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

包括气候变化和新出现的污染在内的环境问题正变得越来越复杂。特别是，这些环境问题正在对包括公共健康和生态系统在内的区域环境媒介产生实时不利影响。以前，环境多媒体的命运，运输和暴露评估模型作出了重大贡献，以控制风险和现场清理。例如，申请人针对主要环境介质（包括空气、土壤、地下水和地表水）提出了以下环境模型，并在过去十年中报告了现场应用：石油污染土壤和地下水的多相多组分化学归宿和迁移模型，模拟加拿大近海水域污染物扩散的耦合近场和远场数值模型，以及一个GIS支持的空间多源空气质量模型，并应用于加州。* 然而，有以前的环境多媒体建模研究的挑战，包括不一致的模型精度，多方面的不确定性与不同的媒体，和数据问题。这些挑战与进一步推进模型和解决数据差距的需要有关。例如，从地面站和遥感器收集的数据量正在爆炸性地增长，数据处理花费了上述模型开发和应用的一半或更多的时间和精力。 因此，本研究计划提出以大数据技术为支撑的新的环境建模和评估概念和系统，以战略性地扩展最近的研究进展。该研究计划将（1）通过有效的数值求解算法和非结构化计算网格推进先前开发的单介质和多介质化学归宿和传输模型，（2）进一步开发高分辨率卫星数据处理方法，以有效支持环境建模和评估，并与现场智能传感器一起，（3）开发大数据支持的环境数据分析，包括基于网络的数据处理和云计算算法，用于新的基于分布式模型的决策工具，（4）开发用于复杂环境的系统不确定性量化模块，（5）开发用户友好的基于模型的决策支持系统。拟议的研究计划将有助于建立区域级大数据支持的新一代环境风险测绘和决策支持工具，以管理动态建筑和自然环境中的新污染物和主要环境问题。

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.***