NSF East Asia and Pacific Summer Institute (EAPSI) for FY 2013 in Singapore

2013 财年 NSF 东亚及太平洋地区暑期学院 (EAPSI) 在新加坡举行

• 批准号: 1310798
• 负责人: Jennifer Apell
• 金额: $ 0.01万
• 依托单位: Apell Jennifer N
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2014-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1310798&HistoricalAwards=false
• 关键词: NSF; East; Asia; Pacific; Summer

This action funds Jennifer Apell of Massachusetts Institute of Technology to conduct a research project in Engineering during the summer of 2013 at National University of Singapore in Singapore. The project title is "Comparison of Food Web Models Constructed with Traditional and Passive Sampling Data." The host scientist is Dr. Barry Kelly.Food web modeling, which allows for the prediction of chemical concentrations in biota based on concentrations measured in sediments, water, or air, is often used for remediation decisions and to establish cleanup goals for contaminated sites. Yet, the accuracy of the model is dependent on how well the contamination is characterized. Traditional sediment sampling techniques often incorrectly estimate the concentration of contaminants that are readily accumulated by biota, which is referred to as the bioavailable fraction. However, passive samplers, constructed of a polymeric material, can act as surrogate for biota and have been shown to be a more accurate estimate of the bioavailable concentration. This research uses contaminant concentrations of polychlorinated biphenyls, measured using both traditional and passive sampling techniques, in a Gobas food web model to compare the outcomes as well as evaluate the impact these sampling techniques have on the model.Broader impacts of an EAPSI fellowship include providing the Fellow a first-hand research experience outside the U.S.; an introduction to the science, science policy, and scientific infrastructure of the respective location; and an orientation to the society, culture and language. These activities meet the NSF goal to educate for international collaborations early in the career of its scientists, engineers, and educators, thus ensuring a globally aware U.S. scientific workforce. Furthermore, results from this research could lead to innovation in the way contaminated sediment sites are evaluated and remediated. Findings will be presented to parties involved in the remediation of Superfund sites as well as graduate and undergraduate students at MIT.

该行动资助麻省理工学院的 Jennifer Apell 于 2013 年夏天在新加坡国立大学开展工程研究项目。 该项目的标题是“用传统和被动采样数据构建的食物网模型的比较”。主持科学家是巴里·凯利博士。食物网模型可以根据沉积物、水或空气中测量的浓度来预测生物群中的化学浓度，通常用于修复决策和建立污染场地的清理目标。然而，模型的准确性取决于污染特征的程度。传统的沉积物采样技术经常错误地估计生物群容易积累的污染物的浓度，这被称为生物可利用分数。然而，由聚合材料制成的被动采样器可以作为生物群的替代品，并且已被证明可以更准确地估计生物可利用浓度。这项研究在 Gobas 食物网模型中使用传统和被动采样技术测量的多氯联苯污染物浓度来比较结果并评估这些采样技术对模型的影响。 EAPSI 奖学金的更广泛影响包括为研究员提供美国以外的第一手研究经验；各自地点的科学、科学政策和科学基础设施的介绍；以及对社会、文化和语言的定位。 这些活动符合 NSF 的目标，即在科学家、工程师和教育工作者职业生涯的早期进行国际合作教育，从而确保美国科学劳动力具有全球意识。 此外，这项研究的结果可能会带来污染沉积物场地评估和修复方式的创新。研究结果将提交给参与超级基金场地修复的各方以及麻省理工学院的研究生和本科生。