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

在美国通过《清洁空气法》50年后，据估计，到2020年，美国近一半的人口将生活在和呼吸污染的空气中。最近，气候变化引发的野火数量和强度加快，加上毁灭性的新冠肺炎大流行，使得空气污染对公众健康的影响成为一个更加令人担忧的问题。这一智能互联社区项目在弱势社区建立和加强技术、经济、政治和社会基础设施，以更好地感知和监测空气质量，防止暴露在呼吸危险中，并提高对空气质量错误信息的认识和打击。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“促进国民健康、繁荣和福利”的使命产生了共鸣。研究团队与马萨诸塞州波士顿罗克斯伯里和洛杉矶圣约翰浸信会教区两个社区的当地基层组织、社区、研究中心和规划机构合作。该项目产生的新技术方法和空气质量数据以及社区的深度参与将潜在地影响和加强政策，并使服务不足的社区更公平地获得健康环境。在本研究项目中，研究团队(1)开发数据驱动的方法，将人类流动性(即一个人在居民区内外的足迹)与通过空气质量监测仪测量的呼吸危险无缝联系起来；(2)设计实时人工智能技术，将支持基于网络的干预的信息和社区连接起来；(3)在两个具有代表性的当地社区部署该方法，该社区的特点是少数民族和多样性，都受到空气污染的负担。这项研究侧重于挥发性有机化合物(VOCs)和颗粒物(例如PM2.5)对健康的已知影响，这些影响会导致肺癌、心血管疾病和呼吸系统疾病。工作的主要成果包括：(1)数据驱动和基于实时人工智能的框架，将多模型数据流(例如，地理位置、空气质量、社交网络等)联系在一起。为了量化和预测与人类足迹相关的空气污染暴露；(2)与两个空气污染负担沉重的社区(马萨诸塞州波士顿罗克斯伯里和洛杉矶新奥尔良的圣约翰教区)互动开发并测试的基于社会网络的干预战略；以及(3)主流智能手机的实时人工智能驱动的移动应用程序(App)，它允许实时估计空气暴露并支持基于网络的干预。该奖项反映了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。

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.