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Global AQ Monitoring Network

An American Thoracic Society Workshop Report: Air Pollution Monitoring for Health Research and Patient Care

Link to Workshop Report: Coming Soon

Air quality data from satellites and low-cost sensor systems, along with output from air quality models, have the potential to augment high-quality, regulatory-grade data in countries with in situ monitoring networks and provide much needed air quality information in countries without them. Each of these technologies has strengths and limitations that need to be considered when integrating them to develop a robust and diverse global air quality monitoring network.  To address these issues the American Thoracic Society, Environmental Protection Agency, National Aeronautics and Space Administration, and National Institute of Environmental Health Sciences convened a workshop in May 2017 to bring together global experts from across multiple disciplines and agencies to discuss current and near-term capabilities to monitor global air pollution. The participants focused on four topics: current and near-term capabilities in air pollution monitoring; data assimilation from multiple technology platforms; critical issues for air pollution monitoring in regions without a regulatory-quality stationary monitoring network; and risk communication and health messaging. Recommendations for research and improved utilization were identified during the workshop including a recognition that the integration of data across monitoring technology groups is critical in order to maximize the effectiveness (i.e., data accuracy, as well as spatial and temporal coverage) of these monitoring technologies. Taken together these recommendations will advance the development of a global air quality monitoring network that takes advantage of emerging technologies to ensure the availability of free, accessible and reliable air pollution data and forecasts to health professionals, as well, as to all global citizens.

Low-cost sensors, instruments on drones and satellites, and air quality models are becoming important sources of information that will complement traditional regulatory-grade surface monitors. (Image source:

HAQAST Leads: Dr. Kevin Cromar (NYU), Dr. Bryan N. Duncan (; NASA)

Other HAQAST Participants: Dr. Yang Liu (Emory U.), Dr. Ted Russel (Georgia Tech)

Other Participants: see workshop report