Dataset Information

Finely Resolved On-Road PM_2.5 and Estimated Premature Mortality in Central North Carolina.

ABSTRACT: To quantify the on-road PM_2.5 -related premature mortality at a national scale, previous approaches to estimate concentrations at a 12-km × 12-km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on-road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. In this study, we compared the on-road PM_2.5 -related premature mortality in central North Carolina with two different concentration estimation approaches-(i) using the Community Multiscale Air Quality (CMAQ) model to model concentration at a coarser resolution of a 36-km × 36-km grid resolution, and (ii) using a hybrid of a Gaussian dispersion model, CMAQ, and a space-time interpolation technique to provide annual average PM_2.5 concentrations at a Census-block level (?105,000 Census blocks). The hybrid modeling approach estimated 24% more on-road PM_2.5 -related premature mortality than CMAQ. The major difference is from the primary on-road PM_2.5 where the hybrid approach estimated 2.5 times more primary on-road PM_2.5 -related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on-road PM_2.5 premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near-road primary PM_2.5 and suggesting that previous studies may have underestimated premature mortality due to PM_2.5 from traffic-related emissions.

SUBMITTER: Chang SY

PROVIDER: S-EPMC7784485 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Finely Resolved On-Road PM<sub>2.5</sub> and Estimated Premature Mortality in Central North Carolina.

Chang Shih Ying SY Vizuete William W Serre Marc M Vennam Lakshmi Pradeepa LP Omary Mohammad M Isakov Vlad V Breen Michael M Arunachalam Saravanan S

Risk analysis : an official publication of the Society for Risk Analysis 20170228 12

To quantify the on-road PM<sub>2.5</sub> -related premature mortality at a national scale, previous approaches to estimate concentrations at a 12-km × 12-km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on-road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. ...[more]

PMID: 28244115

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Project description:BackgroundApproximately nine million adults in the United States are living with chronic obstructive pulmonary disease (COPD), and positive associations between short-term air pollution exposure and increased risk of COPD hospitalizations in older adults are consistently reported. We examined the association between short-term PM2.5 exposure and hospitalizations and assessed if there is modification by long-term exposure in a cohort of individuals with COPD.MethodsIn a time-referent case-crossover design, we used a cohort of randomly selected individuals with electronic health records from the University of North Carolina Healthcare System, restricted to patients with a medical encounter coded with a COPD diagnosis from 2004-2016 (n = 520), and estimated ambient PM2.5 concentrations from an ensemble model. Odds ratios and 95% confidence intervals (OR (95%CI)) were estimated with conditional logistic regression for respiratory-related, cardiovascular (CVD), and all-cause hospitalizations. Exposures examined were 0-2 and 0-3 day lags of PM2.5 concentration, adjusting for daily census-tract temperature and humidity, and models were stratified by long-term (annual average) PM2.5 concentration at the median value.ResultsWe observed generally null or low-magnitude negative associations with short-term PM2.5 exposure and respiratory-related (OR per 5 µg/m3 increase in 3-day lag PM2.5: 0.971 (0.885, 1.066)), CVD (2-day lag: 0.976 (0.900, 1.058) and all-cause (3 day lag: 1.003 (0.927, 1.086)) hospitalizations. Associations between short-term PM2.5 exposure and hospitalizations were higher among patients residing in areas with higher levels of annual PM2.5 concentrations (OR per 5 µg/m3 in 3-day lag PM2.5 for all-cause hospitalizations: 1.066 (0.958, 1.185)) than those in areas with lower annual PM2.5 concentrations (OR per 5 µg/m3 in 3-day lag PM2.5 for all-cause hospitalizations: 0.914 (0.804, 1.039)).ConcluisonsDifferences in associations demonstrate that people in areas with higher annual PM2.5 exposure may be associated with higher risk of hospitalization during short-term increases in PM2.5 exposure.

Project description:IntroductionDespite the London Underground (LU) handling on average 2.8 million passenger journeys per day, the characteristics and potential health effects of the elevated concentrations of metal-rich PM2.5 found in this subway system are not well understood.MethodsSpatial monitoring campaigns were carried out to characterise the health-relevant chemical and physical properties of PM2.5 across the LU network, including diurnal and day-to-day variability and spatial distribution (above ground, depth below ground and subway line). Population-weighted station PM2.5 rankings were produced to understand the relative importance of concentrations at different stations and on different lines.ResultsThe PM2.5 mass in the LU (mean 88 μg m-3, median 28 μg m-3) was greater than at ambient background locations (mean 19 μg m-3, median 14 μg m-3) and roadside environments in central London (mean 22 μg m-3, median 14 μg m-3). Concentrations varied between lines and locations, with the deepest and shallowest submerged lines being the District (median 4 μg m-3) and Victoria (median 361 μg m-3 but up to 885 μg m-3). Broadly in agreement with other subway systems around the world, sampled LU PM2.5 comprised 47% iron oxide, 7% elemental carbon, 11% organic carbon, and 14% metallic and mineral oxides. Although a relationship between line depth and air quality inside the tube trains was evident, there were clear influences relating to the distance from cleaner outside air and the exchange with cabin air when the doors open. The passenger population-weighted exposure analysis demonstrated a method to identify stations that should be prioritised for remediation to improve air quality.ConclusionPM2.5 concentrations in the LU are many times higher than in other London transport Environments. Failure to include this environment in epidemiological studies of the relationship between PM2.5 and health in London is therefore likely to lead to a large exposure misclassification error. Given the significant contribution of underground PM2.5 to daily exposure, and the differences in composition compared to urban PM2.5, there is a clear need for well-designed studies to better understand the health effects of underground exposure.

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Finely Resolved On-Road PM_2.5 and Estimated Premature Mortality in Central North Carolina.

Publications

Finely Resolved On-Road PM<sub>2.5</sub> and Estimated Premature Mortality in Central North Carolina.

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Finely Resolved On-Road PM2.5 and Estimated Premature Mortality in Central North Carolina.

Publications

Finely Resolved On-Road PM<sub>2.5</sub> and Estimated Premature Mortality in Central North Carolina.

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OmicsDI is part of the ELIXIR infrastructure

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Finely Resolved On-Road PM_2.5 and Estimated Premature Mortality in Central North Carolina.