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

Abstract

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.

Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children

“…Prior approaches have utilized self-organizing maps, a form of unsupervised learning (Pearce et al, 2016), multipollutant indicators that combine measured pollutant concentrations with emissions data (Oakes et al, 2014b), k-means and hierarchical clustering (Austin et al, 2012), and Bayesian clustering techniques that account for the uncertainty in the identification of the pollutant profiles (Molitor et al, 2016). All of these approaches have been shown to identify and estimate the health effects of air pollutant profiles.…”

Spatiality in Health: The Distribution of Health Conditions Associated with Electronic Waste Processing Activities at Agbogbloshie, Accra

“…To emphasize this point, air quality within urban environments involves a mixture of gaseous and particulate concentrations that are affected by a variety of emission sources, local topographies, and meteorological conditions [4,6,26,29,30,38,42,43,57]. As such, complex spatial patterning can occur in urban air quality making the variability of such phenomena difficult to characterize as different pollutants often exhibit differential spatial patterns (e.g., ozone vs. nitrogen dioxides) [58].…”

“…However, recent advancements made in the development of spatial methods for studying spatial variation in health outcomes have made it possible to study spatial variability of more concrete health outcomes such as injuries, skin conditions and scars with very high degree of certainty; although admittedly, most public health and epidemiological studies have not fully embraced the application of advanced spatial methods probably due to limited understanding of the application of these new spatial methods [47][48][49]. While there is scientific consensus that ecological studies are more reliably conducted over fairly large spatial areas over which multiple socioeconomic and environmental factors acting over distinct spatial scales occur in more spatially explicit manner, a few other robust spatial statistical methods which can be applied to study environmental health phenomena over small spatial scales also exist and are widely applied with high degree of success in spatial epidemiology [49,51,53,[58][59][60]. Such methods include the one we applied in this study in which a hand-held GPS was used to map out small areas so that scars and other physical/concrete health events were reliably counted on e-waste workers who work within these small spaces on daily basis.…”

Model choice for estimating the association between exposure to chemical mixtures and health outcomes: A simulation study

“…The next two groups are dimension reduction techniques (UDR and SDR) that transform exposure data to reduce the dimension of the predictor and, therefore, the required parameter space. UDR methods such as k-means [23,24] transform exposure data without regard to the health outcome [25][26][27][28]. SDR methods, including supervised principle components analysis [29], let the outcome inform exposure data transformation [30][31][32][33][34][35].…”