About
The Baltimore air quality dashboard is developed by a team of researchers at Johns Hopkins University and offers maps and other analytics about air quality trends in Baltimore city. The maps and figures are created based on outputs of statistical models [1,2] that use air quality data on Baltimore from multiple sources (Maryland Department of Energy, the SEARCH low-cost sensor network [3], and the PurpleAir low-cost sensor network). The dashboard currently offers both city-level and neighborhood-level maps and trends on fine particulate matter (PM2.5) data in Baltimore city from 2019 to 2024. See the FAQ page for more details on the maps, figures, and methodology. In the future, near-real time air-quality maps and information on other pollutants will be added.
Please send any feedback, possible data issues, bug reports, and data requests to airquality@live.johnshopkins.edu.
Contact: airquality@live.johnshopkins.edu
Team
Acknowledgement
We thank Drew Gentner, Misti-Levy Zamora, Colby Buehler, Claire Heffernan and Roger Peng for contributing to development of the SEARCH low-cost sensor network in Baltimore and statistical methods for calibration of the network. We gratefully acknowledge use of the facilities at the Joint High Performance Computing Exchange (JHPCE) in the Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health that have contributed to the analysis presented in the website. We also acknowledge and thank all individuals who made their Purple Air Sensor data freely available for scientific analysis, which was downloaded from PurpleAir.com. The SEARCH network was developed under Assistance Agreement no. RD835871 awarded by the U.S. Environmental Protection Agency to Yale University. It has not been formally reviewed by the Environmental Protection Agency (EPA). The views expressed in this website are solely those of the developers and do not necessarily reflect those of the Agency. The EPA does not endorse any products or commercial services mentioned in this publication. Additional support is from the Department of Energy Urban Integrated Field Laboratory program, through award DE-SC0023217. Development of statistical methods for the calibration was supported by National Institute of Environmental Health Sciences (NIEHS) Grant R01 ES033739. Development of this dashboard was supported by 2024 JHU DSAI Demonstration Award.
References
A dynamic spatial filtering approach to mitigate underestimation bias in field calibrated low-cost sensor air pollution data
Heffernan, C., Peng, R., Gentner, D. R., Koehler, K., & Datta, A. (2023)
The annals of applied statistics, 17(4), 3056.
Unified calibration and spatial mapping of fine particulate matter data from multiple low-cost air pollution sensor networks in Baltimore, Maryland
Heffernan, C., Koehler, K., Gentner, D. R., Peng, R. D., & Datta, A. (2024)
arXiv preprint arXiv:2412.13034.
Stationary and portable multipollutant monitors for high-spatiotemporal-resolution air quality studies including online calibration. Atmospheric measurement techniques
Buehler, C., Xiong, F., Zamora, M. L., Skog, K. M., Kohrman-Glaser, J., Colton, S., ... & Gentner, D. R. (2021)
Atmospheric measurement techniques, 14(2), 995-1013.