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: 2020677
• 负责人: Daniel Westervelt
• 金额: $ 96.4万
• 依托单位: Columbia 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=2020677&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卫生危机，每年在全球造成490万例早期死亡。与空气污染有关的疾病和死亡越来越多地发生在最少有技术能力，计划和资源来解决这些疾病的地方。干净的空气监控和解决方案网络（CAMS-NET）建立了一个国际网络网络，该网络将科学家，决策者，城市管理员，公民群体，私营部门以及其他本地利益相关者共同开发新的方法和实践，以实时空气质量数据收集，数据共享和解决空气质量改进的解决方案。 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项目为国际合作建立了一种机制，建立技术能力，分享知识，并培训下一代空气质量从业人员和倡导者，包括研究生和博士后研究人员。提供公开可用的，开放式和高质量的空气污染数据的关键组成部分和关键服务是提供公开可用的，这是及时的，因为空气质量可以在许多高度人口稠密的地方进一步降级，随着经济体的增长，随着经济体的增长，cams-net将加速有效的空中研究，以促进空中的质量，但要促进良好的资源来促进良好的良好的资源，但要促进良好的资源范围，劳力不佳。改善城市空气质量的一种传统方法是管理计划的制定和实施，该计划通常由收集实时数据的高质量，研究级测量设备的网络锚定，再加上实时数据，并基于该数据来分析和做出决策。所谓的低成本传感器（LCS）有可能彻底改变清洁空气解决方案和刺激法规行动，尤其是在中低收入国家。 LCS正在全世界部署。 然而，尚无全球财团来帮助标准化最佳实践，共享部署策略，确保质量控制并校准传感器以研究级质量。 CAMS-NET旨在最大程度地提高LC通过能力建设，知识交流和新型研究加速而获得的不明质量数据的科学和社会的价值。 CAMS网络研究方向还包括应用LCS网络评估空气质量模型，精炼卫星衍生的空气质量产品，告知空气质量标准的实施，并估算细胞污染物的暴露量以进行健康影响分析。学生和博士后研究人员将参加学者交流，并在CAMS-NET中扮演领导角色，为全球空气质量的职业做好准备。 CAMS-NET将创建一个旨在缩小空气污染数据和知识差距的强大，持续的全球网络。通过国际网络到网络合作（Accelnet）计划的加速研究旨在加速科学发现的过程，并准备下一代美国研究人员进行多我们国际合作的美国研究人员。 Accelnet计划支持美国研究网络和国外互补网络之间的战略联系，这些联系将利用研究和教育资源来应对需要重大协调的国际努力的宏伟科学挑战。该奖项反映了NSF的法定使命，并认为通过使用该基金会的智力和更广泛的影响来评估CRITERIA的评估。

项目成果

First Measurements of Ambient PM2.5 in Kinshasa, Democratic Republic of Congo and Brazzaville, Republic of Congo Using Field-calibrated Low-cost Sensors

• DOI: 10.4209/aaqr.200619
• 发表时间: 2021-07-01
• 期刊: AEROSOL AND AIR QUALITY RESEARCH
• 影响因子: 4
• 作者: McFarlane, Celeste;Isevulambire, Paulson Kasereka;Westervelt, Daniel M.
• 通讯作者: Westervelt, Daniel M.

Health impacts of smoke exposure in South America: increased risk for populations in the Amazonian Indigenous territories

南美洲烟雾暴露对健康的影响：亚马逊土著地区人口的风险增加

• DOI: 10.1088/2752-5309/acb22b
• 发表时间: 2023
• 期刊: Environmental Research: Health
• 作者: Bonilla, E X;Mickley, L J;Raheja, G;Eastham, S D;Buonocore, J J;Alencar, A;Verchot, L;Westervelt, D M;Castro, M C
• 通讯作者: Castro, M C

Automated Machine Learning to Evaluate the Information Content of Tropospheric Trace Gas Columns for Fine Particle Estimates Over India: A Modeling Testbed

• DOI: 10.1029/2022ms003099
• 发表时间: 2023-03
• 期刊: Journal of Advances in Modeling Earth Systems
• 影响因子: 6.8
• 作者: Zhonghua Zheng;A. Fiore;D. Westervelt;G. Milly;J. Goldsmith;A. Karambelas;G. Curci;C. Randles;Antonio R. Paiva;Chi Wang;Qingyun Wu;S. Dey

Community-based participatory research for low-cost air pollution monitoring in the wake of unconventional oil and gas development in the Ohio River Valley: Empowering impacted residents through community science

• DOI: 10.1088/1748-9326/ac6ad6
• 发表时间: 2022-06-01
• 期刊: ENVIRONMENTAL RESEARCH LETTERS
• 影响因子: 6.7
• 作者: Raheja, Garima;Harper, Leatra;Westervelt, Daniel M.
• 通讯作者: Westervelt, Daniel M.

Validation of In-field Calibration for Low-Cost Sensors Measuring Ambient Particulate Matter in Kolkata, India

印度加尔各答测量环境颗粒物的低成本传感器的现场校准验证

• DOI: 10.4209/aaqr.230010
• 发表时间: 2023
• 期刊: Aerosol and Air Quality Research
• 影响因子: 4
• 作者: Nobell, Siddharth;Majumdar, Arnab;Mukherjee, Shovon;Chakraborty, Sukumar;Chatterjee, Sanjoy;Bose, Soumitra;Dutta, Anindita;Sethuraman, Sandhya;Westervelt, Daniel M.;Sengupta, Shairik
• 通讯作者: Sengupta, Shairik