Collaborative Research: Optimization of metal attenuation in biologically-active remediation systems

合作研究:生物活性修复系统中金属衰减的优化

基本信息

  • 批准号:
    1336247
  • 负责人:
  • 金额:
    $ 17.78万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2013
  • 资助国家:
    美国
  • 起止时间:
    2013-10-01 至 2017-07-31
  • 项目状态:
    已结题

项目摘要

CBET 1336496/1336247Colleen Hansel/Cara SantelliWoods Hole Ocean Inst. /Smithsonian InstututionCoal-mining activities have resulted in worldwide environmental pollution due to the production of acidic, metal-rich waters that damage entire ecosystems and contaminate water supplies compromising public health. Coal mine drainage (CMD) throughout the Appalachian region contains particularly elevated concentrations of dissolved manganese (Mn), that at such high levels may lead to neurological disorders. One of the most promising and economically feasible approaches to treat metal-laden CMD containing elevated Mn are biologically active limestone treatment beds. Limestone is used to raise the pH of the contaminated waters to promote growth of microorganisms that can transform (via oxidation reactions) soluble Mn to solid Mn oxide minerals that are subsequently retained within the treatment beds. Formation of these minerals effectively removes Mn from the water and also produces a substrate that serves as a water treatment filter, effectively removing additional contaminants, such as cobalt, zinc, and nickel, from CMD. At this time, the successful removal of Mn and other metal contaminants from mine waters is highly variable and as low as 20% removal of Mn in some systems in Pennsylvania. Success of these treatment systems is currently limited by an insufficient knowledge of the individual and collective activities of microbial populations and the optimal conditions for biologically mediated Mn oxide formation. This research will address these knowledge gaps by simulating limestone treatment systems under controlled laboratory conditions to better establish the most effective biogeochemical conditions for stimulating both microbial growth and subsequent metal attenuation in CMD treatment systems. Specifically, the project will first identify the most effective microbial species and nutrient conditions (e.g., organic carbon and nitrogen composition) stimulating optimal Mn oxide formation by pure and mixed laboratory cultures of bacteria, fungi, and algae previously isolated from CMD treatment systems. These vital nutrient and microbiological conditions will then be employed and tested in laboratory-simulated treatment systems to further optimize Mn removal and precipitation efficiencies by complex microbial assemblages and the activity of key microbial species. Throughout the experiments, the microbial population structure and community interactions that impact Mn removal and Mn oxide formation will be identified. The composition and stability of the biologically precipitated Mn oxide minerals and their efficacy in removing metal contaminants will also be assessed. The development of successful and cost-effective approaches for cleaning contaminated environments and water supplies is an immediate priority. This project will answer key scientific questions limiting the success of biologically stimulated treatment processes and optimize low-cost, green technologies currently employed throughout the world in an attempt to clean environments devastated by mine drainage. Essential knowledge gained by this project will be conveyed to scientists, engineers, educators, and government regulators for direct application to limestone treatment systems currently being used at hundreds of sites in Appalachia to treat coal mine drainage. An equally important goal of this project is to educate future generations and the general public on the causes, effects, and solutions to mine drainage. The PIs will integrate this research into two outreach activities, including (1) high school science teacher internships to aid in the development of new curricula that will engage underrepresented students in STEM fields and introduce them to green technologies used to treat environmental pollution and (2) informal presentations and inquiry-based learning exercises at the National Museum of Natural History, Smithsonian Institution, to communicate science activities and products to the general public and provide opportunities for visitors to ask questions and personally interact with the scientists.
CBET 1336496/1336247Colleen Hansel/Cara Santelliwoods Hole Ocean Inst。 /史密森尼企业开采活动因产生酸性,金属丰富的水的生产而损害了整个生态系统并污染供水损害公共卫生,因此导致了全球环境污染。整个阿巴拉契亚地区的煤矿排水(CMD)包含溶解的锰(MN)的浓度特别高,可能会导致神经系统疾病。含有升高MN的金属CMD的最有前途和经济上最可行的方法之一是生物活跃的石灰石处理床。石灰石用于提高受污染水域的pH值,以促进微生物的生长,这些微生物可以(通过氧化反应)转化为可溶性MN,以随后保留在治疗床中的固体Mn氧化物矿物质。 这些矿物的形成有效地从水中清除了MN,还产生了作为水处理过滤器的底物,从CMD中有效地去除了其他污染物,例如钴,锌和镍。 目前,在宾夕法尼亚州某些系统中,成功从矿水中成功去除MN和其他金属污染物是高度可变的。这些治疗系统的成功目前受到对微生物种群的个体和集体活动的了解以及生物学介导的Mn氧化物形成的最佳条件的限制。 这项研究将通过在受控实验室条件下模拟石灰石处理系统来解决这些知识差距,以更好地建立最有效的生物地球化学条件,以刺激CMD治疗系统中的微生物生长和随后的金属衰减。具体而言,该项目将首先确定最有效的微生物物种和营养条件(例如有机碳和氮组成),从而刺激细菌,真菌和藻类的纯和混合实验室培养物刺激最佳的Mn氧化物形成,并以前从CMD治疗系统中分离出来。然后将在实验室模拟的治疗系统中使用这些重要的营养和微生物条件,并通过复杂的微生物组合以及关键微生物物种的活性来进一步优化MN的去除和降水效率。在整个实验中,将确定影响MN去除和MN氧化物形成的微生物种群结构和社区相互作用。还将评估生物沉淀的Mn氧化物矿物质的组成和稳定性及其在去除金属污染物中的功效。 开发成功且具有成本效益的方法来清洁受污染的环境和供水是当务之急。该项目将回答关键的科学问题,限制了生物学刺激的治疗过程的成功,并优化了目前在全球范围内采用的低成本绿色技术,以试图清洁矿山排水灾难的环境。 该项目获得的基本知识将被传达给科学家,工程师,教育者和政府监管机构,以直接应用于目前在阿巴拉契亚州数百个地点使用的石灰石处理系统,以治疗煤矿排水。 该项目同样重要的目标是教育后代和公众就矿井排水的原因,效果和解决方案进行教育。 The PIs will integrate this research into two outreach activities, including (1) high school science teacher internships to aid in the development of new curricula that will engage underrepresented students in STEM fields and introduce them to green technologies used to treat environmental pollution and (2) informal presentations and inquiry-based learning exercises at the National Museum of Natural History, Smithsonian Institution, to communicate science activities and products to the general public and provide opportunities for visitors to ask questions and personally与科学家互动。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

暂无数据

数据更新时间:2024-06-01

Cara Santelli的其他基金

NSF Convergence Accelerator Track L: Innovative chemical microsensor development for in situ, real-time monitoring of priority water pollutants to protect water quality
NSF Convergence Accelerator Track L:创新化学微传感器开发,用于对重点水污染物进行原位实时监测,以保护水质
  • 批准号:
    2344373
    2344373
  • 财政年份:
    2024
  • 资助金额:
    $ 17.78万
    $ 17.78万
  • 项目类别:
    Standard Grant
    Standard Grant
CAREER: Genome-enabled investigations into the mechanisms and ecological controls on selenium transformations by fungi
职业:通过基因组研究真菌硒转化的机制和生态控制
  • 批准号:
    1749727
    1749727
  • 财政年份:
    2018
  • 资助金额:
    $ 17.78万
    $ 17.78万
  • 项目类别:
    Continuing Grant
    Continuing Grant
Collaborative Research: Optimization of metal attenuation in biologically-active remediation systems
合作研究:生物活性修复系统中金属衰减的优化
  • 批准号:
    1743046
    1743046
  • 财政年份:
    2017
  • 资助金额:
    $ 17.78万
    $ 17.78万
  • 项目类别:
    Standard Grant
    Standard Grant

相似国自然基金

车联网中基于多智能体系统的协同优化机制研究
  • 批准号:
    62302062
  • 批准年份:
    2023
  • 资助金额:
    30.00 万元
  • 项目类别:
    青年科学基金项目
基于多算法组合协作的城市空中交通建模分析与优化管控研究
  • 批准号:
    72301278
  • 批准年份:
    2023
  • 资助金额:
    30.00 万元
  • 项目类别:
    青年科学基金项目
面向边缘智能的无线网络协作计算与资源优化研究
  • 批准号:
    62301307
  • 批准年份:
    2023
  • 资助金额:
    30 万元
  • 项目类别:
    青年科学基金项目
不确定环境下多式联运系统优化与协作机制研究
  • 批准号:
    72371169
  • 批准年份:
    2023
  • 资助金额:
    41 万元
  • 项目类别:
    面上项目
多冗余度机器人的跨层协作神经动力学优化策略研究
  • 批准号:
    62373157
  • 批准年份:
    2023
  • 资助金额:
    50 万元
  • 项目类别:
    面上项目

相似海外基金

Collaborative Research: An Integrated Framework for Learning-Enabled and Communication-Aware Hierarchical Distributed Optimization
协作研究:支持学习和通信感知的分层分布式优化的集成框架
  • 批准号:
    2331710
    2331710
  • 财政年份:
    2024
  • 资助金额:
    $ 17.78万
    $ 17.78万
  • 项目类别:
    Standard Grant
    Standard Grant
Collaborative Research: An Integrated Framework for Learning-Enabled and Communication-Aware Hierarchical Distributed Optimization
协作研究:支持学习和通信感知的分层分布式优化的集成框架
  • 批准号:
    2331711
    2331711
  • 财政年份:
    2024
  • 资助金额:
    $ 17.78万
    $ 17.78万
  • 项目类别:
    Standard Grant
    Standard Grant
Collaborative Research: AF: Medium: Algorithms Meet Machine Learning: Mitigating Uncertainty in Optimization
协作研究:AF:媒介:算法遇见机器学习:减轻优化中的不确定性
  • 批准号:
    2422926
    2422926
  • 财政年份:
    2024
  • 资助金额:
    $ 17.78万
    $ 17.78万
  • 项目类别:
    Continuing Grant
    Continuing Grant
Collaborative Research: SaTC: CORE: Medium: Differentially Private SQL with flexible privacy modeling, machine-checked system design, and accuracy optimization
协作研究:SaTC:核心:中:具有灵活隐私建模、机器检查系统设计和准确性优化的差异化私有 SQL
  • 批准号:
    2317232
    2317232
  • 财政年份:
    2024
  • 资助金额:
    $ 17.78万
    $ 17.78万
  • 项目类别:
    Continuing Grant
    Continuing Grant
Collaborative Research: SaTC: CORE: Medium: Differentially Private SQL with flexible privacy modeling, machine-checked system design, and accuracy optimization
协作研究:SaTC:核心:中:具有灵活隐私建模、机器检查系统设计和准确性优化的差异化私有 SQL
  • 批准号:
    2317233
    2317233
  • 财政年份:
    2024
  • 资助金额:
    $ 17.78万
    $ 17.78万
  • 项目类别:
    Continuing Grant
    Continuing Grant