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Fine particulate matter composition in American Indian vs. Non-American Indian communities.

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Citation: Environmental Research. 237(Pt 2):117091, 2023 Sep 06.PMID: 37683786Institution: MedStar Health Research InstituteForm of publication: Journal ArticleMedline article type(s): Journal ArticleSubject headings: IN PROCESS -- NOT YET INDEXED | Year: 2023 ISSN:

0013-9351

Name of journal: Environmental researchAbstract: BACKGROUND: Fine particulate matter (PM2.5) exposure is a known risk factor for numerous adverse health outcomes, with varying estimates of component-specific effects. Populations with compromised health conditions such as diabetes can be more sensitive to the health impacts of air pollution exposure. Recent trends in PM2.5 in primarily American Indian- (AI-) populated areas examined in previous work declined more gradually compared to the declines observed in the rest of the US. To further investigate components contributing to these findings, we compared trends in concentrations of six PM2.5 components in AI- vs. non-AI-populated counties over time (2000-2017) in the contiguous US.CONCLUSIONS: This study indicates time trend differences of specific components by AI-populated county type. Notably, decreases in sulfate and ammonium may contribute to steeper declines in total PM2.5 in non-AI vs. AI-populated counties. These findings provide potential directives for additional monitoring and regulations of key emissions sources impacting tribal lands. Copyright © 2023 Elsevier Inc. All rights reserved.METHODS: We implemented component-specific linear mixed models to estimate differences in annual county-level concentrations of sulfate, nitrate, ammonium, organic matter, black carbon, and mineral dust from well-validated surface PM2.5 models in AI- vs. non-AI-populated counties, using a multi-criteria approach to classify counties as AI- or non-AI-populated. Models adjusted for population density and median household income. We included interaction terms with calendar year to estimate whether concentration differences in AI- vs. non-AI-populated counties varied over time.RESULTS: Our final analysis included 3108 counties, with 199 (6.4%) classified as AI-populated. On average across the study period, adjusted concentrations of all six PM2.5 components in AI-populated counties were significantly lower than in non-AI-populated counties. However, component-specific levels in AI- vs. non-AI-populated counties varied over time: sulfate and ammonium levels were significantly lower in AI- vs. non-AI-populated counties before 2011 but higher after 2011 and nitrate levels were consistently lower in AI-populated counties.All authors: Li M, Do V, Brooks JL, Hilpert M, Goldsmith J, Chillrud SN, Ali T, Best LG, Yracheta J, Umans JG, van Donkelaar A, Martin RV, Navas-Acien A, Kioumourtzoglou MAFiscal year: FY2024 Digital Object Identifier:

https://dx.doi.org/10.1016/j.envres.2023.117091

Date added to catalog: 2023-12-20

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Title notes ( 5 )

Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Journal Article	MedStar Authors Catalog	Article	37683786	Available		37683786

BACKGROUND: Fine particulate matter (PM2.5) exposure is a known risk factor for numerous adverse health outcomes, with varying estimates of component-specific effects. Populations with compromised health conditions such as diabetes can be more sensitive to the health impacts of air pollution exposure. Recent trends in PM2.5 in primarily American Indian- (AI-) populated areas examined in previous work declined more gradually compared to the declines observed in the rest of the US. To further investigate components contributing to these findings, we compared trends in concentrations of six PM2.5 components in AI- vs. non-AI-populated counties over time (2000-2017) in the contiguous US.

CONCLUSIONS: This study indicates time trend differences of specific components by AI-populated county type. Notably, decreases in sulfate and ammonium may contribute to steeper declines in total PM2.5 in non-AI vs. AI-populated counties. These findings provide potential directives for additional monitoring and regulations of key emissions sources impacting tribal lands. Copyright © 2023 Elsevier Inc. All rights reserved.

METHODS: We implemented component-specific linear mixed models to estimate differences in annual county-level concentrations of sulfate, nitrate, ammonium, organic matter, black carbon, and mineral dust from well-validated surface PM2.5 models in AI- vs. non-AI-populated counties, using a multi-criteria approach to classify counties as AI- or non-AI-populated. Models adjusted for population density and median household income. We included interaction terms with calendar year to estimate whether concentration differences in AI- vs. non-AI-populated counties varied over time.

RESULTS: Our final analysis included 3108 counties, with 199 (6.4%) classified as AI-populated. On average across the study period, adjusted concentrations of all six PM2.5 components in AI-populated counties were significantly lower than in non-AI-populated counties. However, component-specific levels in AI- vs. non-AI-populated counties varied over time: sulfate and ammonium levels were significantly lower in AI- vs. non-AI-populated counties before 2011 but higher after 2011 and nitrate levels were consistently lower in AI-populated counties.

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