First study to shed light on ambient particulate matter in tribal lands across the U.S. — ScienceDaily

“Our results underscore the need to strengthen air pollutionregulations and prevention implementation in tribal territories and areas where AI populations live,” said Maggie Li, PhD candidate in the Department of Environmental Health Sciences at Columbia Mailman School, and first author.”Until now, insight into PM_2.5 exposure burdens in AI communities has been limited, in turn contributing to the lack of air pollution epidemiological studies which include this population in the contiguous U.S.”

The researchers compared ambient PM_2.5 average concentrations and trends in AI-populated vs. non-AI-populated counties in the contiguous U.S. from 2000 to 2018. They conducted their analysis at the county level, the most relevant unit to inform regulatory action. Population data and household income were obtained from the U.S. 2010 Census.

Average concentrations were 1.46 ?g/m³ lower for modeled PM_2.5 in AI-populated counties vs. non-AI-populated counties in 2000; however, towards the end of the study period, PM_2.5 concentrations in AI-populated counties became significantly higher compared to non-AI-populated counties. By 2018, concentrations were on average 0.66µg/m³higher in AI-populated counties than non-AI-populated counties. “Although we observed that average PM_2.5 concentrations were higher in non-AI-populated counties at baseline and across most of the study period, the gap between AI- and non-AI-populated counties decreased over time; after approximately 2015, average PM_2.5 concentrations were higher in AI-populated counties,” observed Li.

Short- and long-term exposure to particulate matter ? 2.5 µm (PM_2.5) increases the risk of cardiovascular and respiratory disease, among other health outcomes. In the U.S., socioeconomically disadvantaged communities are often exposed to higher PM_2.5 exposure levels. They bear a disproportionate burden of disease, even at levels well below air quality standards set by the U.S. Environmental Protection Agency.

“Our findings build upon existing studies showing that socioeconomically disadvantaged communities experience disproportionate burdens of environmental hazards, such as ambient air pollution. Native Americans may be particularly susceptible to the adverse health effects of PM_2.5 and air pollution, but research quantifying air pollution exposures and impacts on health among this population is lacking,” said Marianthi-Anna Kioumourtzoglou, ScD, assistant professor of environmental health sciences at Columbia Mailman School, and senior author. “These communities already face a large disease burden attributable to environmental pollution due, for instance, to extensive mining and water contamination on Tribal Lands.”

Cardiovascular disease, one of the outcomes associated with exposure to PM_2.5, and the leading cause of death in AI populations, occurs at significantly higher rates than in white populations. “There is a critical need for future investigations of health impacts associated with air pollution exposure in AI populations, as well as of interventions to ensure the observed inequalities can be eliminated,” noted Ana Navas-Acien, MD, PhD, professor of environmental health sciences at Columbia Mailman School, and a senior co-author.

Co-authors are Markus Hilpert, Jenni A. Shearston, and Jeff Goldsmith, Columbia University Mailman School of Public Health; Jada Brooks, the University of North Carolina School of Nursing at Chapel Hill; Steven Chillrud, Lamont-Doherty Earth Observatory, Columbia University; Tauqeer Ali, University of Oklahoma Health Sciences Center; Jason Umans, Georgetown-Howard Universities Center for Clinical and Translational Sciences; Lyle Best and Joseph Yracheta, Missouri Breaks Industries Research, Inc.; Aaron van Donkelaar and Randall Martin, Department of Energy, Environmental & Chemical Engineering, Washington University at St. Louis.

Support for the study was provided by the National Institute of Environmental Health Sciences, grants T32 ES007322, P30 ES09089, R01 ES030616, and P42ES010349.