Comparing the Health Effects of Ambient Particulate Matter Estimated Using Ground-Based versus Remote Sensing Exposure Estimates

Jerrett, Michael; Turner, Michelle C.; Beckerman, Bernardo; Pope, C. Arden; Donkelaar, Aaron van; Martin, Randall V.; Serre, Marc L.; Crouse, Dan L.; Gapstur, Susan M.; Krewski, Daniel; Diver, W. Ryan; Coogan, Patricia F.; Thurston, George D.; Burnett, Richard T.

doi:10.1289/ehp575

Cited by 118 publications

(92 citation statements)

References 41 publications

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“…A modification to the ACS coefficient of around 30% to 50% for total personal exposure is broadly consistent with previously published evidence suggesting increased coefficients for personal exposure in time-series studies [37,38]. Similarly, studies adjusting coefficients for exposure measurement error using regression calibration techniques have tended to suggest that central monitoring sites generally provide a reasonable representation of overall personal exposures [39][40][41]. However, regression calibration requires detailed information on personal exposures (for instance, based on personal monitoring or remotely sensed data), which commonly does not account for indoor exposures.…”

Section: Discussionsupporting

confidence: 86%

An Exposure-Mortality Relationship for Residential Indoor PM2.5 Exposure from Outdoor Sources

Milner

Armstrong

Davies

et al. 2017

Climate

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A large proportion of particulate air pollution exposure in urban areas occurs due to the penetration of outdoor pollution into the residential indoor environment. Theoretical considerations suggest that quantifying health effects due to changes to indoor particulate concentrations derived from outdoor sources requires the adjustment of exposure-response coefficients based on epidemiological studies of outdoor air. Using the PM 2.5 -mortality coefficient from the American Cancer Society (ACS) cohort study as an example, we developed a theoretical model to quantify the relationship between the published coefficient and one based on personal exposure, and explored how this adjusted coefficient might be applied to changes in indoor PM 2.5 from outdoor sources. Using a probabilistic approach, our estimated average mortality coefficient for personal PM 2.5 exposure is 30-50% greater than the ACS coefficient. However, since the indoor PM 2.5 of outdoor origin accounts for only a proportion of the overall exposure, the average net adjustment required for indoor exposure is very modest. The results suggest that it is generally appropriate to apply unadjusted exposure-response functions derived from cohort studies to assess the health impact of changes in indoor particle concentrations from outdoor sources. However, it may be important to re-scale the coefficients for assessing exposures of population groups who spend a greater proportion of their time at home.

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Section: Discussionsupporting

confidence: 86%

An Exposure-Mortality Relationship for Residential Indoor PM2.5 Exposure from Outdoor Sources

Milner

Armstrong

Davies

et al. 2017

Climate

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“…However, previous studies are mostly based on single model for the PM 2.5 exposure estimate, which bring much uncertainty. To better express modeling estimate uncertainty, the multi-model method has been applied in several studies especially studies on climate and health, which can be applied in the PM 2.5 exposure estimates (Kinney et al, 2008; Jerrett et al, 2017; Silva et al, 2017). …”

Section: Introductionmentioning

confidence: 99%

A county-level estimate of PM 2.5 related chronic mortality risk in China based on multi-model exposure data

Wang

et al. 2018

Environment International

132

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Background Ambient fine particulate matter (PM2.5) pollution is currently a serious environmental problem in China, but evidence of health effects with higher resolution and spatial coverage is insufficient. Objective This study aims to provide a better overall understanding of long-term mortality effects of PM2.5 pollution in China and a county-level spatial map for estimating PM2.5 related premature deaths of the entire country. Method Using four sets of satellite-derived PM2.5 concentration data and the integrated exposure-response model which has been employed by the Global Burden of Disease (GBD) to estimate global mortality of ambient and household air pollution in 2010, we estimated PM2.5 related premature mortality for five endpoints across China in 2010. Result Premature deaths attributed to PM2.5 nationwide amounted to 1.27 million in total, and 119,167, 83,976, 390,266, 670,906 for adult chronic obstructive pulmonary disease, lung cancer, ischemic heart disease, and stroke, respectively; 3995 deaths for acute lower respiratory infections were estimated in children under the age of 5. About half of the premature deaths were from counties with annual average PM2.5 concentrations above 63.61 μg/m3, which cover 16.97% of the Chinese territory. These counties were largely located in the Beijing-Tianjin-Hebei region and the North China Plain. High population density and high pollution areas exhibited the highest health risks attributed to air pollution. On a per capita basis, the highest values were mostly located in heavily polluted industrial regions. Conclusion PM2.5-attributable health risk is closely associated with high population density and high levels of pollution in China. Further estimates using long-term historical exposure data and concentration-response (C-R) relationships should be completed in the future to investigate longer-term trends in the effects of PM2.5.

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“…However, limitations in using satellite AOD to provide adequate spatial variability in PM concentrations for health effects studies have been noted [49]. A recent examination of several exposure models found that health effects estimated based on models integrating ground-based information and satellite AOD to produce PM 2.5 estimates at a 30 m resolution were larger and had smaller standard errors [50]. Thus, a hybrid approach incorporating information on spatial gradients from roads is possibly the best approach to capture subgrid-scale gradients in satellite observations, particularly when estimating PM exposures that are also temporally resolved.…”

Section: Discussionmentioning

confidence: 99%

Characterization of Subgrid-Scale Variability in Particulate Matter with Respect to Satellite Aerosol Observations

et al. 2018

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Recent use of satellite observations of aerosol optical depth (AOD) to characterize surface concentrations of particulate matter (PM) air pollution has proven extremely valuable in estimating exposures for health effects studies. While the spatial resolutions of satellite data provide far better coverage than existing fixed site surface monitoring stations, they are not able to capture atmospheric processes such as dilution of primary pollutants that vary at small spatial scales. As a result, small-scale variability due to highly localized sources such as traffic may be poorly represented, which in turn may lead to exposure measurement error in epidemiological analyses. Using a fixed spatial grid representing 4.4 km Multiangle Imaging SpectroRadiometer (MISR) aerosol observations, we examined the spatial variability in fine and coarse mode PM (PM 2.5 and PM 2.5-10 respectively) measured at ground monitors from a unique spatially-dense sampling campaign in Southern California. We found that while the variance in measured PM 2.5 differed seasonally (warm 6.82 µg 2 /m 6 and cool 24.5 µg 2 /m 6 ) across the study region, the average subgrid (<4.4 km) variance did not (warm 2.03 µg 2 /m 6 and cool 2.43 µg 2 /m 6 ) and was significantly smaller. On the other hand, ground monitor PM 2.5-10 concentrations showed large variance in warm (18.6 µg 2 /m 6 ) and cool (20.6 µg 2 /m 6 ) seasons, as well as seasonal differences in subgrid variance (warm 8.90 µg 2 /m 6 and cool 3.28 µg 2 /m 6 ). Geostatistical analysis of the semivariance as a function of distance indicated that variability in measured PM 2.5 and PM 2.5-10 concentrations was relatively constant for spatial scales of one to five kilometers, but there was evidence of small-scale (~500 m) variability in PM 2.5-10 concentrations in the cool season. The lack of small-scale spatial variability in the warm season was likely due to large photochemical contributions to regional PM 2.5 , and greater regional contributions to PM 2.5-10 from windblown dust. In contrast, in the cool season there tends to be greater localized concentrations from primary traffic sources due to stronger nocturnal inversions and delayed morning winds reducing dilution that contribute to greater spatial heterogeneity. Overall, these results suggest that regional contributions tend to dominate PM 2.5 , and spatial resolutions of satellite observations including the 4.4 km MISR and 3 km MODIS aerosol products aptly capture relevant spatial variability. Coarse PM 2.5-10 can have seasonally dependent localized contributions, leading to small-scale variability below current satellite aerosol product resolutions.

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Comparing the Health Effects of Ambient Particulate Matter Estimated Using Ground-Based versus Remote Sensing Exposure Estimates

Cited by 118 publications

References 41 publications

An Exposure-Mortality Relationship for Residential Indoor PM2.5 Exposure from Outdoor Sources

An Exposure-Mortality Relationship for Residential Indoor PM2.5 Exposure from Outdoor Sources

A county-level estimate of PM 2.5 related chronic mortality risk in China based on multi-model exposure data

Characterization of Subgrid-Scale Variability in Particulate Matter with Respect to Satellite Aerosol Observations

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