Collaborative Research: AccelNet: Clean Air Monitoring and Solutions Network (CAMS Net)

合作研究：AccelNet：清洁空气监测和解决方案网络（CAMS Net）

• 批准号: 2020673
• 负责人: Randall Martin
• 金额: $ 49.81万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2020673&HistoricalAwards=false
• 关键词: Collaborative; Research; AccelNet; Clean; Air

Air pollution is causing a global public health crisis, responsible for around 4.9 million premature deaths worldwide each year. Air pollution-related disease and death increasingly occur in places least equipped with the technical capacity, planning, and resources to address them. The Clean Air Monitoring and Solutions Network (CAMS-Net) establishes an international network of networks that unites scientists, decision-makers, city administrators, citizen groups, the private sector, and other local stakeholders in co-developing new methods and practices for real-time air quality data collection, data sharing, and solutions for air quality improvements. CAMS-Net brings together at least 32 multidisciplinary member networks from North America, Europe, Africa, and India. This AccelNet project establishes a mechanism for international collaboration, builds technical capacity, shares knowledge, and trains the next generation of air quality practitioners and advocates, including graduate students and postdoctoral researchers. A crucial component and key service to society of the network of networks is the provision of publicly available, open-access and high-quality air pollution data, which is timely because air quality is poised to degrade further in many highly populated places as climate changes and as economies grow.CAMS-Net will accelerate effective solutions for clean air by promoting novel research into a promising but largely untapped resource for cost-effective air quality monitoring. A traditional approach for improving air quality in cities is the development and implementation of a management plan, which is typically anchored by a network of high-quality, research-grade measurement devices collecting real-time data coupled with the technical expertise to analyze and make decisions based on that data. So-called low-cost sensors (LCS) have the potential to revolutionize clean air solutions and spur regulatory action, especially in lower- and middle-income countries. LCS are being deployed all over the world. Yet no global consortium exists to help standardize best practices, share deployment strategies, ensure quality control, and calibrate sensors towards research-grade quality. CAMS-Net seeks to maximize the value to science and society of the current proliferation of unknown quality data acquired by LCS through capacity building, knowledge exchange, and acceleration of novel research. CAMS-Net research directions also include applying LCS networks to evaluate air quality models, refining satellite-derived air quality products, informing the implementation of air quality standards, and estimating fine-scale pollutant exposure for health impact analyses. Students and postdoctoral researchers will participate in scholar exchanges and take on leadership roles within CAMS-Net, preparing them for careers in global air quality. CAMS-Net will create a strong, sustained global network of networks focused on closing the air pollution data and knowledge gaps.The Accelerating Research through International Network-to-Network Collaborations (AccelNet) program is designed to accelerate the process of scientific discovery and prepare the next generation of U.S. researchers for multiteam international collaborations. The AccelNet program supports strategic linkages among U.S. research networks and complementary networks abroad that will leverage research and educational resources to tackle grand scientific challenges that require significant coordinated international efforts.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.

空气污染正在造成全球公共健康危机，每年导致全球约490万人过早死亡。与空气污染有关的疾病和死亡越来越多地发生在缺乏应对这些疾病的技术能力、规划和资源的地方。清洁空气监测和解决方案网络(CAMS-Net)建立了一个国际网络网络，将科学家、决策者、城市管理人员、公民团体、私营部门和其他当地利益攸关方联合起来，共同开发新的方法和实践，用于实时空气质量数据收集、数据共享和空气质量改善解决方案。CAMS-Net汇集了来自北美、欧洲、非洲和印度的至少32个多学科成员网络。这个AccelNet项目建立了国际合作机制，建设技术能力，共享知识，并培训包括研究生和博士后研究人员在内的下一代空气质量从业者和倡导者。网络网络的一个重要组成部分和对社会的关键服务是提供公开、开放获取和高质量的空气污染数据，这是及时的，因为随着气候变化和经济增长，许多人口稠密地区的空气质量将进一步恶化。CAMS-Net将通过促进对一种有前景但基本上未被开发的资源的新研究，加速清洁空气的有效解决方案，以实现具有成本效益的空气质量监测。改善城市空气质量的传统方法是制定和实施管理计划，该计划通常以收集实时数据的高质量研究级测量设备网络为基础，再加上根据这些数据进行分析和决策的技术专长。所谓的低成本传感器(LCS)有可能给清洁空气解决方案带来革命性的变化，并刺激监管行动，特别是在中低收入国家。LC正在世界各地部署。然而，目前还没有全球联盟来帮助标准化最佳实践、共享部署策略、确保质量控制以及将传感器校准为研究级质量。CAMS-Net试图通过能力建设、知识交流和加速新的研究，最大限度地提高LCS目前获得的未知质量数据对科学和社会的价值。CAMS-Net的研究方向还包括应用LCS网络评估空气质量模型，提炼来自卫星的空气质量产品，为空气质量标准的实施提供信息，以及估计用于健康影响分析的细尺度污染物暴露。学生和博士后研究人员将参与学者交流，并在CAMS-Net中担任领导角色，为他们在全球空气质量领域的职业生涯做好准备。CAMS-Net将创建一个强大、持续的全球网络网络，专注于弥合空气污染数据和知识鸿沟。加速通过国际网络到网络合作研究(AccelNet)计划旨在加快科学发现的进程，并为多团队国际合作培养下一代美国研究人员。AccelNet计划支持美国研究网络和海外互补网络之间的战略联系，这些网络将利用研究和教育资源来应对需要大量国际协调努力的重大科学挑战。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。

项目成果

Integrating Fixed Monitoring Systems with Low-Cost Sensors to Create High-Resolution Air Quality Maps for the Northern China Plain Region

• DOI: 10.1021/acsearthspacechem.1c00174
• 发表时间: 2021-10-18
• 期刊: ACS EARTH AND SPACE CHEMISTRY
• 影响因子: 3.4
• 作者: Chao, Chun-Ying;Zhang, Huang;Biswas, Pratim
• 通讯作者: Biswas, Pratim

Evaluating the Performance of Low-cost PM Sensors over Multiple COALESCE Network Sites

• DOI: 10.4209/aaqr.220390
• 发表时间: 2023
• 期刊: Aerosol and Air Quality Research
• 影响因子: 4
• 作者: Vishal R. Dharaiya;V. Malyan;Vikas Kumar;M. Sahu;C. Venkatraman;P. Biswas;K. Yadav;Deeksha Haswani;R. S. Raman;Ruqia Bhat;Tanveer Ahmad Najar;A. Jehangir;R. Patil;G. Pandithurai;S. Duhan;Jitendra Singh Laura

Advances in Simulating the Global Spatial Heterogeneity of Air Quality and Source Sector Contributions: Insights into the Global South

• DOI: 10.1021/acs.est.2c07253
• 发表时间: 2023-04
• 期刊: Environmental Science & Technology
• 影响因子: 11.4
• 作者: Dandan Zhang;R. Martin;Liam Bindle;Chi Li;S. Eastham;A. van Donkelaar;L. Gallardo