Location-based modeling and management of critical contaminant risks through a Spatial Decision Support System Approach

加拿大的经济以农业、林业、石油生产和城市化等集约化活动为基础，这些活动生产或消耗大量化学品，导致存在关键和新出现的污染物，如致癌水消毒副产品、纳米级农药和药物残留。关键的是，由于污染物引入的速率大于评估的速率，并且由于方法学上的缺陷妨碍了风险的量化和控制，因此对这些持久性和危险性污染物的了解很弱。拟议的研究将使我们能够检查关键污染物及其转化产物的风险，从而在安全的生活环境和公众健康方面产生重大好处。开发的系统工具和实际应用结果对负责碳排放控制管理的监管机构和机构至关重要，包括加拿大环境部、化学品管理监管机构、加拿大渔业和海洋管理局以及工业界。该研究在管理碳污染方面优于传统的风险评估方法，通过(i)在环境流动中新颖地结合超细颗粒相互作用的相关模拟，以推进污染物命运和运输的区域尺度空间建模；（ii）在模型驱动或基于评估者的决策过程中，使用混合统计、模糊随机和智能网络技术有效量化系统不确定性。(3)通过地理参考分散建模、空间可视化报告和专家决策引擎提供空间决策支持。因此，在执行任何决定之前，可以根据所建议的方法进行有用的环境战略分析，以打破技术障碍。提出的前沿模型驱动的空间决策分析工具将用于解决新出现的CCs问题，以保障清洁环境和人类健康。研究成果将通过加拿大版本的新型系统评估方法来管理关键污染物，从而提高加拿大作为高科技、环境友好和社会负责任国家的声誉。

Canada's economy is based on intensive activities such as agriculture, forestry, petroleum production and urbanization that produce or consume vast quantities of chemicals resulting in the presence of critical and emerging contaminants such as carcinogenic water disinfection byproducts, nano-scaled pesticide and medication residues. Critically, these persistent and risky contaminants are weakly understood because the rate of contaminants introduction is greater than that of assessment, and because methodological shortcomings prevent quantification and control of the risks. Proposed research will yield a major benefit in terms of safe living environment and public health by enabling us to examine the risks from critical contaminants (CCs) and their transformation products. The developed system tools and practical application results are essential to regulators and agencies responsible for management of CCs, including Environment Canada, Chemicals Management Regulatory Agency, Fisheries and Oceans Canada, and industry. The proposed research has superiorities over traditional methods of risk assessment in managing CCs through (i) novel incorporation of related simulation of ultrafine particle interactions in environmental flows for advancing regional-scale spatial modeling of pollutants fate and transport, (ii) effective quantification of system uncertainties using hybrid statistical, fuzzy stochastic, and intelligent network techniques throughout model-driven or assessor-based decision making process, and (iii) spatial decision support through geo-referenced dispersal modeling, spatial visualization reporting, and expert decision engine. Thus, useful environmental strategic analysis based on the proposed methods can be made available for breaking technical barriers before any decision becomes implemented. The proposed front edge model-driven spatial decision analysis tools will be applied to address emerging CCs problems to safeguard clean environment and human health. The research results will enhance Canada's reputation of being a high-tech, environmentally friendly, and socially responsible country in the world through the Canadian version of novel systematic assessment approach to manage critical contaminants.