Bioremediation of mine-affected water

地球化学、基因组和信息学科学和技术的迅速发展正在改变工程师设计、监测、控制和操作复杂的基于生物的受污染水修复过程的方式，这是世界大多数地区高度关注的问题。采矿是加拿大一个重要的经济行业的例子，在这个行业，其可持续性取决于满足越来越严格的水质准则。与传统化学方法相比，利用自然地化和生物过程的水处理技术作为从受地雷影响的水中去除化合物以保护水生生态系统的更具成本效益和更可靠的方法而得到支持。由于缺乏对影响其有效性的各种相互关联的因素的了解，这些基于自然的水处理方法的广泛接受和采用受到阻碍。*我们将重点关注那些由于对环境的迫在眉睫的影响而需要紧急关注的高优先因素。其中包括硒、硝酸盐、铵和硫酸盐等含氮化合物。目前去除这些污染物的方法需要几个步骤，耗能大，而且会产生大量的污泥。我们将开发的新工艺利用来自矿场的浓缩微生物群的代谢潜力，这些微生物群具有同时清除一种以上化合物的能力。这有可能极大地简化MIW治疗，降低成本并提高有效性。然而，由于微生物-微生物相互作用的复杂性以及环境变量对微生物种群组成和代谢动态的影响，维持可靠的生物反应器性能和成功的处理是具有挑战性的。在我们的工作中，我们建议更好地了解这些复杂性并构建动态模型，这些模型将用于开发新的生物增强和生物刺激策略，以使MIW处理更可靠、更具成本效益且对环境的影响更小。*为了实现这些目标，我们将使用高分辨率表征和监测使能技术。我们建议使用层析和矿物学方法将污染物的去除机理分解到化学物种水平。遗传生物标记物和整个DNA的测序将用于识别微生物并组装它们的基因组，以便可以随着时间的推移和不同条件下的微生物种群结构和功能进行跟踪。来自实验室和现场生物反应器的化学和遗传数据将使用新兴的机器学习方法进行特征选择和建模，这将为我们将测试的新过程控制理念提供信息，以提高MIW处理的有效性和可靠性。

Rapid new advances in geochemical, genomic and informatic science and technology are transforming the way that engineers design, monitor, control and operate complex biologically-based processes for remediation of contaminated water, a cause for great concern in most parts of the world. Mining is an example of one economically important Canadian industry where its sustainability depends on meeting increasingly more stringent water quality guidelines. Water treatment technologies that leverage natural geochemical and biological processes versus conventional chemical methods are gaining support as more cost-effective and reliable approaches to remove chemical compounds from mine-influenced water (MIW) so as to protect aquatic ecosystems. Widespread acceptance and adoption of these naturally based water treatment approaches are hindered by lack of knowledge on the myriad of interrelated factors that influence their effectiveness.******We will focus on high-priority constituents of concern that require urgent attention due to their imminent impact on the environment. These include selenium, nitrogen compounds such as nitrate and ammonium and sulphate. Current methods to remove these require several steps, are energy intensive and produce large volumes of sludge. The new processes that we will develop leverage the metabolic potential of enriched microbial consortia from mine sites that have the capacity to simultaneously remove more than one compound at a time. This has the potential to greatly simplify MIW treatment reducing costs and increasing effectiveness. However, due to the complexity of microbe-microbe interactions and the effect of environmental variables on the dynamics of microbial population composition and metabolism, it is challenging to maintain reliable bioreactor performance and successful treatment. In our work, we propose to better understand these complexities and construct dynamic models that will be used to develop novel bioaugmentation and biostimulation strategies to make MIW treatment more reliable, cost-effective and less impactful on the environment. ******To achieve these goals we will use high-resolution characterization and monitoring enabling technologies. We propose to resolve mechanisms of contaminant removal to the chemical species level using chromatography and mineralogical methods. Sequencing of genetic biomarkers and whole DNA will be used to identify microorganisms and assemble their genomes so that microbial population structure and function can be tracked over time and under different conditions. Chemistry and genetic data from laboratory and field bioreactors will be combined using emerging machine-learning approaches for feature selection and modeling that will inform new process control philosophies that we will test to improve the effectiveness and reliability of MIW treatment.