Campuses:

Mathematical Modeling of Urban Air Quality: A Case Study for Medellin, Colombia

Tuesday, June 5, 2018 - 1:25pm - 2:25pm
Lind 409
Olga Lucia Quintero Montoya (Universidad EAFIT)
Urban population growth generates increased pollutant emissions with negative consequences for the environment and public health. Further, there are several physical phenomena occurring in the low atmosphere of cities that affect the concentration of pollutants and the chemistry composition of atmosphere. These problems are more critical in complex terrains where ventilation is limited.

Mathematical models are the main scientific tool for understanding and predicting the potential response of the atmosphere to perturbations such as different meteorological conditions, altered emissions, and land-use/land-cover modifications. These models contain nonlinear and complex differential equations derived from the physics of the phenomenon, such as Navier-Stokes equations, and their numerical solution presents a series of challenges related to their sensitivity and uncertainty when solutions are required in a nonhomogeneous domain, with restrictions in boundary and initial conditions.

Well-known models that are relevant for urban air quality include the WRF (Weather Research and Forecasting) model for short-term meteorological forecast and LOTOS EUROS (developed by TNO, Netherlands) for chemical and transport dynamics. Our work seeks to identify significant sources of uncertainty in WRF, reduce solution uncertainty, analyze the sensitivity of the model and numerical solutions, and couple results with LOTOS EUROS for pseudo-real-time
prediction.

The geographical focus of this effort is Medellin and the Aburrá river valley, Colombia, an area with complex topography, accelerating population growth, and associated surface alterations and pollutant emissions. This work is being done in international collaboration with TNO, Netherlands. Future work will include integration with a human pollutant exposure model, reducing prediction uncertainty taking into account the computational properties of the models, and application of the developed methodology to European metropolitan areas.

Bio
Lucia Quintero received her Control Engineer degree from the National University of Colombia-Medellin and her Ph.D. in Control Engineering Systems from the Institute of Automation at Universidad Nacional de San Juan, Argentina in 2008. From 2008 to 2011 she was a consultant for oil and gas companies and was also involved in developments for the telecommunications market in Latin America. She was also on the faculty at Universidad San Francisco de Quito, Ecuador. In 2011 Dr. Quintero joined the Mathematical Sciences Department in Universidad EAFIT, Medellin, Colombia, where she is an Associate Professor, serves as the Academic Director of the Mathematical Engineering Ph.D. Program, and leads the Mathematical Modeling Research Group. She is also Senior Researcher with the Colombian governmental research organization, Colciencias, and a member of the Executive Committee of the IFAC Industry Committee. Her research interests are in control systems, Bayesian filtering, multidimensional signal analysis, artificial intelligence, and machine learning.