SCC-IRG Track 2: Toxic-Free Footprints to Improve Community Health against Respiratory Hazards

SCC-IRG 第 2 轨道：无毒足迹改善社区健康，预防呼吸系统危害

• 批准号: 2125326
• 负责人: Qi Wang
• 金额: $ 150万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125326&HistoricalAwards=false
• 关键词: SCC; IRG; Track; Toxic; Free

Fifty years after the passing of the Clean Air Act in the U.S., nearly half of the nation’s population is estimated to live with and breathe polluted air in 2020. The recent acceleration in the number and intensity of wildfires driven by climate change and the devastating COVID-19 pandemic have made air pollution effects on public health an even more heightened concern. This Smart and Connected Communities project builds and enhances technological, economic, political, and social infrastructures in vulnerable communities to better sense and monitor air quality, prevent exposure to respiratory hazards, and raise awareness of and battle misinformation on air quality. The research aims to build smart and connected toxic-free communities, which resonates with NSF’s mission “to advance the national health, prosperity, and welfare.” The research team collaborates with local grass-root organizations, communities, research centers, and planning agencies in two communities, Roxbury, Boston, MA and St. John the Baptist Parish, LA. The new technological approaches and air quality data generated by the project along with the deep community engagement will potentially influence and enhance policies and benefit underserved communities with more equitable access to a healthy environment.In this research project, the research team (1) develops data-driven approaches to seamlessly link human mobility (i.e., a person’s footprints within and beyond the residential place) and respiratory hazards measured by air quality monitors; (2) designs real-time AI technologies to connect information and communities that support both network-based interventions; and (3) deploys the methodology in two representative local communities characterized with minority and diversity both burdened by air pollution. The research focuses on volatile organic compounds (VOCs) and particulate matter (e.g., PM2.5) for their known health impacts of causing lung cancers and cardiovascular and respiratory diseases. The key deliverables of work include: (1) a data-driven and real-time AI-based framework that links multi-model data streams (e.g., geolocations, air quality, social networks, etc.) to quantify and predict air pollution exposures associated with human footprints; (2) social network-based interventions strategies developed interactively with and tested by two communities burdened by air pollution (Roxbury, Boston, MA and St. John Parish, New Orleans, LA); and (3) real-time AI-powered mobile application (App), Toxic-Free Life, for mainstream smartphones that allows real-time estimations of air exposure and supports network-based interventions.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.

美国《清洁空气法》通过50年后，据估计，到2020年，美国近一半的人口将生活在受污染的空气中。最近，气候变化和2019冠状病毒病（COVID-19）毁灭性大流行导致的野火数量和强度增加，使空气污染对公共卫生的影响更加令人担忧。这个智能和互联社区项目在脆弱社区建设和加强技术、经济、政治和社会基础设施，以更好地感知和监测空气质量，防止接触呼吸系统危害，提高对空气质量的认识，并打击有关空气质量的错误信息。这项研究旨在建立智能和互联的无毒社区，这与美国国家科学基金会“促进国家健康、繁荣和福利”的使命相呼应。研究团队与两个社区的当地基层组织、社区、研究中心和规划机构合作，分别是马萨诸塞州波士顿的罗克斯伯里和洛杉矶的圣约翰浸信会教区。项目产生的新技术方法和空气质量数据，以及社区的深入参与，将有可能影响和加强政策，使服务不足的社区更公平地获得健康环境。在这个研究项目中，研究团队(1)开发了数据驱动的方法，将人类的流动性（即一个人在住宅内外的足迹）和空气质量监测器测量的呼吸危害无缝连接起来；(2)设计实时人工智能技术，以连接支持基于网络的干预措施的信息和社区；(3)将该方法应用于两个具有代表性的地方社区，这些社区具有少数民族和多样性的特征，都受到空气污染的影响。这项研究的重点是挥发性有机化合物（VOCs）和颗粒物（如PM2.5），因为它们已知会导致肺癌、心血管和呼吸系统疾病。工作的主要成果包括：(1)一个数据驱动和实时的基于人工智能的框架，该框架将多模型数据流（例如，地理位置、空气质量、社交网络等）联系起来，以量化和预测与人类足迹相关的空气污染暴露；(2)基于社会网络的干预策略与两个空气污染严重的社区（马萨诸塞州波士顿的罗克斯伯里和洛杉矶新奥尔良的圣约翰教区）互动发展并进行了测试；(3)面向主流智能手机的实时人工智能移动应用程序“无毒生活”（Toxic-Free Life），可实时评估空气暴露并支持基于网络的干预措施。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Percolation Transitions in Urban Mobility Networks in America's 50 Largest Cities

• DOI: 10.1016/j.scs.2023.104435
• 发表时间: 2023-01
• 期刊: Sustainable Cities and Society
• 影响因子: 11.7
• 作者: Ruoxi Wang;Wang Qi;Nan Li

Estimating comparable distances to tipping points across mutualistic systems by scaled recovery rates

通过缩放回收率来估计互惠系统中到临界点的可比距离

• DOI: 10.5281/zenodo.6784072
• 发表时间: 2022
• 期刊: Nature ecology evolution
• 作者: Zhang, Huixin;Wang, Qi;Zhang, Weidong;Havlin, Shlomo;Gao, Jianxi
• 通讯作者: Gao, Jianxi

A spatiotemporal decay model of human mobility when facing large-scale crises.

• DOI: 10.1073/pnas.2203042119
• 发表时间: 2022-08-16
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1

RELIANT: Fair Knowledge Distillation for Graph Neural Networks

• DOI: 10.48550/arxiv.2301.01150
• 发表时间: 2023-01
• 作者: Yushun Dong;Binchi Zhang;Yiling Yuan;Na Zou;Qi Wang;Jundong Li