Data gaps and resources to support short-term air quality forecasting and long-term pollution management

Data is the crucial element of all forms modeling from emissions modeling based on activity data to health impacts modeling based on observed or estimated concentration profiles and using this information to support short-term pollution alert systems via forecasting. In a data sparse environment, several assumptions are made to construct a reliable modeling framework to support public and policy dialogue. These assumptions include interpolation of observations from one zone to another and extrapolation of operational conditions from one country to another. In this paper, we will present data gaps identified while conducting emissions and pollution modeling in Indian cities, how these gaps were addressed using emerging technology and big data feeds, and how the same methods can be expanded to other regions. Examples from Air Pollution knowledge Assessments (APnA) program implemented for 60 Indian cities and expansions to Balkan countries and Africa region will be presented. References: Guttikunda, S. K., Nishadh, K. A., and Jawahar, P. (2019) Air pollution knowledge assessments (APnA) for Indian cities Urban Climate @ https://doi.org/10.1016/j.uclim.2018.11.005 Ganguly, G., K.L. Selvaraj, and S.K. Guttikunda (2020) National Clean Air Programme (NCAP) for Indian cities: Review and outlook of clean air action plans Atmospheric Environment @ https://doi.org/10.1016/j.aeaoa.2020.100096