Reference number : GC-2017-Q1-03572, amendment number : , agreement type : G, recipient legal name : Chen, Zhi (Concordia University)|Chen, Zhi (Université Concordia), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Today&rsquo;s environmental problems including climate changes are getting exponentially more complex. Accordingly, huge volumes of information and data are involved in both daily and strategic environmental management. Based on data, environmental modeling and assessment tools have been playing an essential role in understanding those complex environmental problems and in helping vital decision-making processes. However, the critical gap between the data and the state of the art models has significantly limited the applicability of modeling tools due to data availability and data management issues. For example, the applicant has put forward the following environmental models with reported field applications in the past decade: a multiphase multicomponent chemical fate and transport model for the petroleum-contaminated soil and groundwater, a coupled near- and far- field numerical model for modeling contaminant's dispersion in Canadian offshore water, and a GIS-supported spatial multi-source air quality model with application to California. Data processing costs half or more of the time and efforts for the above-mentioned model developments and applications. 
 With related research on environmental informatics and data management by the applicant, big data concept is understood to be evolving and implemented for enhanced insight and decision making through cost-effective and innovative forms of information processing, intended for large volume data, high variety of heterogeneous data, fast data processing, with true veracity of the captured and analyzed data. The veracity, roughly termed data value or data usability, is especially important in practice to support environmental modeling and assessment. 
 Therefore, this research program proposes to develop new environmental modeling and assessment concept and system supported by the big data technology based on the recent research activities. This proposed research will extend the previously developed single- and multi- media chemical fate and transport models with efficient numerical solving algorithms and unstructured computational mesh, optimize high-resolution satellite data processing to effectively support environmental modeling and assessment together with field smart sensors, develop Big Data supported environmental data analytics involving web-based data processing and cloud computing to advance model based decision tools, develop systematic uncertainty quantification module for complex environment, and develop user-friendly model-based decision support system. The proposed research program will help building national level big data supported new-generation environmental risk mapping and decision support tools to manage emerging chemical contaminants and major environmental issues within the dynamic built and natural environment., program purpose french : Today&rsquo;s environmental problems including climate changes are getting exponentially more complex. Accordingly, huge volumes of information and data are involved in both daily and strategic environmental management. Based on data, environmental modeling and assessment tools have been playing an essential role in understanding those complex environmental problems and in helping vital decision-making processes. However, the critical gap between the data and the state of the art models has significantly limited the applicability of modeling tools due to data availability and data management issues. For example, the applicant has put forward the following environmental models with reported field applications in the past decade: a multiphase multicomponent chemical fate and transport model for the petroleum-contaminated soil and groundwater, a coupled near- and far- field numerical model for modeling contaminant's dispersion in Canadian offshore water, and a GIS-supported spatial multi-source air quality model with application to California. Data processing costs half or more of the time and efforts for the above-mentioned model developments and applications. 
 With related research on environmental informatics and data management by the applicant, big data concept is understood to be evolving and implemented for enhanced insight and decision making through cost-effective and innovative forms of information processing, intended for large volume data, high variety of heterogeneous data, fast data processing, with true veracity of the captured and analyzed data. The veracity, roughly termed data value or data usability, is especially important in practice to support environmental modeling and assessment. 
 Therefore, this research program proposes to develop new environmental modeling and assessment concept and system supported by the big data technology based on the recent research activities. This proposed research will extend the previously developed single- and multi- media chemical fate and transport models with efficient numerical solving algorithms and unstructured computational mesh, optimize high-resolution satellite data processing to effectively support environmental modeling and assessment together with field smart sensors, develop Big Data supported environmental data analytics involving web-based data processing and cloud computing to advance model based decision tools, develop systematic uncertainty quantification module for complex environment, and develop user-friendly model-based decision support system. The proposed research program will help building national level big data supported new-generation environmental risk mapping and decision support tools to manage emerging chemical contaminants and major environmental issues within the dynamic built and natural environment., agreement title english : Big Data and Model Supported Environmental Decision Making under Uncertainty, agreement title french : Big Data and Model Supported Environmental Decision Making under Uncertainty, agreement number : RGPIN, agreement value : 21000.0, 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 2018-2019), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2018-2019), 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