Urban air pollution, like fine particles, is driven by emissions from vehicles, buildings, power plants, and industrial operations. Air pollution is not distributed evenly because of many factors, including urban planning decisions on where to locate industrial and transportation infrastructure. The result is an inequitable exposure to air pollution, with majority Black and Brown communities experiencing greater air pollution exposure than majority white communities. Addressing these environmental justice issues requires an understanding of historical influences and information on air pollution conditions at the community level, known as hyperlocal air pollution data, which is generally unavailable from current monitoring networks.
The Scientific Challenge
Understanding the causes of and potential remedies for air pollution hot spots is scientifically challenging due to the complex interactions between multiple pollutants, uncertainties in pollution sources, and complicated urban conditions.
Trends in sensor technology, spatial data collection and retrieval, and data analytics are converging to enable the generation of hyperlocal air pollution data. It is now, in principle, possible to bring together detailed information on individual and community mobility patterns, data from high-quality mobile sensors, and new competing and modeling techniques. This has the potential to produce the hyperlocal air pollution data needed to characterize, understand, and provide the information decision-makers need to address air pollution inequities.
Continuing disparities in air pollution highlight the need for interdisciplinary research that bridges social, political, biological, and physical sciences. This work suggests several useful actions to address current data limitations that could help improve air quality and equity, including increased funding for hyperlocal air quality monitoring and modeling in cities, innovation in estimating individual exposures, and engaging community groups and citizen scientists in the development and use of data networks.
Yang Zhang, firstname.lastname@example.org, corresponding author,
Steven J Smith, email@example.com (PNNL corresponding author)
Michelle Bell, firstname.lastname@example.org, corresponding author
This work was funded internally as a follow-up to proposal development.
Published: July 20, 2021
Zhang Y., S.J. Smith, M. Bell, A. Mueller, M. Eckelman, S. Wylie, and E. Sweet, et al. "Pollution Inequality 50 years after the Clean Air Act: The Need for Hyperlocal Data." Environ. Res. Lett. 16, 071001, (2021). [DOI: 10.1088/1748-9326/ac09b1]