Identifying Vulnerable Populations through an Examination of the Association Between Multipollutant Profiles and Poverty

Molitor, John; Su, Jason; Molitor, Nuoo-Ting; Gómez‐Rubio, Virgilio; Richardson, Sylvia; Hastie, David I.; Morello‐Frosch, Rachel; Jerrett, Michael

doi:10.1021/es104017x

Cited by 51 publications

(53 citation statements)

References 28 publications

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“…It is reasonable to consider, however, that air pollution exposure-response effects on adverse birth outcomes, such as TLBW, vary spatially within an urban setting. First and foremost, air pollutant mixtures or components of PM air pollution can be autocorrelated spatially within urban environments -depending on local-scale air pollution sources, the intensity of emissions, and meteorology (among other factors) (Hajat et al, 2013;Molitor et al, 2011;Su et al, 2012). As a result, the intrinsic toxicity of PM 2.5 mixtures is likely to be spatially dependent.…”

Section: Introductionmentioning

confidence: 99%

Modeling spatial effects of PM2.5 on term low birth weight in Los Angeles County

Coker

Ghosh

Jerrett

et al. 2015

Environmental Research

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Air pollution epidemiological studies suggest that elevated exposure to fine particulate matter (PM2.5) is associated with higher prevalence of term low birth weight (TLBW). Previous studies have generally assumed the exposure-response of PM2.5 on TLBW to be the same throughout a large geographical area. Health effects related to PM2.5 exposures, however, may not be uniformly distributed spatially, creating a need for studies that explicitly investigate the spatial distribution of the exposure-response relationship between individual-level exposure to PM2.5 and TLBW. Here, we examine the overall and spatially varying exposure-response relationship between PM2.5 and TLBW throughout urban Los Angeles (LA) County, California. We estimated PM2.5 from a combination of land use regression (LUR), aerosol optical depth from remote sensing, and atmospheric modeling techniques. Exposures were assigned to LA County individual pregnancies identified from electronic birth certificates between the years 1995-2006 (N=1,359,284) provided by the California Department of Public Health. We used a single pollutant multivariate logistic regression model, with multilevel spatially structured and unstructured random effects set in a Bayesian framework to estimate global and spatially varying pollutant effects on TLBW at the census tract level. Overall, increased PM2.5 level was associated with higher prevalence of TLBW county-wide. The spatial random effects model, however, demonstrated that the exposure-response for PM2.5 and TLBW was not uniform across urban LA County. Rather, the magnitude and certainty of the exposure-response estimates for PM2.5 on log odds of TLBW were greatest in the urban core of Central and Southern LA County census tracts. These results suggest that the effects may be spatially patterned, and that simply estimating global pollutant effects obscures disparities suggested by spatial patterns of effects. Studies that incorporate spatial multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effective.

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Section: Introductionmentioning

confidence: 99%

Modeling spatial effects of PM2.5 on term low birth weight in Los Angeles County

Coker

Ghosh

Jerrett

et al. 2015

Environmental Research

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“…The method has been shown to perform well in comparisons with traditional approaches [18,19]. To date, SOM applications in the field of air pollution have primarily focused on source apportionment with mixed success [20-22]; however, our objective here is more similar to clustering approaches to multipollutant environments [5,6,8] and thus our application of SOM is tailored accordingly.…”

Section: Methodsmentioning

confidence: 99%

“…This presents considerable challenges for health investigators, and several methodological strategies appear in the literature [1,3,4]. One prospective solution under active investigation is to use classifications or groupings as a means to characterize aspects of the multipollutant environment [5-8]. This is appealing to health investigators because classification of complex multipollutant data into specific categories can elucidate combinatorial patterns of interest and can be used to compare risk of an adverse health outcome observed within one air quality classification to that observed in another.…”

Section: Introductionmentioning

confidence: 99%

“…For example, multipollutant combinations could be discriminated using prior knowledge of hypothesized biological pathways of effect [10] (e.g., inflammation) or known emissions sources (e.g., traffic) [11]. Alternatively, investigators without a priori information are turning to statistical methods that construct groupings by ‘learning’ from the data [5,7,8,12,13]. These approaches encompass a number of techniques that focus on the discovery of patterns and trends in data and can be categorized as being either ‘supervised’ or ‘unsupervised’ [14].…”

Section: Introductionmentioning

confidence: 99%

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Using self-organizing maps to develop ambient air quality classifications: a time series example

et al. 2014

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BackgroundDevelopment of exposure metrics that capture features of the multipollutant environment are needed to investigate health effects of pollutant mixtures. This is a complex problem that requires development of new methodologies.ObjectivePresent a self-organizing map (SOM) framework for creating ambient air quality classifications that group days with similar multipollutant profiles.MethodsEight years of day-level data from Atlanta, GA, for ten ambient air pollutants collected at a central monitor location were classified using SOM into a set of day types based on their day-level multipollutant profiles. We present strategies for using SOM to develop a multipollutant metric of air quality and compare results with more traditional techniques.ResultsOur analysis found that 16 types of days reasonably describe the day-level multipollutant combinations that appear most frequently in our data. Multipollutant day types ranged from conditions when all pollutants measured low to days exhibiting relatively high concentrations for either primary or secondary pollutants or both. The temporal nature of class assignments indicated substantial heterogeneity in day type frequency distributions (~1%-14%), relatively short-term durations (<2 day persistence), and long-term and seasonal trends. Meteorological summaries revealed strong day type weather dependencies and pollutant concentration summaries provided interesting scenarios for further investigation. Comparison with traditional methods found SOM produced similar classifications with added insight regarding between-class relationships.ConclusionWe find SOM to be an attractive framework for developing ambient air quality classification because the approach eases interpretation of results by allowing users to visualize classifications on an organized map. The presented approach provides an appealing tool for developing multipollutant metrics of air quality that can be used to support multipollutant health studies.

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“…Recently, many techniques have been presented for characterizing multipollutant exposure (Oakes, Baxter et al 2014); however, very few have been applied in spatial settings (Molitor, Su et al 2011, Austin, Coull et al 2013). Although limited, findings from these studies have noted significant spatial variation in multipollutant exposures within and across cities in the US.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the spatial distribution of multiple pollutants and populations at risk in Atlanta, Georgia

Pearce

Waller

Sarnat

et al. 2016

Spatial and Spatio-temporal Epidemiology

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Background Development of exposure metrics that identify contrasts in multipollutant air quality across space are needed to better understand multipollutant geographies and health effects from air pollution. Objective Our aim is to improve understanding of: 1) long-term spatial distributions of multiple pollutants across urban environments; and 2) demographic characteristics of populations residing within areas that experience differing long-term air quality in order to assist in the development of future epidemiologic studies. Methods Data available for this study included seven years of spatiotemporally resolved concentrations for ten ambient air pollutants across the Atlanta metropolitan area. To analyze, we first distinguish the long-term behavior of air pollution at each grid location (n=253) by calculating study period means for each pollutant (n=10). Then, we apply the self-organizing map (SOM) technique to derive patterns in the multipollutant combinations observed among grid cells, i.e., multipollutant spatial types (MSTs), project results onto an ‘organized map’, and classify each grid cell under its most similar MST. Finally, we geographically map grid cell classifications to delineate regions of similar multipollutant characteristics and characterize associated population demographics using geographic information systems. Results We found six MSTs well describe the nature of multipollutant combinations experienced at locations in our study area. MST profiles highlighted a range of combinations, from locations experiencing generally clean air quality (all pollutants low) to locations experiencing conditions that were relatively dirty (high long-term concentrations of several pollutants). Mapping the spatial distribution of MSTs revealed strong within-class contiguity and highlighted that downtown areas were dominated by primary pollution and that suburban areas experienced relatively higher levels of secondary pollution. Demographics show that the largest proportion of the overall population of metro Atlanta resided in downtown locations experiencing relatively high levels of primary pollution. Moreover, higher proportions of nonwhites and children in poverty reside in these areas when compared to populations that resided in suburban areas exhibiting relatively lower pollution, moderate secondary pollution, or relatively high SO2. Conclusion Placing multipollutant air quality within a geographic regionalization problem reveals the nature and spatial distribution of differential pollutant combinations across urban environments and provides helpful insights for identifying spatial exposure and demographic contrasts for future health studies.

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Identifying Vulnerable Populations through an Examination of the Association Between Multipollutant Profiles and Poverty

Abstract: TitleIdentifying vulnerable populations through an examination of the association between multipollutant profiles and poverty Permalink https://escholarship.org/uc/item/49f6f7v9 Journal

Cited by 51 publications

References 28 publications

Modeling spatial effects of PM2.5 on term low birth weight in Los Angeles County

Modeling spatial effects of PM2.5 on term low birth weight in Los Angeles County

Using self-organizing maps to develop ambient air quality classifications: a time series example

Characterizing the spatial distribution of multiple pollutants and populations at risk in Atlanta, Georgia

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