SBIR Phase I: Ecosystem Design Tool

SBIR 第一阶段：生态系统设计工具

• 批准号: 1938665
• 负责人: Daniel Fleischer
• 金额: $ 22.49万
• 依托单位: Hyphae Design Lab
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2021-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1938665&HistoricalAwards=false
• 关键词: SBIR; Phase; Ecosystem; Design; Tool

The broader impact of this SBIR Phase I project is to enable tools to inform methods to reduce air pollution. Air pollution increases the risk of cancer, stroke, heart disease, respiratory infections; further, it may play a role in Alzheimer's disease and diabetes, resulting in trillions of dollars of health care costs globally each year. Half of these costs may be attributed to roadway traffic sourced pollution. Traffic sourced pollution is possibly intercepted by trees, although improperly configured roadside trees do not produce these benefits and creating the opportunity for improved health via targeted engineering of roadside tree placements. This project will develop analysis and design tools to make sure that such tree plantings optimize benefits for ecosystem designers, health insurance companies, governments, and local businesses.This SBIR project will develop a system to optimize high-resolution site-specific tree placement for pollution reduction. Trees are known to reduce air pollution by acting like filters as their high surface area attracts pollutants. Tightly packed trees of a type with high surface area placed on the leeward side of a busy roadway can intercept 30% of traffic sourced pollutants. This project will perform ground-based, high-resolution vegetation analysis on neighborhoods under study. These data will be used to train an algorithm to infer high-resolution vegetation properties from widely available, low-cost data streams. These inferred vegetation metrics will be integrated with a geospatial ecosystem design automation pipeline to create a software tool. To accurately predict the capture, high-resolution vegetation surface area measurements, tree crown spacing information, wind speed, wind direction and roadway traffic volumes will be aggregated in a comprehensive model.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

SBIR第一阶段项目的更广泛影响是使工具能够提供减少空气污染的方法。空气污染增加了癌症、中风、心脏病和呼吸道感染的风险；此外，它还可能在阿尔茨海默氏症和糖尿病中发挥作用，导致全球每年花费数万亿美元的医疗保健费用。这些成本的一半可能归因于道路交通来源的污染。交通来源的污染可能会被树木拦截，尽管配置不当的路边树不会产生这些好处，并通过有针对性地设计路边树的放置来创造改善健康的机会。该项目将开发分析和设计工具，以确保这种植树优化生态系统设计师、健康保险公司、政府和当地企业的利益。该SBIR项目将开发一个系统，以优化高分辨率特定地点的树木放置，以减少污染。众所周知，树木通过充当过滤器来减少空气污染，因为它们的高表面积吸引了污染物。在繁忙道路的背风侧放置表面积较大的密植树木，可以拦截30%的交通来源污染物。该项目将对正在研究的社区进行基于地面的高分辨率植被分析。这些数据将被用来训练一种算法，从广泛可用的低成本数据流中推断出高分辨率的植被特性。这些推断的植被指标将与地理空间生态系统设计自动化管道集成，以创建一个软件工具。为了准确地预测捕获，高分辨率的植被表面积测量、树冠间距信息、风速、风向和道路交通量将被综合在一个模型中。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。