop an understanding of utilising model and satellite observations for monitoring, predicting, and assessing air quality. This will further facilitate informed decision-making.
Expected outcomes
After the training, participants will gain a more precise grasp of satellite, model, and assimilated data products and their practical applications. They will utilise this knowledge to support decision-making in the region. Furthermore, they will have access to a vast AQ data platform for capacity building, training, and other purposes. Participants will actively engage in using the satellite-derived AQ data to understand air quality challenges in their region and will present their work to the group.
Expected participants
25 participants from Bangladesh, Bhutan, India, Nepal, and Pakistan representing universities, government organisations, and development agencies are expected to join the training.
Resource persons/facilitators
ICIMOD
Arun B Shrestha, Strategic Group Lead: Reducing Climate and Environmental Risks
Bertrand Bessagnet, Action Area Coordinator, Stimulating Action for Clean Air
Bhupesh Adhikary, Senior Air Quality Specialist, Stimulating Action for Clean Air
Birendra Bajracharya, Chief of Party, SERVIR-HKH
Rajesh Bahadur Thapa, Senior Remote Sensing and Geoinformation Specialist, SERVIR-HKH
Arshini Saikia, Air Quality Modelling Analyst, Stimulating Action for Clean Air
Suman Sanjel, Air Quality Application Development Associate, Stimulating Action for Clean Air
Poonam Tripathi, Geospatial Training Analyst
NASA-Applied Science Team (AST)
Dan Irwin, Head-Global Program Manager, SERVIR
Aaron Naeger, Principal Investigator, Research Scientist at NASA Marshall Space Flight Center
Jonathan Case, Co-Investigator, Research Meteorologist at ENSCO, Inc.
Kevin Fuell, Co-Investigator, University of Alabama Huntsville (UAH) Research Scientist / Transition/Training Specialist at NASA Short-term Prediction Research and Transition Center (SPoRT)
SERVIR – Science Coordination Office
Alqamah Sayeed, Air Quality Scientist, Universities Space Research Association (USRA)
Meryl Kruskopf, SERVIR Science Coordination Office
Background
Air pollution is a significant threat to the environment and public health in the HKH region, exacerbated by robust emission sources and widespread pollutant transport. The Kathmandu Valley in Nepal faces particularly high levels of air pollution due to its geographical features, which trap pollutants, leading to adverse health effects. In addition, other major cities such as Delhi, Dhaka, and Lahore in the HKH region also grapple with air pollution challenges driven by vehicular emissions, industrial activities, and agricultural burning. Traditional ground-based sensors are commonly used for air quality monitoring but often lack coverage, hindering the characterisation of pollutant gradients. Space-borne remote sensing from low Earth orbit (LEO) and geosynchronous equatorial orbit (GEO) satellites can provide high spatiotemporal information on trace gases and aerosols, improving AQ monitoring and forecasting capabilities and enabling better-informed decision-making. Integrating high-resolution satellite observations into model simulations enhances air quality forecasts, facilitating timely alerts for the public.
