Reference number : GC-2017-Q1-01677, amendment number : , agreement type : G, recipient legal name : Aliabadi, Amir Abbas (University of Guelph), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Cities occupy approximately 2% of the earth's surface and accommodate more than 50% of the world's population. Understanding and mitigating the growing environmental impact of cities require design and prediction tools, such as the Urban Atmospheric Models (UAMs) . UAMs predict the urban climate (air temperature, wind, precipitation, and humidity) and pollution dispersion (distribution of air pollutants in the urban environment). UAMs help understand, develop, and regulate more sustainable cities toward the control of the urban air temperature, moisture, and winds and the improvement of air quality within urban spaces. 
 
The proposed interdisciplinary research program attempts to develop novel, fast, and accurate operational UAMs based on reduced-order and Artificial Intelligence (AI) approaches to account for the realistic urban environment. The realistic urban environment is currently not well represented in operational models or is otherwise too computationally expensive to model. The UAMs will account for 1) complexity of the urban morphology such as variety of geometries and length scales in buildings, landscapes, roads, and other urban components, 2) thermal effects such as differential heating on urban surfaces or the influence of stable/unstable boundary layers, and 3) pollution dispersion from realistic anthropogenic emissions by vehicles and buildings. 
 
The scientific approach includes development of experimental and numerical databases to formulate and validate the UAMs. Experimental databases will be developed from a novel blimp (balloon) platform (first one of its kind in Canada), wind tunnel, water channel, field, and remote sensing measurements. Numerical databases will be developed from high spatiotemporal resolution models for parametric study of realistic urban environments using computational fluid dynamics, energy balance, dispersion, and atmospheric models. These databases will be used to develop the new UAMs based on a 1D vertical diffusion (reduced-order) and Artificial Neural Network (AI) models. 
 
This research program contributes to knowledge of complex physics influencing the urban atmosphere and the ability to develop models to predict it. It results in UAMs that can be commercialized as stand-alone design and prediction tools for use by various architectural firms, engineering consulting firms, and urban planners. It will train HQP with data science by collection, generation, processing, and analysis of big data, and by building and validating simple or complex models for the urban atmosphere. It provides environmental benefits by improving operational forecast and air quality models, both in Canada or internationally, with computational speed and accuracy. It provides social benefits toward more sustainable urban development for the future generations by reducing their energy consumption and improving their environmental conditions., program purpose french : Cities occupy approximately 2% of the earth's surface and accommodate more than 50% of the world's population. Understanding and mitigating the growing environmental impact of cities require design and prediction tools, such as the Urban Atmospheric Models (UAMs) . UAMs predict the urban climate (air temperature, wind, precipitation, and humidity) and pollution dispersion (distribution of air pollutants in the urban environment). UAMs help understand, develop, and regulate more sustainable cities toward the control of the urban air temperature, moisture, and winds and the improvement of air quality within urban spaces. 
 
The proposed interdisciplinary research program attempts to develop novel, fast, and accurate operational UAMs based on reduced-order and Artificial Intelligence (AI) approaches to account for the realistic urban environment. The realistic urban environment is currently not well represented in operational models or is otherwise too computationally expensive to model. The UAMs will account for 1) complexity of the urban morphology such as variety of geometries and length scales in buildings, landscapes, roads, and other urban components, 2) thermal effects such as differential heating on urban surfaces or the influence of stable/unstable boundary layers, and 3) pollution dispersion from realistic anthropogenic emissions by vehicles and buildings. 
 
The scientific approach includes development of experimental and numerical databases to formulate and validate the UAMs. Experimental databases will be developed from a novel blimp (balloon) platform (first one of its kind in Canada), wind tunnel, water channel, field, and remote sensing measurements. Numerical databases will be developed from high spatiotemporal resolution models for parametric study of realistic urban environments using computational fluid dynamics, energy balance, dispersion, and atmospheric models. These databases will be used to develop the new UAMs based on a 1D vertical diffusion (reduced-order) and Artificial Neural Network (AI) models. 
 
This research program contributes to knowledge of complex physics influencing the urban atmosphere and the ability to develop models to predict it. It results in UAMs that can be commercialized as stand-alone design and prediction tools for use by various architectural firms, engineering consulting firms, and urban planners. It will train HQP with data science by collection, generation, processing, and analysis of big data, and by building and validating simple or complex models for the urban atmosphere. It provides environmental benefits by improving operational forecast and air quality models, both in Canada or internationally, with computational speed and accuracy. It provides social benefits toward more sustainable urban development for the future generations by reducing their energy consumption and improving their environmental conditions., agreement title english : Fast and Accurate Urban Atmospheric Models, agreement title french : Fast and Accurate Urban Atmospheric Models, agreement number : RGPIN, agreement value : 105000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada