Big Data Supported Strategic Advancement in Model-Based Environmental Assessment and Management

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Environmental problems including climate changes and emerging contamination are getting exponentially more complex. Particularly, these environmental issues are having real-time adverse effects on the regional environmental media including public health and ecosystem. Previously, environmental multimedia fate, transport, and exposure assessment models have contributed significantly to control the risks and to site cleanups. For example, the applicant has put forward the following environmental models for major environmental media including air, soil, groundwater, and surface water 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. However, there have been challenges of the previous environmental multimedia modeling studies including inconsistent model accuracy, multifaceted uncertainties associated with different media, and data issues. These challenges are related to the need of further model advancement and resolving data gaps. For instance, the volume of data collected from both ground station and remote sensors is explosively growing, data processing costs half or more of the time and efforts for the above-mentioned model developments and applications. Therefore, this research program proposes to put forward new environmental modeling and assessment concept and system supported by the big data technology to strategically extend the recent research developments. This research program will (1) advance the previously developed single- and multi- media chemical fate and transport models with efficient numerical solving algorithms and unstructured computational mesh, (2) further develop high-resolution satellite data processing method to effectively support environmental modeling and assessment together with field smart sensors, (3) develop big data supported environmental data analytics involving web-based data processing and cloud computing algorithm for new distributed model based decision tools, (4) develop systematic uncertainty quantification module for complex environment, and (5) develop user-friendly model-based decision support system. The proposed research program will help building regional level big data supported new-generation environmental risk mapping and decision support tools to manage emerging contaminants and major environmental issues within the dynamic built and natural environment.