Dataset Information

Geographic Variations in Urban-Rural Particulate Matter (PM2.5) Concentrations in the United States, 2010-2019.

ABSTRACT: Fine particulate matter 2.5 (PM2.5) is a widely studied pollutant with substantial health impacts, yet little is known about the urban-rural differences across the United States. Trends of PM2.5 in urban and rural census tracts between 2010 and 2019 were assessed alongside sociodemographic characteristics including race/ethnicity, poverty, and age. For 2010, we identified 13,474 rural tracts and 59,065 urban tracts. In 2019, 13,462 were rural and 59,055 urban. Urban tracts had significantly higher PM2.5 concentrations than rural tracts during this period. Levels of PM2.5 were lower in rural tracts compared to urban and fell more rapidly in rural than urban. Rural tract annual means for 2010 and 2019 were 8.51 [2.24] μg/m3 and 6.41 [1.29] μg/m3, respectively. Urban tract annual means for 2010 and 2019 were 9.56 [2.04] μg/m3 and 7.51 [1.40] μg/m3, respectively. Rural and urban majority Black communities had significantly higher PM2.5 pollution levels (10.19 [1.64] μg/m3 and 9.79 [1.10] μg/m3 respectively), in 2010. In 2019, they were: 7.75 [1.1] μg/m3 and 7.09 [0.78] μg/m3, respectively. Majority Hispanic communities had higher PM2.5 levels and were the highest urban concentration among all races/ethnicities (8.01 [1.73] μg/m3), however they were not the highest rural concentration among all races/ethnicities (6.22 [1.60] μg/m3) in 2019. Associations with higher levels of PM2.5 were found with communities in the poorest quartile and with higher proportions of residents age<15 years old. These findings suggest greater protections for those disproportionately exposed to PM2.5 are needed, such as, increasing the availability of low-cost air quality monitors.

SUBMITTER: Kilpatrick DJ

PROVIDER: S-EPMC11368819 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature

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Geographic Variations in Urban-Rural Particulate Matter (PM2.5) Concentrations in the United States, 2010-2019.

Kilpatrick Daniel J DJ Hung Peiyin P Crouch Elizabeth E Self Stella S Cothran Jeremy J Porter Dwayne E DE Eberth Jan M JM

GeoHealth 20240902 9

Fine particulate matter 2.5 (PM2.5) is a widely studied pollutant with substantial health impacts, yet little is known about the urban-rural differences across the United States. Trends of PM2.5 in urban and rural census tracts between 2010 and 2019 were assessed alongside sociodemographic characteristics including race/ethnicity, poverty, and age. For 2010, we identified 13,474 rural tracts and 59,065 urban tracts. In 2019, 13,462 were rural and 59,055 urban. Urban tracts ...[more]

PMID: 39234600

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Project description:Elevated levels of ambient air pollution has been implicated as a major risk factor for morbidities and premature mortality in India, with particularly high concentrations of particulate matter in the Indo-Gangetic plain. High resolution spatiotemporal estimates of such exposures are critical to assess health effects at an individual level. This article retrospectively assesses daily average PM2.5 exposure at 1 km × 1 km grids in Delhi, India from 2010-2016, using multiple data sources and ensemble averaging approaches. We used a multi-stage modeling exercise involving satellite data, land use variables, reanalysis based meteorological variables and population density. A calibration regression was used to model PM2.5: PM10 to counter the sparsity of ground monitoring data. The relationship between PM2.5 and its spatiotemporal predictors was modeled using six learners; generalized additive models, elastic net, support vector regressions, random forests, neural networks and extreme gradient boosting. Subsequently, these predictions were combined under a generalized additive model framework using a tensor product based spatial smoothing. Overall cross-validated prediction accuracy of the model was 80% over the study period with high spatial model accuracy and predicted annual average concentrations ranging from 87 to 138 μg/m3. Annual average root mean squared errors for the ensemble averaged predictions were in the range 39.7-62.7 μg/m3 with prediction bias ranging between 4.6-11.2 μg/m3. In addition, tree based learners such as random forests and extreme gradient boosting outperformed other algorithms. Our findings indicate important seasonal and geographical differences in particulate matter concentrations within Delhi over a significant period of time, with meteorological and land use features that discriminate most and least polluted regions. This exposure assessment can be used to estimate dose response relationships more accurately over a wide range of particulate matter concentrations.

Dataset Information

Geographic Variations in Urban-Rural Particulate Matter (PM2.5) Concentrations in the United States, 2010-2019.

Publications

Geographic Variations in Urban-Rural Particulate Matter (PM<sub>2.5</sub>) Concentrations in the United States, 2010-2019.

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